Reviewerly/exHarmony huggingface dataset:
Hacks Hit the Phish: Phish Attack Detection Based on Hacks Search

Phishing is a simple but fatal cyber-attack that deceives users to visit a fake website to steal sensitive information such as username, password, and other credentials. The search engine-based phishing detection approaches provide interpretability and credibility well. While, the limitation of these approaches is that it is difficult to choose an accurate search string to collect valid information. The uncertain search results caused by search strings will influence the detection accuracy. This paper proposes a Hacks search based phishing detection approach—Hacks Hit the Phishing (HHP), in which the Hacks search is an advanced search syntax provided by search engines. The approach not only collects valid information accurately by Hacks search syntax, but also can detect phishing sites hosted on compromised servers by the resource decision strategy. From the experimental results, it is observed that HHP achieved an accuracy of 98.3% in Alexa, and 95.59% in openphish.
Performance Evaluation of LSB Sequential and Pixel Indicator Algorithms in Image Steganography

Digital photo images are everywhere around us over the Internet. Due to the advancements in digital cameras, photo images have become a rich subject of manipulation. Images are used as cover media by image-based steganography to conceal hidden data. The objective of the approach is to hide a message under an image without creating any doubt. Encoder of this can be used for hiding message in an image so that when we send the image to another person using any network, that does not create unwanted attention to any unauthorized person about the existence of the message. But the person who has decoder can easily decode the message from the image. This tool manipulates so less portion of the image that it cannot be visible in naked eyes and only those who already know about existence of the message can decode it. In this way, message can be passed to the actual receiver without notifying the existence of it even in insecure network. The common technique used replacing the least significant bits (LSBs) of the image pixels with the hidden bits intended. On the other hand, pixel indicator technique (PIT) benefits from the advantages of several older steganography methods. Here, a performance comparison among LSB sequential substitution, PIT and coded LSB substitution are carried out over image quality, capacity, and security issues.
Standard R 7.0.100-2018: from difficulties to opportunities

The article presents an overview of the problems that arise in the process of practical application of the national standard regulating the rules of bibliographic description in bibliographic activities. Special attention is paid to the features of the bibliographic description that have not received sufficient coverage in the standard. The article emphasizes that the standard is focused on a creative approach to the application of description rules. It offers various options, the choice of which depends on the specifics of a particular library, the purpose of the description and the information needs of the user. At the same time, the methodological decisions of the compilers of the bibliographic description are subjective. They depend not only on the tasks facing the bibliographic institution, but also on the qualifications of the bibliographers. It is necessary to adjust the adoption of methodological decisions by preparing normative and methodological materials that explain and detail the provisions of the basic standard.
Real-Time Scheme for Covert Communication Based VoIP

Abstract Covert communication is an important necessity for governments and companies due to the ease of eavesdropping and access to information. A private channel with robust encryption can provide the required protection for the communications. However, a public channel is cheaper and less restrictive (from any place and any communication devices) than private channels. This paper proposes a real-time covert communication scheme by creating a secret speech channel in a public speech channel based on audio steganography. The scheme compresses the secret speech using a very fast and simple process based on lifting wavelet transform. Also, the cover speech is compressed by G.711 encoder at synchronous time periods with respect to the secret speech to meet the networks of voice over internet protocol (VoIP). Subsequently, the secret data hides in a compressed cover speech data in real-time. The embedding procedure employs the only high strength data for embedding to maintain the quality of the cover data after embedding at greater than 40 dB in terms of signal to noise ratio (SNR). The secret data is retrieved 100% without error in case of a lossless channel.
Development of Natural Science Literacy in Basic School Algebra Lessons Using Interdisciplinary Connections

The article deals with the issue of natural science literacy formation in teaching mathematics in middle school with the use of intersubject connections. Research methodology and techniques. The research is based on systemic-structural and activity approaches.&#x0D; Results. First of all, attention is paid to the consideration of the very concept of natural science literacy; the issue of assessing the level of development of students’ natural science literacy and means of its diagnostics in process of teaching mathematics, for example, at algebra lessons, is considered. Examples of specific tasks of interdisciplinary nature are given, which are used to determine the level of results achievement as a requirement of the Federal State Educational Standard of Basic General Education. Conditions for conducting integrated classes and their application as a means of developing natural science literacy of high school students at algebra lessons are considered. Issues of educational material synchronization of subject areas are considered as a key condition for implementation of intersubject connections between mathematics and physics. &#x0D; Practical significance of the work. The directions of changes in approaches to the implementation of intersubject connections of mathematics with other areas of knowledge as a basis for achieving metasubject educational results as a requirement of the Federal State Standard of Basic General Education are identified. Metasubject assignments that contribute to the achievement of metasubject and subject educational results at algebra and physics lessons have been identified. Particular attention is paid to giving integrated classes as a condition for development of natural science literacy of students, as well as a means of assessing the level of natural science literacy formation. In this regard, examples of tasks in physics are considered that require the use of mathematical literacy, which directly affects the ability to analyze the situation and draw conclusions within the framework of the natural-scientific picture of the world. The main recommendations for the implementation of interdisciplinary relationships to achieve metasubject educational results are formulated.
Video Compression Artifacts Removal with Efficient Non-local Block

