Reviewerly/exHarmony huggingface dataset:
Expert System Diagnosis Corn Pests And Diseases Using Certainty Factor Method

Abstract The development of technology has been very significant, not only in the fields of information, industry, education, but in agriculture. Therefore technological sophistication is also utilized by corn farmers to obtain information about corn crops. Corn is included in plantation commodities in Indonesia. However, in corn cultivation there are various problems associated with the disease that is likely to attack the plant. Therefore, the authors designed the expert system of corn disease diagnosis using certainty method. This system will analyze based on symptoms - symptoms that are in the input by the user so that in obtaining a result of disease identification on corn in the name of the disease, the cause of the disease, the percentage value and how to handle it. This expert system is developed by using the visual basic application and Microsoft Access database with Certainty Factor method to measure the value of certainty of a hypothesis against a fact.
Cause and origin of moire interferences in recursive processes and with fixed-point and floating-point data types

In the calculation of the map of periods of the Mandelbrot Set and working with fixed-point and floating-point real number representation systems, moire interference patterns always appear. These result from the discretization that every computer sets in the finite encoding of its real values. It is not known from which original layers such interferences are obtained and how these initial layers are generated. This article aims at answering these two questions. In order to search these original layers thousands of images —created by means of two different encodings of the real values and with different accuracies— of the map of periods were analyzed. Some points with constant location, called Source Points, around which regular patterns with hyperbolic configuration are generated, were located as well. This answers the questions regarding the cause and origin of moire interferences. Some characteristics of Source Points are described hereinafter. It is also justified why moire interferences in the plane of periods have a hypersensitive behavior. Finally, it is shown how in dynamic systems accuracy does not only affect the precision of results, but also the configuration of the results themselves. The following interesting reflection is, therefore, open to discussion: if the real world should be seen with a fractal way of looking, accuracy configurates then the essence of everything.
A Literature Review on Sentiment Analysis and its Foundational Technologies

Sentiment Analysis is the computational treatment of opinion or sentiment expressed in a source of data. A cross-section of Natural Language Processing and Machine Learning, Sentiment Analysis applications deal with opinions as a tangible commodity often used to leverage big data for the sake of gaining a competitive advantage. To date it appears Sentiment Analysis has no formal frameworks or seminal texts that one can turn to for an accessible but comprehensive look into the field. This literature review based paper addresses this absence by providing a technical summary of common modern frameworks, the taxonomy of its parent fields and a brief analysis of related research dating back to the early 1990s.
Discussion Games for Preferred Semantics of Abstract Dialectical Frameworks

Abstract dialectical frameworks (ADFs) are introduced as a general formalism for modeling and evaluating argumentation. However, the role of discussion in reasoning in ADFs has not been clarified well so far. The current work provides a discussion game as a proof method for preferred semantics of ADFs to cover this gap. We show that an argument is credulously acceptable (deniable) by an ADF under preferred semantics iff there exists a discussion game that can defend the acceptance (denial) of the argument in question.
A Parametric Version of Probabilistic Distance Clustering

Probabilistic distance (PD) clustering method grounds on the basic assumption that the product between the probability of the unit belonging to a cluster and the distance between the unit and the cluster center is constant, for each statistical unit. This constant is a measure of the classificability of the point, and the sum of the constant over units is referred to as the joint distance function (JDF). The parameters that minimize the JDF maximize the classificability of the units. The goal of this paper is to introduce a new distance measure based on a probability density function, specifically, we use the multivariate Gaussian and Student-t distributions. We show using two simulated data sets that the use of a distance based on these two density functions improves the performance of PD clustering.
A complex number approach to elliptic curve cryptosystems over finite fields: implementations and experiments

Network security is a general idea to ensure information transmission over PC and portable systems. Elliptic curve cryptosystems are nowadays widely used in public communication channels for network security. Their security relies upon the complexity of clarifying the elliptic curve discrete alogarithm issue. But, there are several general attacks in them. Elliptic bend number juggling is actualized over complex fields to enhance the security of elliptic curve cryptosystems. This paper starts with the qualities of elliptic curve cryptosystems and their security administrations. At that point we talk about limited field number-crunching and its properties, prime field number-crunching, twofold field math and complex number-crunching, and elliptic bend number-crunching over prime field and parallel field. This paper proposes how to execute the unpredictable number of math under prime field and double field utilizing java BigInteger class. also, we actualize elliptic bend math and elliptic bend cryptosystems utilizing complex numbers over prime field and double field and talk about our trials that got from the usage.
A Forensic Evidence Acquisition Model for Data Leakage Attacks

