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Electronic voting security 10 years after the help america vote act.

Merle S. King, executive director of the Center for Election Systems at Kennesaw State University, and Brian Hancock, director of voting system testing and certification at the US Election Assistance Commission (EAC), discuss e-voting security 10 years ...

E-voting in the 2005 local elections in Estonia and the broader impact for future e-voting projects

In this project, we analyze the introduction of voting by internet (e-voting) in political elections. In particular, we focus on the Estonian municipal elections held on 16 October 2005, where the possibility of casting a vote via the internet was newly ...

New Research Results for Electronic Voting

Voting systems have become controversial in the years following the multiple election disasters that occurred in the United States during and after 2000. Of particular note were the electronic voting (e-voting) systems that were widely deployed to ...

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• Electoral Systems
• Electronic Voting
• Internet Voting
• Remote Voting
• Voting Machines
• e-Democracy
• e-Participation
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• Political Science
• Political Methodology

DESIGN AND IMPLEMENTATION OF AN E-VOTING SYSTEM

• January 2019
• Affiliation: Nnamdi Azikiwe University, Awka

• Nnamdi Azikiwe University, Awka

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Colorado clerk urges Supreme Court to block trial over voting machine password leak

Former Mesa County clerk-recorder Tina Peters claims she has constitutional immunity from prosecution by "hostile state actors."

WASHINGTON (CN) — A former Colorado election official asked the Supreme Court on Friday for emergency intervention to prevent the start of her trial over leaked voting machine passwords. 

Tina Peters, who served as Mesa County’s clerk and recorder during the 2020 election cycle, faces criminal charges for giving a security company unauthorized access to voting machines during a system update in 2021. 

In an attempt to avert her July 29 trial, Peters filed an emergency application with the justices, claiming federal elections protections give her immunity from the state’s prosecution. 

“An injunction stopping the state trial while this court considers this case is necessary to preserve the status quo and prevent an irreparable injury to the institutional interests of the federal government and to Ms. Peters’ right not to be subjected to state trial for executing her duty under federal law,” Peters wrote in her application. 

Peters said prosecutors’ charges relate to her duty to preserve election records as Colorado’s chief election official. She argued her actions reflected official responsibilities under federal law, giving her immunity under the 14th Amendment.  

The controversy kicked off when Colorado announced an upgrade to their election management system. Concerned that records of the 2020 presidential would be deleted, prosecutors say Peters hired Gerald Wood to help her back up Dominion voting machines. 

A video was then posted on the social media site Telegram and the Gateway Pundit blog of the country’s equipment being updated including system passwords. 

Peters’ claims of unusual activity and deleted records were vindicated but an investigation identified those instances as human error, not election fraud. 

Colorado prosecutors charged Peters with seven felony counts related to attempting to influence a public servant, impersonation and identity theft. Peters also faces several misdemeanor charges of official misconduct, violating her duties and failing to comply with the secretary of state’s requirements. 

Peters pleaded not guilty , claiming the charges were a political hitjob due to her support for former President Donald Trump.

Colorado Secretary of State Jena Griswold sued to remove Peters as clerk and recorder for the 2022 elections. Peters decided to run for secretary of state to unseat Griswold. A judge blocked Peters from overseeing county elections and she lost the Republican primary by more than 15 points.

Peters filed a federal lawsuit against a slew of state officials and U.S. Attorney General Merrick Garland, claiming she was wrongly prosecuted in violation of her First Amendment rights. 

U.S. District Judge Nina Wang, a Joe Biden appointee, granted Mesa County District Attorney Daniel Rubinstein’s request to dismiss the suit in January. Wang said Peters could present her challenges in state court. 

In February, the 10th Circuit refused to delay Peters’ trial while she challenged the ruling. The appeals court terminated her appeal in June. 

At the Supreme Court, Peters asked the justices to postpone her trial so they could review her immunity appeal. Peters said supremacy clause immunity had been extensively litigated by the lower courts and the justices’ intervention was overdue. 

“Arising as it does from the Constitution’s foundational allocation of power between the federal and state governments, it is critically important that the immunity provided by the supremacy clause for federal actors contending with hostile state officials be properly applied,” Peters wrote. 

