Reviewerly/exHarmony huggingface dataset:
Single Image Super-Resolution via Convolutional Sparse Coding with One Group of Filters

Using the characteristic that image patches can be represented sparsely on a selected over-complete dictionary, the sparse coding based super-resolution (ScSR) method pioneers a new avenue for the SR technology. However, ScSR will result in obvious block-effect because of the block preprocessing of the image. For this purpose, Zeiler et al. proposed convolutional sparse coding based SR (CSC-SR) method to solve this problem. Because it needs to learn two groups of filters and mapping function between HR and LR feature maps, CSC-SR usually puts forward high demand to storage and relatively large the calculation expense. In this paper, we propose a modified CSC-SR method to single image super-resolution. The proposed method only needs to train one group of filers, and explores directly the mapping function by least squares algorithm, which can effectively reduce the workload of model training. Experimental results for different test images show that our model has achieved effective improvement over the previous methods.
New Roles of the Human against the Background of Contemporary Screen Space

The aim of this paper is to analyze maintenance, essence and types of roles of a man formed within the limits of virtual screen space. These scientific searches seem actual as a role-play behaviour of a man in the real society and newly changed virtual reality differs substantially, that, in turn, influences on the evolution of modern culture and art processes of a modern state. Results. The paper shows that the rich in the content constituent of virtual screen space formed exceptionally at the active voice of man effects on the processes of one’s personal authentication. The paper clarifies that for the purposes of this research, it has considered more generally as a set of self-manifesting displays of the self, depending on the needs, interests and individual characteristics of a particular person. Novelty. The author argued that the following criteria should be taken into account when forming a typology of a person’s social roles within the virtual screen space: the purpose that the person pursues; one’s own assessment of self, one’s capabilities and achievements; those forms of interpersonal interaction that one chooses over the Internet.
PENENTUAN DAERAH RAWAN KECELAKAAN DENGANPENDEKATAN METODE JARINGAN SYARAF TIRUAN

Technology improvement gives positive impacts on increasing transportation mode. But it has a negativeimpact such as traffic jam and increasing number in traffic accident, so road safety issues must be a commonconcern. One of the efforts to prevent tha accident is to identify accident-prone areas as a warning system foruser. Eleven road sections in Malang District and supported data from Satkorlantas Polres Malang Districtis used as scope of discussion in this study. In this study, the factors that caused accidents such as roadcharacteristic, geometric and environment condition is used for identifcation the accident-prone area. Basedon the data, database mapping was done and the pattern of potential accident-prone areas was determined.It can be used for analysis and decision. Mapping and testing process uses a neural network approachbecause the accuracy of this method has been already proven in various applications. The results approachon prone area identification indicates a precision with a variance of 0.015% in compare with accident-baseddata analysis through the validation process. This result shows that neural network approach can be used toidentify the accident-prone areas as one of the solution in accident prevention and efforts in road safetyimprovement.
Innovative approaches in reshaping activities of the Department “Information and Library Systems”

This article discusses the specific use of foreign languages in the activities of the department “Information and Library Systems” of the Tashkent University of Information Technologies, as well as the introduction in a curriculum a new special subjects and teaching them in English.
An Efficient Security Mechanism for Cloud Data Using Elliptic Curve Digital Signature Algorithm with Wake–Sleep

As cloud computing ends up common, increasingly touchier data are being concentrated into the cloud. For the refuge of information protection, delicate information more often than need not to be mixed before redistributing, which makes viable information use a difficult assignment. The proposed structure gives the protection to information in order. In this structure, cloud service provider (CSP) chooses the best host utilizing wake–sleep algorithm. After server choice, client encrypts their information utilizing homomorphic encryption. But it is lagging to reduce computation time and needs an additional security so we adopt and utilize elliptic curve digital signature algorithm (ECDSA). This additional encryption method gives the high security during transmission of data and also avoids bandwidth, less computation time and finally less storage space attained on both the sides.
Characterization of Functions Using Artificial Intelligence to Reproduce Complex Systems Behavior

In the field of signal processing, for forecasting purposes, the characterization of functions is a key factor to be faced. In most of the cases, the characterization can be achieved by applying least square estimation (LSE) to polynomial functions; however, it is not fully in all cases. To contribute in this field, this article proposes a variant of artificial intelligence based on fuzzy characterization patterns initialized by Lagrange interpolators and trained with neuro-adaptive system. The aim is to minimize a cost function based on the absolute value between samples and their prediction. The proposal is applied to the characterization of cardiac PQRST complex as case study. The results show a satisfactory performance providing an error of around 1.42% compared to the normalized PQRST complex signal.
Burst Probability Based Dynamic Load Balancing Algorithm in Cloud Environment

