Reviewerly/exHarmony huggingface dataset:
Automatic Classification of Error Types in Solutions to Programming Assignments at Online Learning Platform

Online programming courses are becoming more and more popular, but they still have significant drawbacks when compared to the traditional education system, e.g., the lack of feedback. In this study, we apply machine learning methods to improve the feedback of automated verification systems for programming assignments. We propose an approach that provides an insight on how to fix the code for a given incorrect submission. To achieve this, we detect frequent error types by clustering previously submitted incorrect solutions, label these clusters and use this labeled dataset to identify the type of an error in a new submission. We examine and compare several approaches to the detection of frequent error types and to the assignment of clusters to new submissions. The proposed method is evaluated on a dataset provided by a popular online learning platform.
Social Disciplines and humanities in Engineering Training in the Context of the FSES 3++

The article discusses the role of social disciplines and humanities in new educational programmes of engineering training, updated in connection with the introduction of HE FSES 3++. The notion of “general cultural competencies” is no longer used in the new educational standards although the “universal competencies” of HE FSES 3++ still correlate with the indicators of general human culture. The article considers the possibilities of the development of universal competencies in approximate basic educational programmes taking the project in the educational direction of 03.09.01 “Computer science and computing” as an example. The importance of social disciplines and humanities to form universal competences is noted. These disciplines provide the continuity of the educational process of the younger generation. The article gives a brief summary of the content of the proposed social disciplines and humanities, describes the conditions for its variation, possible forms of learning activities that ensure the development of universal competencies of future engineers.
NATIONAL SUBSCRIPTION AND LIBRARY ACQUISITION. PART ONE. MATERIAL AND ELECTRONIC ACQUISITION BUDGETS IN RUSSIA

Introduction. Electronic acquisition budgets are indicators of the information supply of research and education in the organization and affect its research output. The national level subscription to the global citation indexes Web of Science Core Collection and Scopus and the Freedom Collection of Elsevier released 70% of eleсtronic acquisition expenditures in the Russian libraries. The article studies the effect of the state-funded national subscription on the structure and temporal dynamics of material and electronic acquisition budgets in the Russian libraries. Materials and Methods. Materials comprise results of the data processing of the surveys on acquisition budgets in 2012–2018 and published results of the international surveys. Following our previous studies, we analyzed three main reference groups: universities (with a separate group of the Russian leading universities), research institutions and public libraries. For the purpose of this study, we created additional sub-groups of leading institutions (centers of excellence) in each reference group. From the survey data we calculated the average unit costs (both of the material and electronic acquisition budgets) in each reference group/sub-group. We also calculated relative shares of electronic and print resources in the library material budgets. Results. Since 2016, the material budgets of Russian libraries have grown after the fall caused by the economic crisis in 2014. The state-funded national subscription to the global citation indexes and the Freedom Collection of Elsevier resulted in the 2–4 times decrease in material and electronic acquisition budgets of the centers of excellence in each reference group. Discussion and Conclusions . For the first time, our study revealed the negative effect of the state-funded national subscription on the material and acquisition budgets in Russian organizations. We registered a decrease in the material budgets of the major Russian public and scientific libraries and universities.
Practical Verification of Data Encryption for Cloud Storage Services

Sensitive data is usually encrypted to protect against data leakage and unauthorized access for cloud storage services. Generally, the remote user has no knowledge of the actual data format stored in the cloud, even though a cloud server promises to store the data with encryption. Although a few works utilize data encapsulation and remote data checking to detect whether the sensitive data is protected securely in the cloud, they still suffer from a number of limitations, such as heavy computational cost at the user side and poor practicality, that would hinder their adoptions. In this paper, we propose a practical verification scheme to allow users to remotely evaluate the actually deployed data encryption protection in the cloud. We employ the pseudo-random number generator and present a data encapsulation solution, which can benefit users with significant cost savings. By imposing monetary rewards or penalties, our proposed scheme can help ensure that the cloud server stores data encrypted at rest honestly. Extensive experiments are conducted to further demonstrate the efficiency and practicality of the proposed scheme.
A World Following Farmer Almanac

Interaction design for domestic settings is popular recently, and there is a great deal of general literature on it. There is little specific guidance, however, on how local culture interacts with interactive technologies. Using Taiwanese Farmer Almanac as an example, through cultural probes and speculative design, we hope to deepen the discussion of the possibility of the home life that interweaves local folk religion and IoT smart home technology in the future.
Collective Biographies of Women: A Machine Learning Approach to Paragraph Annotation

