Reviewerly/exHarmony huggingface dataset:
Cybercrimes in the Russian Federation: Criminological and Criminal Law Analysis of the Situation

The rapid development of communication systems and the use of computer equipment for the exchange of information lead to the emergence of new social relations that require criminal law protection. The Russian Federation does not stay away from this, and although it joined the high tech race late, the development of its legal norms in this sphere follows global trends. Law aimed at counteracting cybercrimes is no exception. The emergence of the global information space gives rise to previously unknown threats to national and international security. Key characteristics of information space, such as its trans-border character, anonymity, speed, difficulty of identifying the actor in the network have led to the growth in the number of IT users and increased the attractiveness of the information infrastructure from the standpoint of using the Internet for criminal purposes. Counteracting these threats has become an important element of the complex of measures to ensure security at the national and the global levels. The authors of the article present the comparative evaluation of the level of cyber-threats in Russia, offer a generalized assessment of the place that cybercrimes hold among other global threats and of the economic damage inflicted by cybercrimes. They examine the key sources of cyber threats, the critical problems and technologies. The authors also make a prediction of the possible threats associated with the introduction of artificial intelligence systems. Besides, they carry out the analysis of the current criminal law norms which prohibit illegal activities in the Internet and pay attention to the debatable scientific and practical issues.
Research on information security in text emotional steganography based on machine learning

To effectively protect sensitive information, this paper proposes a method of text Emotional modulation steganography based on machine learning. Firstly, we explore an intelligent dynamic expansion method for text emotional lexicon based on deep learning in detail. Secondly, the cosine similarity algorithm is used to combine the two most similar emotional words into emotional word pairs, which is the basis of the proposed approach. Thirdly, we combine emotional word pairs with matrix encoding algorithm to calculate the minimum modification unit, so as to minimize the rewriting of carrier text, and hence improve the privacy, embedding rate and capacity of sensitive information. Fourthly, we evaluate and validate the proposed algorithms through several experiments. Finally, the design of a NoC based organizational covert and secure autonomous intelligent online team work system is carried out. Experiments show that the efficiency, security, concealment and robustness of the proposed algorithms are sound.
Learning-Based Correspondence Estimation for 2-D/3-D Registration

In many minimally invasive procedures, image guidance using a C-arm system is utilized. To enhance the guidance, information from pre-operative 3-D images can be overlaid on top of the 2-D fluoroscopy and 2-D/3-D image registration techniques are used to ensure an accurate overlay. Despite decades of research, achieving a highly reliable registration remains challenging. In this paper, we propose a learning-based correspondence estimation, which focuses on contour points and can be used in combination with the point-to-plane correspondence model-based registration. When combined with classical correspondence estimation in a refinement step, the method highly increases the robustness, leading to a capture range of 36mm and a success rate of 98.5%, compared to 14mm and 71.9% for the purely classical approach, while maintaining a high accuracy of 0.430.08mm of mean re-projection distance.
3D Object Detection from LiDAR Data using Distance Dependent Feature Extraction

This paper presents a new approach to 3D object detection that leverages the properties of the data obtained by a LiDAR sensor. State-of-the-art detectors use neural network architectures based on assumptions valid for camera images. However, point clouds obtained from LiDAR are fundamentally different. Most detectors use shared filter kernels to extract features which do not take into account the range dependent nature of the point cloud features. To show this, different detectors are trained on two splits of the KITTI dataset: close range (objects up to 25 meters from LiDAR) and long-range. Top view images are generated from point clouds as input for the networks. Combined results outperform the baseline network trained on the full dataset with a single backbone. Additional research compares the effect of using different input features when converting the point cloud to image. The results indicate that the network focuses on the shape and structure of the objects, rather than exact values of the input. This work proposes an improvement for 3D object detectors by taking into account the properties of LiDAR point clouds over distance. Results show that training separate networks for close-range and long-range objects boosts performance for all KITTI benchmark difficulties.
A stochasticlocation privacy protection scheme for edge computing

Location-based Service has become the fastest growing activity related service that people use in their daily life due to the boom of location-aware mobile devices. In edge computing along with the benefits brought by LBS, privacy preservation becomes a more challenging issue because of the nature of the paradigm, in which peers may cooperate with each other to collect and analyze user's location data. To avoid potential information leakage and usage, user's exact location should not be exposed to the edge node. In this paper, we propose a stochastic location privacy protection scheme for edge computing, in which the geographical distribution of surrounding users is obtained by analyzing proposed long-term density map and short-term density map. The cloaking scheme transfers user's exact location to a cloaked location to satisfy predefined probability of having <i>k</i>-users in that area. Our scheme does not reveal any exact location information, thus it is practicable for the real scenario when edge computing is honest but curious. Extensive experimental results are conducted to verify the efficiency and effectiveness of our method. By varying the privacy protection requirements, the corresponding performance have been examined and discussed.
Leveraging Error Correction in Voice-based Text Entry by Talk-and-Gaze

