Reviewerly/exHarmony huggingface dataset:
A Method of the Result Preparation in Addition-Based Circuits

This paper is devoted to development of a method of the results preparation for development of the digital circuits carrying out addition operation which is basic for arithmetic calculations. The method of the results preparation is widely presented in information technologies. By this method all libraries, including libraries of the software and IP-core libraries are organized. The method gained development in design of the fixed-point adders on FPGA with the LUT-oriented architecture for the organization of operation of the logical elements in the dynamic arithmetic mode with the accelerated propagation of carry signal. We offer development of a method of the results preparation for realization of arithmetic addition of the floating-point numbers which plays an important role in processing of approximate data. Floating-point addition operates with the numbers presented by mantissas and exponents. The method of the results preparation provides paralleling of processing the exponents and mantissas which are traditionally carried out consistently: at first operations with exponents determine shift sizes, and then the shift of mantissas is executed. Parallelization of processes is carried out by preparation of a set of the sums for various options of their relative positioning after shift. The size of shift is used for the choice of the sum corresponding to it from a set of the prepared sums. The offered method is estimated by time of operation performance in floating-point addition and complexity of its realization for decisions with various level of circuit parallelism.
Metadata Integration with Labeled-Property Graphs

The work reflects on the use case developed by FREYA project that employs labeled-property graph for the integration of metadata from diverse sources. The role of persistent identifiers in metadata integration is discussed, and a solution is proposed for the dynamic characterization of the integrated graph. The result of this characterization can be considered a metadata model for the labeled-property graph and can be used for the graph exploration, the graph content monitoring, also for the graphs comparison and for the automated generation of machine interfaces (APIs).
A User Study of the Visualization-Assisted Evaluation and Management of Network Security Detection Events and Policies

Intrusion detection systems and other network security components detect security-relevant events based on policies consisting of rules. If an event turns out as a false alarm, the corresponding policy has to be adjusted in order to reduce the number of false positives. Modified policies, however, need to be tested before going into productive use. We present a visual analysis tool for the evaluation of security events and related policies which integrates data from different sources using the IF-MAP specification and provides a “what-if” simulation for testing modified policies on past network dynamics. In this paper, we will describe the design and outcome of a user study that will help us to evaluate our visual analysis tool.
An Enhanced Model for Digital Reference Services

Digital Reference Service (DRS) play a vital role in the Digital Library (DL) research. DRS is a very valuable service provided by DL. Unfortunately, the reference service movement towards digital environment begins late, and this shift was not model based. So, a journey towards a digital environment without following a proper model raises some issues. A few researchers presented a general process model (GPM) in the late 1990s, but this process model could not overcome the problems of DRS. This paper proposes an enhanced model for DRS that use the storage and re-use mechanism with other vital components like DRS search engine and ready reference for solving the issues in DRS. Initially, storage and re-use mechanism are designed and finally, DRS search engine is designed to search appropriate answers in the knowledge base. We improved the GPM by incorporating the new components. The simulation results clearly states that the proposed model increased the service efficiency by reducing the response time from days to seconds for repeated questions and decreased the workload of librarians.
Static Analysis Method on Portable Executable Files for REMNUX based Malware Identification

Malware is one of the most dangerous threats in the digital world today and in the future. Today, technological developments do not only give benefits but also reap serious challenges. One of the threats is the breach of computer network security system. Malware can be inserted anywhere, especially on various types of files that can be downloaded from the internet. It is important to analyze malware for of its complex development. This research projects our work on testing and analyzing executable files by utilizing various tools on REMnux operating system. It is aimed to recognize whether a file is safe or contains malware. The results of this study indicated that REMnux can be an appropriate tool to check a file's characteristics in the form of malware or not based on anomalous data check, metadata of file integrity, section entropy, and function that will be executed by that executable file. In addition, the results can also estimate the impact of malware performance if the file execution is not intentionally done by reverse engineering.
Face Recognition Using LBP on an Image Transformation Based on Complex Network Degrees

The following topics are dealt with: image segmentation; learning (artificial intelligence); convolutional neural nets; medical image processing; data visualisation; image classification; feature extraction; object detection; image representation; image recognition.
Supporting Software Architecture Maintenance by Providing Task-Specific Recommendations

During software maintenance, developers have different information needs (e.g., to understand what type of maintenance activity to perform, the impact of a maintenance activity and its effort). However, information to support developers may be distributed across various sources. Furthermore, information captured in formal architecture documentation may be outdated. In this paper, we put forward a late breaking idea and outline a solution to improve the productivity of developers by providing task-specific recommendations based on concrete information needs that arise during software maintenance.
SIROCCO 2019 Review

