Reviewerly/exHarmony huggingface dataset:
A Comprehensive Study of Contemporary IoT Technologies and Varied Machine Learning (ML) Schemes

Currently, Internet of things (IoT) is advancing quickly and different applications have turned out from the scholarly community and industry. In this context, quick improvements in equipment and programming-based associated gadgets with corresponding innovations present Internet of things (IoT) in a different look. IoT, the innovation trendy expression of the Web has picked up consideration among specialists, researchers, engineers, programming designers, and the scholarly world. The Internet of things (IoT) incorporates billions of savvy gadgets that can speak with each other with negligible human mediation. It is one of the quickest created fields throughout the entire existence of figuring with an expected 50 billion gadgets before the finish of 2020. With the implementation of machine learning (ML) algorithms, the performance of IoT applications can be enriched. Furthermore, artificial intelligence (AI) systems can perform complex undertakings by gaining from data instead of adhering to pre-modified principles. One of the middle focuses of AI is to prepare PCs to use data or past experience to handle a given issue. Today, AI is employed in various applications and is a focal thought for keen structures. IoT innovation empowers correspondence among genuine items to connect in a wise manner. Thus, IoT is about network and robotization that require shrewd calculations to make the brilliant articles cooperate. AI can likewise help machines or a huge number of machines that get together to comprehend what individuals need from the information made by people. Additionally, AI assumes a fundamental job in IoT angle for handling the tremendous measure of data produced by those machines. On this viewpoint, this survey paper intends to talk about the innovation components of IoT and how ML can be implemented on IoT applications.
A Novel Detection Framework for Detecting Abnormal Human Behavior

Public safety issues have always been the focus of widespread concern of people from all walks of life. With the development of video detection technology, the detection of abnormal human behavior in videos has become the key to preventing public safety issues. Particularly, in student groups, the detection of abnormal human behavior is very important. Most existing abnormal human behavior detection algorithms are aimed at outdoor activity detection, and the indoor detection effects of these algorithms are not ideal. Students spend most of their time indoors, and modern classrooms are mostly equipped with monitoring equipment. This study focuses on the detection of abnormal behaviors of indoor humans and uses a new abnormal behavior detection framework to realize the detection of abnormal behaviors of indoor personnel. First, a background modeling method based on a Gaussian mixture model is used to segment the background image of each image frame in the video. Second, block processing is performed on the image after segmenting the background to obtain the space-time block of each frame of the image, and this block is used as the basic representation of the detection object. Third, the foreground image features of each space-time block are extracted. Fourth, fuzzy C-means clustering (FCM) is used to detect outliers in the data sample. The contribution of this paper is (1) the use of an abnormal human behavior detection framework that is effective indoors. Compared with the existing abnormal human behavior detection methods, the detection framework in this paper has a little difference in terms of its outdoor detection effects. (2) Compared with other detection methods, the detection framework used in this paper has a better detection effect for abnormal human behavior indoors, and the detection performance is greatly improved. (3) The detection framework used in this paper is easy to implement and has low time complexity. Through the experimental results obtained on public and manually created data sets, it can be demonstrated that the performance of the detection framework used in this paper is similar to those of the compared methods in outdoor detection scenarios. It has a strong advantage in terms of indoor detection. In summary, the proposed detection framework has a good practical application value.
Breaking Trivium Stream Cipher Implemented in ASIC Using Experimental Attacks and DFA

One of the best methods to improve the security of cryptographic systems used to exchange sensitive information is to attack them to find their vulnerabilities and to strengthen them in subsequent designs. Trivium stream cipher is one of the lightweight ciphers designed for security applications in the Internet of things (IoT). In this paper, we present a complete setup to attack ASIC implementations of Trivium which allows recovering the secret keys using the active non-invasive technique attack of clock manipulation, combined with Differential Fault Analysis (DFA) cryptanalysis. The attack system is able to inject effective transient faults into the Trivium in a clock cycle and sample the faulty output. Then, the internal state of the Trivium is recovered using the DFA cryptanalysis through the comparison between the correct and the faulty outputs. Finally, a backward version of Trivium was also designed to go back and get the secret keys from the initial internal states. The key recovery has been verified with numerous simulations data attacks and used with the experimental data obtained from the Application Specific Integrated Circuit (ASIC) Trivium. The secret key of the Trivium were recovered experimentally in 100% of the attempts, considering a real scenario and minimum assumptions.
Evaluating Input Representation for Language Identification in Hindi-English Code Mixed Text

