Reviewerly/exHarmony huggingface dataset:
Construction Projects Problems Optimization Using PSO and GSA

Construction projects are a combination of high complicated procedures that rarely go with the plan. The greatest dangers projects are the construction since it linked with an extraordinary amount of ambiguity and threat and that because of the business activities nature, procedures, and the outside surroundings. This paper investigates the problems during the pre-construction phase and the optimal solution for this problem by using to algorithm, partial swarm and Gravitational search algorithm. The results show that the construction problems have a severe effect on both time and cost and these problems must be treated immediately and this requires sophisticated techniques by using computer science. GSA and PSO are both used and show excellent results in solving these problems, the GSA algorithm shows better results in both the velocity is taken to find the solutions and in the accuracy. PSO is still a good technique in finding the solution and their future recommendation in making an expert system to find the solution more than one project and their interdependency.
Formative digital and online technologies in news and journalism

Online news systems share some affordances of Turing’s universal machine, especially configurability, but the early generation of web standards enabled data sharing, interoperability, and ultimately frameworks to reasoning about digital resources. At the backend of online news, indexing, mark-up languages, and applied logic, provide a base for machine intelligence that ultimately extends to cloud servers and big data. However, XML languages, like RSS, enabled the first phase of sharing stories in the form of newsfeeds. Specific mark-up for online news, such a NewsML, also defined layout and other features of news sites. Tim Berners-Lee established the W3C for online standards in the 1980s, and then on the cusp of the 21st century he proposed semantic and structured approaches for meaningful data sharing online. However, in subsequent years entrepreneurs have appropriated semantic approaches for different ends. The atomisation of data also introduces “personalised” data preferences to pitch news stories.
Towards three-dimensional point cloud reconstruction of fish swimming

Zebrafish is extensively used in behavioral, pharmacological, and neurological studies due to a number of method- ological and practical advantages, including genetic and neurobiological homologies with humans and a fully se- quenced genome. Critical to a biologically-based understanding of zebrafish behavior is the ability to reconstruct their complex behavioral repertoire in three-dimensions. Toward this aim, several efforts have been made to score their ethogram in three-dimensions, but most of these studies are constrained by a single-view imaging. A promising line of approach to extract refined information about the mechanosensory and perceptual systems of zebrafish is point cloud reconstruction. Here, we provide an initial review of the state of knowledge in zebrafish tracking and we propose a potential methodology that can capture the dynamic three-dimensional geometry of fish swimming. We utilize a stereo vision camera, calibrated with a pinhole camera model with refraction cor- rection to allow for multi-medium imaging. The corrected pinhole camera model accounts for refraction through multiple mediums and allows for more accurate point cloud reconstruction from two cameras. From the point cloud data, we could recreate the three-dimensional geometric model of the fish and analyze its swimming be- havior in three dimensions. The extracted dynamic fish geometry should allow for an improved understanding of mechanosensation and perception, which are critical to elucidate how zebrafish process visual cues and perceive flow structures.
Combined Genetic Programming and Neural Network Approaches to Electronic Modeling

An approach combining genetic programming (GP), neural network and electrical knowledge equations is presented for electronic device modeling. The proposed model includes a GP-generated symbolic function accurately representing device behavior within the training range, and a knowledge equation providing reliable tendencies of electronic behavior outside the training range. A correctional neural network is trained to align the knowledge equations with the GP-generated symbolic functions at the boundary of training data. The proposed method is more robust than the GP-generated symbolic functions alone because of improved extrapolation ability, and more accurate than the knowledge equations alone because of the genetic program's ability to learn non-ideal relationships inherent in the practical data. The method is demonstrated by applying it to a practical high-frequency, high-power transistor called a HEMT (High-Electron Mobility Transistor) used in wireless transmitters.
The Promotion and Placement of Scholarly Journals

