Reviewerly/exHarmony huggingface dataset:
Evaluation of Vector Transformations for Russian Word2Vec and FastText Embeddings

Authors of Word2Vec claimed that their technology could solve the word analogy problem using the vector transformation in the introduced vector space. However, the practice demonstrates that it is not always true. In this paper, we investigate several Word2Vec and FastText model trained for the Russian language and find out reasons of such inconsistency. We found out that different types of words are demonstrating different behavior in the semantic space. FastText vectors are tending to find phonological analogies, while Word2Vec vectors are better in finding relations in geographical proper names. However, we found out that just four out of fifteen selected domains are demonstrating accuracy more that 0.8. We also draw a conclusion that in a common case, the task of word analogies could not be solved using a random word pair taken from two investigated categories. Our experiments have demonstrated that in some cases the length of the vectors could differ more than twice. Calculation of an average vector leads to a better solution here since it closer to more vectors.
Design and Implementation of Work Training Certificate Verification Based On Public Blockchain Platform

The purpose of this research is to develop a job training document storage system based on the public blockchain platform. The use of public platforms is used to secure certificate data making it difficult to falsify. The use of smart contracts is used to form data that will be used in blocks to be sent to the Ethereum blockchain network. The InterPlanetary File System (IPFS) is used to store certificate files in a distributed environment so that access is easy and safe to do. The results showed that certificate data can be stored in public blockchain Ethereum infrastructure and its supporting files stored in the IPFS environment. This means that certificate data is more secure from counterfeiting because it is stored in a distributed and transparent blockchain environment.
Sensitivity Context Aware Privacy Preserving Text Document Summarization

Text data is one of the major types of unstructured data generated in the real world i.e. the data that doesn't have a predefined data model. It is already known that, the healthcare domain consists of the patient data in text type, which are available in the form of documents/reports. Especially, the hospital discharge summary document consists of the patient's details, medications, and clinical procedures. The major issue here is, when these documents are subjected to text analytics operations like summarization, which is a process of extracting or describing the important aspects of document data as a summary, by Third Party or In House Data Scientist there is a possibility of patients' privacy breach. To overcome the patient privacy breach, this research work has proposed a machine learning-based approach called sensitivity context aware privacy preserving text document summarization.
ACOTSP-MF: A memory-friendly and highly scalable ACOTSP approach

Ant Colony Optimization (ACO) is a population-based meta-heuristic inspired by the social behavior of ants. It is successfully applied in solving many NP-hard problems, such as the Traveling Salesman Problem (TSP). Large-sized instances pose two memory problems to the ACOTSP algorithm: the memory size and the memory bandwidth. This work has focused on developing ACOTSP-MF, a new ACOTSP algorithm proposed to adequately manage the memory issues that arise while solving large TSP instances. ACOTSP-MF uses the nearest neighbor list, introducing a novel class of cities, the backup cities, while grouping cities into three classes: the nearest neighbor cities, the backup cities, and the rest of the cities (the majority). ACOTSP-MF also modifies how the base ACOTSP carries out the tour construction and pheromone update phases depending on the group to which a city belongs. This way, ACOTSP-MF reduces both the memory requirements of its data structures (from O(n∗n) to O(n)), and the memory bandwidth needs (thanks to better exploitation of the memory data locality). In this paper, we have carried out an in-depth analysis of ACOTSP-MF performance for medium and large TSP instances, covering vectorization and scalability issues and showing its main bottlenecks. For medium-size instances, the paper reports speedup factors of 20-500X for the rl11849 instance compared to the base ACOTSP version. ACOTSP-MF is intended and especially adequate for large-size instances. In this context, the paper reports excellent execution time for the Tour Construction phase, with less than 500 ms per iteration for the earring-200k instance. Finally, a study about the solution quality of ACOTSP-MF has been included, showing that ACOTSP-MF paired with local search offers high solution quality (within 2% of the best-known solution).
GFD-Retina: Gated Fusion Double RetinaNet for Multimodal 2D Road Object Detection

In the field of Advanced Driver-Assistance Systems, road traffic actors detection is a vital task in order to avoid human errors in driving. Unlike camera only-based convolutional neural networks for 2D object detection, multimodality using improve object detectors accuracy and robustness. In this paper, we propose Stacked Fusion Double RetinaNet (SFD-Retina) and Gated Fusion Double RetinaNet (GFD-Retina), two convolutional neural networks taking multimodal data (RGB, Depth from Stereo, Optical Flow, LIDAR Point Cloud) as input. These networks combine efficiently sensor specific properties by using both early fusion and middle fusion for detecting road objects and their 2D localization. Evaluation of SFD-Retina and GFD-Retina on the challenging KITTI object detection benchmark shows that using sensor fusion improve significantly object detection accuracy. Moreover, GFD-Retina with Gated Fusion Unit outperforms SFD-Retina with Stacked Fusion Unit, and obtain satisfying results against state-of-the-art algorithms.
Usability and User eXperience Evaluation of Conversational Systems