Data leakage attack is a serious threat to daily business operations. Reconstructing scenes after attacks is critical because the reconstructed scenarios help security analysts to understand these attacks and prevent future incidents. In this paper, we have proposed a systematic approach to reconstruct attack scenes based on a forensic evidence acquisition model. We first build the model, i.e., data leakage-evidence tree, from which digital forensic examiners can collect forensic evidence, then we formalize the tree and evaluate the semantics of the tree based on the evidence found on digital devices and their supporting environments. Finally, we reconstruct the data leakage scenarios based on the semantics of the tree. Our empirical study reconstructs a data breach scenario using a real-world example.
Konkani Script to Speech Conversion by Concatenation of recognized Hand written Konkani Text Using Neural Network

This paper demonstrates a system for transforming the text inscribed in Konkani dialect into Speech by utilizing artificial neural network. Many visually challenged individuals use Text to Speech framework as a tool for communication. The ability to convert text to voice lessens the reliance, dissatisfaction, and feeling of defenselessness of these individuals. India is called as the land of unity and diversity, there are 22 official languages. TTS frameworks are mostly accessible in English; in any case, it has been watched that individuals feel more comfortable in hearing their own native dialect. Handwritten optical character recognition is the most challenging research zone, because of its intricacy in segmenting the character that grows on account of Devnagari Script because of Modifiers and compound characters. The document comprising Konkani text is scanned and fed to the system. In this framework the character recognition is done by utilizing Neural Network, in this manner the structure can be upgraded to work with letters written in different styles. After the characters in the Documents are viably recognized by neural network, it is composed to a text document, the entered text document is analyzed, the syllabification is accomplished in view of the phonological guidelines and the syllables are secured autonomously. At that point the syllable coordinating speech file is linked and the silence existing in the linked discourse is confined. breaks within the discourse are removed at syllable limits without diminishing the superiority of speech.
Blur and noisy image restoration for near real time applications

Image restoration of blur and noisy images can be performed in either of the two ways i.e. denoising after deblurring or deblurring after denoising. While performing deblurring after denoising, the residual noise is greatly amplified due to the subsequent deblurring process. In case of denoising after deblurring, the denoising stage severely blurs the image and leads to inadequate restoration. Denoising can be done mainly in two ways namely, linear filtering and non-linear filtering. The former one is fast and easy to implement. However, it produces a serious image blurring. Nonlinear filters can efficiently overcome this limitation and results in highly improved filtering performance but at the cost of high computational complexity. Few filtering algorithms have been proposed for performing image denoising and deblurring simultaneously. This paper presents a novel algorithm for the restoration of blur and noisy images for near real time applications. The proposed algorithm is based on PSF (Point Spread Function) estimation and Wiener filtering. The Wiener filter removes the additive noise and inverts the blurring simultaneously and thus performs an optimal trade-off between inverse filtering and noise suppressing. The Wiener filtering minimizes the overall mean square error in the process of noise suppressing. The PSF used for Wiener filtering is estimated using blind deconvolution. This is a noniterative process and provides faster results.
Unsupervised Domain Adaptation Using Full-Feature Whitening and Colouring

It is a very well known fact in computer vision that classifiers trained on source datasets do not perform well when tested on other datasets acquired under different conditions. To this end, Unsupervised Domain adaptation (UDA) methods address the shift between the source and target domain by adapting the classifier to work well in the target domain despite having no access to the target labels. A handful of UDA methods bridge domain shift by aligning the source and target feature distributions through embedded domain alignment layers that are based on batch normalization (BN) or grouped whitening. Contrarily, in this work we propose to align feature distributions with domain specific full-feature whitening and domain agnostic colouring transforms, abbreviated as $$\text {F}^{2}\text {WCT}$$ . The proposed $$\text {F}^{2}\text {WCT}$$ optimally aligns the feature distributions by ensuring that the source and target features have identical covariance matrices. Our claim is also substantiated by the experimental results on Digits datasets for both single source and multi source unsupervised adaptation settings.
User Authentication Scheme for Greenhouse Remote Monitoring System using WSNs/IOT