We present the design and evaluation of Talk-and-Gaze (TaG), a method for selecting and correcting errors with voice and gaze. TaG uses eye gaze to overcome the inability of voice-only systems to provide spatial information. The user's point of gaze is used to select an erroneous word either by dwelling on the word for 800 ms (D-TaG) or by uttering a "select" voice command (V-TaG). A user study with 12 participants compared D-TaG, V-TaG, and a voice-only method for selecting and correcting words. Corrections were performed more than 20% faster with D-TaG compared to the V-TaG or voice-only methods. As well, D-TaG was observed to require 24% less selection effort than V-TaG and 11% less selection effort than voice-only error correction. D-TaG was well received in a subjective assessment with 66% of users choosing it as their preferred choice for error correction in voice-based text entry.
O3S-MTP: Oriented star sampling structure based multi-scale ternary pattern for texture classification

This paper presents an effective hand-crafted texture operator for texture recognition, referred to as oriented star sampling structure based multi-scale ternary pattern (O3S-MTP) which is expected to better represent salient local texture structure. Instead of heuristic code constructions, the proposed O3S-MTP descriptor is defined as a gray-scale invariant texture measure, derived from a general definition of texture in the neighborhood of a 3 × 3 square patch thanks to the flexibility of the graph theory and dominating set. Different from the existing non-oriented local graph structure based texture descriptors, which cannot fully represent the extent of pixel difference, the essence of O3S-MTP operator is to encode the structure of local neighborhood by analyzing the differential excitation and orientation information using new effective oriented star sampling local structures. Extensive experiments on twelve challenging representative widely-used texture datasets show that O3S-MTP can achieve competitive and superior texture classification performance than a large number of recent most promising state-of-the-art texture descriptors. Furthermore, the obtained classification results were statistically validated through the Wilcoxon signed rank test based ranking method.
CUI

The use of speech as an interaction modality has grown considerably through the integration of Intelligent Personal Assistants (IPAs- e.g. Siri, Google Assistant) into smartphones and voice based devices (e.g. Amazon Echo). Such engineering advances in speech processing present a unique opportunity for enabling users to interact with interface in a truly conversational way. However, we have yet to see current voice-enable interface fully becoming Conversational User Interfaces (CUIs) as afforded by the underlying speech and natural language capabilities. For example, from a conversational / dialogue perspective, there remain significant gaps in using theoretical frameworks to understand user behaviours and choices and how they may applied to specific speech interface interactions. On a design and Human-Computer Interaction level, we don't yet have the proper tools such as validated design guidelines to help us improve the usability of such interfaces. On the speech processing side, variability in speech, language, and conversation still pose problem, and error-recovery strategies often lead to degraded user experience. From a critical perspective, issues of ethics and privacy remain yet to be addressed.
Analysis of the guiding rules of the nurse technician’s practice in Brazil

to analyze the normative bases that guide the nurse technician's practice, clarifying how the performance of this category happens.this is a qualitative study of exploratory and descriptive nature and of documentary type, held on the basis of the resolutions of the Brazilian Federal Nursing Council (Cofen). From a total of 364 resolutions published from 1975 to 2018, 15 accounted for the objective of the study and were therefore selected.this study systematized two analytical categories: areas of practice of the nurse technician, according to Cofen resolutions; and description of the professional's activities based on the resolutions.the analysis of the normative bases that guide the nurse technician's practice leads to the conclusion of a possible frailty of theoretical and normative content to justify the practice of these professionals.
ATR-FTIR and XRD quantification of solid mixtures using the asymptotic constant ratio (ACR) methods. Application to geological samples of sodium and potassium feldspars

Two asymptotic constant ratio methods applied to the quantification of individual components of solid samples using attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) and X-ray diffraction (XRD) are described. The methods involve the measurement of the peak current/peak areas of selected signals (diffraction peaks in XRD and absorption bands in ATR-FTIR) relative to the signal of standard added in constant proportion to the sample and the sample enriched with the analyte following the usual standard additions methodology. The proposed method compensates the absorption effects appearing in XRD and the presence of overlapping absorption bands of interferents by means of an asymptotic representation thus avoiding the need of the knowledge of the absorption parameters of the matrix and analytes. The method was tested for mixtures of different metal oxides and sodium and potassium feldspars with satisfactory results.
Truver