The Collective Biographies of Women Project (CBW) seeks to annotate a large corpus of nineteenth and twentieth century British and American biographical texts about women. These annotations, applied at the paragraph level, draw from a controlled vocabulary known as BESS, Biographical Elements and Structure Schema. The BESS vocabulary terms are grouped into five major categories - Stage Of life, Persona, Event, Topos, Discourse. The corpus is drawn from 1,270 known books, comprising around 13,000 chapters of about 8,000 women. Because manual annotation is painstaking, time-consuming, and error-prone, there is a need to automate the annotation process for the entire corpus. Using the BESS vocabulary as labels and the currently annotated paragraphs as a training set, we developed a supervised machine learning classifier to aid in this process. Employing several methods, including Logistic Regression, Random Forest and Language models, we achieved an accuracy of ~87%. In addition to aiding in the work of annotation, we have made recommendations about further developing the BESS vocabulary.
Using C4.5 algorithm to predict students monthly payment on islamic boarding school

Abstract Assalafi AlFithrah is islamic boarding school located in Meteseh Semarang. In 2012 until 2016, it received 262 students from various regions in Indonesia. Fulfillment of life necessities of students is one of the things that should be considered by boarding school management. The basic necessities of life is food, clothing, place, education and health care. The monthly payment needed to cover the necessities of students in Assalafi AlFithrah. There are number of students whos payment were delayed. From these problems, it was necessary to predict the delay of monthly payment to estimate whether students will be late to pay or not. Data mining as solution to predict the delay of the monthly payment on students. One of a robust classification technique of data mining on categorical data is C4.5. Experiment conducted based on Knowledge Discovery Database (KDD) with C4.5 algorithm resulted average accuracy 81.15%, with average value of precision 77.62% and average value of recall 91.90%. This Application can give recommendations for scholarship to poor families. With this application can improve service and trust of parents towards the survival of students in schools.
Location-based Cryptographic Techniques: Its Protocols and Parameters

Storing data via online increased rapidly and this massive data has become significant to be secured due to the data invasion. Hence, encryption to transmit data securely in open networks has been applied. However, this single layer of security is insufficient. Location-based cryptographic technique is proposed by adding another layer of security on top of existing encryption method. It requires location information which are latitude and longitude coordinates, time and velocity. Those parameters used to generate an additional encryption and decryption keys for location-based encryption process. Therefore, protocols are required in cryptographic techniques to ensure the security and integrity of data while transit over a network connection. This paper discusses The Geo-encryption Algorithm, Location Dependent Encryption Algorithm (LDEA), Dynamic Toleration Distance (DTD) and Timed, Efficient Stream Loss-tolerant Authentication (TESLA) as the protocols for location-based cryptographic techniques. Its parameter used in that particular protocol and the roles in some applications such as location-based security in digital cinema distribution for sending large media file to multiple theatre locations and location-based authentication in bank cloud storage for accountant access at specific location and timeframe.
Survey of RSA Vulnerabilities

Rivest et al. patented (US) RSA.RSA forms the basis of most public encryption systems.It describes a public key encryption algorithm and certification process, which protects user data over networks.The patent expired in September 2000 and now is available for general use.According to Marketsandmarkets.com, the global network encryption market size is expected to grow from USD 2.9 billion in 2018 to USD 4.6 billion by 2023, at a compound annual growth rate (CAGR) of 9.8%.Major growth drivers for the market include increasing adoption of optical transmission, an increasing demand to meet various regulatory compliances and a growing focus on shielding organizations from network security breaches.In short, RSA forms the basis of almost all public encryption systems.This, however, is not without risk.This chapter explores some of these vulnerabilities in a mathematical context and provides the reader with an appreciation of the strength of RSA.
Secure communication for Industrie 4.0

Abstract Industrie 4.0 fosters the concept of systems within one company to communicate with external systems in other companies or cloud services. Such communication across security domains creates new challenges as multiple stakeholders with different, sometimes conflicting objectives are involved. Typical examples are confidentiality versus monitoring. Current security architectures and protocols are not designed to resolve these challenges.
Deciphering and Analyzing Software Requirements employing the techniques of Natural Language Processing

Deciphering human language by Requirement Analysts is the key issue in Software Development. Clients communicate their software requirements in raw form. In this paper, we are presenting certain techniques of Natural Language processing which work out greatly to extract information properly and minimizing the bugs that may generate in later parts of Software Development. In today's era, the latest technological development in Artificial Intelligence has enabled machines to process the text to a certain level. Natural Language understanding is so far the most critical problem; the Software community is facing today in requirements gathering. In this study, using the techniques of Natural Language Interpretation, Testers and Software Developers can chalk out the more exact requirements from customers which can improve the Quality of Software to a certain level.
Ontology negotiation: Knowledge interchange between distributed ontologies through agent negotiation