Natural language processing (NLP) techniques have become mainstream in the recent decade. Most of these advances are attributed to the processing of a single language. More recently, with the extensive growth of social media platforms focus has shifted to code-mixed text. The code-mixed text comprises text written in more than one language. People naturally tend to combine local language with global languages like English. To process such texts, current NLP techniques are not sufficient. As a first step, the text is processed to identify the language of the words in the text. In this work, we focus on language identification in code-mixed sentences for Hindi-English mixed text. The task of language identification is formulated as a token classification task. In the supervised setting, each word in the sentence has an associated language label. We evaluate different deep learning models and input representation combinations for this task. Mainly, character, sub-word, and word embeddings are considered in combination with CNN and LSTM based models. We show that sub-word representation along with the LSTM model gives the best results. In general sub-word representations perform significantly better than other input representations. We report the best accuracy of 94.52% using a single layer LSTM model on the standard SAIL ICON 2017 test set.
Graph Neural Network and Context-Aware Based User Behavior Prediction and Recommendation System Research

Due to the influence of context information on user behavior, context-aware recommendation system (CARS) has attracted extensive attention in recent years. The most advanced context-aware recommendation system maps the original multi-field features into a shared hidden space and then simply connects it to a deep neural network (DNN) or other specially designed networks. However, for different areas, the ability of modeling complex interactions in a sufficiently flexible and explicit way is limited by the simple unstructured combination of feature fields. Therefore, it is hard to get the accurate results of the user behavior prediction. In this paper, a graph structure is used to establish the interaction between context and users/items. Through modeling user behavior, we can explore user preferences in different context environments, so as to make personalized recommendations for users. In particular, we construct a context-user and context-item interactions graph separately. In the interactions graph, each node is composed of a user feature field, an item feature field, and a feature field of different contexts. Different feature fields can interact through edges. Therefore, the task of modeling feature interaction can be transformed into modeling the node interaction on the corresponding graph. To this end, an innovative model called context-aware graph neural network (CA-GNN) model is designed. Furthermore, in order to obtain more accurate and efficient recommendation results, first, we innovatively use the attention mechanism to improve the interpretability of CA-GNN; second, we innovatively use the degree of physical fatigue features which has never been used in traditional CARS as critical contextual feature information into our CA-GNN. We simulated the Food and Yelp datasets. The experimental results show that CA-GNN is better than other methods in terms of root mean square error (RMSE) and mean absolute error (MAE).
A Deep Learning Approach in the DCT Domain to Detect the Source of HDR Images

Although high dynamic range (HDR) is now a common format of digital images, limited work has been done for HDR source forensics. This paper presents a method based on a convolutional neural network (CNN) to detect the source of HDR images, which is built in the discrete cosine transform (DCT) domain. Specifically, the input spatial image is converted into DCT domain with discrete cosine transform. Then, an adaptive multi-scale convolutional (AMSC) layer extracts features related to HDR source forensics from different scales. The features extracted by AMSC are further processed by two convolutional layers with pooling and batch normalization operations. Finally, classification is conducted by a fully connected layer with Softmax function. Experimental results indicate that the proposed DCT-CNN outperforms the state-of-the-art schemes, especially in accuracy, robustness, and adaptability.
Attribute-Guided Adversarial Training for Robustness to Natural Perturbations

While existing work in robust deep learning has focused on small pixel-level norm-based perturbations, this may not account for perturbations encountered in several real world settings. In many such cases although test data might not be available, broad specifications about the types of perturbations (such as an unknown degree of rotation) may be known. We consider a setup where robustness is expected over an unseen test domain that is not i.i.d. but deviates from the training domain. While this deviation may not be exactly known, its broad characterization is specified a priori, in terms of attributes. We propose an adversarial training approach which learns to generate new samples so as to maximize exposure of the classifier to the attributes-space, without having access to the data from the test domain. Our adversarial training solves a min-max optimization problem, with the inner maximization generating adversarial perturbations, and the outer minimization finding model parameters by optimizing the loss on adversarial perturbations generated from the inner maximization. We demonstrate the applicability of our approach on three types of naturally occurring perturbations --- object-related shifts, geometric transformations, and common image corruptions. Our approach enables deep neural networks to be robust against a wide range of naturally occurring perturbations. We demonstrate the usefulness of the proposed approach by showing the robustness gains of deep neural networks trained using our adversarial training on MNIST, CIFAR-10, and a new variant of the CLEVR dataset.
Virtual Museum Technologies and the Modern Educational Process