During the job application process, applicants are asked to upload/send their resumes, certificates, recommendation letters, etc. However, how can employers ensure that the education and certifications are legitimate? Digital copies of physical documents cannot be trusted. The current verification process is inefficient, time consuming, and expensive, causing many delays and difficulties for the Human Resources. To address this problem, we propose Truver, a blockchain based platform which (a) provides a distributed and secure system on which every individual can store his/her verified credentials and share them with all potential employers, and (b) allows employers to verify an applicant's credentials instantaneously.
Token-wise sentiment decomposition for ConvNet: Visualizing a sentiment classifier

Convolutional neural networks are one of the most important and widely used constructs in natural language processing and AI in general. In many applications, they have achieved state-of-the-art performance, with training time faster than the other alternatives. However, due to their limited interpretability, they are less favored by practitioners over attention-based models, like RNNs and self-attention (Transformers), which can be visualized and interpreted more intuitively by analyzing the attention-weight heat-maps. In this work, we present a visualization technique that can be used to understand the inner workings of text-based CNN models. We also show how this method can be used to generate adversarial examples and learn the shortcomings of the training data.
A Comparison Study of Control Devices for an Omnidirectional Wheelchair

High performance wheelchairs are in great need for both ever-growing populations of the elderly and those with disabilities caused by illness or injury. Wheelchairs can provide the user with many benefits such as maintaining mobility, continuing or broadening community and social activities, conserving strength and energy, and enhancing quality of life. In order to support the user in such ways, the wheelchair body must be able to flexibly make different movements and should be capable to deal with various control methods to meet diverse needs. In this paper, we present the omnidirectional wheelchair and compare some control devices.
Theoretical comparative analysis of cascading, iterative, and hybrid approaches to IT project life cycle management

The absence of a common and universal approach to IT project management allows us to formulate a problem to analyze and study when choosing the most efficient project management methodology.The relatively small number of scientific works summarizing practical experience of a theoretical approach allowed us to formulate a generalized mathematical model for a common IT project lifecycle estimation in this work using waterfall, agile or hybrid approaches for the project management.Based on the advantages and disadvantages of existing methodologies that we revealed, it appears that use
Cross-Domain Recommendation Based on Sentiment Analysis and Latent Feature Mapping

Cross-domain recommendation is a promising solution in recommendation systems by using relatively rich information from the source domain to improve the recommendation accuracy of the target domain. Most of the existing methods consider the rating information of users in different domains, the label information of users and items and the review information of users on items. However, they do not effectively use the latent sentiment information to find the accurate mapping of latent features in reviews between domains. User reviews usually include user’s subjective views, which can reflect the user’s preferences and sentiment tendencies to various attributes of the items. Therefore, in order to solve the cold-start problem in the recommendation process, this paper proposes a cross-domain recommendation algorithm (CDR-SAFM) based on sentiment analysis and latent feature mapping by combining the sentiment information implicit in user reviews in different domains. Different from previous sentiment research, this paper divides sentiment into three categories based on three-way decision ideas—namely, positive, negative and neutral—by conducting sentiment analysis on user review information. Furthermore, the Latent Dirichlet Allocation (LDA) is used to model the user’s semantic orientation to generate the latent sentiment review features. Moreover, the Multilayer Perceptron (MLP) is used to obtain the cross domain non-linear mapping function to transfer the user’s sentiment review features. Finally, this paper proves the effectiveness of the proposed CDR-SAFM framework by comparing it with existing recommendation algorithms in a cross-domain scenario on the Amazon dataset.
Citation Recommendations Considering Content and Structural Context Embedding

The number of academic papers being published is increasing exponentially in recent years, and recommending adequate citations to assist researchers in writing papers is a non-trivial task. Conventional approaches may not be optimal, as the recommended papers may already be known to the users, or be solely relevant to the surrounding context but not other ideas discussed in the manuscript. In this work, we propose a novel embedding algorithm DocCit2Vec, along with the new concept of "structural context", to tackle the aforementioned issues. The proposed approach demonstrates superior performances to baseline models in extensive experiments designed to simulate practical usage scenarios.
A Hybrid Co-occurrence and Ranking-based Approach for Detection of Implicit Aspects in Aspect-Based Sentiment Analysis