Use of modern machinery and technology has significantly reduced the labor work in agriculture. The next level was to make growth of vegetables/ crops independent of seasons. This was made possible by introducing greenhouses. Essence of greenhouse lies in control of environmental parameters like temperature, humidity, light, pressure and CO 2 as per specific crop needs. Integrating IOT technology with greenhouse enables us to remotely monitor the greenhouse environment and control its parameters intelligently. OT and WSNs deployed in greenhouse require a secure and efficient connectivity with monitoring/ control room. While deploying a WSN for greenhouse following aspects are worth considering. Hardware Connectivity User Authentication The objective of this research is to devise a user authentication scheme for deployment of IOT in greenhouse environment.
A Mathematical Model and Control Problems of Traffic Flows in Urban Road Networks

A study is focused on formulation of traffic flow control problems in the urban roads network. The problems of parametric identification, optimal control, and control synthesis are considered. Initially, the problem of obtaining a mathematical model of traffic flows as a control object is described. To obtain a model, it is proposed to use an approach based on the controlled networks theory. Evolutionary algorithms are used to solve the considered control problems.
Assisting women in career change towards software engineering

Technological growth affects the way we live, communicate, and work. Over the coming decades, up to 60% of existing jobs might be lost to automation. This creates pressure on the job market, demanding more tech professionals, while creating job insecurity for positions that might be automated. Although tech and computing is an appealing career choice with high salary, job security, working flexibility, space for creativity and career growth, vast majority of women are dropping their interest in tech during adolescence, with very limited recovery in later years, when preventable reasons are stopping them from reconsidering their decision and joining tech.
A Study on Effectiveness of Processing in Computational Clouds Considering Its Cost

The paper concerns issues related to evaluation of processing in computational clouds. The effectiveness of the computational clouds depends on many factors and different metrics can be used to asses effects of processing. In this work potentiality for evaluation of processing in cloud virtual machines that takes into account also financial cost constraints is investigated. There are considered KVM and Hyper–V based cloud platform configurations. In the paper the APPI index (Application Performance and Price Index) that can be used for selection of recommended configuration considering acceptable price of processing is defined. Presented experiments show the impact of use of the proposed metric on the choice of recommended platform for processing.
Keystroke Dynamics Identification System using ABC Algorithm

Password authentication system is a widely known security system that is commonly used by many in this day and age. However, this authentication system is vulnerable and easily manipulated as impostors are only required to know the password to access a private space. Hence, keystroke dynamics is introduced to the conventional password authentication system to strengthen the security of the system. Keystroke dynamics is a method of identifying or authenticating an individual according to their manner and the rhythm of typing. The challenge of working with biometrics is the ability to categorize data of users and recognize the users accurately. This research features a keystroke authentication system using the Artificial Bee Colony algorithm as a classifier to identify different individuals based on their keystroke biometric which are pressure, dwell time and flight time. The different parameters of the system were tested and analyzed for the best combination to accurately identify the users.
Forming the professional skills of a future physics teacher

In the presented paper, the authors discuss the formation of professional skills of a future Physics teacher, the characteristics of the system of professional competencies and key competences in the third millennium based on the required competencies that teachers will have to pass on to their pupils defined in ISCED standards and the shift in the understanding of key competences of the Physics teacher in the rest of the period. The specific area on which the authors focus in the paper is the technical communication of the teacher of Physics and the means for its realization.
Towards Automatic Manipulation of Arbitrary Structures in Connectivist Paradigm with Tensor Product Variable Binding

Building a bridge between symbolic and connectionist level of computations requires constructing a full pipeline that accepts symbolic structures as an input, translates them to distributed representation, performs manipulations with this representation equivalent to symbolic manipulations and translates it back to the symbolic structure. This work proposes neural architecture that is capable of joining two structures which is an essential part of structure manipulation step in the connectionist pipeline. Verification of the architecture demonstrates scalability of the solution, a set of advice for engineering practitioners was elaborated.
Random Forests in a Glassworks: Knowledge Discovery from Industrial Data

This paper presents the use of random forests for knowledge discovery from large data sets. The data was obtained from sensors of glassworks technological process monitoring system. Based on original and standardized data, several random forest models were created. Then they were used to establish a significance ranking of explanatory variables. The ranking presents information about explanatory variables that most affect the product defects.
Perception of Differences in Directed Acyclic Graphs: Influence Factors &amp; Cognitive Strategies

Visual comparison of graphs has become widespread in various disciplines. To support this task, it is necessary to generate graph drawings that facilitate the perception of differences. However, while readability of single graphs has been widely investigated, knowledge about the recognition of differences in graphs in general and directed acyclic graphs (DAGs) specifically is quite limited.
ALBAYZIN 2018 spoken term detection evaluation: a multi-domain international evaluation in Spanish

