Reviewerly/exHarmony huggingface dataset:
User behaviour analysis using data analytics and machine learning to predict malicious user versus legitimate user

Research-based on user behavior analysis for authentication is the motivation for this research. We move ahead using a behavioral approach to identify malicious users and legitimate users. In this paper, we have explained how we have applied big data analytics to application-layer logs and predicted malicious users by employing a Machine Learning algorithm based on certain metrics explained later in the paper. Machine Learning would present a list of IP addresses or user identification tokens (UIT),deduced from live data which would be performing a malicious activity or are suspected of malicious activity based on their browsing behavior. We have created an e-commerce web application and induced vulnerabilities intentionally for this purpose. We have hosted our setup on LAMP [1] stack based on AWS cloud [2]. This method has a huge potential as any organization can imply this to monitor probable attackers thus narrowing down on their efforts to safeguard their infrastructure. The idea is based on the fact that the browsing pattern, as well as the access pattern of a genuine user,varies widely with that of a hacker. These patterns would be used to sort out the incoming traffic from and list out IP addresses and UIT that are the most probable cases of hack attempts.
Implications for Agricultural Producers of Using Blockchain for Food Transparency, Study of 4 Food Chains by Cumulative Approach

In agro-food, Blockchain has been recently implemented in order to improve transparency. Blockchain raises great expectations of data decentralization and better efficiency–cost ratio, integration speed, and data protection that appear as promises of gains in all areas. The fundamental assumption was that transparency prevents or reduces illegitimate forms of power. However, discussions are emerging about how digitization is likely to exacerbate power inequalities in food systems, as transparency can become tyrannical when it contributes to the proliferation of audits, evaluations, and assessment measures. The objective of this research is to contribute by providing knowledge about the implications of this digitization for farmers. For a first exploratory study, we conducted 53 interviews with actors of digitalization of agri-food, and we used 9 press releases, 3 webinars, and 1 article published in a specialized French journal. These materials evoke 12 different agro-food chains recently equipped with blockchain in France. From this pool of chains, we focused on four through in-depth analysis of interviews and literature readings using NVivo software. The first results highlight that the use of blockchain for transparency rarely delivers on its promises. Blockchain tends to centralize control since few actors have access to the distributed ledger, and the visibility brought to farmers, at the consumer level, tends to become a form of control. While blockchain seems to provide some benefits to producers, it raises the issue of overloaded technology and the problem of their data privacy.
Learning Opinion Summarizers by Selecting Informative Reviews

Opinion summarization has been traditionally approached with unsupervised, weaklysupervised and few-shot learning techniques.In this work, we collect a large dataset of summaries paired with user reviews for over 31,000 products, enabling supervised training.However, the number of reviews per product is large (320 on average), making summarization -and especially training a summarizerimpractical.Moreover, the content of many reviews is not reflected in the human-written summaries, and, thus, the summarizer trained on random review subsets hallucinates.In order to deal with both of these challenges, we formulate the task as jointly learning to select informative subsets of reviews and summarizing the opinions expressed in these subsets.The choice of the review subset is treated as a latent variable, predicted by a small and simple selector.The subset is then fed into a more powerful summarizer.For joint training, we use amortized variational inference and policy gradient methods.Our experiments demonstrate the importance of selecting informative reviews resulting in improved quality of summaries and reduced hallucinations.
Task-Agnostic Continual Learning Using Online Variational Bayes with Fixed-Point Updates

Catastrophic forgetting is the notorious vulnerability of neural networks to the changes in the data distribution during learning. This phenomenon has long been considered a major obstacle for using learning agents in realistic continual learning settings. A large body of continual learning research assumes that task boundaries are known during training. However, only a few works consider scenarios in which task boundaries are unknown or not well defined: task-agnostic scenarios. The optimal Bayesian solution for this requires an intractable online Bayes update to the weights posterior. We aim to approximate the online Bayes update as accurately as possible. To do so, we derive novel fixed-point equations for the online variational Bayes optimization problem for multivariate gaussian parametric distributions. By iterating the posterior through these fixed-point equations, we obtain an algorithm (FOO-VB) for continual learning that can handle nonstationary data distribution using a fixed architecture and without using external memory (i.e., without access to previous data). We demonstrate that our method (FOO-VB) outperforms existing methods in task-agnostic scenarios. FOO-VB Pytorch implementation is available at https://github.com/chenzeno/FOO-VB.
Bitcoin Price Prediction Using Machine Learning and Artificial Neural Network Model