People are increasingly using Web sites and Web services to express their opinions since the inception of Internet. Sentiment analysis is an active research domain that aims at the extraction of sentiments or opinion from the user text, thereby getting the associated sentiment orientation. Although sentiments can be analyzed at document level and phrase level, unfortunately these options are not sufficient for fine-grained analysis of sentiments. Therefore, it makes sense to focus on aspect-based sentiment analysis which is very promising in terms of providing accurate predictions on user sentiments. There is a lot of scope for the research community to provide solutions for various challenges involved in performing sentiment analysis at the aspect level. The primary goal of this work is to extract the implicit aspects from opinionated document using the co-occurrence of aspects with feature indicators and ranking the pair based on their frequency of co-occurrence. As a first step toward achieving this objective, a novel algorithm is proposed to detect the implicit aspects through co-occurrence and ranking. The proposed algorithm is reliable as it uses the association between explicit aspects and sentiment words to detect implicit aspects.
THE EFFECTIVENESS OF PICTURES IN ENHANCE WRITING SKILL OF SENIOR HIGH SCHOOL STUDENTS

Abstract. The purpose of this research was to know the effectiveness of pictures in writing class of senior high school students. It employed a quantitative method using the quasi-experimental design. The subject of the research was the third year students of on senior high school in Sidrap Regency, South Sulawesi, Indonesia. The total number of population is 100 students which are distributed in 5 classes. Each class consists of 20 students. The sampling technique used in this research is cluster random sampling. The instrument was a writing test to obtain data on the students’ writing achievement through the pictures. Based on the result the researcher concluded that the pictures enhance the writing skill of senior high schools’ students.
Learning a 3D Gaze Estimator With Adaptive Weighted Strategy

As a method of predicting the target's attention distribution, gaze estimation plays an important role in human-computer interaction. In this paper, we learn a 3D gaze estimator with adaptive weighted strategy to get the mapping from the complete images to the gaze vector. We select the both eyes, the complete face and their fusion features as the input of the regression model of gaze estimator. Considering that the different areas of the face have different contributions on the results of gaze estimation under free head movement, we design a new learning strategy for the regression net. To improve the efficiency of the regression model to a great extent, we propose a weighted network that can adjust the learning strategy of the regression net adaptively. Experimental results conducted on the MPIIGaze and EyeDiap datasets demonstrate that our method can achieve superior performance compared with other state-of-the-art 3D gaze estimation methods.
Zero-Shot Sign Language Recognition: Can Textual Data Uncover Sign Languages?

We introduce the problem of zero-shot sign language recognition (ZSSLR), where the goal is to leverage models learned over the seen sign class examples to recognize the instances of unseen signs. To this end, we propose to utilize the readily available descriptions in sign language dictionaries as an intermediate-level semantic representation for knowledge transfer. We introduce a new benchmark dataset called ASL-Text that consists of 250 sign language classes and their accompanying textual descriptions. Compared to the ZSL datasets in other domains (such as object recognition), our dataset consists of limited number of training examples for a large number of classes, which imposes a significant challenge. We propose a framework that operates over the body and hand regions by means of 3D-CNNs, and models longer temporal relationships via bidirectional LSTMs. By leveraging the descriptive text embeddings along with these spatio-temporal representations within a zero-shot learning framework, we show that textual data can indeed be useful in uncovering sign languages. We anticipate that the introduced approach and the accompanying dataset will provide a basis for further exploration of this new zero-shot learning problem.
Deep snow: synthesizing remote sensing imagery with generative adversarial nets

In this work we demonstrate that generative adversarial networks (GANs) can be used to generate realistic pervasive changes in RGB remote sensing imagery, even in an unpaired training setting. We investigate some transformation quality metrics based on deep embedding of the generated and real images which enable visualization and understanding of the training dynamics of the GAN, and provide a useful measure in terms of quantifying how distinguishable the generated images are from real images. We also identify some artifacts introduced by the GAN in the generated images, which are likely to contribute to the differences seen between the real and generated samples in the deep embedding feature space even in cases where the real and generated samples appear perceptually similar.
HEVC Video Encryption Algorithm Based on Integer Dynamic Coupling Tent Mapping

A selective encryption algorithm is proposed to improve the efficiency of high efficiency video coding (HEVC) video encryption and ensure the security of HEVC videos. The algorithm adopts the integer dynamic coupling tent mapping optimization model as the pseudo-random sequence generator, and multi-core parallelization is used as the sequence generation mechanism. The binstrings during the process of context adaptive binary arithmetic coding are selected for encryption, which conforms to the features of invariable binstream and compatible format in terms of video encryption. Performance tests for six types of standard videos with different resolutions were performed. The results indicated that the encryption algorithm has a large key space and benefits from a high encryption effect.