Since at least the 1980s, there have been significant changes in the marketing of scholarly journals utilized by the majority of scholarly publishers. This shift meant that traditional advertising (which was very effective for many decades) and direct mail pieces (which were rather ineffective because of 1.5% to 2.0% response rates) were de-emphasized but not eliminated completely. They were supplanted somewhat by the growing use of social media and emails. This chapter provides an overview of print and digital journal distribution strategies, procedures, and platforms. Attention is paid to the US and global journal markets and subscriptions, including data on library expenditures. A sample journal’s contract with an author is presented and analyzed. There is a discussion of Sci-Hub, open access, article processing charges, the development of Plan S and Plan U, and research funding policies.
Python and Dynare Code

In this chapter a variety of different sample codes are provided in Python, complementing the Matlab code in the main text. In addition, Dynare (an open source add-on to Matlab) code is provided for the business cycle model.
VERIFIKASI KARTU MENUJU SEJAHTERA (KMS) MENGGUNAKAN SISTEM PENDUKUNG KEPUTUSAN BERBASIS WEB DESA DUTOHE KECAMATAN KABILA

Kartu Menuju Sehat  is part of a government program in the context of handling poverty that is always updated every year receiving data, so that assistance reaches the hands of the poor, the data collection of families must be as expected, although many parameters / indicators of poverty result in less effective poverty management programs. The results of the analysis of the data of poor families after re-verification conducted by Dutohe Village, Kabila Subdistrict, Bone Bolango District, showed that the cause of the misidentification of poor families was due to errors during the data collection process, errors in the calculation process, and due to fraud during the data collection. This study designed a decision support system using the Technique For Others Reference Method by Similarity to Ideal Solution (TOPSIS) in the Verification process Kartu Menuju Sehat (KMS) in order to produce the right and accurate decision making that involves the privacy value or preference value of the KMS recipient. The results showed an increase in terms of effectiveness and efficiency in card verification processing
Efficient Certificateless Privacy-Preserving Public Auditing for Cloud-Based IIoT

Continuous development of the Industrial Internet of Things (IIoT) is making organization to adopt cloud for data storage and computation as it is cost-effective and less computation for IoT devices. Since cloud is untrusted, authentication of data stored in the cloud is one of the major concerns. To address this issue, several schemes are proposed using certificateless signatures but these schemes are inefficient. In this paper, we propose an efficient certificateless privacy-preserving public auditing for cloud-based IIoTs using certificateless short signature. Unlike previous schemes, our scheme has low computational cost for signature generation and verification. Further, our scheme enables the public auditor to verify the authentication of data and also protects the privacy of data from public auditor during verification. Security of this scheme is proved against Type-I, Type-II and Type-III adversaries. The performance analysis shows that our scheme is efficient and feasible for the IIoTs.
The Research and Design of Virtual Reality Technology-Based Teaching Cloud Platform

At present, educational informatization has become an inevitable choice for the development of the times. As a new teaching method combining virtual reality technology with traditional education, “VR education” has been recognized and favoured by more and more educational circles. This paper mainly analyses the design process of VR-based teaching cloud platform. The platform provides a new way of thinking, new method and new means for the transformation of new educational methods such as “interactive” learning and “flipped classroom” into immersive practical teaching. It will certainly play a positive role in promoting the reform and development of teaching.
Undergraduate Thesis Supervisor Recommendation Based On Text Similarity

The measurement of the similarity of texts is very extensive research. The most common implementation is finding documents that match the search keywords. And the current implementation is to measure the plagiarism of scientific documents. In the academic field, the topic of undergraduate students needs to be done an examination of similarity, in addition to knowing the title of the previous thesis that is similar, also to provide recommendations for suitable supervisors. In this study, we propose a system that can help undergraduate students determine supervisors based on the undergraduate thesis title to be proposed. The experimental results show that the proposed method is quite good as a recommendation system for undergraduate thesis supervisors with an accuracy of 80% in the field of research and 87.5% in the field of lecturer research. Keywords: Text similarity, Dice Coefficient, Supervisor Recommendation.
Noise Tolerance of an Energy-Scalable Deep Learning Model with Two Extreme Bit-Precisions

