Reviewerly/exHarmony huggingface dataset:
Radar Phrase Recognition Algorithm Based on New Word Discovery

Aiming at the problem that the previous recognition algorithms based on supervised learning are difficult to apply to non-cooperative multi-function radar, this paper proposes a radar phrase recognition algorithm based on new word discovery. The algorithm can effectively identify radar phrases without prior knowledge. It is verified by simulation experiments that with the increase of the input data size, the algorithm can overcome the problem of decreased phrase recognition rate caused by the loss of data quality. To a certain extent, the algorithm achieves the purpose of compensating for data “quality” with data “quantity”, and has good engineering applicability.
Graph-based Topic Extraction Using Centroid Distance of Phrase Embeddings on Healthy Aging Open-ended Survey Questions

Open-ended questions are a very important part of research surveys. However, they can pose a challenge when it comes to processing since manual processing requires a labour-intensive human effort. Automation of the task requires application of NLP methods since free text does not ensure standardized structure. To tackle this problem, we present a solution for topic discovery and analysis of open-ended survey items. We use graph-based representation of the text that adds structure and enables easier manipulation and keyphrase retrieval. Additionally, we use pre-trained fastText aligned word vectors to cluster similar phrases even if they are written in different languages. The goal is to produce topic word and phrase representatives that are easy to interpret by a domain expert. We compare the method with traditional LDA and two state-of-the-art algorithms: BTM and WNTM. The resulting keyphrases representing topics are more intuitive to the domain experts than the ones obtained by reference topic models in similar experimental settings.
An Approach for Attacking Speck on Microcontroller with Correlation Power Analysis

In recent years, some lightweight cipher algorithms for IoT devices have been proposed. These algorithms can be applied to a variety of IoT devices without occupying too much memory and power consumption, and compared to traditional ciphers, such as AES, these algorithms have higher efficiency. The Speck algorithm is a lightweight block cipher, proposed by the NSA in June 2013, and it is specifically optimized for IoT microcontrollers. A few previous studies have shown that the Speck algorithm can be attacked through power analysis. Based on Arduino Uno as the implementation platform of the Speck 128/128 algorithm, in this paper, we propose a method to attack the Speck 128/128 algorithm. Our experiment results have shown that when the attack point in the last round of Speck 128/128 XOR operation, the round key can be successfully recovered by the Correlation Power Analysis attack.
Reproving the basic principles of fuzzy sets for the decisions

Abstract Principles in the logic and the sets form the basis of many societal solutions that are present with physical as well as non-physical phenomena. Non-physical phenomena, for example present language variables that require the presence of the fuzzy logic and the fuzzy sets. Although specific verification of the rules used remains the same, the systematic verification for the fuzzy sets requires a strategy, including when implementing it into a computer program. The final problem has the answer to the change in the operator implemented in the program.
Representation Learning for Constructive Comments Classification

While the common scenario nowadays in social networks is the proliferation of offensive language, the focus of attention in this work is the identification of constructive online comments. In order to automatically identify constructive online comments we implement both traditional and deep learning models based on the use of sparse and dense vector semantics. We evaluate these classifiers on a recently created constructive comments corpus comprised of 12,000 annotated news comments, intended to improve the quality of online discussions. The obtained results show how our model based on learning embeddings (dense vectors) is able to match the performance of complicated architectures like recurrent and convolutional neural networks.
Research on the Development and Reform of Electronic Physics Laboratory Based on Computer

Abstract As a basic and innovative natural science discipline, physics has been an important knowledge carrier method for people to study modern natural sciences since ancient times. In the study of science, the subject of physics is also an important science and engineering subject besides chemistry. The school has always attached great importance to the teaching of physics [1] . Due to the shortcomings of the traditional and outdated physics laboratory in recent years, people have been discovered. People are no longer satisfied with the use of the original laboratory. In particular, it will also cause huge losses to the school and the students and their teachers. In order to meet the needs of the school’s physics laboratory, my country’s educational circles have developed an electronic physics laboratory. With the support of computer technology, the emergence of such laboratories will become an important change in physics teaching experiments.
An Analysis of Jonathan Harvey’s Speakings for Orchestra and Electronics