The conference was held from July 1st to July 4th at Gran Sasso Science Institute (GSSI) in the heart of L'Aquila, Abruzzo, a historic city in Italy to the east of Rome. First Day On the rst day, a tutorial on Permissioned Blockchains took place. There, Roger Wattenhofer covered a variety of topics on permissioned and permissionless blockchains. The rst part started from the very basics, targeting an audience that has never heard of fault-tolerant distributed systems. In the second part, permissionless blockchains were explained, followed by the third and nal part, in which more advanced topics and current research were discussed. The 6-hour tutorial was well attended by researchers and students from various areas and the interactive format allowed the members of the audience to steer the talk into their preferred directions. In the evening, after the registration, the welcome reception with lots of delicious snacks and refreshing drinks took place.
Authenticated-Encrypted Analog-to-Digital Conversion Based on Non-Linearity and Redundancy Transformation

We have proposed a generic architecture that can integrate the aspects of confidentiality and integrity into the A/D conversion framework. A conceptual account of the development of the proposed architecture is presented. Using the principle of this architecture we have presented a CMOS circuit design to facilitate a fully integrated Authenticated-Encrypted ADC (AE-ADC). We have implemented and demonstrated a partial 8-bit ADC Analog Front End of this proposed circuit in 0.18µm CMOS with an ENOB of 7.64 bits.
Hardware Implementation of Simeck Cipher as a Lightweight Hash Function

Internet of Things brings a galore of lightweight connected devices which triggered a need for befitting security mechanisms. A lightweight hash function is one of the major step taken to accomplish that requirement. In this paper, capabilities of Simeck cipher for implementing hash function are explored. A novel architecture is proposed that serve as a cipher as well as hash function. It nullifies the use of two separate functions in power constrained devices where both the operations are required. The preliminary requirements for power and area are ascertained by implementing it on FPGA and developing an ASIC prototype. The hardware utilization and a number of clocks required are compared with SHA and another hash function and the proposed hash function performs better in these aspects. The basic security analysis of the hash function is also performed.
2 satisfiability logic programming in radial basis function neural networks

The training of a radial basis function neural network (RBFNN) involves finding the optimal number of hidden neurons in the hidden layer and finding the RBFNN parameters such as center, width, and the output weight. 2 Satisfiability logic programming will be embedded in RBFNN during the training phase. Two training techniques, no-training, and half-training are proposed in this paper. The experiment of both techniques has been examined by using Microsoft Visual Studio 2008 C# Express software. The detailed comparison of the performance of two different techniques in performing 2SAT is discussed in term of root mean square error (RMSE), the number of the hidden neurons and CPU time. The results obtained from the computer simulation have shown that RBFNN-2SAT in half-training technique outperforms than RBFNN-2SAT in no-training technique due to the terms of RMSE, the number of hidden neurons and CPU time are typically much less than the number of data points, and the centers are not restricted to be data points.
Performance Analysis of the Shore-to-Reef Atmospheric Continuous-Variable Quantum Key Distribution

The effects of sea salt concluded in oceanic atmosphere are ubiquitous in practical wireless optical links. Here a shore-to-reef atmospheric continuous-variable quantum key distribution (CVQKD) model is established on the basis of Mie scattering theory, with the aim to characterize the complex case of beam propagation in the atmosphere caused by sea salt particles. The effects on performance of shore-to-reef atmospheric CVQKD under the sea salt particles and relative humidity are also studied. Simulation results show that the increase of particle radius and relative humidity will lead to the degeneration of secret key rate. Extending the channel distance also reduces the secret key rate. This paper provides a basis for the establishment of practical shore-to-reef atmospheric CVQKD model. The research of this paper also gives momentous reference for the study of optical communication channel models with other suspended particles over the ocean.
Developing Automatic Markerless Sign Language Gesture Tracking and Recognition System

Machine-based interpretations of sign language hand postures and gestures have long been a specialized research topic in human-computer interaction. Involvement of human subjects and multi-faceted nature of the problem requiring expertise in a multitude of disciplines make sign language interpretation an exigent research problem. Purpose of this paper is to present the experience and findings of international collaborative research conducted over several years on vision-based sign language translation. At first, this paper extends a discussion on sign language and its variations, machine-based translation and its significance. Secondly, a discussion on how three main tasks within the machine translation, namely: a) hand localization and tracking, b) hand posture interpretation, and c) hand gesture interpretation, can be addressed. Finally, research challenges, possible approaches, and future extensions are discussed.
Periodization of creative self-realization