Summary With the proliferation of knowledge source on the internet as well as the widely professional agents, the knowledge interchange is drawing much attention. Ontology is recognized as the crucial technology due to their nature of sharing, formalization, and conceptualization to integrate and share the knowledge. In this paper, by interpreting and negotiating the communication content, a unified understanding of knowledge is formed; then, we can realize the interoperability between ontologies. We have developed the ontology automatic negotiation by agent elect protocol (AEP) to elect optimal participants and encourage agents to obey the protocol, concept mapping protocol (CMP) to find the corresponding concept mappings with the highest relevancy, and in addition, agent negotiation protocol (ANP). In ANP, we define the simultaneous negotiation protocol and agents' strategies to combine distributed ontologies interchange with agent negotiation. Finally, the implementation and preliminary results are given to verify the validity of the proposed ontology negotiation.
Take the Initiative: Mixed Initiative Dialogue Policies for Pedagogical Agents in Game-Based Learning Environments

Pedagogical agents have been shown to be highly effective for supporting learning in a broad range of contexts, including game-based learning. However, there are key open questions around how to design dialogue policies for pedagogical agents that support students in game-based learning environments. This paper reports on a study to investigate two different agent dialogue policies with regard to conversational initiative, a core consideration in dialogue system design. In the User Initiative policy, only the student could initiate conversations with the agent, while in the Mixed Initiative policy, both the agent and the student could initiate conversations. In a study with 67 college students, results showed that the Mixed Initiative policy not only promoted more conversation, but also better supported the goals of the game-based learning environment by fostering exploration, yielding better performance on in-game assessments, and creating higher student engagement.
Intelligent Production and Detection Template of Outlier Dataset Using Clustering

Outliers are data with anomalous behaviors to other datasets. There are three different types of outliers, namely point anomaly, collective anomaly, and conditional anomaly. Different density-, clustering-, distance-, and distribution-based methods are used to detect outliers. It is obvious that before testing detection algorithms, a dataset that encompasses different types of outliers is required. In this paper an intelligent clustering algorithm is presented to produce a dataset consisting of different outliers. The other important point in this paper is the probability of two uninvestigated types of collective data among datasets that the anomalies are called type I and II. Results show that the proposed algorithm is capable of producing a dataset including different types of outliers. This dataset can be used in all outlier detection techniques. In addition to detection of point anomalies, it can detect all collective anomalies.
Intelligent Video Monitoring System: Technical and Economic Aspects

The article is devoted to the implementation effect study of the intellectual video monitoring system in a small enterprise. The considered system was developed in the scientific study framework of the authors' group in Engineering and Technological Academy (TRTI, TSURE, ITA). The system is able to count the cars crossing the enterprise checkpoint line automatically, to determine the type of vehicle body of a visitor, and to determine the location of the vehicle visitor of the company territory in dynamics.
Spectrally-resolved white-light phase-shifted interferometry for 3D measurements of multilayer films

As a summary of of the authors' previous paper of Ref 1, we describe a new scheme of a Linnik interferometric configuration based on spectrally-resolved white-light interferometry for simultaneous measurement of top surface and its underlying film surfaces in multilayer film structure. Our proposed technique enables accurate measurements of the phase and reflectance over a large range of wavelengths using the iterative least-squares phase-shifting algorithm by suppressing critical phase shift errors, and it provides a better measurement result than conventional methods. To verify our method a complex multilayer film was prepared and we measured it, and compared with well-known conventional techniques. Comparison results show our new method successfully works well with high precision as same as existing methods.
Perspectives on High-Performance Computing in a Big Data World

High-Performance Computing (HPC) and Cyberinfrastructure have played a leadership role in computational science even since the start of the NSF computing centers program. Thirty years ago parallel computing was a centerpiece of computer science research. Naively Big Data surely requires HPC to be processed, and transformational Big Data technology such as Hadoop and Spark exploit parallelism to success. Nevertheless, the HPC community does not appear to be thriving as a leader in Data Science while parallel computing is no longer a centerpiece. Some reasons for this are the dominant presence of Industry in technology futures and the universal fascination with Artificial Intelligence and Machine Learning. Maybe the pendulum will swing back a bit, but I expect the "AI first" philosophy to dominate in the foreseeable future. Thus I describe a future where HPC thrives in collaboration with Industry and AI. In particular, I discuss the promise of MLforHPC (AI for systems) and HPCforML (systems for AI).
The Evolving Treatment of Semantics in Machine Translation