Search on speech (SoS) is a challenging area due to the huge amount of information stored in audio and video repositories. Spoken term detection (STD) is an SoS-related task aiming to retrieve data from a speech repository given a textual representation of a search term (which can include one or more words). This paper presents a multi-domain internationally open evaluation for STD in Spanish. The evaluation has been designed carefully so that several analyses of the main results can be carried out. The evaluation task aims at retrieving the speech files that contain the terms, providing their start and end times, and a score that reflects the confidence given to the detection. Three different Spanish speech databases that encompass different domains have been employed in the evaluation: the MAVIR database, which comprises a set of talks from workshops; the RTVE database, which includes broadcast news programs; and the COREMAH database, which contains 2-people spontaneous speech conversations about different topics. We present the evaluation itself, the three databases, the evaluation metric, the systems submitted to the evaluation, the results, and detailed post-evaluation analyses based on some term properties (within-vocabulary/out-of-vocabulary terms, single-word/multi-word terms, and native/foreign terms). Fusion results of the primary systems submitted to the evaluation are also presented. Three different research groups took part in the evaluation, and 11 different systems were submitted. The obtained results suggest that the STD task is still in progress and performance is highly sensitive to changes in the data domain.
Research on Multimodal Affective Computing Oriented to Online Collaborative Learning

Analyzing the emotion interaction of online collaborative learning, this paper proposes a multimodal affective computing model combined with the logical function. The framework of an affective computing is designed, which includes several modules for collection, processing, analysis and visualization of data, sentiment classification and feedback. Finally, the feasibility and validity of the prototype system is verified.
JacquardToolkit

JacquardTooIkit enables full access to the Levi's Jacquard Jacket and the ability to create gestures such as our new "Force Touch" gesture. We conduct a 25 participant user study testing all available gestures for accuracy and intuitiveness. "Brush In" and "Brush Out" are rated highly intuitive and have high recognition accuracy.
E-DUB-A: A Tangible Educational Resource Editor in Inclusive Classes

Using tangible interfaces for educational purposes presents considerable advantages and can provide an increase in student engagement and motivation, and showed particularly suitable in inclusive education. One of the main problems encountered in the use of tangible tabletop in the educational environment is the lack of software for programming and use that meets educational needs and is easy to use, especially for teachers who don't master programming logic. This work presents an editor of pedagogical activities developed for tangible tabletop to teachers develop pedagogical activities in inclusive context, intuitively and easily. The new editor was designed from a research with a group of undergraduate education course students, which used an available editor and evaluated its usability. After that, was carried out the new editor requirements elicitation, with the modeling and implementation of the same one. A new group of students of the same course used the new developed editor (E-dub-a) and evaluated its usability.
Navigation Technology to meet new people using Fused Location API

There is always a need for improvement in the scope of technology used in our daily life. This paper consists of one such aspect that mainly concentrates on location updates of user. This is made possible by using the concept of Fused location API. The Location APIs theoretical path points of interest in the area innovations, Fused Location Provider is utilized for obtaining area data with as decreased power use as could be expected under the circumstances and action acknowledgment for enabling applications to adjust to the present activity of the Client.One advantage of using Fused Location is the ability to connect friends even if they are from different locations at different periods of time.The main concept was to design and develop an application which helps to keep track of friends. The proposed application is able to gather information of the phone's GPS locations from the phone and present the information in a simple and convenient way.
Composition of basic heuristics for the game 2048

2048 is a simple and intriguing sliding block puzzle game that has been studied for several years. Many complex solvers, often developed using neural nets are available and capable of achieving very high scores. We are, however, interested in using only basic heuristics, the kind that could be conceivably employed by human players without the aid of computation. A common way to implement a 2048 solver involves searching the game tree for the best moves, choosing a move and scoring the game board using some evaluation functions. The choice in heuristic evaluation function can dramatically affect the moves chosen by the solver. Furthermore, two or more possible moves can frequently produce the same score as evaluated by the same heuristic function, requiring either a random choice, or the use of a secondary or back up evaluation function which itself in turn may produce a tie. In this paper, we test the effectiveness of several basic heuristics in a simple 2048 solver. In order to test these, we create a system that takes basic predefined heuristic evaluation functions as input parameters, generates compositions from these functions with certain rules, and automatically tests all of them with a specified number of games. We find that compositions of evaluation functions that maximize empty spaces and monotonicity of tiles on the board-especially those that prioritize high numbers of empty spaces above prioritizing higher monotonicity- perform the best out of all compositions that we test.