People are sensitive in communication and they undergo relationship problems. They are influenced by others’ evaluation striving for adequate personal self-realization. A periodization of personal creative self-realization is presented, based on psychosexual developmental stages and psychosocial developmental stages for achievement of personal growth and life goals. The proposed periodization of creative self-realization comprises nine stages - Pre-natal stage (from conception to birth); Stage of creative longing (from new-born to 1 year and 7 months); Creative verbal stage (from 1 year and 7 months to 2 years and 7 months); Stage of creative and innovative attitudes (from 2 years and 7 months to 6 years); Stage of creative and innovative self-determination (from 6 years to 11 years); Stage of choice of creative self-realization (from 11 years to 19 years); Inspired creative self-realization stage (from 19 years to 24 years); Stage of conscious intentional creative self-realization (from 24 years to 33 years); and Stage of satisfying creative self-realization (after 33 years).
Generalized Iterated Belief Change in Dynamic Epistemic Logic

AGM's belief revision is one of the main paradigms in the study of belief change operations. Recently, several logics for belief and information change have been proposed in the literature. They have been used to encode belief change operations in rich and expressive semantic frameworks. While the connection of AGM-like operations and their encoding in dynamic doxastic logics has been studied before by the exceptional work of Segerberg, most work on the area of Dynamic Epistemic Logics (DEL) has not attempted to characterise belief change operators by means of their logical properties. This work investigates how Dynamic Preference Logic, a logic in the DEL family, can be used to characterise properties of dynamic belief change operators, focusing on well-known postulates of iterated belief change.
Segmentation of Handwritten Text Using Bacteria Foraging Optimization

Text collected for digitization in the form of handwritten paragraphs is difficult to segment into individual characters. Identification of whole words is more difficult, so the idea is to segment the word into individual characters. These characters will be joined to make artificial words. Actual words may be predicted from these artificial words by any word prediction algorithm. A novel bacteria foraging optimization-based pixel model has been used to optimally segment all valid characters from the word. The bacteria colonies arrange in a specific shape determining a character. The offspring bacterial colonies produce the candidate segmented characters. The unhealthy colonies from the candidate segmented characters are eliminated deducing the pruned offspring bacterial colonies identified as optimal segmented characters. Paragraphs written by more than 50 subjects have been segmented with higher character segmentation accuracy.
Examination about Utilization of Active Learning Method for the Purpose of Nurturing Innovation Human Resources

This study is an exploratory case study about an active-learning type lecture for college students as an effort aimed at the realization of innovation and human resource development for social implementation of intellectual assets. Based on the results of lecture evaluation, students’ own sentimental evaluation, text analysis of free responses about consciousness and changes in the aspect of thinking, the aspect of motivation and desire, the aspect of emotion, etc., the lecture in question changed the students’ consciousness in a positive way. In addition, a positive correlation was found between the evaluation of group discussions, which is one of the techniques of active-learning type lectures, and the awareness of the internal and external aspects of other people.
Topic Model-Based Recommender System for Longtailed Products Against Popularity Bias

Future businesses will obtain great profit from long-tailed selling in the reason of meeting the personalized needs of users, however the data sparsity makes long-tailed items hard to be recommended, resulting the problem of popularity bias. To improve the quality of recommendations for long-tailed products, we propose a new topic model-based approach named joint user and social tags model (JUST) to solve the problem, jointly modeling user behavior data, product label information and social relationship. Let labels and social relationship data compensate for the sparsity of the behavior. To assess the effectiveness of our model, we conduct empirical evaluations on the last.fm-2k dataset released by HetRec2011. The experimental results show that the JUST model is better than the compared models in recommending long-tailed products.
Embarrassingly Easy Zero-Shot Image Recognition

Zero-shot Learning (ZSL) aims to transfer knowledge from seen image categories to unseen ones by leveraging semantic information. It is generally assumed that the seen and unseen classes share a common semantic space. A number of methods propose to design a common space to accomplish the projection between image and class embeddings by learning a compatibility function, which make up sample pairs to train the object function. However, considering the drawbacks of previous compatibility function, we design a new compatibility function in this paper. Different from previous compatibility pattern, our proposed compatibility function is more discriminative by employing label vectors, which can measure the similarity between the projected image features and all seen class prototypes. Extensive experiments on four benchmark datasets show the effectiveness of our proposed approach.
The Relationship between TBL Performance and Learning Motivation in Information Literacy Education

