Reviewerly/exHarmony huggingface dataset:
Enhancing Deep-Learning Based Side-Channel Analysis Through Simultaneously Multi-Byte Training

Abstract Preparing a large number of physical traces is an important first step in Side-Channel Analysis, especially in Deep-Learning based Side-Channel Analysis (DL-SCA). With sufficient training data and a proper modeling algorithm, the secret key of cryptographic devices can be successfully recovered with a small number of attacking data. However, in reality, it may be impossible or difficult, in some threat models, to collect sufficient data due to various resource constraints. In this case, the performance of DL-SCA will be severely decreased. In this work, we propose an easy-to-implement method to achieve an efficient DL-SCA with a small number of training data in the scenario of software-based cryptographic implementations. Our simultaneously multi-byte training method, which trains the model with side-channel leakage characteristics of different byte intermediate values, significantly enhances the robustness and performance of DL-SCA. The simulated experiment shows that our method achieves more robust profiling. The success rate of recovering a secret AES key can be improved by 250% with the same collected data. The results of attacking real-world COTS USIM cards are consistent with the ones of simulation-based counterparts. Compared with state-of-the-art data-augmentation techniques, our results show that the proposed method can achieve the same or even better performance without additional generated training data.
The Evaluation of One-to-One Initiatives: Exploratory Results from a Systematic Review

While one-to-one initiatives (that equip each student and teacher with digital devices) have been widely implemented, no systematic review has explored how they are being evaluated. The contribution of this paper is twofold. First, we present exploratory insights from a systematic review on the evaluation of one-to-one initiatives. We focus on the relations inside the related research community and explore the relevant research topics that they have considered, through bibiliometric network analyses and topic modeling. Second, this paper contributes to existing guidelines about systematic reviews with an example that applies the mentioned analyses after the manual in-depth review of the papers (usually they are applied in parallel, or afterwards). Results depict a fragmented community, with little explicit collaborations among the research groups, but that shares a common body of literature providing good practices that can inform future one-to-one implementations. This community has considered a common set of topics (including, the implementation of educational technologies, mobile learning and classroom orchestration). Future evaluations of one-to-one initiatives would benefit if grounded in pedagogical theories and informed by learning analytics. Our approach enabled us to understand the dynamics of the related community, identify the core literature, and define guiding questions for future qualitative analyses.
Alice in (Software Supply) Chains: Risk Identification and Evaluation

The fast pace of modern development paradigms like DevOps boosted the complexity of development pipelines. In particular, developers rely on many external assets and third-party software to build the final product and match the demanding requirements in terms of release cycles and functionalities. However, such a choice impacts all the elements of the development pipeline composing the so-called Software Supply Chain (SSC), degrading its maintainability and security. From a security standpoint, successful attacks can go unnoticed and impact many targets that use the affected software before being resolved. Unfortunately, traditional security assessment methodologies might detect the symptoms (e.g., the piece of vulnerable code) but not the cause, i.e., the attack vector and the affected asset of the SSC, failing to mitigate the risk of new attack campaigns. In this paper, we propose Sunset, a methodology with a two-fold objective. First, it allows the automatic reconnaissance of the SSC assets and dependencies to alleviate the burden of monitoring the composition of the SSC. Then, it computes a risk profile, identifying the SSC risk sources and how they can impact the final software to support the identification of the weakest points of the SSC and activate the necessary organizational and technical countermeasures to prevent future SSC attack campaigns.
Succinct Attribute-Based Signatures for Bounded-Size Circuits by Combining Algebraic and Arithmetic Proofs

Attribute-based signatures allow fine-grained attribute-based authentication and at the same time keep a signer’s privacy as much as possible. While there are constructions of attribute-based signatures allowing arbitrary circuits or Turing machines as an authentication policy, none of them is practically very efficient. Some schemes have long signatures or long user secret keys which grow as the sizes of a policy or attributes grow. Some scheme relies on a vast Karp reduction which transforms public-key and secret-key operations into an arithmetic circuit. We propose an attribute-based signature scheme for bounded-size arbitrary arithmetic circuits with constant-size signatures and user secret keys without relying on such a Karp reduction. The scheme is based on bilinear groups and is proven secure in the generic bilinear group model. To achieve this we develop a new extension of SNARKs (succinct non-interactive arguments of knowledge). We formalize this extension as constrained SNARKs, which can be seen as a simplification of commit-and-prove SNARKs both in syntax and technique. In a constrained SNARK, one can force a prover to use a witness satisfying some constraint by announcing a succinct constraint string which encodes a constraint on a witness. If a proof is valid under some constraint string, it is ensured that the witness behind the proof satisfies the constraint that is behind the constraint string. By succinct, we mean that a constraint string has a constant length independent of the length of the plain description of the constraint, and notably a verifier need not know the (potentially long) plain description of the constraint for verifying a proof. We construct a constrained SNARK in the generic bilinear group model.
Understanding Black-Box Attacks Against Object Detectors from a User’s Perspective