In recent years an increasing number of composers have used speech as source material for instrumental, electronic and electroacoustic music. This article examines this particular intersection of music and language through an analysis of Jonathan Harvey’s Speakings for orchestra and electronics. I attempt to understand how Harvey made an orchestra sound like a human voice by analyzing his use of technology and his compositional techniques, particularly as they relate to existing theories of speech perception, acoustics and articulatory phonetics. This technical achievement is then placed in its broader musical context to examine the role that speech-sounds play in this piece, and the implications of hearing an orchestra speak in the context of this work’s narrative.
Implementation of Anti-Profanity Words in Mobile Application Platform

Abstract Communication technology increases day by day in our daily life due to the rapid growth of variety mobile application platforms. For instance, mobile application users are using chat rooms for sharing data, pictures, videos and documents. However, the users are exposed to receive something inappropriate such as bad words, bad photos and sensitive data in their social media platforms. The developed chat profanity filtering application can be used to detect and filter the bad words before the sender send any messages via the application. One way to detect and filter profanity words is by using the PurgoMalum web service. Therefore, the recipients are unable to view any bad words sent to the chat application. This research has shown that the developed chat profanity filtering application will minimize the potential of users abusing the chat platform in daily communication. Result from this study had successfully demonstrated that the developed chat profanity filtering application could minimize the potentials for users to abuse the chat platforms in communicating various forms of obscenity. Therefore, chat profanity filtering application was developed to minimize the abusing of social media platform in daily communication.
Noise Robust Learning with Hard Example Aware for Pathological Image classification

Label noise among the datasets can be memorized by deep neural networks, which degenerates the robustness of the learned model. However, recent studies proposed to deal with label noise are difficult in distinguishing informative hard examples from harmful noise ones, especially for medical image analysis. In this paper, we propose a noise robust learning algorithm based on hard example aware and Co-weighting to circumvent label noise. Firstly, we identify and exclude noisy samples according to the statistical features of predictive history, which allows retaining informative hard examples. Secondly, we adopt Co-weighting strategy for robust training, which improves Co-teaching algorithm through weighting samples between peer networks. Compared with extensive state-of-the-art methods, our strategy achieves the best performance on two colon tumor histology image datasets under different ratios of noise.
Japan's Act on Wiretapping for Criminal Investigation: How the system is implemented and how it should be

Japan`s Act on Wireapping for Criminal Investigation has been amended in 2016. The amendment was made by adding a wiretapping method using specifically designed device, and came into effect in 2019. Previously, the witness system was used to avoid unauthorized communication eavesdropping by investigative agencies. The amendment allows for the wiretapping without the need for witnesses. For this purpose, special interception system using cryptographic technology has been introduced. However, there has not been sufficient discussion of the issue of the interception system that satisfies the requirements of the law from a technical point of view. This paper discusses an interception system for adequate wiretapping to satisfy the act.
Анализа друштвених мрежа у електронском образовању на Војној академији у Београду

This paper presents the Social Network Analysis in the electronic education at the Military Academy in Belgrade. The objective of the analysis is to examine the ways of communication and cooperation of profesors and students in the area of electronic education. The obtained results should indicate the possibilities of application of social networks in e-education for the purpose of promoting the communication and cooperation between professors and students in the area of higher education.
Crack Detection of Highway Prestressed Concrete Bridge based on Image Recognition

Because the traditional highway prestressed concrete bridge crack detection method has poor ability to detect image edges, the detection rate of the detection method is low. Therefore, a crack detection method of highway prestressed concrete bridge based on image recognition is proposed. Select the image acquisition equipment and acquisition points to complete the bridge image acquisition. The collected image is grayed and enhanced to realize image preprocessing. Taking the above-mentioned image as the detection basis, the threshold method is used to segment the detection to complete the crack edge detection. So far, the design of crack detection method for highway prestressed concrete bridge based on image recognition is completed by comparing with the other two traditional methods, it can be seen that this method has a higher detection rate of cracks, which shows that this method is more effective in use.
Memory network based Knowledge Driven Model for Response Generation in Dialog System

Human-machine conversation is one of the most important topics in artificial intelligence (AI) and has received much attention across academia and industry in recent years. Currently dialogue system is still in its infancy, which usually converses passively and utters their words more as a matter of response rather than on their own initiatives, which is different from human-human conversation. This paper tackles the problem of generating informative responses by integrating knowledge base into the dialogue system’s response generation process, in an end-to-end way. A novel architecture is proposed, namely Memory network based Knowledge Driven Model (MKDM), which can integrate knowledge base by memory manager, and generate knowledge grounded responses. By conducting comparative experiments on automatic metrics demonstrate the effectiveness and usefulness of our model.
Capabilities and aspirations: A multi‐theory approach to information and communications technology for development in rural Panama