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Journal of Materials Chemistry A

Leveraging machine learning in porous media †.

* Corresponding authors

a School of Engineering, Newcastle University, Newcastle Upon Tyne, UK E-mail: [email protected] , [email protected]

b School of Mathematics and Physics, University of Portsmouth, Portsmouth, UK

c Department of Mechanical Engineering, University of Kashan, Kashan, Iran

d School of Mechanical Engineering, Iran University of Science and Technology, Iran

e Department of Chemical Engineering, University of Waterloo, Waterloo, Canada

f School of Metallurgy and Materials Engineering, College of Engineering, University of Tehran, Tehran, Iran

g Department of Chemical Engineering, University of Manchester, Oxford Road, Manchester, UK E-mail: [email protected]

h School of Climate Change and Adaptation, University of Prince Edward Island, Charlottetown, PEI, Canada

i Department of Chemical Engineering & Materials Science, University of Southern California, USA

j Department of Chemistry and Bioscience, Aalborg University, Niels Bohrs Vej 8A, Esbjerg 6700, Denmark

k Department of Chemical and Petroleum Engineering, Khalifa University, Abu Dhabi, UAE

The emergence of artificial intelligence (AI) and, more particularly, machine learning (ML), has had a significant impact on engineering and the fundamental sciences, resulting in advances in various fields. The use of ML has significantly enhanced data processing and analysis, eliciting the development of new and improved technologies. Specifically, ML is projected to play an increasingly significant role in helping researchers better understand and predict the behavior of porous media. Furthermore, ML models will be able to make use of sizable datasets, such as subsurface data and experiments, to produce accurate predictions and simulations of porous media systems. This capability could help optimize the design of porous materials for specific applications and improve the effectiveness of industrial processes. To this end, this review paper attempts to provide an overview of the present status quo in this context, i.e. , the interface of ML and porous media in six different applications, namely, heat exchanger and storage, energy storage and combustion, electrochemical devices, hydrocarbon reservoirs, carbon capture and sequestration, and groundwater, stressing the advances made in the application of ML to porous media and offering insights into the challenges and opportunities for future research. Each section also entails a supplementary database of the literature as a spreadsheet, which includes the details of ML models, datasets, key findings, etc. , and mentions relevant available online datasets that can be used to train ML models. Future research trends include employing hybrid models by combining ML models with physics-based models of porous media to improve predictions concerning accuracy and interpretability.

• This article is part of the themed collection: Journal of Materials Chemistry A Recent Review Articles

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Leveraging machine learning in porous media

M. Delpisheh, B. Ebrahimpour, A. Fattahi, M. Siavashi, H. Mir, H. Mashhadimoslem, M. A. Abdol, M. Ghorbani, J. Shokri, D. Niblett, K. Khosravi, S. Rahimi, S. M. Alirahmi, H. Yu, A. Elkamel, V. Niasar and M. Mamlouk, J. Mater. Chem. A , 2024, Advance Article , DOI: 10.1039/D4TA00251B

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Sinner: a reward-sensitive algorithm for imbalanced malware classification using neural networks with experience replay.

1. Introduction

• It presents SINNER, i.e., a DRL-based classifier, which leverages a reward function slightly modified compared to that proposed in [ 28 ].
• It provides an extended benchmark analysis that involves a state-of-the art DL-based malware family classifier that can deal with class skew at the algorithm level.

2. Preliminaries

2.1. malware analysis, 2.1.1. static analysis, 2.1.2. dynamic analysis, 2.2. deep reinforcement learning, 2.2.1. deep q-network, 2.2.2. double deep q-network, 2.2.3. dueling network, 2.2.4. prioritized experience replay, 3. related work, 3.1. deep learning for api-based malware classification, 3.2. imbalanced multi-class malware classification, 3.3. deep reinforcement learning for malware analysis, 3.4. motivation, 4. methodology, 4.1. environment setting.