Due to recent developments in object detection systems, and the realistic threat of black-box adversarial attacks on object detector models, we argue the need for a contextual understanding of the attacks from the users’ perspective. Existing literature reviews either do not provide complete and up-to-date summaries of such attacks or focus on the knowledge from the researchers’ perspective. In this research, we conducted a systematic literature review to identify state-of-the-art black-box attacks and extract the information to help users evaluate and mitigate the risks. The literature review resulted in 29 black-box attack methods. We analyzed each attack from the following main aspects: attackers’ knowledge needed to perform the attack, attack consequences, attack generalizability, and strategies to mitigate the attacks. Our results demonstrate an emerging increase in highly generalizable attacks, which now make up more than 50% of the landscape. We also reveal that more than 50% of recent attacks remain untested against mitigation strategies.
Towards Effective Blended Learning Through the Eyes of Students: A Survey Study in Transition into Face-to-Face Education

Many researchers consider that blended or hybrid learning implies a meaningful combination of online and face-to-face activities. Before the COVID-19 pandemic, studies had shown promising results of blended learning to improve student performance. However, the design and implementation of effective combinations is far from trivial, considering students’ differences regarding their demographics and self-regulatory capacities. This paper presents a survey study developed in an Engineering school of a Latin American university that transitioned from online learning to a hybrid format in mid-2021. Quantitative data was collected throughout an online questionnaire applied to a convenience sample of 1,124 students. Subgroup differences were identified by means of exploratory factor analysis and clustering. Two different subgroups emerged from the data: those who prefer online learning and those who prefer face-to-face activities. This difference is particularly observed in students from different cohorts and regions of origin: students who are closer to graduation preferred online activities, as well as students who come from regions outside the campus location. Students’ preferences varied regarding feedback delivery and collaboration with peers, which are usually synchronous activities. Further implications are discussed to inform instructional design of blended and hybrid approaches beyond COVID-19.
Analysing Capacity Bottlenecks in Rail Infrastructure by Episode Mining

We introduce a methodology to identify and analyse capacity bottlenecks in railway networks. It is based on operational data that has been recorded in real operation. In a first step, network areas that exhibit frequent and significant train delays are determined. Next, the actual causes of such delays are investigated by analysing interdependences between train runs and by distinguishing between primary and secondary delays. This is achieved by employing episode mining techniques to enable the systematic identification of temporal patterns that occur frequently in the data about train runs.
e-FeeD4Mi: Automating Tailored LA-Informed Feedback in Virtual Learning Environments

The provision of personalized and timely feedback can become challenging when shifting from face-to-face to online learning. Feedback is not only about providing support to students, but also about identifying when and which students need what kind of support. Usually, educators carry out such activities manually. However, the manual identification, personalization and provision of feedback might turn unmanageable, especially in large-scale environments. Previous works proposed the use of data-driven tools to automate the feedback provision with the active involvement of human agents in its design. Nevertheless, to the best of our knowledge, these tools do not guide instructors in the process of feedback design and sense-making of the data-driven information. This paper presents e-FeeD4Mi, a web-based tool developed to support instructors in the design and automatic enactment of feedback in multiple virtual learning environments. We developed e-FeeD4Mi following a Design-Based Research approach and its potential for adoption has been evaluated in two evaluation studies.
What Teachers Would Expect from a Pedagogical Agent System Working at a Classroom Level: A Focus Group Study