Lossy video compression is widely applied in the process of video transmission and storage. With the pursuit of higher compression ratio, the demand for compressed video-enhancement is derived from the annoying degradation. Recently some deep-neural-network-based approaches have achieved impressive performance in this task. Unfortunately, most of them are plainly designed for images without exploiting the high temporal redundancy for video restoration. To this end, we present a two-stage Video compression Artifacts Removal Neural Network (VARNN) with deformable convolutional kernels and modified non-local blocks. Conventional non-local block is incapable of capturing dependencies among channels and suffers from high computation and memory cost. We thus introduce a Divided Non-Local Block (DNLB), in which global dependencies can be captured in a fast and low space-cost way. Finally, experiments show that the proposed VARNN outperforms some state-of-the-art methods.
Internal Motion Estimation during Free-Breathing via External/Internal Correlation Model

Determination the locations of vessels and tumors are the key components in radiofrequency ablation (RFA). Traditionally, patients are required to hold their breath during puncture for precisely locating their target structures. However, it's difficult for patients to do so since it is hard to avoid the free breathing completely. In this paper, we propose a novel external-internal correlation model to predict the liver surface first, and then estimate the locations of the blood vessels and tumors inside the liver during free-breathing via non-rigid registration. 4D-CT scanner is used to obtain the external and internal motions simultaneously, here we represent the body surface with 6 surface landmarks, while reconstruct the 3D model of internal liver surface. After that, we apply the moving least squares (MLS) method on surface landmarks to predict the partial motion of liver surface. Considering that laparoscopic stereo stream has been successfully employed to register the given preoperative volume on liver surface, we adopt the pre-trained convolution neural network (CNN) on partial liver surface data to get the displacement of heterogeneous liver, which can be used to estimate the motion of internal structures. The experimental results show that our method can well predict the displacement of interesting internal liver structures.
Pareto Solutions vs Dataset Optima: Concepts and Methods for Optimizing Competing Objectives with Constraints in Retrieval

When ranking search results for multiple objectives, such as maximizing the relevance and diversity of retrieved documents, competing objectives can induce a space of optimal solutions, each reflecting a different optimal trade-off over objectives. This raises several important questions. Firstly, what Pareto optimal solution set is induced by objective functions? Secondly, what are the best solutions achievable on a given dataset? Finally, how closely do the best dataset solutions approach the true Pareto optimal? We present a clear conceptual framing of these questions, with supporting terminology and visualizations, distinguishing Pareto vs. "dataset-best" solutions and providing strong intuition about how and why different optimization problems lead to different shapes and forms of solutions (regardless of optimization technique). We also provide benchmark problems for verifying the correctness of any Pareto machinery and show how existing multi-objective optimization (MOO) and filter methods can be used to provide accurate and interpretable answers to the above questions. Finally, we show how user-defined constraints imposed on the solution space can be effectively handled. In sum, we provide conceptual, translational, and practical contributions to solving MOO problems in retrieval.
WhisperChord: Scalable and Secure Node Discovery for Overlay Networks

Node discovery is a fundamental service for any overlay network, including anonymization networks. Although anonymization and node discovery are two disjoint services, the node discovery has a direct impact on the anonymization. Centralized methods require a trusted third party, limit the network scalability, and are vulnerable to intersection (statistical disclosure) attacks. Therefore, several distributed node discovery methods were proposed to meet the security requirements of anonymization networks through additional structures within Distributed Hash Tables (DHTs). However, they require a high management overhead, a strict cooperation between nodes, and are susceptible to active and passive attacks.We propose WhisperChord—an alternative distributed node discovery approach, which incorporates gossiping into structured overlays. WhisperChord is based on a Chord DHT and neither creates any additional structures within the DHT nor requires any trusted third party. Via simulations, we show that our method provides superior protection against active attacks than prior methods and can effectively thwart information leakages.
Trajectory Planning Method of UAVs Considering the Error Correction