• Training data provide the observation space S ; therefore, each training sample represents an observation s t for a specific timestep t . Note that S ∈ R m × n , with m the number of samples within the training set and n the number of features.
• The action space A consists of all known labels for classes. Therefore, given K classes, A = { 1 , 2 , . . . , K } , i.e., | A | = K .
• The reward function f R represents the main component of the proposed cost-sensitive approach according to the following formula: r = f R ( s t , a t , l t ) = λ t = m l t − 1 Λ , if a t = l t − λ t , otherwise (7) where Λ = | | m 1 − 1 , m 2 − 1 , . . . , m K − 1 | | 2 , l t refers to the true label of the observed s t , and m l t represents the number of samples in the l t -th class. In this way, the agent can adjust the learning to be more sensitive to minority classes because the higher the m l t , the lower the λ t . Furthermore, in [ 28 ], the authors found that the use of the normalization factor Λ improves the learning performance, having as the main effect the scaling of r so that it falls in [ 0 , 1 ] .
• Finally, according to the definition of S , the states-transition probability ϕ is deterministic; thus, the agent advances from s t to s t + 1 , as determined by the order of the samples within S .

4.2. Reward-Sensitive Training Analysis

4.3. modified reward function, 5. experimental setup, 5.1. approaches used for benchmark.

• According to the literature review provided in Section 3 , the following DL models are selected and combined with the cost-sensitive strategies proposed in [ 11 , 62 ], the working principle of which is shown in Table 2 : - LSTM [ 79 ]: This popular model belongs to the class of RNNs. The structure of this network consists of three gates in its hidden layers: an input gate, an output gate, and a forget gate. These entities form the so-called memory cell, which traces the data flow, i.e., remembers or forgets information over time. In such a way, LSTMs can maintain long-term dependencies on sequential data. The LSTM used in our experiments has 100 units (size of hidden cells) connected to a final layer, which is a multi-class classification layer ( K nodes, each having a softmax activation). Between these connections, there is a dropout layer to mitigate overfitting using a chance of 20 % of randomly discarding a neuron. - BiLSTM [ 80 ]: This differs from the aforementioned model regarding the adoption of a bidirectional layer, which enables the forwarding and backwarding of the input to two separate recurrent nets, both of which are connected to the same output layer (having the same properties of the LSTM last layer). - BiGRU [ 81 ]: This method uses a bidirectional approach to analyze sequences in both directions as the previous DL model described, involving as a main model the so-called GRU, which is an LSTM variant. In fact, the GRU has gating units (update and reset gates) that control the flow of information inside each unit without having separate memory cells. The update gate helps the model to determine how much of the past information (from previous time steps) must be passed to the future. In contrast, the reset gate is used by the model to determine how much of the past information is to be forgotten. In this case, the dropout rate is fixed so that a neuron can be discarded with a probability of 0.3. - TabNet [ 82 ]: This is a DL architecture specifically designed for tabular data. During each of the N s t e p s decision steps, such a model exploits a sequential attention mechanism to select N d features useful to perform a specific prediction, according to the aggregated information collected (the aggregation in N a dimension is realized by the attentive transformer component of the TabNet encoder). This property enhances the explainability of the model (because of the presence of a feature masking component, which is part of the TabNet decoder, i.e., the module delegated to reconstruct the features generated by the encoder). According to the suggestions provided by the authors of the original paper, N s t e p s = 3 , N a = N d = 16 . Lastly, all the above DL models optimize the loss function using the Adam optimization algorithm and sampling mini-batches of 128, 64, and 1024 training samples for LSTM, bidirectional models, and TabNet, respectively. A number of 50 epochs were considered for the first three models, whereas the last model was trained on 100 training epochs.
• RTF [ 16 ]: This model consists of an ensemble of ξ homogeneous (equivalent structure of base estimators) pre-trained transformer models. Each is fine-tuned to implement a sequence classification layer using a subset of ξ training data obtained as a result of a stratified (to retain the class distribution coming from the original set) bootstrap sampling technique. Each i -th model, with i = 1 , ⋯ , ξ , generates a probability employed in a majority voting schema, which leads to a traditional bagging method (exploiting the robustness of such an algorithmic procedure with respect to class skew). BERT and CANINE (including CANINE-C and CANINE-S variants) were evaluated as pre-trained models. The setting was the same as that proposed in the experimental evaluation of the original article (Table 7 in [ 16 ]).