Cloud Computing is a distributed or shared pool of configurable computer system resources which can be instantly available on demand with minimum management intervention over the internet. Load balancing is an important factor which relates with resource distributions in such a way that overloading does not take place at any machine and resources with optimal utilization. Successful balancing of load in cloud environment makes cloud computing more methodical and fulfills user satisfaction. This feature of cloud computing should be paid more attention. This paper deals with the subject of dynamic load balancing for cloud environment. The proposed mechanism has been implemented and it shows satisfactory results by considering burst time of all loaded tasks, total number of loaded task and remaining memory size in RAM in all virtual machines in a cloud environment where all virtual machines have same configuration of processors and different memory size in RAM. The proposed algorithm shows better results in terms of average turnaround time, average response time, average waiting time than other existing algorithms on load balancing like FCFS, RR and algorithms [1–3].
Combined Learning Models Based on Constructionism Applied in Modern Learning of Biology

The purpose of this research has been to identify modern and effective learning models, based on constructionism, in learning biological concepts. The author shows that the cognitive, formative, and emotional benefits, of combined learning models to obtain the best results in the construction of concepts, by students' effort, in accordance with Papert's constructionism ideas. The researcher recommends using the techno-empirio-psycho-sociocentric (TEPS) combined model, based on information and communication technologies (ICTs), in individual-and team-based rediscovery of new biological concepts. When no ICTs are used, the empirio-psycho-socio-centric model is recommended. As an alternative or in combination with, these learning models, the researcher promotes the techno-logo-psychocentric (TLP) model in presentations of science as a finished product when ICTs are used or the logo-psychocentric modernized (LPM) model, with attractive procedures, without ICTs.
Developing a Mobile Application to Detect Improper Sitting Using Regression Analysis and an Accelerometer

Sitting improperly can impact on the amount of pressure put on the back of the body, causing the spine to degrade prematurely. Therefore, this paper proposed a method of computing sitting angles and the creation of a mobile application to alert users when they are sitting improperly. First, a digital protractor and an accelerometer-equipped with a smartphone were employed to gather data. Next, regression equations derived from the collected data were applied to compute the angles between the upper and lower body as well as the angles on the left and right sides of the upper body while sitting. The computed angles were used to analyze the sitting posture. To expedite the testing process, an Android Sensor Fusion application was used to stream the accelerometer data over a local network through a router to a PC-based program. Finally, a mobile application was created. The results showed that the proposed method could be used to obtain sitting angles and alert users when they were sitting improperly.
Bokeh Effect in Images on Objects Based on User Interest

Humans pay visual attention to those objects in the visual field that they are most interested in seeing. The Bokeh effect is a popular blurring effect in photography, where the object of interest is emphasized by blurring other objects. In this paper, we apply the principle of visual attention to the user's object of interest to post processing of photos taken using a smartphone. We simulate the Bokeh effect of blurring objects in the image except those that the user is interested. This adds a biologically inspired effect to the camera and gallery apps in the smartphone. We first define a hierarchy of user interests in different categories. We then create a user interest profile based on the user's demographics, apps and URLs. We build a user interest vector out of this hierarchy by using a word embedding model, and take the weighted average of the vectors of the words corresponding to the user interests. After this, we detect objects in the image and calculate the similarity of the detected objects with the user interest vector, returning a sorted list of objects the user is interested. The Bokeh effect is applied to the image to blur other objects, thus giving a realistic touch to the image. Finally, we conduct a user study to validate the effectiveness of the system.
Jumping Finite Automata for Tweet Comprehension

Every day, over one billion social media text messages are generated worldwide, which provides abundant information that can lead to improvements in lives of people through evidence-based decision making. Twitter is rich in such data but there are a number of technical challenges in comprehending tweets including ambiguity of the language used in tweets which is exacerbated in under resourced languages. This paper presents an approach based on Jumping Finite Automata for automatic comprehension of tweets. We construct a WordNet for the language of Kenya (WoLK) based on analysis of tweet structure, formalize the space of tweet variation and abstract the space on a Finite Automata. In addition, we present a software tool called Automata-Aided Tweet Comprehension (ATC) tool that takes raw tweets as input, preprocesses, recognise the syntax and extracts semantic information to 86% success rate.
Artificial Intelligence in Operating System

