Reviewerly/exHarmony huggingface dataset:
Some Easy Instances of Ideal-SVP and Implications on the Partial Vandermonde Knapsack Problem

In this article, we generalize the works of Pan et al. (Eurocrypt’21) and Porter et al. (ArXiv’21) and provide a simple condition under which an ideal lattice defines an easy instance of the shortest vector problem. Namely, we show that the more automorphisms stabilize the ideal, the easier it is to find a short vector in it. This observation was already made for prime ideals in Galois fields, and we generalize it to any ideal (whose prime factors are not ramified) of any number field. We then provide a cryptographic application of this result by showing that particular instances of the partial Vandermonde knapsack problem, also known as partial Fourier recovery problem, can be solved classically in polynomial time. As a proof of concept, we implemented our attack and managed to solve those particular instances for concrete parameter settings proposed in the literature. For random instances, we can halve the lattice dimension with non-negligible probability.
A Method of Deep Learning Model Optimization for Image Classification on Edge Device

Due to the recent increasing utilization of deep learning models on edge devices, the industry demand for Deep Learning Model Optimization (DLMO) is also increasing. This paper derives a usage strategy of DLMO based on the performance evaluation through light convolution, quantization, pruning techniques and knowledge distillation, known to be excellent in reducing memory size and operation delay with a minimal accuracy drop. Through experiments regarding image classification, we derive possible and optimal strategies to apply deep learning into Internet of Things (IoT) or tiny embedded devices. In particular, strategies for DLMO technology most suitable for each on-device Artificial Intelligence (AI) service are proposed in terms of performance factors. In this paper, we suggest a possible solution of the most rational algorithm under very limited resource environments by utilizing mature deep learning methodologies.
PF-ViT: Parallel and Fast Vision Transformer for Offline Handwritten Chinese Character Recognition

Recently, Vision Transformer (ViT) has been widely used in the field of image recognition. Unfortunately, the ViT model repeatedly stacks 12-layer encoders, resulting in a large number of model computations, many parameters, and slow training speed, making it difficult to deploy on mobile devices. In order to reduce the computational complexity of the model and improve the training speed, a parallel and fast Vision Transformer method for offline handwritten Chinese character recognition is proposed. The method adds parallel branches of the encoder module to the structure of the Vision Transformer model. Parallel modes include two-way parallel, four-way parallel, and seven-way parallel. The original picture is fed to the encoder module after flattening and linear embedding processing operations. The core step in the encoder is the multihead attention mechanism. Multihead self-attention can learn the interdependence between image sequence blocks. In addition, the use of data expansion strategies increases the diversity of data. In the two-way parallel experiment, when the model is 98.1% accurate on the dataset, the number of parameters and the number of FLOPs are 43.11 million and 4.32 G, respectively. Compared with the ViT model, whose parameters and FLOPs are 86 million and 16.8 G, respectively, the two-way parallel model has a 50.1% decrease in parameters and a 34.6% decrease in FLOPs. This method has been demonstrated to effectively reduce the computational complexity of the model while indirectly improving image recognition speed.
A <scp>Solicitous</scp> Approach to Smart Contract Verification

Smart contracts are tempting targets of attacks, as they often hold and manipulate significant financial assets, are immutable after deployment, and have publicly available source code, with assets estimated in the order of millions of dollars being lost in the past due to vulnerabilities. Formal verification is thus a necessity, but smart contracts challenge the existing highly efficient techniques routinely applied in the symbolic verification of software, due to specificities not present in general programming languages. A common feature of existing works in this area is the attempt to reuse off-the-shelf verification tools designed for general programming languages. This reuse can lead to inefficiency and potentially unsound results, as domain translation is required. In this article, we describe a carefully crafted approach that directly models the central aspects of smart contracts natively, going from the contract to its logical representation without intermediary steps. We use the expressive and highly automatable logic of constrained Horn clauses for modeling and instantiate our approach to the Solidity language. A tool implementing our approach, called Solicitous , was developed and integrated into the SMTChecker module of the Solidity compiler solc. We evaluated our approach on an extensive benchmark set containing 22,446 real-world smart contracts deployed on the Ethereum blockchain over a 27-month period. The results show that our approach is able to establish safety of significantly more contracts than comparable, publicly available verification tools, with an order of magnitude increase in the percentage of formally verified contracts.
FAC-V: An FPGA-Based AES Coprocessor for RISC-V