Abstract Large national development projects often overlook site‐specific theoretical approaches to information and communications technology for development (ICT4D). To address this dilemma, we created a unique multi‐theoretical lens by combining a capabilities approach with an aspirations‐based approach. We use this combination to present an abductive analysis of our ethnographic study in a Black/Afro‐Caribbean community (Basti) in Bocas del Toro, Panama. The study highlights a community/computer center (Infoplaza) that is part of an initiative to close Panama's digital divide. The study objectives were to identify major barriers to post‐secondary education and computer access in Basti, and to assess how the Infoplaza might affect those barriers and people's education and life aspirations. We conclude that while beneficial, the Infoplaza's impact on education, computer access, and influence on education and life aspirations is minimal. This article emphasizes aspirations requiring digital literacy, and it suggests that the Infoplaza (and local schools) prioritize literacy, typing and internet knowledge as a development strategy to increase digital literacy and people's opportunities. Analyzing this study using our combined approach uncovers nuanced inequality in Basti and reveals limited capabilities for people to actualize what they value doing or being. This unique study and analysis is valuable to the Infoplaza initiative and to similar ICT4D research.
Research on the Teaching Reform Strategy of Professional Engineering Survey Course for Architecture Specialty based on the Internet under the Background of MOOC

Abstract In the “Internet +” era, MOOC is a new form of online open course, which has caused the change of teaching concept and teaching mode. MOOC is a learning experience that integrates high-quality educational resources, which brings new learning experience to learners. However, there are still some problems in MOOC, which will affect learners’ difficulty to enter the deep learning state. Firstly, this paper analyzes the importance of MOOC. Then, this paper puts forward some questions. Finally, some suggestions are put forward.
ECEM - Generating Adversarial Logs under Black-box Setting in Web Security

Researchers are making efforts on detection and prevention in web security by deploying machine learning models, but such models are vulnerable to adversarial examples. We introduce Exploratory Character Edit Method (ECEM), requiring only detection labels to generate adversarial logs in web security. It is applicable to real-world black-box detection models, such as an IDS (Intrusion Detection System). Experiments on an open data set show that our adversarial attack outperforms two textual adversarial attacks in following points: (1) The success rate of attack is higher; (2) The quality of adversarial examples are higher (larger similarity and smaller distance); (3) The generation process requires fewer iterations; We further show the consequences of causative attacks by injecting adversarial logs into the training data set and a possible defensive mechanism with adversarial training.
Online and energy-efficient task-processing for distributed edge networks

User equipment produces a series of tasks that are processed locally or remotely, falling into three categories: (i) local computing only, (ii) a fraction of the task is computed locally and the remaining task unprocessed is offloaded for remote computation, and (iii) the entire task is offloaded. Each case has attracted substantial attention in recent studies, where a delay-constrained non-linear optimization problem is often formulated. The solutions employed are either based on Lagrange duality, heuristic search, or dynamic programming. To our knowledge, there is no unifying task-processing orchestrator that is an online tailored solver for learning the model-free problems, encapsulating the three cases above. We fill this gap and present the first attempt on an innovative actor-critic reinforcement learning approach in consideration of the energy-efficiency, to compute the asymptotically optimal solutions via decomposing the comprehensive optimization into sub-problems. Rigorous theoretical analyses and experience-driven simulations demonstrate significant advantages over the benchmark approaches, in terms of task-processing delay, power efficiency, and convergence time.
Introduction: Fuzzy Logic’s Four Art-Related Principles and Five Phenotypes in the Musical Process

The research program for the book is designed from both the FL side and the music side and aims at a specifically music-related appropriation of the FL. With reference to L. Zadeh, categories and ways of thinking of FL and its ‘intra-interdisciplinary’ approach, also under the sign of the paradox of “Precise fuzziness”, the many and manifold relationships between musical process, artwork and FL are reflected. This applies initially to the relationship between modal, polyvalent and musical logic. thereby respecting the asymmetric relation between the infinite possibilities and finite reality. Four art-specific principles will be developed from FL’s concept of fuzziness and precision: similarity, sharpening - as filtering and as crystallization -, blurring and variation. These principles are the guiding categories and analytical basis for the book and will be applied systematically and in detail during the course of the investigation, and in turn will be continuously concretized.
Towards Hybrid Model for Automatic Text Summarization