Conversational Systems (CSs) are software that uses the user's voice to perform some action. Before these systems were available to users, they must be evaluated. In this sense, Usability and User eXperience (UX) evaluations contribute to the verification of software quality, since they evaluate several aspects such as efficiency, effectiveness, immersion, and user satisfaction. Therefore, the goal of our Systematic Mapping Study (SMS) is to identify the evaluation technologies (methods, techniques, models, among others) used by researchers and professionals to evaluate Usability and UX of CSs. We selected 39 papers for data extraction and, based on these works, we identified 31 different evaluation technologies. Besides, our SMS extracted the characteristics of technologies, CSs, and empirical studies described in the papers. Our results identify a lack of evaluation technologies of CSs that unite the concepts of Usability and UX and undergo empirical evaluations. Moreover, we observed researchers tend to create their questionnaires according to the needs of the study. Overall, our SMS presents data about the researched topic, describing the gaps, and contributing to the scientific community that evaluates Usability and UX of CSs.
Wheelchair Navigation System using EEG Signal and 2D Map for Disabled and Elderly People

Disabled and elderly people may be very difficult to control an electrical wheelchair by using a joystick in environments with tight spaces, narrow pathways, and obstacles. This paper proposes a wheelchair system with hands-free control to assist users to overcome this difficulty. In particular, a combination of EEG signals and a 2-dimensional (2D) map is applied in this wheelchair system. Firstly, a human brain-computer communication system for interfacing with human facial gestures to execute commands using the EEG signals is designed. The EEG signals are processed using filtering, scaling, and then features are extracted to connect with the input of one neural network for classifying the EEG signals corresponding to control commands. Secondly, environmental information with obstacle and free spaces are collected for building a 2D map using an RGB-D camera system. Therefore, user inputs are used to a navigation plan along with the 2D safety map for a navigation wheelchair system with collision-free control during movement. Experimental results demonstrate to illustrate the feasibility and effectiveness of the proposed method.
Motivation, persuasion and healthy eating

Countries are confronting a variety of problems, among them those related to obesity and other illness due to uncontrolled consume of highly processed and inorganic food. Our question is: how the information systems and the human-computer interaction fields can help to improve the human behavior towards the consumption of healthy food for wellbeing? This paper searches for answers to this question with a case study in which we attempt to motivate and persuade people to eat healthy food by using an information system and, on the other hand, influence owners of restaurants to offer more healthy food. In this work we present a persuasive information system, designed using the Persuasive OpenDesign framework (a socially aware platform for open design) and discuss preliminary results of using it. We conclude this paper with future lines of work on this subject.
Leveraging Multi-view Learning for Human Anomaly Detection in Industrial Internet of Things

One of the most prominent human anomalous behaviour is fall. Fall detection for elderly care is important in the smart home care system. Fall detection is one of the smart home applications. In home-based assisted living, the detection of human anomalous behaviour is crucial. Home-based assisted living is becoming prevalent in elderly care. Aging brings several health issues and prevents the elderly to live an independent life. Therefore, it is important to detect human anomalous behaviour to ensure the safety of older adults. In this paper, we propose a multi-view learning approach to detect human anomalous behaviour. Our multi-view algorithms are running on edge devices. Multi-view learning is basically a method of learning from multiple views. It involves data fusion or integration from learned multiple feature sets. We evaluate our approaches for fall detection on the publicly available dataset “MobiFall_Dataset_v2.0”. We construct 2 distinct views of accelerometer and gyroscope data and apply co-training methods to train the classifiers. We perform experiments on the extracted feature sets of two different views and combined them using 3 different methods. We introduce 3 feature fusion methods to leverage the inadequacy of single-view learning. Multi-view approach used in this work has an improvement over single view learning and produces better accuracy in classification of falls.
Skills Obsolescence and Education Global Risks in the Fourth Industrial Revolution