In the new Internet of Things (IoT) era, embedded Field-Programmable Gate Array (FPGA) technology is enabling the deployment of custom-tailored embedded IoT solutions for handling different application requirements and workloads. Combined with the open RISC-V Instruction Set Architecture (ISA), the FPGA technology provides endless opportunities to create reconfigurable IoT devices with different accelerators and coprocessors tightly and loosely coupled with the processor. When connecting IoT devices to the Internet, secure communications and data exchange are major concerns. However, adding security features requires extra capabilities from the already resource-constrained IoT devices. This article presents the FAC-V coprocessor, which is an FPGA-based solution for an RISC-V processor that can be deployed following two different coupling styles. FAC-V implements in hardware the Advanced Encryption Standard (AES), one of the most widely used cryptographic algorithms in IoT low-end devices, at the cost of few FPGA resources. The conducted experiments demonstrate that FAC-V can achieve performance improvements of several orders of magnitude when compared to the software-only AES implementation; e.g., encrypting a message of 16 bytes with AES-256 can reach a performance gain of around 8000× with an energy consumption of 0.1 μJ.
Data Mining Applications in Banking Sector While Preserving Customer Privacy

In real-life data mining applications, organizations cooperate by using each other’s data on the same data mining task for more accurate results, although they may have different security and privacy concerns. Privacy-preserving data mining (PPDM) practices involve rules and techniques that allow parties to collaborate on data mining applications while keeping their data private. The objective of this paper is to present a number of PPDM protocols and show how PPDM can be used in data mining applications in the banking sector. For this purpose, the paper discusses homomorphic cryptosystems and secure multiparty computing. Supported by experimental analysis, the paper demonstrates that data mining tasks such as clustering and Bayesian networks (association rules) that are commonly used in the banking sector can be efficiently and securely performed. This is the first study that combines PPDM protocols with applications for banking data mining. Doi: 10.28991/ESJ-2022-06-06-014 Full Text: PDF
Using motivation to improve learning achievement with a chatbot in blended learning

Chatbots have the potential to be used as motivational learning tools, particularly for boosting academic performance. The purpose of this study is to construct a Facebook Messenger chatbot to promote accomplishment through the use of blended learning, guided by the ARCS (attention, relevance, confidence, satisfaction) motivation model that compares how engagement works, and explores the chatbot in terms of its usability. Integrated with Facebook Messenger, chatbot software was designed to answer inquiries based on the chatbot's communication framework. This included course alerts, a gradebook for each student, attendance statistics, and assignment feedback. Using a quasi-experimental research approach, the influence of the chatbot on student motivation and academic achievement was empirically investigated. The trial covered 18 weeks, and the sample comprised 48 students enrolled in a course on Information Technology for Learning. The results suggest that the chatbot increased the learning accomplishment of the students to a considerable extent, and that a motivational setting may lead to a better outcome than a blended learning environment. Overall, our approach produced reliable findings which validated the chatbot's capacity to communicate with students. The students agreed that the chatbot facilitated their learning, but that a few modifications were required in terms of ongoing development. Keywords: Motivation; Learning Achievement; Chatbot; Blended Learning
Deep-Sentiment: Sentiment Analysis Using Ensemble of CNN and Bi-LSTM Models

With the popularity of social networks, and e-commerce websites, sentiment analysis has become a more active area of research in the past few years. On a high level, sentiment analysis tries to understand the public opinion about a specific product or topic, or trends from reviews or tweets. Sentiment analysis plays an important role in better understanding customer/user opinion, and also extracting social/political trends. There has been a lot of previous works for sentiment analysis, some based on hand-engineering relevant textual features, and others based on different neural network architectures. In this work, we present a model based on an ensemble of long-short-term-memory (LSTM), and convolutional neural network (CNN), one to capture the temporal information of the data, and the other one to extract the local structure thereof. Through experimental results, we show that using this ensemble model we can outperform both individual models. We are also able to achieve a very high accuracy rate compared to the previous works.
Case tests as a tool for identifying of teacher’s professional deficits in the interpretation of complex knowledge

