Reviewerly/exHarmony huggingface dataset:
A generic and computationally efficient automated innovization method for power-law design rules

Automated Innovization (AutoI) originally aimed to extract power-law based rules from a design optimization task without any human intervention. Existing AutoI methods have employed Evolutionary Algorithms (EAs) twice: first to search for Pareto-optimal (PO) solutions, and second to extract rules hidden in them. Furthermore, these methods are limited in scope, in that, they are not capable of tackling both continuous and discrete variables for either single-cluster or multi-cluster rules. In a unique departure from the state-of-the-art, this paper presents a computationally efficient, single EA-based, AutoI method capable of simultaneously extracting single-cluster or multi-cluster rules for both continuous and discrete variable spaces. The robustness of the proposed method is evident from its successful rule extraction tasks even with small-size datasets, where existing AutoI methods fail to apply. The generic scope, computational efficiency, and robustness of the proposed method are demonstrated through a number of benchmark design problems.
Sentiment Analysis based Multi-person Multi-criteria Decision Making Methodology: Using Natural Language Processing and Deep Learning for Decision Aid.

Decision making models are constrained by taking the expert evaluations withpre-defined numerical or linguistic terms. We claim that the use of sentimentanalysis will allow decision making models to consider expert evaluations innatural language. Accordingly, we propose the Sentiment Analysis basedMulti-person Multi-criteria Decision Making (SA-MpMcDM) methodology, whichbuilds the expert evaluations from their natural language reviews, and evenfrom their numerical ratings if they are available. The SA-MpMcDM methodologyincorporates an end-to-end multi-task deep learning model for aspect basedsentiment analysis, named DMuABSA model, able to identify the aspect categoriesmentioned in an expert review, and to distill their opinions and criteria. Theindividual expert evaluations are aggregated via a criteria weighting throughthe attention of the experts. We evaluate the methodology in a restaurantdecision problem, hence we build the TripR-2020 dataset of restaurant reviews,which we manually annotate and release. We analyze the SA-MpMcDM methodology indifferent scenarios using and not using natural language and numericalevaluations. The analysis shows that the combination of both sources ofinformation results in a higher quality preference vector.
Speaker Adaptation Using i-Vector Based Clustering

experimental results on resource management (RM) dataset.Therefore, more than 30% substantial new contributions are added to the methods, experiments, and analysis of the results.
Adaptive Multi-Hierarchical signSGD for Communication-Efficient Distributed Optimization

In this work, we investigate a communicationefficient multi-hierarchical signSGD (MH-signSGD) algorithm with an adaptive learning rate. Under the symmetric assumption of the stochastic gradient distribution, we show that, without the need for learning rate tuning, our proposed MH-signSGD matches the state-of-art sublinear convergence rate O(1/√K) in nonconvex settings, where K is the number of iterations. Our adaptive learning strategy is based on stochastically approximating the learning rate found by greedily minimizing an error upper bound between two successive iterations. Moreover, by leveraging a normal approximation technique to characterize stochastic gradient sign error, we are able to sharpen the convergence analysis of MH-sighSGD with a fixed learning rate 1/√K and establish a strong result in the large-system regime, which says that the MH-signSGD algorithm asymptotically converges to a stationary point at rate O(1/√M), where M is the number of workers. In comparison, most existing work on signSGD can only prove a weaker finite neighborhood convergence in the large system regime. We validate our theoretical results experimentally both on synthetic data and real-world datasets.
Can Defect Prediction Be Useful for Coarse-Level Tasks of Software Testing?

It is popular to use software defect prediction (SDP) techniques to predict bugs in software in the past 20 years. Before conducting software testing (ST), the result of SDP assists on resource allocation for ST. However, DP usually works on fine-level tasks (or white-box testing) instead of coarse-level tasks (or black-box testing). Before ST or without historical execution information, it is difficult to get resource allocated properly. Therefore, a SDP-based approach, named DPAHM, is proposed to assist on arranging resource for coarse-level tasks. The method combines analytic hierarchy process (AHP) and variant incidence matrix. Besides, we apply the proposed DPAHM into a proprietary software, named MC. Besides, we conduct an up-to-down structure, including three layers for MC. Additionally, the performance measure of each layer is calculated based on the SDP result. Therefore, the resource allocation strategy for coarse-level tasks is gained according to the prediction result. The experiment indicates our proposed method is effective for resource allocation of coarse-level tasks before executing ST.
Digital Learning Theater with Automatic Instant Assessment of Body Language and Oral Language Learning