The thrust of the Fourth Industrial Revolution and the pressure of industry stakeholders require employers to develop and implement new workforce strategies. In the engineering field, current requirements must address the additional challenges related to planned obsolescence in technology. This phenomenon has represented in recent years a triggering risk for other labor obsolescences, with devastating effects for many companies and educational institutions that were unprepared for these cataclysmic changes. The current panorama is frantic and especially damaging for educational institutions in Latin America, to the point that the worst facet of technology obsolescence, known as systematic, causes a kind of "mirror" obsolescence in academic programs in engineering institutions. The objectives of this Work-in-Progress study are to: (i) identify problems related to technological change skewed by skills in the technology sector labor markets and (ii) assess different initiatives that educational institutions in engineering have addressed, including Higher Education and Continuing Education. This document also briefly presents a statement of the implications for educational practice with focus on actions, possible frameworks of teaching and learning techniques, and a summary of the research preliminary results and findings.
A survey of text classification models

With the rapid development of artificial intelligence, text classification method based on deep learning model has surpassed traditional machine learning method in various aspects. This paper introduces dozens of deep learning models for text classification according to the different network structures of the models. In addition, this paper briefly introduces the evaluation indicators and application scenarios of text classification, summarizes and forecasts the current challenges and future development trend of text classification.
Semantic Role Labeling with Heterogeneous Syntactic Knowledge

Recently, due to the interplay between syntax and semantics, incorporating syntactic knowledge into neural semantic role labeling (SRL) has achieved much attention.Most of the previous syntax-aware SRL works focus on explicitly modeling homogeneous syntactic knowledge over tree outputs.In this work, we propose to encode heterogeneous syntactic knowledge for SRL from both explicit and implicit representations.First, we introduce graph convolutional networks to explicitly encode multiple heterogeneous dependency parse trees.Second, we extract the implicit syntactic representations from syntactic parser trained with heterogeneous treebanks.Finally, we inject the two types of heterogeneous syntax-aware representations into the base SRL model as extra inputs.We conduct experiments on two widely-used benchmark datasets, i.e., Chinese Proposition Bank 1.0 and English CoNLL-2005 dataset.Experimental results show that incorporating heterogeneous syntactic knowledge brings significant improvements over strong baselines.We further conduct detailed analysis to gain insights on the usefulness of heterogeneous (vs.homogeneous) syntactic knowledge and the effectiveness of our proposed approaches for modeling such knowledge.
Medical Image Compression and Encryption using Chaos based DNA Cryptography

In digital communication, the transmission of medical images over communication network is very explosive. We need a communication system to transmit the medical information rapidly and securely. In this manuscript, we propose a cryptosystem with novel encoding strategy and lossless compression technique. The chaos based DNA cryptography is used to enrich security of medical images. The lossless Discrete Haar Wavelet Transform is used to reduce space and time efficiency during transmission. The cryptanalysis proves that proposed cryptosystem is secure against different types of attacks. The compression ratio and pixel comparison is performed to verify the similarity of retained medical image.
A Distributed Tracking Algorithm for Counting People in Video by Head Detection

We consider the problem of people counting in video surveillance. This is one of the most popular tasks in video analysis because this data can be used for predictive analytics and improvement of customer services, traffic control, etc. Our method is based on the object tracking in video with low framerate. We use the algorithm from [1] as a baseline and propose several modifications that improve the quality of people counting. One of the main modifications is to use a head detector instead of a body detector in the tracking pipeline. Head tracking is proved to be more robust and accurate as the heads are less susceptible to occlusions. To find the intersection of a person with a signal line, we either raise the signal lines to the level of the heads or perform a regression of bodies based on the available head detections. Our experimental evaluation has demonstrated that the modified algorithm surpasses the original in both ac- curacy and computational efficiency, showing a lower counting error on a lower detection frequency.
Quantum algorithm for a set of quantum 2SAT problems*

We present a quantum adiabatic algorithm for a set of quantum 2-satisfiability (Q2SAT) problem, which is a generalization of 2-satisfiability (2SAT) problem. For a Q2SAT problem, we construct the Hamiltonian which is similar to that of a Heisenberg chain. All the solutions of the given Q2SAT problem span the subspace of the degenerate ground states. The Hamiltonian is adiabatically evolved so that the system stays in the degenerate subspace. Our numerical results suggest that the time complexity of our algorithm is $O(n^{3.9})$ for yielding non-trivial solutions for problems with the number of clauses $m=dn(n-1)/2\ (d\lesssim 0.1)$. We discuss the advantages of our algorithm over the known quantum and classical algorithms.
Convolutional Neural Network based Traffic-Sign Classifier Optimized for Edge Inference