Introduction. The formation of functional (mathematical) literacy of students has been the basic trend of mathematical education in secondary school in recent years in connection with the results of the international PISA testing. Pedagogical experience, theory and practice, requests and challenges of real life show that fundamental mathematical abilities and situations of overcoming difficulties in mastering complex knowledge play a central role in various levels determining in the formation of mathematical literacy success. The purpose of the study is to identify the subject and methodological deficiencies of teachers in the interpretation of complex knowledge as a factor in the formation of mathematical literacy of students, using the original competence meters in case tests e form and using Google Forms. Materials and methods. Materials: diagnostic case-tests; online service Google Forms. Methods of descriptive statistics: arithmetic mean, standard deviation, etc. Results. It was revealed that teacher’s majority of (80%) adequately assesses their scientific and methodological potential, are tolerant of innovations in didactics. Low average results of teachers (43.7%) are shown, indicating the presence of subject deficits in mathematics teachers in the field of applying mathematical knowledge in solving practice-oriented tasks. The average results of teachers' methodological deficits (62.3%) indicate the teacher's readiness for professional self-development, the ability to adapt and knowledge using of science’s modern problems in solving practice-oriented tasks with student’s motivation and self-organization effects. Conclusions. The subject and methodological competencies of a teacher should cover the content and ways of complex knowledge adapting (modern achievements in science) to school mathematics, the variability of forms and means of generalized constructs presenting, knowledge of mathematical and computer modeling methods, knowledge of samples and standards of popular applications of complex knowledge to real life, technologies and industries. Practice-oriented tasks arise in teaching mathematics at the same time as a component of student’s project and research activities.
Characterising Bias in Compressed Models

The popularity and widespread use of pruning and quantization is driven by the severe resource constraints of deploying deep neural networks to environments with strict latency, memory and energy requirements. These techniques achieve high levels of compression with negligible impact on top-line metrics (top-1 and top-5 accuracy). However, overall accuracy hides disproportionately high errors on a small subset of examples; we call this subset Compression Identified Exemplars (CIE). We further establish that for CIE examples, compression amplifies existing algorithmic bias. Pruning disproportionately impacts performance on underrepresented features, which often coincides with considerations of fairness. Given that CIE is a relatively small subset but a great contributor of error in the model, we propose its use as a human-in-the-loop auditing tool to surface a tractable subset of the dataset for further inspection or annotation by a domain expert. We provide qualitative and quantitative support that CIE surfaces the most challenging examples in the data distribution for human-in-the-loop auditing.
Accuracy Analysis of Sports Performance Prediction Based on BP Neural Network Intelligent Algorithm

With the increasing attention and popularity of competitive sports, the continuous progress of artificial intelligence, and deep learning theory, people’s sports performance prediction technology for professional athletes or sports students is also developing. Accurate and effective prediction can help athletes and students to carry out more targeted training, so as to further improve their performance. BP neural network (BPNN) is a multilayer feedforward neural network (NN). Therefore, based on the BPNN algorithm, this work conducts a deep research on the prediction of sports performance. First of all, this work uses the three-layer structure of BPNN to design the algorithm and then selects the weight, oxygen saturation, systolic and diastolic blood pressure, the previous best score, the worst score in the past period, and the average score of one week before the examination as the feature vector of the input sample. The students’ scores of two classes of physical education major in a university are selected as the prediction objects, and the time period of students’ relevant information data is selected from September 2018 to December 2018. The quantity of hidden units is calculated to be 15 by training. After the successful construction of BPNN sports performance estimation method, the PSO search approach is utilized to enhance the BPNN sports performance prediction model. Finally, the relationship between the two classes’ performance and the students’ performance is analyzed. 49% of the total number of times the error of class A was predicted to be 0, and the number of times that the error of class B was 0 was 50%, 58%, and 75%, respectively. There is no strong linear correlation between sports performance and body weight, but a high correlation systolic, pulse pressure, and plasma oxygen levels. This shows that the BPNN sports prediction model established in this work has high accuracy in predicting sports performance.
Deep learning for quality control of receiver functions

Receiver function has been routinely used for studying the discontinuity structure in the crust and upper mantle. The manual quality control of receiver functions, which plays a key role in high-quality data selection and accurate structural imaging, has been challenged by today’s booming data volumes. Traditional automatic quality control methods usually require tuning hyperparameters and fail to generalize to low signal-to-noise ratio data. Deep learning has been increasingly used to deal with extensive seismic data. However, it generally requires a manually labeled dataset, and its performance is highly related to the network design. In this study, we develop and compare four different deep learning network designs with manual and traditional quality control methods using 53293 receiver functions from three broadband seismic stations. Our results show that a combination of convolutional and long-short memory layers achieves the best performance of ∼91% accuracy. We also propose a fully automatic training schema that requires zero manually labeled receiver function yet achieves similar performance to that using carefully labeled ones. Compared with the traditional automatic method, our model retrieves ∼5 times more reliable receiver functions from relatively small earthquakes with magnitudes between 5.0 and 5.5. The average waveforms and H-κ stacking results of these receiver functions are comparable to those obtained by manual quality control from earthquakes with magnitudes larger than 5.5, which further demonstrates the validity of our method and indicates its potential for making use of smaller earthquakes in the receiver function analysis.
Smaller progress measures and separating automata for parity games