Applications of pedagogical agent (PA) systems incorporating animated characters in school settings have mainly addressed students at an individual level. However, how these systems could be used and designed for supporting teachers while taking advantage of artificial intelligence (AI) technology is an open question. Therefore, we carried out a focus group to understand what teachers would expect and need from such a system at a classroom level. Our focus group protocol sought to discover design and practical considerations in four dimensions. 1) System design considerations, where teachers expect the system to incorporate speech recognition and to “learn” from them while doing their practice. 2) System collaboration, teachers wanted support in their pedagogy by considering students’ achievement profiles, and by finding and sorting learning material as needed. 3) PA role in the classroom, we identified the following roles: annotator, scaffolder, peacekeeper, and substitute. 4) PA ethical considerations, teachers perceive PA as a possible replacement threat and controversial opinions on the use and meaning making of this technology. We discuss our findings and present future research directions to develop a PA that could empower teachers with AI pedagogy in the classroom, hence, indirectly supporting learning.
Masked face recognition with principal random forest convolutional neural network (PRFCNN)

Masked face recognition embarks the interest among the researchers to find a better algorithm to improve the performance of face recognition applications, especially in the Covid-19 pandemic lately. This paper introduces a proposed masked face recognition method known as Principal Random Forest Convolutional Neural Network (PRFCNN). This method utilizes the strengths of Principal Component Analysis (PCA) with the combination of Random Forest algorithm in Convolution Neural Network to pre-train the masked face features. PRFCNN is designed to assist in extracting more salient features and prevent overfitting problems. Experiments are conducted on two benchmarked datasets, RMFD (Real-World Masked Face Dataset) and LFW Simulated Masked Face Dataset using various parameter settings. The experimental result with a minimum recognition rate of 90% accuracy promises the effectiveness of the proposed PRFCNN over the other state-of-the-art methods.
Safemedchain — drug counterfeit prevention and recommendation using blockchain and machine learning

Counterfeit drugs are without a doubt becoming a greater hazard to consumers and the pharmaceutical sector. As a result, real-time visibility of drug manufacturing and management is required. The proposed system uses Ethereum blockchain as the main technology. The primary advantage of blockchain technology is that the transactions are maintained in immutable digital ledger format and it may be read easily without jeopardizing the users’ security and privacy. In our proposed system, the admin validates and adds the manufacturers. The manufacturer after registering and logging in can perform tasks like adding the drug and seller list. The seller can place order to the manufacturer which the manufacturer can accept or reject. The seller can update status of order of accepted orders to delivered. The customer can view the order details by entering the serial number on the drug package. Any transaction or exchange that occurs in the network is recorded in the chain. It functions similarly to other networks, but blockchain technology is distinguished by the fact that no data can be removed or altered by anyone in the network. No changes to the network can be made unless it has been validated by all of the network’s authorized users. All the information stored can be read by anybody so to incorporate more security, AES has been used to store data in the blockchain. The use of AES encryption technique distinguishes this system from all the existing implementations. Thus, this makes it easy to trace to the exact point in the supply chain and detect any counterfeit drugs in movement. As an extension to the drug counterfeit prevention system a Drug Recommendation System is also performed using the ensemble model with a combination of Random Forest and Logistic Regression for sentiment analysis training. Furthermore, when compared to the existing Linear SVM model, which has an accuracy of 90.39%, the suggested model has the best accuracy of 93.31%. Using the obtained sentiment for each drug, the drug is predicted accurately for the specified medical condition.
An augmented semantic search tool for multilingual news analytics

News feeds generate colossal amount of data consisting of important information hidden in the intricacies. State of the art methods are still at infancy in providing a very generic and publicly available solution to skim through the important information in the news from various sources and an ability to search using specific keywords in different languages. This paper focuses on designing a tool to extract semantic details from news articles published through various internet sources in various languages. The semantic information is stored within DBMS for ease of organizing and retrieving the data. Further, a querying facility to search through entire articles based on the keyword or date-based search is also proposed to view the crisp content. The news articles in English, and two Indian languages - Hindi and Malayalam are considered for experimentation. The proposed strategy consists of two main components namely, Generative model creation and Query engine. Generative model aims to extract important entities and keywords along with their relevance to the article and other similar articles using Latent Dirichlet Allocation(LDA) and Named Entity Recognition(NER). Query engine is to facilitate on the fly retrieval of semantic content from the database, based on user keyword. The search engine, along with database indexing, reduces the access time to the database thereby retrieving the information in less time. Experimental results show that the proposed method is effective in terms of quality of information and time consumed for information retrieval.
Design and Implementation of encryption software based on classical cryptography algorithms