It is necessary to correct the errors of an unmanned aerial vehicle (UAV) for successfully accomplishing its tasks, especially considering the large error accumulation during a long flight. To realize the trajectory planning of UAVs with error correction, we propose a modeling method with multi-steps optimization. The optimal trajectory of the UAV is obtained by solving the program developed on MATLAB. The results illustrate that this modeling method is feasible and reasonable, which achieves the objectives that trajectory length is as short as possible and that the correction points the UAV passes are as few as possible.
Research on Effective Reading Teaching Model for English Majors Based on Network and Information Technology under the Background of New Liberal Arts

In 2018 Chinese University Teaching Forum emphasized the significance of the “Golden Course” and the determination of its construction of the Ministry of Education, and proposed to comprehensively advance the construction of “new engineering, new medicine science, new agricultural science and new liberal arts”. New liberal arts mean the new requirements and the new technology. This paper studies the reform path of reading teaching for English majors under the background of new liberal arts, discusses the effect of English reading teaching on the construction of new liberal arts for English majors, and discusses how to establish an efficient reading teaching model with the great help of the network and information technology, hoping to provide help for the construction of English majors under the background of new liberal arts in China.
Text Classification Model Based on BERT-Capsule with Integrated Deep Learning

Current text classification methods based on traditional capsule network models cannot properly reflect the importance of different words in a text sequence, and cannot effectively extract multi-level semantic features in text. To address the shortcomings of the traditional capsule network model, a text classification model based on BERT-Capsule integrated deep learning is proposed, which takes advantage of BERT's bi-directional encoding of text features and the improved routing mechanism of the capsule network as the basis. The model not only extracts the contexts information of text more comprehensively, but also learns the local word features and global semantic features of text to ensure the stability of the result classification. Finally, the BERT model before integration, the capsule network model and the BERT-Capsule model after integration are evaluated by instances on IMDB, AG News, and Reuters-21578 datasets. The experimental results demonstrate that the BERT-Capsule integrated model outperforms the BERT model before integration and the capsule network model in terms of accuracy of text classification, and the generalization error is lower, and the model can be used for text classification.
Efficient Estimation of Sensor Biases for the 3-D Asynchronous Multi-Sensor System

An important preliminary procedure in multi-sensor data fusion is \textit{sensor registration}, and the key step in this procedure is to estimate sensor biases from their noisy measurements. There are generally two difficulties in this bias estimation problem: one is the unknown target states which serve as the nuisance variables in the estimation problem, and the other is the highly nonlinear coordinate transformation between the local and global coordinate systems of the sensors. In this paper, we focus on the 3-dimensional asynchronous multi-sensor scenario and propose a weighted nonlinear least squares (NLS) formulation by assuming that there is a target moving with a nearly constant velocity. We propose two possible choices of the weighting matrix in the NLS formulation, which correspond to classical and weighted NLS estimation and maximum likelihood (ML) estimation, respectively. To address the intrinsic nonlinearity, we propose a block coordinate descent (BCD) algorithm for solving the formulated problem, which alternately updates different kinds of bias estimates. Specifically, the proposed BCD algorithm involves solving linear LS problems and nonconvex quadratically constrained quadratic program (QCQP) problems with special structures. Instead of adopting the semidefinite relaxation technique, we develop a much more computationally efficient algorithm based on the alternating direction method of multipliers (ADMM) to solve the nonconvex QCQP subproblems. The convergence of the ADMM to the global solution of the QCQP subproblems is established under mild conditions. The effectiveness and efficiency of the proposed BCD algorithm are demonstrated via numerical simulations.
Thinking with GDPR: A guide to better system design

Europe’s General Data Protection Regulation (GDPR) has a fearsome reputation as “the law that can fine you €20 million.” But behind that scary slogan lies a text that can be a very helpful guide to designing data processing systems. This paper explores that side of the GDPR: how understanding it can produce more effective - and more trustworthy - systems. Three popular myths often take designers down the wrong track: that GDPR is about stopping processing, is about users, and is about consent. Instead we consider, from a design perspective, the GDPR’s source material, its Principles, and its Lawful Bases for processing. Three examples - from the field of education, but widely applicable - show how “thinking with GDPR” has improved both the effectiveness and safety of large-scale data processing systems.
Specialists training in a technical university in the transition to a robotic society