Calude et al. have recently shown that parity games can be solved in quasi-polynomial time, a landmark result that has led to a number of approaches with quasi-polynomial complexity. Jurdinski and Lasic have further improved the precise complexity of parity games, especially when the number of priorities is low (logarithmic in the number of positions). Both of these algorithms belong to a class of game solving techniques now often called separating automata: deterministic automata that can be used as witness automata to decide the winner in parity games up to a given number of states and colours. We suggest a number of adjustments to the approach of Calude et al. that lead to smaller statespaces. These include and improve over those earlier introduced by Fearnley et al. We identify two of them that, together, lead to a statespace of exactly the same size Jurdzinski and Lasic's concise progress measures, which currently hold the crown as smallest statespace. The remaining improvements, hence, lead to a further reduction in the size of the statespace, making our approach the most succinct progress measures available for parity games.
Exosoul: Ethical Profiling in the Digital World

The development and the spread of increasingly autonomous digital technologies in our society pose new ethical challenges beyond data protection and privacy violation. Users are unprotected in their interactions with digital technologies and at the same time autonomous systems are free to occupy the space of decisions that is prerogative of each human being. In this context the multidisciplinary project Exosoul aims at developing a personalized software exoskeleton which mediates actions in the digital world according to the moral preferences of the user. The exoskeleton relies on the ethical profiling of a user, similar in purpose to the privacy profiling proposed in the literature, but aiming at reflecting and predicting general moral preferences. Our approach is hybrid, first based on the identification of profiles in a top-down manner, and then on the refinement of profiles by a personalized data-driven approach. In this work we report our initial experiment on building such top-down profiles. We consider the correlations between ethics positions (idealism and relativism) personality traits (honesty/humility, conscientiousness, Machiavellianism and narcissism) and worldview (normativism), and then we use a clustering approach to create ethical profiles predictive of user’s digital behaviors concerning privacy violation, copyright infringements, caution and protection. Data were collected by administering a questionnaire to 317 young individuals. In the paper we discuss two clustering solutions (k = 2 and k = 4) in terms of validity and predictive power of digital behavior.
Overperception of moral outrage in online social networks inflates beliefs about intergroup hostility

As individuals and political leaders increasingly interact in online social networks, it is important to understand how the affordances of social media shape social knowledge of morality and politics. Here, we propose that social media users overperceive levels of moral outrage felt by individuals and groups, inflating beliefs about intergroup hostility. Utilizing a Twitter field survey, we measured authors’ moral outrage in real time and compared authors’ reports to observers’ judgments of the authors’ moral outrage. We find that observers systematically overperceive moral outrage in authors, inferring more intense moral outrage experiences from messages than the authors of those messages actually reported. This effect was stronger in participants who spent more time on social media to learn about politics. Pre-registered confirmatory behavioral experiments found that overperception of individuals’ moral outrage causes overperception of collective moral outrage and inflates beliefs about hostile communication norms, group affective polarization and ideological extremity. Together, these results highlight how individual-level overperceptions of online moral outrage produce collective overperceptions that have the potential to warp our social knowledge of moral and political attitudes.
Twitter Data Analysis using Distill BERT and Graph Based Convolution Neural Network during Disaster

Abstract TDS (Twitter Data Analysis) in Social media plays an essential role in spreading information during disasters and needy situations. May it be for seeking help or sharing and updating seriousness of situation or communicating the status or response to public in need social media plays a major role in case of emergencies. This research focuses on to prepare the data by performing cleansing and various transformations, to generate word embedding vectors using DistillBERT and generate a similarity matrix , to construct the(Graph based Convolution Network) GCN model to classify text sequences, to analyze the performance of the DistillBERT with GCN model in Text classification. To implement this, contextual word embedding’s introduced for generating word vectors. The contextual word embedding’s and concept of graph neural networks which has gained its importance to capture contextual relationship among the text which further improves the performance of classification. In this work proposed a semi supervised ML model to detect and classify Need and Availability of Resources tweets with accuracy of 98% as compared with state-of-the-art approaches.