Reviewerly/exHarmony huggingface dataset:
Recent advances in leveraging human guidance for sequential decision-making tasks

A longstanding goal of artificial intelligence is to create artificial agents capable of learning to perform tasks that require sequential decision making. Importantly, while it is the artificial agent that learns and acts, it is still up to humans to specify the particular task to be performed. Classical task-specification approaches typically involve humans providing stationary reward functions or explicit demonstrations of the desired tasks. However, there has recently been a great deal of research energy invested in exploring alternative ways in which humans may guide learning agents that may, e.g., be more suitable for certain tasks or require less human effort. This survey provides a high-level overview of five recent machine learning frameworks that primarily rely on human guidance apart from pre-specified reward functions or conventional, step-by-step action demonstrations. We review the motivation, assumptions, and implementation of each framework, and we discuss possible future research directions.
Fast NEON-Based Multiplication for Lattice-Based NIST Post-quantum Cryptography Finalists

This paper focuses on high-speed NEON-based constant-time implementations of multiplication of large polynomials in the NIST PQC KEM Finalists: NTRU, Saber, and CRYSTALS-Kyber. We use the Number Theoretic Transform (NTT)-based multiplication in Kyber, the Toom-Cook algorithm in NTRU, and both types of multiplication in Saber. Following these algorithms and using Apple M1, we improve the decapsulation performance of the NTRU, Kyber, and Saber-based KEMs at the security level 3 by the factors of 8.4, 3.0, and 1.6, respectively, compared to the reference implementations. On Cortex-A72, we achieve the speed-ups by factors varying between 5.7 and 7.5 $$\times $$ for the Forward/Inverse NTT in Kyber, and between 6.0 and 7.8 $$\times $$ for Toom-Cook in NTRU, over the best existing implementations in pure C. For Saber, when using NEON instructions on Cortex-A72, the implementation based on NTT outperforms the implementation based on the Toom-Cook algorithm by $$14\%$$ in the case of the MatrixVectorMul function but is slower by $$21\%$$ in the case of the InnerProduct function. Taking into account that in Saber, keys are not available in the NTT domain, the overall performance of the NTT-based version is very close to the performance of the Toom-Cook version. The differences for the entire decapsulation at the three major security levels (1, 3, and 5) are $$-4$$ , $$-2$$ , and $$+2\%$$ , respectively. Our benchmarking results demonstrate that our NEON-based implementations run on an Apple M1 ARM processor are comparable to those obtained using the best AVX2-based implementations run on an AMD EPYC 7742 processor. Our work is the first NEON-based ARMv8 implementation of each of the three NIST PQC KEM finalists.
A Comparative Study of Credit Card Fraud Detection Using the Combination of Machine Learning Techniques with Data Imbalance Solution

Due to the rapid spread of fraud and cybersecurity risks in digital economy, fraud detection stands as a prime issue of modern technology. However, the analysis of fraud cases is computationally difficult because, fraud cases conjure less than 0.2% of the transactions. Thus to figure out the best classification technique to use for fraud detection, this paper has conducted a thorough experimentation of Machine Learning (ML) techniques. It has implemented six ML techniques i.e. Logistic Regression (LR), Support Vector Machine (SVM), Naíve Bayes (NB), Random forest (RF), Decision Tree (DT), and K-nearest neighbour (KNN) classifiers to detect credit card fraud. The investigation used five type of datasets i.e. imbalanced data, Under Sampled (US) data, Over Sampled (OS) data, sampled data using Synthetic Minority Over Sampling Technique (SMOTE) and Adaptive Synthetic Sampling Method for Imbalanced Data (ADASYN). The best combination of these classification approaches is selected based on five performance evaluation criteria i.e. Accuracy, Area Under the Curve (AUC), Precision, Recall score and fl-score. After evaluation of the classifiers it has showed that among 30 different classification approaches, RF classifier with over sampling (OS) technique was found to be the best approach in terms of all the performance criteria. It showed 99.99 % accurate and precise results with 99.99 % AUC, fl-score and 100 % Recall rate. Our choosen approach has obtained the highest accuracy over other studies on the same dataset. The banking sector as well as other financial institutions might use this suggested machine learning based combination approach to minimize (debit/credit card) frauds.
Multi-modality Search and Recommendation on Palestinian Cultural Heritage Based on the Holy-Land Ontology and Extrinsic Semantic Resources