5.2. Datasets Selected for This Study

• APIs statically extracted from the PE structure of malware samples collected from two main providers, i.e., VirusShare ( https://virusshare.com/ , accessed on 16 May 2024) and VirusSample ( https://www.virussamples.com/ , accessed on 16 May 2024), which were labeled using the VirusTotal ( https://www.virustotal.com/ , accessed on 16 May 2024) engine [ 83 ]. These two datasets differ in the number of samples and malware families within each, but they share the feature space size as in [ 16 ].
• API sequences traced by dynamically analyzing each malware sample using the Cuckoo sandbox. These are collected in two different datasets, namely Catak [ 63 ] and Oliveira [ 84 ]. Note that while Catak represents the state-of-the-art in the category of multi-class malware classification problems using APIs, the Oliveira dataset was released as suitable for binary classification problems. Therefore, only the malware contained in the latter dataset were used and labeled, so they were assigned to the malware family indicated by the VirusTotal service. In this assignment process, statistical units without associated classes and malware families with fewer than 100 samples were discarded [ 16 ].

5.3. Metrics

• The F1 score, as the harmonic mean of precision (PREC) and true positive rate (TPR), defined as PREC = TP TP + FP and TPR = TP TP + FN , i.e., F 1 score = TP TP + FP + FN 2 . Specifically, the macro-averaged metric was examined because it assumes that each class has the same impact regardless of its skew [ 86 ].
• The area under receiver operating characteristic curve (AUC) computed by identifying the surface below the graph that relates the false positive rate (FPR) to the TPR.

5.4. Setting of the Proposed Methodology

5.5. hardware settings and implementation details, 6. results and discussion, 6.1. reward influence analysis.

• The three algorithms that achieve the highest F1 score among all evaluated DRL configurations in the case of the Catak dataset are dueling DDQN, dueling DQN with PER and dueling DQN. This trio shares a key finding: the reward function used is Equation ( 11 ). With the same configuration, adopting Equation ( 7 ) results in a performance degradation that is more evident in the F1 score than in the AUC.
• Using the Oliveira dataset, among the top three performers, there are, once more, dueling DQN and dueling DDQN, followed by the dueling of DDQN with PER. As before, the best results are obtained using r s as the reward; in fact, it is remarkable that the three opposite algorithms achieve F1 scores that are half of those achieved by the algorithms using Equation ( 11 ). Similarly, using r s rather than r improves the AUC.
• Using the PER technique for the VirusSample dataset brings benefits that are reflected in the performance achieved by the dueling of DQN (which performs effectively also using the ER not prioritized) and DDQN algorithms, respectively. The dueling of DQN with PER, adopting r s for reward-sensitive training, reaches an F1 close to 80%, outperforming the same algorithm configuration trained using Equation ( 7 ) by ∼ 20 % . An improvement in F1 scores is also found for the remaining two algorithms using r s instead of r . In contrast, the opposite trend is shown by evaluating the AUC.
• The benefit achieved by introducing the revised reward formulation is confirmed for the VirusShare dataset, for which the top performers are given by the following three algorithms: dueling DDQN, dueling DQN, and dueling DDQN with PER.