The prerequisites for the research of the topic arise from the tasks of society modernization and social training of professional staff for these purposes. The latter has traditionally been based on sociology as a science appropriate to the present moment, studying societal problems at the group and individual levels of individual behavior. However, during the transition to the Bologna model of education, it was withdrawn from the Federal State Educational Standard for training future engineers and dramatically downgraded in status, which had a negative impact on their personal potential as professionals. This was stated by employers who had been given the task of using these graduates for the purposes of Russia’s transition to an information, robotic society. The purpose of the research is to draw the attention of society and the authorities to the importance of strengthening the social training of groups capable of keeping up with the scientific and technological progress of society, using the base of sociological knowledge. For the methodological basis of the topic were used: the sociology of higher education, theories of personal role behavior, as well as the concept of social humanism. Three sets of sources and literature on the topic have been studied, revealing the main problem of society, which centers on the situation with “artificial sociality” as a condition of socialization of student youth, and the conjunction of their behavior with “digital risks”; as well as the impact of these risks on both natural and social environments. It is argued that students who are already taking these risks today are potential creators of new “digital” and other technologies, who have not mastered the skills of forecasting social and professional behavior and have not formed themselves as a whole person. The hypothesis formulated in the article about the tendency to reduce the hours of social training in universities for engineering personnel has been confirmed. The reason for this trend is the neoliberal policy of austerity on education.
Comparative Analysis for Improving Accuracy of Image Classification Using Deep Learning Architectures

Image classification is a classic problem in areas pertaining to Computer Vision, Image Processing, and Machine Learning. This paper aims to compare the various Deep Learning Architectures to improve the accuracy of Image Classification to select the best Deep Learning Architecture by implementing and testing various Deep Learning Architectures in combination with Dense Neural Networks. This comparative study helps to improve the accuracy of image separation in both training and testing databases. For targeted training and testing, 3000 training images and 1000 test images were used. The result of the Deep Learning-based classification of images using the platform as Google Colab showed how accurate classification was done by comparing various deep learning architectures.
HMA: An Efficient Training Method for NLP Models

Nowadays, deep learning has been widely used for solving natural language processing (NLP) problems. Embedding matrices are common-used in the NLP deep models for automatical feature learning. However, the sparsity of embedding matrices makes it challenging to efficiently train the NLP models in data parallelism. When training with synchronous optimization methods, the aggregation on sparse gradients brings high communication cost and low scalability for distributed training. In this paper, we combine Model Average (MA) and synchronous optimization methods together, and propose HMA, a hybrid training method for NLP deep models. Furthermore, we implement HMA method in Horovod+TensorFlow training framework and conduct experimental evaluation with representative NLP models. For NLP models with a large number of sparse parameters, HMA saves over 30% wall-clock time compared with the state-of-the-art distributed training framework, while maintaining the same final training loss.
Collaborating with an AI Swarm in an Adversarial Game

Towards close collaboration between a human and a large number of AI systems, we propose to design an AI agent with two technical elements. The first is the use of a modeling approach that enables us to know what AI agents are trying to do. The second is the use of a multi-agent consensus building algorithm. A good combination of these two, a human and a group of AI agents were put together as one team. In this paper, we explain a configuration using a Behavior Tree and a Contract Net Protocol as a concrete example. In addition, we propose a method of applying reinforcement learning in which the intentions of the AI agents can be easily grasped by a human. The effectiveness and feasibility of this approach were evaluated with teams in a simulated Tail Tag game. Matches were held with up to 29 AI agents and 1 person on one team and 30 people on the other team. The results indicate that our approach works almost evenly with human-human collaboration by sharing roles between a human and AI swarm.
Study on Big Data Frameworks

Big data analytics is becoming more and more popular every day as a tool for evaluating large volumes of data on demand. Apache Hadoop, Spark, Storm, and Flink are four of the most widely used big data processing frameworks. Although all four architectures support big data analysis, they vary in how they are used and the infrastructure that supports it. This paper defines a general collection of main performance metrics, which include Processing Time, CPU Use, Latency, Execution Time, Performance, Scalability, and Fault-tolerance, and contrasting the four big data architectures against these KPIs in a literature review. When compared to Apache Hadoop and Apache Storm frameworks for non-real-time results, Spark was found to be the winner over multiple KPIs, including processing time, CPU usage, Latency, Execution time, and Scalability. In terms of processing time, CPU consumption, latency, execution time, and performance, Flink surpassed Apache Spark and Apache Storm architectures.