Objective: This paper explains the working of the linear regression and Long Short-Term Memory model in predicting the value of a Bitcoin.Due to its raising popularity, Bitcoin has become like an investment and works on the Block chain technology which also gave raise to other crypto currency.This makes it very difficult to predict its value and hence with the help of Machine Learning Algorithm and Artificial Neural Network Model this predictor is tested.Methodology: In this study, we have used data sets for Bitcoin for testing and training the ML and AI model.With the help of python libraries, the data filtration process was done.Python has provided with a best feature for data analysis and visualization.After the understanding of the data, we trim the data and use the features or attributes best suited for the model.Implementation of the model is done and the result is recorded.Finding: It was discovered that the linear regression model's accuracy rate is very high when compared to other Machine Learning models from related works; it was found to be 99.87 percent accurate.The LSTM model, on the other hand, shows a mini error rate of 0.08 percent.This, in turn, demonstrates that the neural network model is more optimized than the machine learning model.Novelty: In this work, a small GUI has been created using the tkinter library that will allow the user to input the High, Low, and Open features values and then predict the next value for the coin.This paper compares the prediction outcomes of a machine learning model and an artificial neural network model.Because linear regression provided the highest accuracy compared to the other machine learning models, we used it to compare it to the LSTM model.
&amp;#8220;You don&amp;#8217;t understand me!&amp;#8221;: Comparing ASR Results for L1 and L2 Speakers of Swedish

The performance of Automatic Speech Recognition (ASR) systems has constantly increased in state-of-the-art development.However, performance tends to decrease considerably in more challenging conditions (e.g., background noise, multiple speaker social conversations) and with more atypical speakers (e.g., children, non-native speakers or people with speech disorders), which signifies that general improvements do not necessarily transfer to applications that rely on ASR, e.g., educational software for younger students or language learners.In this study, we focus on the gap in performance between recognition results for native and non-native, read and spontaneous, Swedish utterances transcribed by different ASR services.We compare the recognition results using Word Error Rate and analyze the linguistic factors that may generate the observed transcription errors.
Improved guess-and-determine and distinguishing attacks on SNOW-V

In this paper, we investigate the security of SNOW-V, demonstrating two guess-and-determine (GnD) attacks against the full version with complexities 2384 and 2378, respectively, and one distinguishing attack against a reduced variant with complexity 2303. Our GnD attacks use enumeration with recursion to explore valid guessing paths, and try to truncate as many invalid guessing paths as possible at early stages of the recursion by carefully designing the order of guessing. In our first GnD attack, we guess three 128-bit state variables, determine the remaining four according to four consecutive keystream words. We finally use the next three keystream words to verify the correct guess. The second GnD attack is similar but exploits one more keystream word as side information helping to truncate more guessing paths. Our distinguishing attack targets a reduced variant where 32-bit adders are replaced with exclusive-OR operations. The samples can be collected from short keystream sequences under different (key, IV) pairs. These attacks do not threaten SNOW-V, but provide more in-depth details for understanding its security and give new ideas for cryptanalysis of other ciphers.
Exploring Underexplored Limitations of Cross-Domain Text-to-SQL Generalization

Recently, there has been significant progress in studying neural networks for translating text descriptions into SQL queries under the zeroshot cross-domain setting.Despite achieving good performance on some public benchmarks, we observe that existing text-to-SQL models do not generalize when facing domain knowledge that does not frequently appear in the training data, which may render the worse prediction performance for unseen domains.In this work, we investigate the robustness of text-to-SQL models when the questions require rarely observed domain knowledge.In particular, we define five types of domain knowledge and introduce Spider-DK (DK is the abbreviation of domain knowledge), a human-curated dataset based on the Spider benchmark for text-to-SQL translation.NL questions in Spider-DK are selected from Spider, and we modify some samples by adding domain knowledge that reflects real-world question paraphrases.We demonstrate that the prediction accuracy dramatically drops on samples that require such domain knowledge, even if the domain knowledge appears in the training set, and the model provides the correct predictions for related training samples. 1
DIALKI: Knowledge Identification in Conversational Systems through Dialogue-Document Contextualization

Identifying relevant knowledge to be used in conversational systems that are grounded in long documents is critical to effective response generation.We introduce a knowledge identification model that leverages the document structure to provide dialogue-contextualized passage encodings and better locate knowledge relevant to the conversation.An auxiliary loss captures the history of dialogue-document connections.We demonstrate the effectiveness of our model on two document-grounded conversational datasets and provide analyses showing generalization to unseen documents and long dialogue contexts.
An efficient <i>k</i>‐modes algorithm for clustering categorical datasets