In this paper, we perform the noise analysis on an energy-scalable deep learning model with two extreme bit-precisions, named MixNet. In real-world applications, there might be a great deal of noisy inputs that are collected from mobile sensors, and the training is performed on those noisy datasets. According to our initial set of experiments, MixNet has lower sensitivity to the noise in the training dataset, when compared to the original CNN model with high-precision. As a result, it is expected that the MixNet can be trained better even in a noisy environment than the original high-precision deep learning models.
Situated Learning Through the Use of Cooperative Techniques and Academic Controversy Applied to the Provision of Cryptographic Confidentiality

Cybersecurity teaching must be oriented to show the student the dichotomy that professional performance demands, to learn how to provide confidentiality, integrity, and authenticity services and at the same time to develop the ability to find weaknesses in any system it evaluates. Because today´s students have different ways of learning, teaching these topics becomes a new challenge for teachers. This need has generated that the improvement of the teaching–learning processes becomes a continuous search in the engineering area. In this sense, this article shows a proposal of strategies where the students of the course of Critical Security Systems can clearly identify the context and the relation of what they learn with reality, reason why the situated learning helps to consolidate the knowledge, to perceive the utility and validity of what is learned. Using techniques of cooperative techniques and academic controversy, the efficiency is exercised in the design of symmetrical cryptographic systems of public key destined to provide the service of confidentiality of the information.
Diritto e OGM. Una storia complicata

Genetically modified organisms are one of the most innovative products in the food sector and, also, one of the most controversial scientific “phenomena”. They have important potential for development and sustainability, but they also involve risks related to the necessary safety standards, the maintenance of certain quality levels of the product and the consumer protection. The essay aims to analyse the regulations on GMO, at national and European level, trying to clarify and understand the current opportunities and problems that actually exist today with regard to the category of genetically modified organisms (including the the most recent gene editing techniques).
Obvious and Improbable in Kuhnian Normal Science

The article is devoted to the analysis of some specific characteristics of the language of normal science described by Thomas Kuhn. We would like to draw attention to two problems associated with some features of the concept of paradigms. The first problem relates to the question, how scientists belonging to one paradigm record the position of a group of scientists adhering to another paradigm. Precisely, the article examines how the problem of “synchronous fragmentation of knowledge” is solved in the language of science. The second issue concerns the age of “normal” knowledge and the question, how the anomalous content of knowledge can appear and accumulate, and what is the status of scientists developing the “anomalous” knowledge. We reveal some possible parameters by which we can determine the early stage of the functioning of normal science, the periods of its heyday and decline. In this article, we try to find an approach to these problems by examining the natural language of scientists, using techniques of content analysis, as well as complex linguistic analysis, including discursive, semantic and pragmatic components. Linguistic analysis can’t finally solve the problems of philosophical analysis of scientific knowledge, in particular, the state of the paradigm concept by Thomas Kuhn. But it helps us to identify the boundaries of paradigms, as well as the state of normal knowledge. The problem of fragmentation of knowledge by paradigms, as well as the problem of “aging” of knowledge inside a “normal science” are not directly expressed by scientists. But they can be recorded by analysis of everyday language, which often becomes entangled with the language of science. The high rate of words that semantically indicate the “obvious” knowledge in scientific texts points to a “good” state of the paradigm. And vice versa, the words denoting “improbable” indirectly indicate its crisis state or express an attitude to the knowledge belonging to a different paradigm. The analysis of the data shows that the alleged complete replacement of Kuhn's concept of a paradigm by the concept of “trading zones” by Peter Galison does not appear to be accomplished. Just as the concept of scientific paradigm did not completely replace the falsificationalism, the Galison’s “trading zones” do not fully reflect the real state of affairs in science. Therefore, the Kuhnian paradigms are recorded at the lexical level in the communication of scientists.
IaaS Reservation Optimization for Multi-QoS Level based SaaS Provisioning.