In modern conditions, education acquires democratic forms of openness and integration with various cultural institutions.The interaction of educational institutions and cultural centers acquires the features of a systematic and structured process.However, the emerging new forms of culture, which should include virtual museums, are not used to the full extent.In this regard, the purpose of this article is to reveal the cultural and educational potential of the virtual form of the museum as a modern technology for teaching and educating younger students.The leading research methods were: theoretical analysis of scientific sources, generalization of historical experience, as well as empirical testing methods and generalization of experimental results.The article presents the historical retrospective of the development of museums as cultural, educational and educational centers in their relationship with educational institutions.The article attaches special importance to virtual museums as the most accessible and educational potential form of culture, and also substantiates the position that virtual museums can be represented as a pedagogical technology.The article presents the results of experimental work on the development of creative activity of younger students by means of a virtual museum.The obtained data confirmed the growth in the number of primary school children with high and medium levels of creative activity, which proves the high efficiency of using Museum technology in the form of a virtual Museum in the development of the creative potential of the student's personality.The theoretical and empirical materials presented in the article indicate the need to improve the training of primary school teachers in mastering Museum pedagogy, which contains the potential for the development of students, including by means of a virtual museum.
Analysis of the Influence of Terrain Orientation on the Design of PV Facilities with Single-Axis Trackers

This paper investigates how to optimally orient the photovoltaic solar trackers of an axis parallel to the terrain, applying the sky model of Hay–Davies. This problem has been widely studied. However, the number of studies that consider the orientation (inclination and azimuth of the terrain) is very limited. This paper provides an examination of incident solar irradiance that can be extended to terrain with variable orientation and in consideration of different azimuths of the axis of rotation. Furthermore, a case study of the south of Spain is provided, considering different inclination and orientation terrain values. The results obtained in this study indicate, as a novelty, that for lands that are not south facing, the rotation axis azimuth of solar trackers should be different from zero and adjusted to the same direction as the land azimuth in order to maximize energy production. Annual energy production is sensitive to changes in the rotation axis azimuths of solar trackers (an influence of around 3% of annual energy production).
Time-Aware Learning Framework for Over-The-Top Consumer Classification Based on Machine- and Deep-Learning Capabilities

With the widespread use of over-the-top (OTT) media, such as YouTube and Netflix, network markets are changing and innovating rapidly, making it essential for network providers to quickly and efficiently analyze OTT traffic with respect to pricing plans and infrastructure investments. This study proposes a time-aware deep-learning method of analyzing OTT traffic to classify users for this purpose. With traditional deep learning, classification accuracy can be improved over conventional methods, but it takes a considerable amount of time. Therefore, we propose a novel framework to better exploit accuracy, which is the strength of deep learning, while dramatically reducing classification time. This framework uses a two-step classification process. Because only ambiguous data need to be subjected to deep-learning classification, vast numbers of unambiguous data can be filtered out. This reduces the workload and ensures higher accuracy. The resultant method provides a simple method for customizing pricing plans and load balancing by classifying OTT users more accurately.
Advantages and Risks of Using the Digital Educational Environment

The article describes the digitalization stages of secondary and higher education in the Russian Federation and considers the main risks that may be caused by this trend.Among the health risks, there are disorders of the musculoskeletal system, impaired brain development, nervous strain and sleep problems, skin and respiratory diseases.The following communicative risks prevail: the risk of weakening motivation to learn, empathy, leadership skills.The main risks in the field of information security are the risks associated with violation of confidentiality, integrity, accessibility, authenticity and non-repudiation of participants in the educational process.These negative consequences may become relevant with the widespread introduction of a unified digital educational environment.Their relevance is evidenced by a survey of teachers of secondary, secondary specialized and higher educational institutions of Russia, which was conducted in October-November 2019.It is noted that each type of risk is relevant and can cause negative consequences for participants in the educational process.The work also identified the advantages of digitalization of education, but they do not allow minimizing the identified risks.Based on the risks identified in the study, the main recommendations were formulated, which, with proper implementation, can avoid the negative impact on participants in the educational process.Such recommendations include, among other things, the use of distance technology for the needs of continuing education and elective classes.If it is necessary to conduct distance learning lessons, use electronic platforms that meet the requirements of integrated security, as well as develop the technology of "safe school Internet".
Security Proof Against Collective Attacks for an Experimentally Feasible Semiquantum Key Distribution Protocol