Mining clusters from data is an important endeavor in many applications. The $k$-means method is a popular, efficient, and distribution-free approach for clustering numerical-valued data, but does not apply for categorical-valued observations. The $k$-modes method addresses this lacuna by replacing the Euclidean with the Hamming distance and the means with the modes in the $k$-means objective function. We provide a novel, computationally efficient implementation of $k$-modes, called OTQT. We prove that OTQT finds updates to improve the objective function that are undetectable to existing $k$-modes algorithms. Although slightly slower per iteration due to algorithmic complexity, OTQT is always more accurate per iteration and almost always faster (and only barely slower on some datasets) to the final optimum. Thus, we recommend OTQT as the preferred, default algorithm for $k$-modes optimization.
Feature Fusion Based on Convolutional Neural Network for Breast Cancer Auxiliary Diagnosis

Cancer is one of the leading causes of death in many countries. Breast cancer is one of the most common cancers in women. Especially in remote areas with low medical standards, the diagnosis efficiency of breast cancer is extremely low due to insufficient medical facilities and doctors. Therefore, in-depth research on how to improve the diagnosis rate of breast cancer has become a hot spot. With the development of society and science, people use artificial intelligence to improve the auxiliary diagnosis of diseases in the existing medical system, which can become a solution for detecting and accurately diagnosing breast cancer. The paper proposes an auxiliary diagnosis model that uses deep learning in view of the low rate of human diagnosis by doctors in remote areas. The model uses classic convolutional neural networks, including VGG16, InceptionV3, and ResNet50 to extract breast cancer image features, then merge these features, and finally train the model VIRNets for auxiliary diagnosis. Experimental results prove that for the recognition of benign and malignant breast cancer pathological images under different magnifications, VIRNets have a high generalization and strong robustness, and their accuracy is better than their basic network and other structures of the network. Therefore, the solution provides a certain practical value for assisting doctors in the diagnosis of breast cancer in real scenes.
Do Long-Range Language Models Actually Use Long-Range Context?

Language models are generally trained on short, truncated input sequences, which limits their ability to use discourse-level information present in long-range context to improve their predictions.Recent efforts to improve the efficiency of self-attention have led to a proliferation of long-range Transformer language models, which can process much longer sequences than models of the past.However, the ways in which such models take advantage of the longrange context remain unclear.In this paper, we perform a fine-grained analysis of two longrange Transformer language models (including the Routing Transformer, which achieves state-of-the-art perplexity on the PG-19 longsequence LM benchmark dataset) that accept input sequences of up to 8K tokens.Our results reveal that providing long-range context (i.e., beyond the previous 2K tokens) to these models only improves their predictions on a small set of tokens (e.g., those that can be copied from the distant context) and does not help at all for sentence-level prediction tasks.Finally, we discover that PG-19 contains a variety of different document types and domains, and that long-range context helps most for literary novels (as opposed to textbooks or magazines).
Data Efficient Video Transformer for Violence Detection

In smart cities, violence event detection is critical to ensure city safety. Several studies have been done on this topic with a focus on 2d-Convolutional Neural Network (2d-CNN) to detect spatial features from each frame, followed by one of the Recurrent Neural Networks (RNN) variants as a temporal features learning method. On the other hand, the transformer network has achieved a great result in many areas. The bottleneck for transformers is the need for large data set to achieve good results. In this work, we propose a data-efficient video transformer (DeVTr) based on the transformer network as a Spatio-temporal learning method with a pre-trained 2d-Convolutional neural network (2d-CNN) as an embedding layer for the input data. The model has been trained and tested on the Real-life violence dataset (RLVS) and achieved an accuracy of 96.25%. A comparison of the result for the suggested method with previous techniques illustrated that the suggested method provides the best result among all the other studies for violence event detection.
Keeping Children Safe Online With Limited Resources: Analyzing What is Seen and Heard

It is every parent's wish to protect their children from online pornography, cyber bullying and cyber predators.Several existing approaches analyze a limited amount of information stemming from the interactions of the child with the corresponding online party.Some restrict access to websites based on a blacklist of known forbidden URLs, others attempt to parse and analyze the exchanged multimedia content between the two parties.However, new URLs can be used to circumvent a blacklist, and images, video, and text can individually appear to be safe, but need to be judged jointly.We propose a highly modular framework of analyzing content in its final form at the user interface, or Human Computer Interaction (HCI) layer, as it appears before the child: on the screen and through the speakers.Our approach is to produce Children's Agents for Secure and Privacy Enhanced Reaction (CASPER), which analyzes screen captures and audio signals in real time in order to make a decision based on all of the information at its disposal, with limited hardware capabilities.We employ a collection of deep learning techniques for image, audio and text processing in order to categorize visual content as pornographic or neutral, and textual content as cyberbullying or neutral.We additionally contribute a custom dataset that offers a wide spectrum of objectionable content for evaluation and training purposes.CASPER demonstrates an average accuracy of 88% and an F1 score of 0.85 when classifying text, and an accuracy of 95% when classifying pornography.
Incorporating Residual and Normalization Layers into Analysis of Masked Language Models