The overflowing of textual data on the web needs an efficient tool that is able to manage and process data. In this context, automatic text summarization has shown a great importance in several application areas. It aims to create a coherent and fluent short version of a document while preserving of the main information. This method allows for a reduction in reading time by condensing relevant information from a large collection of documents. Several automatic text summarization approaches have been proposed in order to entail shorten parts of the document. These methods have good results, but they still need improvements related to the reliability of sentences extraction, redundancy, semantic relationships between sentences, etc. This paper introduces a new hybrid architecture, combining a 2-layer recurrent neural network (RNN) extractive model and a sequence-to-sequence attentional abstractive model. This method uses the advantages of both extractive and abstractive approaches. A given text is first fed into the extractive model to obtain its relevant content, then generalized using the abstractive model, resulting in the text's summary. First experimental results on real-world data show that the proposed model can achieve competitive results for extractive, abstractive and hybrid models.
Design and Development of Digital Painting Online Learning and Communication System Based on Visual Semantics

In the era of rapid development of computer and network, the network learning platform has become an important part of higher education, providing the best space for students to study by themselves in their spare time. At the same time, with the advent of the information age, people's competition is becoming more and more fierce, and the requirements for mastering knowledge are getting higher and higher. People from all walks of life, such as students and teachers, are facing the demand for knowledge. Based on visual semantics and MyEclipse as the development platform, this design uses JSP language and Mysql database related technologies, as well as Photoshop cs5, Dreamweaver and other auxiliary software, according to the learning situation of digital painting students, designs and develops an online learning and communication system for students' digital painting based on B/S mode. The system based on visual semantics has a resource-sharing curriculum group, supports the sharing of learning resources and online learning, and enables learners and teachers to communicate and collaborate spontaneously to build a learning community together.
On the Security of a Proxy-free Privacy-preserving Task Matching with Efficient Revocation

Task matching is an important part to realize task assignment in crowdsourcing computing. However, privacy of tasks and workers is usually ignored in most of exiting task matching schemes. To solve this issue, recently, Shu et al. proposed a privacy-preserving task matching with efficient revocation in Crowdsourcing (IEEE Transactions on Dependable and Secure Computing DOI 10.1109/TDSC.2018.2875682) to ensure privacy protection of tasks and workers and achieve the worker revocation. Their scheme had claimed to be selective IND-CKA secure, and realized efficient revocation of the worker. Unfortunately, in this work, by analyzing the security of Shu et al. scheme, we show that their scheme is insecure. It cannot really provide IND-CKA security and realize the revocation of the worker. This is to say, their scheme does not satisfy the confidentiality of keyword since an adversary can distinguish the ciphertexts of arbitrary keywords without trapdoor information. Finally, after the corresponding attacks are given, we analyze the reason to produce such attacks.
Tasks performed in the legal domain through Deep Learning: A bibliometric review (1987–2020)

Deep Learning (DL) has become the state-of-the-art method for Natural Language Processing (NLP). During the last 5 years DL became the primary Artificial Intelligence (AI) method in the legal domain. In this work we provide a systematic bibliometric review of the publications that have utilized DL as the primary methodology. In particular we analyzed the performed objectives (performed tasks), the corpus utilized to train the models and promising areas of research. The sample includes a total of 137 works published between 1987 and 2020. This analysis starts with the first DL models (formerly Neural Networks) in the legal domain until the latest articles in the ongoing year. Our results show an increment of 300% on the total number of publications during the last 5 years, mainly on information extraction and classification tasks. Moreover, classification is the category with most publications with 39% of the total sample. Finally, we have identified that summarization and text generation as promising areas of research. These findings show that DL in the legal domain is currently in a growing stage, and hence it will be a promising topic of research in the coming years.
Research on Image Segmentation Algorithm Based on Component Tree

Abstract The key part of sea target recognition is to search for the possible ship targets in the image. For the problem that the standard segmentation of threshold algorithm is not effective in the complex sea condition, in this paper, a component tree image segmentation method based on minimum error threshold and ship area is proposed for ship target recognition. Firstly, area criterion is used to filter the image, then minimum error threshold method is used to construct the component tree and determine the filtering mechanism, finally, gray level criterion is used to filter the component tree. The simulation results show that proposed method is effective.