Semiquantum key distribution (SQKD) allows two parties (Alice and Bob) to create a shared secret key, even if one of these parties (say, Alice) is classical.However, most SQKD protocols suffer from severe practical security problems when implemented using photons.The recently developed "Mirror protocol" [1] is an experimentally feasible SQKD protocol overcoming those drawbacks.The Mirror protocol was proven robust (namely, it was proven secure against a limited class of attacks including all noiseless attacks), but its security in case some noise is allowed (natural or due to eavesdropping) has not been proved yet.Here we prove security of the Mirror protocol against a wide class of quantum attacks (the "collective attacks"), and we evaluate the allowed noise threshold and the resulting key rate.
Multi-Agent Collaborative Path Planning Based on Staying Alive Policy

Modern mobile robots tend to be used in numerous exploration and search and rescue applications. Essentially they are coordinated by human operators and collaborate with inspection or rescue teams. Over the time, robots became more advanced and capable for various autonomous collaborative scenarios. Recent advances in the field of collaborative exploration and coverage provide different approaches to solve this objective. Thus scope of this article is to present a novel collaborative approach for multi-agent coordination in exploration and coverage of unknown complex indoor environments. Fundamentally, the task of collaborative exploration can be divided into two core components. The principal one is a sensor based exploration scheme that aims to guarantee complete area exploration and coverage. The second core component proposed is a staying alive policy that takes under consideration the battery charge level limitation of the agents. From this perspective the path planner assigns feasible tasks to each of the agents, including the capability of providing reachable, collision free paths. The overall efficacy of the proposed approach was extensively evaluated by multiple simulation results in a complex unknown environments.
Method for Attack Tree Data Transformation and Import Into IT Risk Analysis Expert Systems

Information technology (IT) security risk analysis preventatively helps organizations in identifying their vulnerable systems or internal controls. Some researchers propose expert systems (ES) as the solution for risk analysis automation since risk analysis by human experts is expensive and timely. By design, ES need a knowledge base, which must be up to date and of high quality. Manual creation of databases is also expensive and cannot ensure stable information renewal. These facts make the knowledge base automation process very important. This paper proposes a novel method of converting attack trees to a format usable by expert systems for utilizing the existing attack tree repositories in facilitating information and IT security risk analysis. The method performs attack tree translation into the Java Expert System Shell (JESS) format, by consistently applying ATTop, a software bridging tool that enables automated analysis of attack trees using a model-driven engineering approach, translating attack trees into the eXtensible Markup Language (XML) format, and using the newly developed ATES (attack trees to expert system) program, performing further XML conversion into JESS compatible format. The detailed method description, along with samples of attack tree conversion and results of conversion experiments on a significant number of attack trees, are presented and discussed. The results demonstrate the high method reliability rate and viability of attack trees as a source for the knowledge bases of expert systems used in the IT security risk analysis process.
Sample Complexity of Policy Gradient Finding Second-Order Stationary Points

The policy-based reinforcement learning (RL) can be considered as maximization of its objective. However, due to the inherent non-concavity of its objective, the policy gradient method to a first-order stationary point (FOSP) cannot guar- antee a maximal point. A FOSP can be a minimal or even a saddle point, which is undesirable for RL. It has be found that if all the saddle points are strict, all the second-order station- ary points (SOSP) are exactly equivalent to local maxima. Instead of FOSP, we consider SOSP as the convergence criteria to characterize the sample complexity of policy gradient. Our result shows that policy gradient converges to an (ε, √εχ)-SOSP with probability at least 1 − O(δ) after the total cost of O(ε−9/2)sinificantly improves the state of the art cost O(ε−9).Our analysis is based on the key idea that decomposes the parameter space Rp into three non-intersected regions: non-stationary point region, saddle point region, and local optimal region, then making a local improvement of the objective of RL in each region. This technique can be potentially generalized to extensive policy gradient methods. For the complete proof, please refer to https://arxiv.org/pdf/2012.01491.pdf.