The rapid advancement in artificial intelligence, semiconductor and electronic technology, software engineering and programming techniques as well as hardware interfaces and communication has raised the demand for advancement in operating system. In this article we have studied the classical and current operating systems and have proposed the advanced components such as new user interfaces, advanced human-to-machine interfaces, communication, artificial intelligence and advanced hardware technology etc. which are necessary for next generation operating systems.
Electronic invoice system database architecture issues and the possible solutions

This paper elucidates electronic invoice system's database architecture requirements and its building issues that one may encounter. Enumerated requirements and issues were taken into consideration and it is suggested to use three types of DB in the system. NoSQL model of database for its numbers of advantages is recommended for the information reused by users to store and querying them. The database is able to split database loadequally with the help of horizontal sharding. Making partition numbers equal to the number which has common divisors as much as possible will let splitting load amount equally among possible additional infrastructure. A special approach is required to each element of system to perfectly create architecture of the information system's database.
Network-Aware Server Consolidation for Wireless Data Centers

Servers in a data center are underutilized due to overprovisioning which contributes heavily toward the high-power consumption of the data centers. Server consolidation can drastically reduce the power consumption of data centers by engaging fewer servers and keeping the unused servers in deep sleep. However, server consolidation alone can adversely affect network performance. Recently, wireless data centers using high-speed millimeter-wave (mmWave) communications have been proposed and shown to drastically reduce the power consumption of the networking equipment. Therefore, server-consolidation strategies should leverage the novel data center network architectures to minimize power consumption with minimal impact on performance. Neither novel server consolidation algorithms for wireless data centers nor the impact of server consolidation on wireless data centers have been explored so far. In this paper, we propose a novel network-aware server consolidation algorithm called Bandwidth-Constrained Consolidation (BCC), for wireless data centers. Our case studies indicate that up to 37% power reduction can be achieved with the proposed consolidation algorithm in a wireless data center. A similar trend in power reduction is observed even with traditional wired data centers. However, the improvement in power consumption is higher in a wireless data center. Moreover, leveraging the network characteristics in the consolidation algorithm even improves the network performance for both wireless and wired data centers, preventing any adverse effect of consolidation on performance.
Creating an OER Collection of Automatically Scored Practice Exercises for Computer Science 1

Open Educational Resources (OER) is one way to help reduce the cost of higher education. We created a repository of 90 (and growing) practice problems for learning introductory programming using Python 3. In order to provide immediate feedback to learners as well as alleviate the scoring burden on instructors, these exercises include tests for a popular automatic web based scoring platform. We have been using and refining these materials for the past four semesters and collecting student user survey data. Overall, students have had a positive reaction to the practice format.
Enriching Intention of Human Motion Prediction

Human motion prediction involves visualization, spatial-temporal data mining, which is a classical problem in artificial intelligence. Recent works focus on recurrent neural network (RNN). However, we find that previous methods could only analyse the inertia motion of body joint nodes. Inertia motion means that joints would maintain the motion state while satisfying environmental factors, such as body structure and gravity. Short-term human motion is similar to inertia motion, but long-term human motion is activity with a clear intention. The intention can be distinguished by action types, such as walking and eating. We propose a new sequence-to-sequence RNN model and gate auto encoder (GAE), to analyze the intention information for different actions. The experimental result shows that our model achieves state-of-the-art performance in long-term motion prediction. Furthermore, our model can generate composite motion by injecting subjective intention.
Image Filtering Algorithm Based On The Analysis Of The Main Components

the paper considers an algorithm that allows to solve the problem of filtering images based on the analysis of the main components, also presents simulation results showing the main features of this algorithm.
Incremental Rapidly Grouping Aggregation Method for Similar Web News Headline

Abstract With the popularity of the Internet, real-time web news has been extensively used in human daily life. Because of competition and repetitive reporting between the media, there may be multiple similar news reporting the same searing event, which may waste a lot of time for users to obtain and read the similar news. Hence, how to rapid aggregating similar news to help users obtain relevant news information accurately is a very meaningful and essential study. However, the accuracy of the existing aggregation method needs to be strengthened, and it not meet real-time incremental processing requirements. In this paper, rapid incremental aggregating a lot of similar news headlines are our main study targets. We proposed a novel Rapidly Grouping Aggregation (RGA) method. It carries out similar news headline aggregation via two core aspects --- Improved Jaro-Winkler (IJW) similarity calculation, normalized mapping and rapidly grouping aggregation. The IJW using hierarchical matching windows to enhance the performance of similarity calculation, and using the MD5 normalization strategy and the Group By function of the MongoDB database to carry out the similarity news headline rapidly grouping. Compared with the state-of-the-art method, the actual system application results show that the RGA method achieves higher performance on similar news headline aggregation tasks.
Achieving the best defuzzifier in terminology of Persian sentences through classified fuzzy method