Communication with other people includes oral language and body language, both of which are important, especially in the workplace. People learn how to react properly by the context and reaction. Therefore, the best way to learn communication is through practical communication exercises. This study proposes an approach where learners can enter digital workplace environment to learn both oral and body language. Meanwhile, with the advance of artificial intelligence technology, the real time assessments of body gestures become feasible. With the proposed digital reality, learns can simultaneously see their performance and get assessments and corrections in real time. The approach was empirically evaluated in a university of technology and the results showed a significant improvement in learning outcomes.
Bibliotecas contemporâneas: dos processos técnicos à busca por competências informacionais

It aims to highlight the educational role that libraries and librarians must play in contemporary society in view of the technological and informational scenario in which they are inserted.It uses as methodology the bibliographical research based on literature review.The current days are marked by the progress of information and communication technologies, consequently the processes of organization, access and use of information have evolved greatly and, with this, information professionals face new challenges.In observing the positive and negative effects of technological advances and the emergence of large informational contents it becomes evident that by combining their consolidated techniques with a social and humanistic approach librarians can collaborate for the construction of individuals with informational skills, for both search and retrieval of information as well as its use for the creation of new knowledge.However, in order to improve libraries to become active agents in the information literacy process, many difficulties still have to be overcome.
Prediction of residential real estate selling prices using neural networks

With the rising housing prices of the last 20 years, the appraisal of real estate has become more difficult. Underlined by the large differences between listing and selling prices, the valuation pr ...
Overcoming Anonymity: How Darknet Markets Provide Private Governance to Enable Encrypted and Anonymous Exchange

Overcoming Anonymity: How Darknet Markets Provide Private Governance to Enable Encrypted and Anonymous Exchange
SECURE CYBER FORENSIC FRAMEWORKS FOR INTERNET OF THINGS

The Internet of computers, tablets, and smart phones in our daily communications connects from leisure purposes to business tasks.An environment is deployed open and usage of resources limitedly make IoT vulnerable against attacks, whereas existing cyber Forensic frameworks and tools for investigation and processes are hard to obtain distributed and heterogeneous features of Internet of Things (IoT).Due to privacy and security protection of IoT, law enforcement agencies and investigators faces the challenges according to those characteristics.In this review, the various objectives in the cyber forensics researches were analyzed to understand the recent IoT techniques and its advantages.The limitation in the existing methods was also reviewed in this paper.
Enriching education through electronic resources in the home process

This article is about Quality software for the creation of electronic libraries allows those who are not specialists in this field to think, collect, correctly compile and distribute collections that contain new information, even without them. This is considered to be very important, because the creation of such collections is the factor of enriching the teaching process and teaching materials. The emergence of the internet or the entire World Wide Web (World Wide Web) allows us all and each of us to gain access to an unlimited amount of information, thereby changing the attitude of society to information. Of course, it is difficult to call the Internet a reliable source for general enlightenment, and the thoughtless use of the information contained therein is even dangerous, such a situation is also, wide spread
Analysis of Grammatical Morpheme Acquisition of Indonesian High School English Learners

L2 learners morpheme acquisition has been studied a lot subsequent to Dulay and Burts work(1974). Similar to other studies responding to Krashens (1977) notion on natural acquisitionorder, this small-scale research aims at investigating whether Indonesian high school English learners also go through similar acquisition order as the respective notion. The data taken froma group of Indonesian high school students writing assignment were analyzed using theObligatory Occasion Analysis (Ellis Barkhuzien, 2005) to investigate the grammaticalmorpheme acquisition order of the students. Subsequently, the finding was analyzed to seewhether the acquisition order was influenced by Krashens hypothesis. The findings showedthat the morpheme acquisition order of the research participants did not go through similaracquisition order as stated in Krashens natural order hypothesis. Further, the participants L1partially contributed to the order.
Ternary sum codes and their modifications

construction of ternary digital devices and computing systems.The main results of the development of the ternary computers are listed, and the possibility of using ternary logic in the development of quantum computers is also noted.The article focuses on the development of the coding procedures and the protection of ternary data.The author offers the methods for constructing a range of sum codes with low redundancy and the ability to detect errors in data vectors.The article provides the descriptions of codes, the examples of their code words, and also notes some of their key features.The article presents the algorithms of sum codes modification, focused on a more efficient use of bits of the control vectors and on minimizing the number of errors that are not detected by codes.Using the presented methods for sum codes constructing can be useful in the future when implementing and using ternary digital devices and systems.
Decomposition Techniques in Distributed Service Architecture