Traffic-Sign Classification is a major task in self-driving cars as well as modern driving assisting systems can be deployed as an inference engine on the Field Programmable Gate Array (FPGA) combined with host processor-based edge device like Zynq Platform considering the property of dynamic reconfigurability of FPGA adoptable to architectural innovation. Hence this paper proposes an optimized Convolutional Neural Network (CNN) architecture based on VGGNet combined with image-preprocessing techniques. The proposed methodology utilizes pruning combined with post-training quantization-based optimization and obtains an accuracy loss of less than 1%. The architecture is trained, tested and validated using German Traffic Sign Detection Benchmark (GTSDB) with Google's TensorFlow framework obtaining a validation accuracy of 99.2% and test accuracy of 100% for novel input inference. The experimental results show the reduction in memory footprint of CNN model readily implementable on FPGA.
Vocational skills learning model strategies during covid-19

The focus of this research is the skills learning model used by teachers, especially those who teach practical subjects. In accordance with the purpose of vocational education which says that graduates from vocational high schools are graduates who are ready to work in the industrial and business world. The purpose of this study is to describe the skills learning model strategy used by teachers when Covid-19 hit Indonesia. The research method used is a qualitative research method with a descriptive approach. Collecting data using observation, interviews, and documentation. The results obtained are: (1) teachers who teach subjects related to the practice of getting obstacles and difficulties because online learning is very influential on internet networks, (2) teachers only use online learning models using video learning media, so that the teacher only sends a learning link to students via WhatsApp Class group, (3) some students experience difficulties in learning online because the internet signal or network is not always good. (4) with the covid-19 all learning in schools is required online, so that teachers must be proficient in using technology.
Analysis of the Internals of MySQL/InnoDB B+ Tree Index Navigation from a Forensic Perspective

In this paper we analyze the structure and generation of InnoDB-indices, as well as navigation in this internal structures with respect to its application in digital forensics. We thus provide an overview on the internal workings of the index that can be used to detect manipulations on the underlying table space. We analyze the physical as well as the logical structure of the index pages and its relation to the very theoretical field of B <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> -tree forensics. There we provide a first usable forensic technique targeting a real life open source database system. Furthermore, we discuss several use cases in the course of forensic investigations, as well as applications of the outlined methods, including possible extensions to the field of file system forensics.
An Overview of UPnP-based IoT Security: Threats, Vulnerabilities, and Prospective Solutions

Advances in the development and increased availability of smart devices ranging from small sensors to complex cloud infrastructures as well as various networking technologies and communication protocols have supported the rapid expansion of Internet of Things deployments. The Universal Plug and Play (UPnP) protocol has been widely accepted and used in the IoT domain to support interactions among heterogeneous IoT devices, in part due to zero configuration implementation which makes it feasible for use in large-scale networks. The popularity and ubiquity of UPnP to support IoT systems necessitate an exploration of security risks associated with the use of the protocol for IoT deployments. In this work, we analyze security vulnerabilities of UPnP-based IoT systems and identify attack opportunities by the adversaries leveraging the vulnerabilities. Finally, we propose prospective solutions to secure UPnP-based IoT systems from adversarial operations.
Predicting Vehicles Appearing from Blind Spots Based on Pedestrian Behaviors

This study aims to enable the prediction of objects appearing from blind spots, such as outside a field-of-view and regions occluded by other objects. Conventional prediction approaches for traffic scenes primarily predict the objects based on their current observations (i.e., not in blind spots). Unlike such approaches, we propose a novel problem definition for traffic scenes that predicts the objects in blind spots based on current observations of other visible objects. To this end, we provides a prediction problem for vehicles appearing from blind spots. We propose a method that predicts vehicles appearing from a blind spot based on the behaviors of visible pedestrians who observe vehicles in the blind spot of a camera. We build a dataset that includes videos capturing real traffic scenes and experimentally confirmed that our proposed method effectively predicted vehicles appearing from blind spots in traffic scenes, which include pedestrians, at a level similar to that afforded by humans. Our dataset except RGB sequences will be made publicly available.
An Anchor-free SAR Ship Detector with Only 1.17M Parameters

An lightweight anchor-free synthetic aperture radar (SAR) ship detector is proposed in this paper. Firstly, According to the SAR image characteristics, ResSARNet with only 0.69M parameters was proposed. Then, with ResSARNet as the backbone network, four improvements have been made to fully convolutional one-stage object detection (FCOS): Center-ness on bounding box regression branch, center sampling, Generalized Intersection over Union (GIoU) loss and adaptive training sample selection (ATSS). To verify the effectiveness of the proposed method, sufficient experiments have been conducted on the widely used SAR Ship Detection Dataset (SSDD). The results show that, the proposed network can achieve 61.5% average precision (AP) and 70.9% average recall (AR) with only 1.17M parameters, which are only 6.5% and 3.2% for [email protected] and [email protected], respectively.