In this study, the relationship between TBL (Team-Based Learning) performance and learning motivation was investigated. Accordingly, TBL experimental classes were conducted in university information literacy. The results revealed a moderate or weak correlation between positive scores of GAMI (Gakugeidai Academic Motivation Inventory Test) and TBL performance indicators (i.e., the pre-study time, problem-solving time, number of utterances, satisfaction level, and belief level in TBL). Furthermore, TBL performance varied depending on the number of members who had high positive scores of GAMI. The TBL performance was low for one or no individuals in a four-person formation team; however, it was high for a three-person team. These results suggest that the TBL performance of a class may improve in relation to team formation when employing positive scores of GAMI.
Towards an Interpretable Metric for DOTA 2 Players: An Unsupervised Learning Approach

DOTA 2 is a Multiplayer Online Battle Arena game, which was released in 2013 and currently has approximately one million players worldwide. In this work, we approach the problem of finding a new metric to characterize and rank DOTA 2 players. We collect an extensive dataset of professional players and we show that the standard metric used in the community, called KDA, is not able to properly rank good players with different sets of skills. Combining unsupervised learning and evolutionary computing, we find a small set of players' attributes that we use to create a new metric, called GDM (General DOTA 2 Metric). In our results, we show that GDM is simple, easy to interpret and that it overcomes many of the issues related to using KDA to rank DOTA 2 players.
Abstract Argumentation for Summarizing Product Reviews: A Case Study in Shopee Thailand

When reading online reviews of products, restaurants, and hotels, internet users usually appreciate argumentative reviews rather than plain opinions. Building intelligent systems capable of summarizing useful reviews can help the users to grasp more information about features of a product and understand if it fits to their needs. In this paper, we conduct an empirical study on computational argumentation (Dung's abstract argumentation) in Shopee.co.th reviews. For that, we first extract positive (in favor of purchase) and negative (against it) arguments from a particular randomly chosen product. Second, we link extracted arguments by hand to understand how they are connected. In such a way, we realize that summarizing product reviews that goes beyond plain opinions, i.e. being a collection of admissible arguments, can be helpful to both online buyers and sellers.
Using auxiliary artifacts during code inspection activity

Code inspection is an important activity to identify defects in the source code and improve software quality. However, even when using techniques such as checklists, inspectors consider implicit decision-making knowledge. In this paper, we perform an exploratory study with groups of inspectors (Group1 and Group2) with two objectives: 1) to present findings on how (and if) auxiliary artifacts interfere in decision making during the code inspection activity, and 2) to show whether there is any influence on the number of defects identified by inspectors when using or not auxiliary artifacts. Both groups used the computational code inspection support of the CRISTA tool, but only Group1 used auxiliary artifacts (requirements, UML diagrams, software metrics). We identified 10 findings. All of them are related to the inspectors' decision making and the influence of using artifacts on defects identification. The findings suggested that when inspectors use auxiliary artifacts, their effectiveness in identifying defects is improved. Besides, their decision making is more homogeneous than that of inspectors who do not use auxiliary artifacts. However, more investigations are necessary to make the results more generalizable. As future work, different strategies for code inspection techniques can be defined based on the findings.
Dialogue Management with Deep Reinforcement Learning: Balancing Exploration and Exploitation

Although there have been many advances in Natural Language Processing in recent years, the reason why dialogue systems do not take off is due to the fragility of Dialog Managers (DM). One approach that has demonstrated recent success for the development of DMs is Deep Reinforcement Learning (DRL). The main advantages are that DRL can handle stochastic environments (dialogue with different users) and does not require as much training data as in supervised learning. This paper proposes a DM system with a DRL algorithm and uses a softmax strategy combined with the transfer learning of the designer's knowledge to balance exploitation and exploration in DRL. The experimental results indicate that our proposal is quite promising.
The Challenges of Implementing ICT in The Indonesia National Education System of The Industrial Revolution Era 4.0

Abstract The Industrial Revolution since the era of 1.0 to 4.0 has facilitated almost all orders of life to change according to the novelty it presents, including the management of education. The management trend of the national education system has made various efforts to change, which among other things are to answer the challenges of the industrial revolution. However, it is good for ICT charged change programs to be approved. This paper discusses some of the basic challenges of the application of ICT in the trend of national education development that supports the challenges of the industrial revolution era 4.0. The method used is a comparison between the target programs in the strategic plan with the results. ICT is constrained by the still weak socialization and the low level of IT literacy among the main managers of education, especially at the level of the education unit management.