In this modern era of cloud computing, distributed service design in web applications is quintessential to harness the benefits of scalability and elasticity.Such design can be achieved either by implementing the service-oriented architecture (SOA) or a finegrained micro service architecture (MSA).Defining the boundaries of the services and decomposing them into separate units is a challenging task in the web service design.Single responsibility principle (SRP) and common closure principle (CCP) are the 2 major guiding principles which drive the decomposition of the backend into micro services. Services can be logically segregated either by (i) delineating the sub-domains of the business served and converting each sub domain into a bunch of services grouped together as a microservice, or (ii) by laying out the major entity model and then building the services related to the capabilities of each entity grouped together as a microservice. Both these approaches have their own benefits and issues. This paper discusses these approaches by taking the real-world examples and explains which pattern is suitable under which circumstances.
Survey around the Impacts of Cloud Computing in Mobile Environments

Mobile Cloud Computing (MCC) which combines mobile computing and cloud computing, has become one of the industry buzz words and a major discussion thread in the IT world since 2009.Despite increasing usage of mobile computing, exploiting its full potential is difficult due to its inherent problems such as resource scarcity, frequent disconnections, and mobility.MCC integrates the cloud computing into the mobile environment and overcomes obstacles related to the performance (e.g., battery life,storage, and bandwidth), environment (e.g., heterogeneity, scalability, and availability), and security (e.g., reliability and privacy) discussed in mobile computing.As MCC is still at the early stage of development, it is necessary to grasp a thorough understanding of the technology in order to point out the direction of future research.With the latter aim, this paper presents a review on the background and principle of MCC, characteristics, recent research work, and future research trends.
Qualitative Perspective of live VM migration techniques in Cloud Computing

Cloud Computing becomes the most reckless developing computing platform which is known as the future of distributed computing. Virtual Machine (VM) is one of the important part of the cloud computing concept. For various type of goals the VMs needs to be migrated from one place to another with in data centre or out of data centre, such as – load balancing, fault tolerance or energy consumption, etc. Live migration is a technique of migrating VM from source to destination in running stage, in result it gives high availability of services. The main objective of this paper is to provide the exhaustive review of literature to analyse the proposed methods of VM migration, purpose and their findings.
Does Economic Growth Responses to Changes in Financing Policies? Signals from Sudan Economy (1997-2018)

Through a simple regression model, the study tries to examine whether the economic growth in Sudan responses to changes in financing policies adopted by the Central Bank of Sudan(CBOS) during the period (1997-2018. Gross Domestic Product growth rate was used to measures economic growth (The dependent variable). While flow of banking finance to economic sectors includes; Agriculture, Industry, Local Trade, Foreign Trade, Transport and Storage, Energy and Mining and Real Estate were used to indicates changes in financing policies (The independent variables). Data were collected from annual reports of the CBOS. The results reveal that economic growth significantly responses to changes in financing policies but with different degrees during the period of the study. Moreover, it is found that imbalanced economic growth is directly related to imbalanced distribution of finance to economic sectors. The study strongly recommends that, the monetary authority should be issuing financing policies lead to equitable distribution of banking finance among economic sectors in order to achieve sustainable and balanced economic growth.
Sinusoidal Neural Networks: Towards ANN that Learns Faster

If everything is a signal and combination of signals, everything can be represented with Fourier representations. Then, is it possible to represent a signal with a conditional dependency to input data? This research is devoted to the development of Sinusoidal Neural Networks (SNNs). The motivation to develop SNNs is to design an artificial neural network (ANN) algorithm that can learn faster. A short review of the history of biological neurons helps to identify components that should be redesigned in ANNs. After the components are identified, a new neural network algorithm called SNN is proposed. Experiments are conducted to show the practical results of the algorithm. According to the experiments, the proposed neural network can reach high accuracy rates faster than the standard neural networks, while an interesting generalization capacity is obtained for the developed algorithm. Even though the promising results are achieved, further research is necessary to test if SNNs are capable of learning faster than existing algorithms in real-life cases.
Advanced selective key point detection techniques

SAR imaging needs precise registration process to get recompensation or setoff between two or more images for a certain area. Generally, operational goal of registration process is a geometrical transformation for the input leading to geometrically agreement for input images. Coregistration step can be used in a lot of fields as change detection, mosaicking, super-resolution images creation, … etc. Registration process can be classified into two main classes: Classical or Automatic methods. For the classical approach, the anchor points are picked out manually and then the transformation modeling is performed, that leads to more time consuming and low output accuracy. Automatic detection of these anchor points aims to recover the functionality of non-automated processes. Registration process should recover many problems such as illumination changes, variations of intensity, Different sensors, noise, … etc. Its applications are mainly dependent on errors, such as multi temporal; multi view; or multi modal, occurred during capturing process. Feature detection, as a step of image registration process, obtains a group of anchors, that are stable and informativeness, or patches under varying conditions. Also, it is critical for the detector to have robustness against viewpoint changes, brightness changes, and other deformations. Detailed discussion and investigation are paid, in this manuscript, for the common corner detectors helping to be familiar with the various applied techniques for feature detection. Our goal is to examine a combination hypothesis of these methods into an advanced selective one.
Reliable Neighbors-Based Collaborative Filtering for Recommendation Systems