The Cultural Heritage (CH) sector and its associated tourism services have been affected notably by the advancement of the Internet as well as the explosive growth of smartphones and other handheld devices. These days, visitors can access reliable CH content using Web and mobile-based interfaces. However, conventional CH systems still lack the ability to provide meaningful semantically overt results that precisely meet user information needs in this domain. In addition, they often ignore the user search context and experience, which hinders their ability to adapt their behavior to the preferences, tasks, interests, and other user functionalities. In this article, we aim to address the issue of designing a precision-oriented multilingual and multi-criteria semantic-based mobile recommender system specifically targeting Palestine's CH, a country with great historical and cultural importance. We aim to better facilitate users’ access to CH content by providing them with multiple search functionalities. In this context, a user can search for relevant information using keywords (a.k.a. tags) or sentence-like queries and the system retrieves all relevant documents based on their semantic similarity. A second option is to search using current location information to retrieve correlated historical places and events. Finally, starting from a picture of interest, a third option makes it possible to extract captions describing its content that can be used to search for additional contextually relevant information. Additionally, the proposed system aims at personalizing users’ experience through progressively delivering output that meets their information needs based on a number of parameters such as users' logging data, interests, previous searches, and location-based information. A prototype of the proposed system has been developed and tested using Android smartphones and a manually constructed ontology enriched with CH links to the Art &amp; Architecture Thesaurus (AAT) and DBpedia. By comparing our system with similar systems in this domain, findings demonstrate that it provides additional search features and functionalities to users. The proposed Holy-Land ontology is the first of its kind attempting to encode knowledge about Palestine's CH. It plays a crucial role in our proposal, serving as a pivotal entity in the combination of language-based, location-based, and visual-based retrieval strategies.
An assessment model for cloud service security risk based on entropy and support vector machine

Summary Cloud services are open, shared, and complex. These characteristics will lead to many security risks and will affect the development of cloud services. Therefore, it is necessary to identify and measure the security risks of cloud services. However, there are still many deficiencies in this area. In light of this, this paper carries out an in‐depth study from the perspective of technical security risk. Firstly, this paper combines the three aspects on cloud service security problems, objectives, and technologies. It attempts to explore the technological solutions to get security risk problems to achieve the expected security goals, and establish the cloud service technology security risk index system. Secondly, because of the strong subjectivity and the deficiency of the data obtained from cloud service providers, this paper establishes a cloud service security risk assessment model based on entropy weight theory and multi‐classification support vector machine. Finally, the experimental results show that the evaluation model is feasible and effective.
Norm Violation in Online Communities – A Study of Stack Overflow Comments

Norms are behavioral expectations in communities. Online communities are also expected to abide by the rules and regulations that are expressed in the code of conduct of a system. Even though community authorities continuously prompt their users to follow the regulations, it is observed that hate speech and abusive language usage are on the rise. In this paper, we quantify and analyze the patterns of violations of normative behaviour among the users of Stack Overflow (SO) - a well-known technical question-answer site for professionals and enthusiast programmers, while posting a comment. Even though the site has been dedicated to technical problem solving and debugging, hate speech as well as posting offensive comments make the community "toxic". By identifying and minimising various patterns of norm violations in different SO communities, the community would become less toxic and thereby the community can engage more effectively in its goal of knowledge sharing. Moreover, through automatic detection of such comments, the authors can be warned by the moderators, so that it is less likely to be repeated, thereby the reputation of the site and community can be improved. Based on the comments extracted from two different data sources on SO, this work first presents a taxonomy of norms that are violated. Second, it demonstrates the sanctions for certain norm violations. Third, it proposes a recommendation system that can be used to warn users that they are about to violate a norm. This can help achieve norm adherence in online communities.
Understanding and Analysing Resource Utilization, Costing Strategies and Pricing Models in Cloud Computing

Abstract This paper is primarily focused on understanding the basics of cloud computing economics – specific pricing models and general cost structure and compares between basic cost structures. It also examines 15 pricing models dependent on various bases and attempts to consolidate the different pricing methods used to guarantee maximum profitability to both the service provider and consumer. To avail the services of cloud computing from the cloud service provider, the organization has to incur individual costs. There are three types of costs: fixed, variable, and semi-variable costs. Each type is computed and analyzed. There are more than 30 pricing models though the pay-as-you-go model, subscription model, and pay-for-resources models are in demand. To consider and discuss the basics of cloud computing, emphasizing its economics and pricing models considering various aspects and costing components, using and estimating plans from various cloud Registering suppliers. The paper also considers identifying gaps, if any, present in the current pricing models that are highly preferred with other pricing models that are designed but not implemented due to practical conditions and fear of higher percentage of risk and error and to compare the basic prices provided by the cloud service providers.
Calibration and location analysis of a heterogeneous binocular stereo vision system