This article uses Visual Studio 2015 to develop, using a variety of encryption algorithms, including Virginia algorithm, Caesar algorithm, single table replacement algorithm technology, to achieve efficient and secure information. The system runs in the Windows system, the function is stable, and the information security is effectively guaranteed. In this paper, the research of the subject is carried out first, and the key technologies of cryptography are sorted out. Present the current research status and introduce the main research contents. Then analyze and organize related technologies, introduce their advantages and disadvantages, and then compare encryption algorithms. Finally, the implementation and evaluation of the algorithm are carried out. The realization of the encryption software based on the classical cryptography algorithm ensures the encryption in the transmission process and solves the problem of data security.
Identification of informational and probabilistic independence by adaptive thresholding

The independence assumptions help Bayesian network classifier (BNC), e.g., Naive Bayes (NB), reduce structure complexity and perform surprisingly well in many real-world applications. Semi-naive Bayesian techniques seek to improve the classification performance by relaxing the attribute independence assumption. However, the study of dependence rather than independence has received more attention during the past decade and the validity of independence assumptions needs to be further explored. In this paper, a novel learning technique, called Adaptive Independence Thresholding (AIT), is proposed to automatically identify the informational independence and probabilistic independence. AIT can respectively tune the network topologies of BNC learned from training data and testing instance under the framework of target learning. Zero-one loss, bias, variance and conditional log likelihood are introduced to compare the classification performance in the experimental study. The extensive experimental results on a collection of 36 benchmark datasets from the UCI machine learning repository show that AIT is more effective than other learning techniques (such as structure extension, attribute weighting) and helps make the final BNCs achieve remarkable classification improvements.
A multi-criteria decision-making approach to analyse the viability of blockchain in software development projects

Blockchain technology is getting famous, and use cases of blockchain range from financial services to the Metaverse. It is considered a platform for web 3.0. As a result, many industries are planning to adopt blockchain. A simple public blockchain is not suitable for most business scenarios, so hybrid and private blockchains came into existence, but it is important to decide which type of blockchain should be adopted during the project planning phase. Various models can be found in the literature to determine if blockchain should be adopted and, if so, which type of blockchain should be adopted. However, these models are already becoming obsolete as they determine the usage of blockchain using simple yes or no. In order to overcome these problems, all these models are converted from binary-based selection to fuzzy-based selection, and decision matrices are created. Various multi-criteria decision analysis methods are applied, and final results are obtained. In addition, a novel model is presented, and a MATLAB application is developed to let the user determine if blockchain can be integrated with any technology or not. This application can be used as a standard in the project’s planning phase and helps avoid losses to the industry.
Research on network teaching platform design of basic Japanese course

At present, with the further deepening of the concept of Internet +, it has become a general trend to optimize and innovate the teaching of basic Japanese courses. Therefore, it is a feasible way to build a new network teaching platform of Japanese basic course based on Internet technology. Based on the actual needs of all kinds of users in the teaching of Japanese basic course, this paper discusses the design of network teaching platform of Japanese basic course in detail from the aspects of system architecture design, database design and so on. At the same time, the related test results show that the performance of the system platform basically meets the expected requirements, and it also plays an important role in improving students’ academic performance.
Online learning effect monitoring system for expression recognition based on computer system

Further in order to better improve the online learning system on the student's learning status, learning level and course activity system to make a systematic monitoring design. Better improve the teaching process between teachers and students to reach a new level of level, so that teachers can better get feedback from students when awarding courses. We have also optimized and improved the online learning effect detection system based on the computer system. In the system, we have built a variety of expression recognition algorithm principles to better capture students' expressions in the classroom, so as to make the conclusions drawn by the online learning system model More authenticity and effectiveness
Development of traditional culture learning platform based on computer system

With the development of society, more and more people are interested in traditional culture. Today, traditional cultural education has become an important part of education. In order to provide a platform for people who like traditional culture to understand traditional culture, publicize traditional culture and ensure the correctness of traditional culture inheritance, this paper constructs a traditional culture learning platform based on computer system. This platform is developed and used B/S structure, which improves the efficiency of system operation and reduces the cost of system operation. The platform uses MySQL as the database, Java as the programming language, and uses the JSP technology to help the system to be applied across platforms. The platform also has user-oriented personalized recommendation, using tag-based recommendation algorithm to recommend learning resources that meet user preferences. This platform provides the public with a channel to understand traditional culture, which can bring good user experience to users and has important practical value for the inheritance of traditional culture.