The sparsity of data and the diversity of ratings have a great effect on the performance of recommendation systems. To deal with these two issues, this paper proposes a reliable neighbors-based collaborative filtering (RNCF) method for recommendation systems. RNCF first uses the item similarity to pre-estimated missing ratings to solve the issue of data sparsity, which enriches the sample data and reduces the impact of extremely sparse ratings on the recommendation effect. Then, RNCF finds a set of reliable users for each item by defining a reliable degree for each item. Next, RNCF adopts the K-means clustering algorithm to find the similar users for a target user. The set of reliable neighbors is the intersection of sets of reliable users and similar users. Finally, RNCF provides a predicted rating of a target user on some item using the pre-estimated ratings of corresponding reliable neighbors. Experimental analysis shows that RNCF can improve the performance at the extreme levels of sparsity and rating diversity in recommendation systems.
Using 3D Virtual VR Technology to Build Rural Landscape Environment

With the development of emerging technologies, the three-dimensional virtual VR technology solves the problem that rural people can’t really feel the design effect in the design of rural landscapes, and is conducive to enhancing the level of village appearance and construction under the revitalization of rural areas. The construction of rural landscape environment uses VR technology to bring different sensory experiences to people. The emergence of VR technology has brought new changes to landscape design. In order to help the development of 3D virtual VR technology in the development of rural landscape environment, this article aims to use Three-dimensional virtual VR technology is used to construct a rural landscape environment.
The Free Speech Blind Spot: Foreign Election Interference on Social Media

The current system for monitoring and removal of foreign election interference on social media is a free speech blind spot. Social media platforms’ standards for what constitutes impermissible interference are vague, enforcement is seemingly ad hoc and inconsistent, and the role governments play in deciding what speech should be taken down is unclear. This extraordinary opacity — at odds with the ordinary requirements of respect for free speech — has been justified by a militarized discourse that paints such interference as highly effective, and “foreign” speech as uniquely pernicious. But, in fact, evidence of such campaigns’ effectiveness is limited and the singling out and denigration of “foreign” speech is at odds with the traditional justifications for free expression. Hiding in the blind spot created by this foreign-threat, securitized framing are more pervasive and fundamental questions about online public discourse, such as how to define appropriate norms of online behavior more generally, who should decide them and how they should be enforced. Without examining and answering these underlying questions, the goal that removing foreign election interference on social media is meant to achieve — reestablishing trust in the online public sphere — will remain unrealized.
Digital Image Compression

This chapter focuses on several common techniques for intraframe coding (removing spatial redundancy in single images). It outlines techniques for hierarchical coding (coding images in such a way that different resolutions or quality levels are easily accessible). The chapter describes the fundamental concepts of arithmetic coding by examining Elias' code and briefly discusses its practical implementations. It discusses two key elements in designing a codebook: determining what vectors should be included in the codebook for best performance, and structuring the codebook to allow for efficient searching. The chapter provides a brief overview of some popular compression techniques and describes how all three components can work together to provide useful and efficient compression of digital images. The downside to representing images in digital form is that a large number of bits is required to represent even a single digital image, and with the rapid advances in sensor technology and digital electronics, this number grows larger with each new generation of products.
Identifying FKP-based Individuals Using the Feature Extraction of the Relaxed Local Ternary Patterns

Identification based on biometric parameters is an effective way to identify people. The fingerprint effect is a feature with small image dimensions, and at the same time, distinguishable features in low image resolution and is used as a reliable biometric identifier. In this paper, a new method for identifying FKP-based individuals using the extraction feature of the Relaxed Local Ternary Patterns (RLTP) is suggested. The RLTP method has been proposed to identify faces and has led to favorable results. In this method, large neighborhood differences that are immune to noise are encoded in two specific states, and small neighborhood disturbances that are vulnerable to noise are encoded in an uncertain state. The chi-square distance criterion is used to calculate the similarity between the extraction features of the input and reference FKP images. The advantage of this method is low computational complexity while improving the high accuracy of recognition. Experimental results on a standard database confirm the success of the proposed method.