In the dairy farming industry, we can obtain the temperature, color, and location information of dairy cows by patrol inspection robot so as to monitor the health status and abnormal behaviors of dairy cows. We build and calibrate a heterogeneous binocular stereo vision (HBSV) system comprising a high-definition color camera and infrared thermal camera and mount it on a patrol inspection robot. First, based on the traditional chessboard, an easy-to-make calibration board for the HBSV system is designed. Second, an accurate locating and sorting algorithm for the calibration points of the calibration board is designed. Then, the cameras are calibrated and the HBSV system is stereo-calibrated. Finally, target locating is achieved based on the above calibration results and Yolo target detection technology. In this paper, several experiments are carried out from many aspects. The target locating average error of HBSV system is 3.11%, which satisfies the needs of the dairy farming environment. The video's FPS captured by using HBSV is 7.3, which is 78% higher than that by using binocular stereo vision system and infrared thermal camera. The results show that the HBSV system has application value to a certain degree.
Deep Learning Enhancing Performance Using Support Vector Machine HTM Cortical Learning Algorithm

Abstract Deep Learning is a function of AI that duplicates the mechanisms of human thought in the processing of information and selection processes. The aim of this study is to apply a technology known as SVMHTMC to improve deep learning. The HTM Cortical Learning Approach and the Support Vector Machine have been combined in this suggested algorithm. The deep learning technique is based on the assumption that the mean absolute percentage error is reduced. Aside from the overlapping duty cycle, the high proportion of which shows the speed of the classifier’s processing function. The findings demonstrate that by halving the value, the suggested set of criteria minimizes the absolute proportion of mistakes. In addition, raise the percentage of overlapping duty cycles by 17%.
OUTLOOKS OF APPLYING AUGMENTED AND VIRTUAL REALITY TECHNOLOGIES IN HIGHER EDUCATION

The article explores the outlooks of the latest technologies related to augmented and virtual reality in the field of innovative higher education.. The relevance of the problem is explained by rapid development and implementation of information and communication technologies in different areas of social activity, including education which is caused by objective reasons and subjective preferences of new generations of students. Theories are applied in on a practice. The investigated technologies are considered as a method of professional communication teacher-student. The objectives of the study include: 1) identifying the current state of implementation of mixed reality technologies in higher education in domestic and foreign practice; 2) determining the attitude of teachers to the use of the latest virtual technologies in the educational process; 3) presentation of practical results in the study area. Methodology. To achieve the purpose of the study, inductive-deductive method and methods of situational analysis are used. The conclusions are based on the principles of determinism, the results of expert interviews are made using the methods of sociological research. Results. The research results in successful implementation of mixed reality technologies in higher education, as well as the identification of a positive attitude of teachers of the Siberian Federal University to the use of the latest technologies of augmented and virtual reality in the educational process. The article describes the AR-project called SFU SCIENCE with application of modern augmented reality technologies. Conclusions. Technologies of augmented and virtual reality have positive results and further prospects of introduction in educational process of domestic and foreign higher education. This process is interdependent: on the one hand, teachers are ready to use the latest technologies for more effective communication teacher-student and already successfully use such educational technologies in their work; on the other hand, students are interested in new products in education and develop projects of AR and VR technologies for different specialties.
Parallel and Distributed Structured SVM Training

Structured Support Vector Machines (structured SVMs) are a fundamental machine learning algorithm, and have solid theoretical foundation and high effectiveness in applications such as natural language parsing and computer vision. However, training structured SVMs is very time-consuming, due to the large number of constraints and inferior convergence rates, especially for large training data sets. The high cost of training structured SVMs has hindered its adoption to new applications. In this article, we aim to improve the efficiency of structured SVMs by proposing a parallel and distributed solution (namely <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FastSSVM</i> ) for training structured SVMs building on top of MPI and OpenMP. FastSSVM exploits a series of optimizations (e.g., optimizations on data storage and synchronization) to efficiently use the resources of the nodes in a cluster and the cores of the nodes. Moreover, FastSSVM tackles the large constraint set problem by batch processing and addresses the slow convergence challenge by adapting stop conditions based on the improvement of each iteration. We theoretically prove that our solution is guaranteed to converge to a global optimum. A comprehensive experimental study shows that FastSSVM can achieve at least four times speedup over the existing solutions, and in some cases can achieve two to three orders of magnitude speedup.
Highly-Parallel Hardwired Deep Convolutional Neural Network for 1-ms Dual-Hand Tracking