Transformer architecture has become ubiquitous in the natural language processing field.To interpret the Transformer-based models, their attention patterns have been extensively analyzed.However, the Transformer architecture is not only composed of the multihead attention; other components can also contribute to Transformers' progressive performance.In this study, we extended the scope of the analysis of Transformers from solely the attention patterns to the whole attention block, i.e., multi-head attention, residual connection, and layer normalization.Our analysis of Transformer-based masked language models shows that the token-to-token interaction performed via attention has less impact on the intermediate representations than previously assumed.These results provide new intuitive explanations of existing reports; for example, discarding the learned attention patterns tends not to adversely affect the performance.The codes of our experiments are publicly available.
Augmented Reality Meets Artificial Intelligence in Robotics: A Systematic Review

Recently, advancements in computational machinery have facilitated the integration of artificial intelligence (AI) to almost every field and industry. This fast-paced development in AI and sensing technologies have stirred an evolution in the realm of robotics. Concurrently, augmented reality (AR) applications are providing solutions to a myriad of robotics applications, such as demystifying robot motion intent and supporting intuitive control and feedback. In this paper, research papers combining the potentials of AI and AR in robotics over the last decade are presented and systematically reviewed. Four sources for data collection were utilized: Google Scholar, Scopus database, the International Conference on Robotics and Automation 2020 proceedings, and the references and citations of all identified papers. A total of 29 papers were analyzed from two perspectives: a theme-based perspective showcasing the relation between AR and AI, and an application-based analysis highlighting how the robotics application was affected. These two sections are further categorized based on the type of robotics platform and the type of robotics application, respectively. We analyze the work done and highlight some of the prevailing limitations hindering the field. Results also explain how AR and AI can be combined to solve the model-mismatch paradigm by creating a closed feedback loop between the user and the robot. This forms a solid base for increasing the efficiency of the robotic application and enhancing the user’s situational awareness, safety, and acceptance of AI robots. Our findings affirm the promising future for robust integration of AR and AI in numerous robotic applications.
Orthogonal Graph Neural Networks

Graph neural networks (GNNs) have received tremendous attention due to their superiority in learning node representations. These models rely on message passing and feature transformation functions to encode the structural and feature information from neighbors. However, stacking more convolutional layers significantly decreases the performance of GNNs. Most recent studies attribute this limitation to the over-smoothing issue, where node embeddings converge to indistinguishable vectors. Through a number of experimental observations, we argue that the main factor degrading the performance is the unstable forward normalization and backward gradient resulted from the improper design of the feature transformation, especially for shallow GNNs where the over-smoothing has not happened. Therefore, we propose a novel orthogonal feature transformation, named Ortho-GConv, which could generally augment the existing GNN backbones to stabilize the model training and improve the model's generalization performance. Specifically, we maintain the orthogonality of the feature transformation comprehensively from three perspectives, namely hybrid weight initialization, orthogonal transformation, and orthogonal regularization. By equipping the existing GNNs (e.g. GCN, JKNet, GCNII) with Ortho-GConv, we demonstrate the generality of the orthogonal feature transformation to enable stable training, and show its effectiveness for node and graph classification tasks.
Quality Assessment of Online Learning in Regional Higher Education Systems

The quality of online learning mechanisms, widely implemented due to the COVID-19 pandemic, is a significant issue for regional higher education systems. The research aims to assess student satisfaction with the quality of online education by identifying discrepancies between their requirements and the actual learning process. In order to examine the gaps between students’ expectations and perceptions, a new approach was proposed based on the integrated use of Gap analysis and SERVQUAL methodology, combining qualitative and quantitative aspects. SERVQUAL questionnaires for measuring student satisfaction with online learning include the following criteria: tangibles, reliability, responsiveness, assurance, empathy. Full- and part-time undergraduates of humanitarian and socio-economic departments of two universities participated in the study. Ural Federal University bachelors, learning via Moodle and Microsoft Teams platforms, could directly communicate with their peers and professors, while students of National Research Nuclear University MEPhI were engaged in massive open online courses (MOOC). As a result, all five criteria were analysed in the proposed model for quality assessment of online learning to reveal the gaps between students’ expectations and perceptions of the educational process. Significant discrepancies in the «empathy» and «responsiveness» criteria in both groups demonstrate low student satisfaction with the quality of communication and individualisation of learning. The research findings can be used to construct resource allocation models for implementing educational programmes and developing support measures for regional higher education institutions.