Fuzzy logic is a multi-valued concept, whose emergence in software sciences has eliminated 0 and 1 computations, putting them within an infinite space of [0,1]. This characteristic of fuzzy logic has resolved ambiguity in numerous previous problems. The sentence roles in Persian language were specified based on the fuzzy logic’s capability to resolve ambiguity. For that purpose, we first obtained the best classification for each defuzzifier, based on which a classified fuzzy was implemented. Nonetheless, the fuzzy system used in this research was classified based on statistical computations. To achieve the best classification, five defuzzification methods (Mean Of Max, Max Of Membership, Largest Of Max, Smallest Of Max, and Central Average) competed in 16 roles each in five classes (different matrices). Finally, Mean of Max with a success rate of 64% proved to be a defuzzifier delivering the best output among 5 different defuzzification methods.
Contextual data classification for a ubiquitous intelligent environment

Recognition of activities from sensors is a key paradigm of ubiquitous computing. Activity recognition systems can be used to label large sets of data. Variability in human activities, sensor deployment characteristics, and application domains has led to the development of best practices and methods to improve the robustness of activity recognition systems. Classification is one of the most important steps in making the recognition process more expressive and reducing uncertainty, thus minimizing representation. The K-medoid algorithm is simple but effective for grouping data according to the similarity that the samples present between them without the need to know each sample’s membership class. In this paper, we propose a classification technique based on unsupervised partitioning algorithm, which allows recognizing activities and overcomes the problem of supervision.
Structural Data Binding for Agile Changeability in Distributed Application Integration

The interaction of distributed applications raises an integration problem that consists in how to satisfy the minimum interoperability requirements while reducing coupling as much as possible. Current integration technologies, such as Web Services and RESTful APIs, solve the interoperability problem but usually entail more coupling than required by the interacting applications. This is caused by sharing data schemas between applications, even if not all features of those schemas are actually exercised. This has its toll in application development agility. This chapter proposes compliance and conformance as the concepts to minimize coupling without impairing interoperability by sharing only the subset of the features of the data schema that are actually used. In addition, data binding between messages and the receiver's schema is done structurally in a universal and application-independent way. This eliminates the need for application-specific stubs and allows clients to use any server with which they comply and servers to replace any server to which they conform.
The Study on Highly Precise Linear Motion Ball Guide System

Linear Motion Ball Guide is widely used over various industrial machines commencing with machine tool, which becomes now an essential necessity as machine component. In the field where required high precision, however, a peculiar phenomenon in Linear Motion Ball Guide called “Waving” has been questioned and which kept some clients at a distance from using Linear Motion Ball Guide. It is known that the generating factor of Waving and Ball Circulation are linked closely, so I decided to apply the load distribution theory to each and every position of moving balls of Linear Motion Ball Guide to ascertain the condition of load distribution and posture displacement.What I found is that the crowning of raceway is intimately related to Waving, and that there is a configuration in crowning which minimize Waving. By applying this technology to the condition of clients using Linear Motion Ball Guide, and I developed the Linear Motion Ball Guide which can be used without problem even in the field of high precision.
Precision interferometric thickness measurement of optical flat using wavelength tuning Fizeau interferometer

Wavelength-tuning interferometry has been widely used for measuring the thickness variation of optical devices used in the semiconductor industry. However, in wavelength-tuning interferometry, the nonlinearity of phase shift causes a spatially uniform error in the calculated phase distribution. In this study, the spatially uniform error is formulated using Taylor series. A new <i>9</i>-sample phase-shifting algorithm is proposed, with which the uniform spatial phase error can be eliminated. Finally, optical-thickness variation of transparent plate is measured using the proposed algorithm and wavelength-tuning Fizeau interferometer.
Regression and Variable Selection

Regression is used for explaining the relationship between a variable Y called response, and a set of one or more variables $$X_1, X_2, \dots , X_N$$ called explanatory variables. The variable selectionVariable selection is the process that choose a reduced number of explanatory variables to describe a response variable in a regression models. Variable selectionVariable selection is used to (i) make the model easier to interpret, removing redundant non-informative variables; (ii) reduce the size of the problem to enable algorithms to run faster; and (iii) reduce the overfittingOverfitting and, consequently, make the model predictive.