1-ms vision systems represent an extreme case of temporal development in video sensing techniques. Moreover, a 1-ms dual-hand tracking system leverages the dexterous functionality of hands and thus serves as a seamless and intuitive interface for Human-Computer Interaction. Deep CNN is promising for high tracking robustness, however, neither GPU-based nor FPGA-based implementation addresses the tracking task with ultra-high-speed. This paper proposes: (a) A paradigm to directly map a deep CNN as a hardwired circuit, so the entire network runs in parallel and high processing speed is obtained. The network is exempted from memory access since all intermediate neural values are implicitly represented in hardware states. And condensed binarization is used to reduce resource utilization; (b) Hardware design of the hardwired network on FPGA, inside which kernel-adapted convolutional trees are devised to maximize the parallelism. The speed bottleneck of the network is therefore removed by implementing convolutional layers as fine-grained pipelines with unified components; (c) FPGA-GPU hetero complementation, which utilizes an auxiliary GPU network to compensate for accuracy of the FPGA network without affecting its speed. The quick primary results on FPGA are intermittently refined using delayed but accurate hints from GPU. Implementation results show that the proposed method reaches 973fps and consumes merely 1.30ms to process on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$640\times 480$ </tex-math></inline-formula> images, while the accuracy is only 4.7% lower compared with the general method on test sequences. Video demonstrations are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://wcms.waseda.jp/em/5f9d020f136e7</uri> .
A Critical Discourse Analysis of Reports about China on the COVID-19 Pandemic in The New York Times

The coronavirus disease 2019 (COVID-19) pandemic is a hot topic in 2020.Through the critical discourse analysis of related reports about the pandemic in China in The New York Times, ideology in media behind the news reports and China's image can be revealed.Based on Norman Fairclough's three-dimensional framework, combined with the methods of quantitative analysis, qualitative analysis and corpus analysis, and with the help of the corpus analysis software AntConc, this thesis makes an in-depth analysis of 35 news reports in The New York Times from September to December of 2020 from three dimensions: description, interpretation and explanation.The description level analyzes the text according to keywords and concordance lines; the interpretation level analyzes from the perspective of news sources and reporting modes; and the explanation level analyzes from the aspects of institutional context and societal context.Through the thorough analysis of the news reports, the thesis finds that The New York Times compliments China's outcome of fighting against COVID-19, but doesn't fully agree with the measures.Besides, The New York Times puts China on the opposite side of the United States and other western countries, and it regards China as a threatening and autocratic country to a certain extent.This thesis is of avail not only to contribute to researches in the field of CDA, especially investigations of news reports based on corpus, but also is useful for readers to understand news reports objectively when they read news coverage of the COVID-19 pandemic about China, and then to objectively view the construction of China's image in foreign mainstream media.
Deep Multi-Image Steganography with Private Keys

In this paper, we propose deep multi-image steganography with private keys. Recently, several deep CNN-based algorithms have been proposed to hide multiple secret images in a single cover image. However, conventional methods are prone to the leakage of secret information because they do not provide access to an individual secret image and often decrypt the entire hidden information all at once. To tackle the problem, we introduce the concept of private keys for secret images. Our method conceals multiple secret images in a single cover image and generates a visually similar container image containing encrypted secret information inside. In addition, private keys corresponding to each secret image are generated simultaneously. Each private key provides access to only a single secret image while keeping the other hidden images and private keys unrevealed. In specific, our model consists of deep hiding and revealing networks. The hiding network takes a cover image and secret images as inputs and extracts high-level features of the cover image and generates private keys. After that, the extracted features and private keys are concatenated and used to generate a container image. On the other hand, the revealing network extracts high-level features of the container image and decrypts a secret image using the extracted feature and a corresponding private key. Experimental results demonstrate that the proposed algorithm effectively hides and reveals multiple secret images while achieving high security.
On the interpretation of linear Riemannian tangent space model parameters in M/EEG

Riemannian tangent space methods offer state-of-the-art performance in magnetoencephalography (MEG) and electroencephalography (EEG) based applications such as brain-computer interfaces and biomarker development. One limitation, particularly relevant for biomarker development, is limited model interpretability compared to established component-based methods. Here, we propose a method to transform the parameters of linear tangent space models into interpretable patterns. Using typical assumptions, we show that this approach identifies the true patterns of latent sources, encoding a target signal. In simulations and two real MEG and EEG datasets, we demonstrate the validity of the proposed approach and investigate its behavior when the model assumptions are violated. Our results confirm that Riemannian tangent space methods are robust to differences in the source patterns across observations. We found that this robustness property also transfers to the associated patterns.