Click here to enlarge figure

6.2. Performance Comparison

• Table 4 reveals that the cost-sensitive strategies proposed in [ 11 ] and [ 62 ] are uniquely beneficial to the BiLSTM model when using the VirusShare dataset. In fact, the remaining three DL algorithms do not produce satisfactory performance, with AUC values (∼0.5) indicating that the algorithms performed random classifications. However, the classification metric scores obtained by the BiLSTM algorithm do not reach the state-of-the-art performance achieved by the RTF algorithm. In addition, it appears to be extremely disadvantageous from the perspective of the required training time, which is the maximum in this list, regardless of the cost-sensitive strategy adopted. The proposed methodology outperforms all of the competitors in terms of F1 score and prediction time. Specifically, SINNER achieves a value of the F1 that is approximately 2% higher compared to the same obtained by RTF, requiring significantly less prediction time. On the other hand, RTF remains advantageous in terms of both the training period and the AUC value that it yields.
• In contrast to the previous case, Table 5 indicates that the results achieved by BiGRU are comparable to those obtained by the BiLSTM. In particular, the scores produced are comparable when adopting a specific cost-sensitive strategy; however, between the two alternatives, the one based on the use of the custom loss function proposed in [ 62 ] performs better in timing performance and F1 score. Furthermore, LSTM and TabNet combined with cost-sensitive strategies are also ineffective in this test. However, as mentioned previously, bidirectional classifiers do not achieve performance comparable to RTF, which is targeted by SINNER. In fact, the proposed methodology generated the closest F1 score to that achieved by RTF, with an inference time that is again shorter, although it is ten times longer in learning time and 10 − 1 less in AUC.
• As shown in Table 6 , SINNER appears to be underperforming when compared with bidirectional DL models combined with the pair of cost-sensitive strategies and RTF, which remains the state-of-the-art model on the Catak dataset with impressive F1 scores and AUC values. Therefore, it appears that SINNER has difficulty learning using an observation space with a large number of variables ( n ).
• According to Table 7 , five top performers are identified using the Oliveira dataset, namely LSTM (that joins the top classifiers for the first time, indicating the presence of a temporal relationship between the variables in each statistical unit, which is a likely condition since the dataset is extracted from a dynamic analysis process); BiLSTM and BiGRU leveraging the strategy proposed in [ 62 ]; RTF, and SINNER. In particular, the five algorithms obtained F1 values between 0.561 and 0.569. While RTF remains the most advantageous in terms of AUC, SINNER stands out with respect to timing performance, requiring the second-lowest training time (LSTM is the top performer for this particular metric) and the lowest prediction time, which, as in all the cases discussed above, is approximately hundredths of seconds.

7. Conclusions

Author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest, abbreviations.

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Coscia, A.; Iannacone, A.; Maci, A.; Stamerra, A. SINNER: A Reward-Sensitive Algorithm for Imbalanced Malware Classification Using Neural Networks with Experience Replay. Information 2024 , 15 , 425. https://doi.org/10.3390/info15080425

Coscia A, Iannacone A, Maci A, Stamerra A. SINNER: A Reward-Sensitive Algorithm for Imbalanced Malware Classification Using Neural Networks with Experience Replay. Information . 2024; 15(8):425. https://doi.org/10.3390/info15080425

Coscia, Antonio, Andrea Iannacone, Antonio Maci, and Alessandro Stamerra. 2024. "SINNER: A Reward-Sensitive Algorithm for Imbalanced Malware Classification Using Neural Networks with Experience Replay" Information 15, no. 8: 425. https://doi.org/10.3390/info15080425

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Machine and deep learning techniques for the prediction of diabetics: a review

• Published: 16 July 2024

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• Sandip Kumar Singh Modak   ORCID: orcid.org/0000-0001-7985-4161 1 &
• Vijay Kumar Jha 2  

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Diabetes has become one of the significant reasons for public sickness and death in worldwide. By 2019, diabetes had affected more than 463 million people worldwide. According to the International Diabetes Federation report, this figure is expected to rise to more than 700 million in 2040, so early screening and diagnosis of diabetes patients have great significance in detecting and treating diabetes on time. Diabetes is a multi factorial metabolic disease, its diagnostic criteria are difficult to cover all the ethology, damage degree, pathogenesis and other factors, so there is a situation for uncertainty and imprecision under various aspects of the medical diagnosis process. With the development of Data mining, researchers find that machine learning and deep learning, playing an important role in diabetes prediction research. This paper is an in-depth study on the application of machine learning and deep learning techniques in the prediction of diabetics. In addition, this paper also discusses the different methodology used in machine and deep learning for prediction of diabetics since last two decades and examines the methods used, to explore their successes and failure. This review would help researchers and practitioners understand the current state-of-the-art methods and identify gaps in the literature.

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A Review for Predicting the Diabetes Mellitus Using Different Techniques and Methods

Recent applications of machine learning and deep learning models in the prediction, diagnosis, and management of diabetes: a comprehensive review

Diabetes prediction model based on an enhanced deep neural network

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Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

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Modak, S.K.S., Jha, V.K. Machine and deep learning techniques for the prediction of diabetics: a review. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-19766-9

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Received : 25 October 2023

Revised : 27 February 2024

Accepted : 20 June 2024

Published : 16 July 2024

DOI : https://doi.org/10.1007/s11042-024-19766-9

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