John Searle and other influential theorists have argued that machine translation (MT) and other natural language processing (NLP) programs can never appreciate meaning in the deepest sense – in other words, that they can never truly exhibit semantics. It is true that MT and many other NLP systems have made steady and impressive progress while use of explicit semantic processing has undergone a rise and fall. It is also true that consensus among researchers on the meaning of meaning has remained elusive. In this chapter, however, we observe renewed interest in semantic representation and processing. Moreover, we foresee gradual adoption of semantic approaches grounded upon audio, visual, or other sensor-based input. We distinguish such perceptually-grounded semantic approaches from most current methods, which have tended to remain perception-free. With respect to philosophical implications, we suggest that perceptually-grounded approaches to automatic natural language processing can display intentionality, and thus foster a truly meaningful semantics. As background for these predictions and suggestions, we survey the role of semantics in machine translation to date in terms of three paradigms: rule-based, statistical, and neural MT. A section on each paradigm discusses its treatment of semantics: rule-based methods have generally emphasized symbolic semantics; statistical methods have generally avoided semantic treatment or employed vector-based semantics; and neural methods have handled meaning as implicit within networks.
Optimal Lightweight Encryption Based Secret Share Creation Scheme for Digital Images in Wireless Sensor Networks

Due to the advances in the computerized world, security has turned into an indivisible issue while transmitting the image. Secret Image Sharing (SIS) scheme is an updated cryptographic strategy which can be utilized to transmit a unique image from the sender to the receiver with incomparable privacy and secrecy in Wireless Sensor Network (WSN). Most traditional security techniques for popular systems, similar to the Internet, are not appropriate for WSN, requesting appropriate examination around there. It manages the procedure utilized to remodel the data among justifiable and deep structures through encryption and decryption strategies under the intensity of the keys. In this part, a high-security model for DI is proposed using a secret image share creation scheme with a novel Light Weight Symmetric Algorithm (LWSA). This cryptographic strategy partitioned the secret image into shares for the security analysis by optimal key selection in LWSA technique; here Adaptive Particle Swarm Optimization (APSO) is used. Moreover, this methodology is applying full encryption to image transmission over WSNs by offloading the computational outstanding task at hand of sensors to a server. The proposed LWSA plot offered better security for shares and furthermore reduced the deceitful shares of the secret image. The trial results and the investigations inferred that the proposed model can viably encrypt the image with quick execution speed and expanded PSNR value.
Research on Knowledge Classification Based on KNN and Naive Bayesian Algorithms

Abstract With the development of the Internet, the speed of knowledge update is getting faster and faster, and the Internet is full of various knowledge data. For knowledge management systems, the knowledge classification function is very important. The main task of this paper is to learn the mapping relationship between text content and text category according to the given text content and text category, so as to get a classification model. Finally, use this classification model to process the text of the unknown classification, and judge or predict the category of the unknown text.
Optimal Stream Encryption for Multiple Shares of Images by Improved Cuckoo Search Model

Security of the media information such as image and video is one of the fundamental prerequisites for broadcasting communications and computer systems. Most of image security system, for famous systems, similar to the Internet, is not reasonable for wireless sensor systems, requesting legitimate examination around there. In the proposed investigation, the security of Digital Images (DI) is upgraded by utilizing a Lightweight encryption algorithm. To ensure security, the images selected for security investigation was shared by a number of copies. With the help of the presented share creation model, for example, Chinese Remainder Theorem (CRT), the image was encoded into a number of shares and the created shares were highly secured by the proposed Stream Encryption (SE) algorithm. The metaheuristic algorithm was introduced by passing the selection of optimal public and private keys to encrypt as well as decrypt the image in secure transmission. With the help of Improved Cuckoo Search Algorithm (ICSA), the optimal keys were selected with fitness function as maximum throughput. The proposed SE-based ICSA algorithm consumed the least time in creating key value to decrypt the image in WSN security model. The simulation result exhibited that the SE-based ICSA algorithm enhanced the exactness of DI security for all input images (Lena, Barbara, baboon, house, and airplane) when compared with existing algorithms.
A RESEARCH STUDY ON INDOOR SPATIAL RADIATION DOSE RATE OF A WOODEN DETACHED HOUSE BEFORE AND AFTER THE DECONTAMINATION WORK IN NUCLEAR POWER PLANT ACCIDENT AREA

This study explored changes in spatial radiation dose rate both indoor and outdoor associated with a decontamination operation for a wooden house in a high dose rate region in Fukushima. As a result, the averaged spatial dose rate at 1m height from the floor before the decontamination was 1.73μSv/h and the dose rate after the decontamination was 0.86μSv/h. Furthermore, the results of a comparison of the dose rate between pre- and post-decontamination indicates a decrease in the dose rate at almost all points. The reduction rate of spatial dose-rate was 40 to 50% at the center of the house.