We design an evolutionary computation based solution for SaaS providers to optimize the cost of reserved IaaS instance subscription. The services provisioned by SaaS providers have multiple options of Quality of Service (QoS) levels. A Bayesian network model is designed to map between the QoS levels of SaaS instances and their actual resource consumption on IaaS instances. A genetic algorithm with heuristics is designed to optimize the reserved IaaS instance subscription. The proposed solution is evaluated via a set of simulated experiments.
Modeling Sharpening a Pencil in Virtual Reality

For the elderly, manual work is effective in activating and training the brain; however, the activity is decreasing because of its danger. Pencil sharpening involves a slight sense of tension by using a knife, which requires skill. In this study, a prototype was developed for pre-training in situations where the task would be dangerous, and the conditions for reproducing a pencil sharpening operation with VR were clarified. As a result of the experiment, the model of the cutting action was clarified with the data. However, the ease of sharpening a pencil varies not only with the pencil-knife angle but also with skill and the method of pencil sharpening. We believe that this result is effective as data for one method of rudimentary manual work from children to adults.
A Robust Eye Localization System for Autostereoscopic Display Using a Multiple Camera

In this paper, a multiple camera eye localization for stereoscopic display is proposed. The multi-camera system contains an infrared camera and a binocular camera. Candidate face area for low-resolution infrared image is obtained by using YOLO-V3 network. Then face distance is calculated by an embedded ARM platform Raspberry Pi-3B+, which provides a priori knowledge for the face detection of the high-resolution visible light camera. AdaBoost and LBF algorithm are used for complete face detection and alignment, and finally they achieve the goal of eye location. Sliding window stereo matching method can improve the efficiency of stereo matching. The experiment results verify the feasibility of the proposed scheme. The speed, accuracy, and robustness of eye tracking are excellent.
Target Model Construct Method Based on Sobel and Snake Algorithm

Traditional target model is inaccurate. In order to solve this problem, this paper proposes a target model construct method based on Sobel and Snake algorithm. Firstly, get the edge information using Sobel operator in the target model area and get the gradient direction information of each edge pixel. The edge pixels are used as control points. Secondly use Snake algorithm to move the control points to get the accurate contour point. Bilinear interpolation is used to construct the target contour. The experimental results show that the new modeling method is more accurate than the traditional method. The pixel dot number is reduced to 10703 which is 50.9% of the traditional model. The calculate time is reduced to 31 ms when using normalized cross-correlation to calculate the correlation coefficient which provides the necessary model support for the following tracking.
Optimised feature selection and entropy-based graph classification of gene expression data

Gene expression (GE) profiles expansively revised to disclose intuition into the multifariousness of cancer furthermore to discover concealed information which provides biological knowledge for the classification of cancer. Precise cancer classification straightly through original GE profiles stays challenging on account of the intrinsic high-dimension feature along with the small magnitude of the data samples. Therefore, choosing high discriminative genes as of the GE data have turn into progressively fascinating in the bioinformatics field. This given paper gives a technique for the GE data classification utilising entropy-based graph classifier. Initially, the proposed technique evaluate the GE data's signal to noise ratio (SNR) values, additionally, selects the relevant features using krill herd (KH) optimization process. The truth is that not all features are helpful for classification, and some redundant together with the irrelevant features might even serve as outlier. To dispose the outliers, feature reduction is done with the assist of Euclidean distance. Classification is made utilising entropy-based graph classifier. The proposed process' effectiveness contrasted with the existing method concerning classifications is established from the experimental outcome.
Quality models for testing cognitive architectures

Cognitive architectures are commonly designed for developing autonomous agents capable to exhibit human-like behavior. This paper shows preliminary efforts for identifying appropriate software quality models for testing the quality of cognitive architectures. As part of this work, a review of current quality models for software quality assurance, as well as efforts done by research community for identifying the right quality metrics for testing cognitive architectures are presented. Additionally, more relevant cognitive architectures found in the literature, as well as processes used to test them are described.
Pixel-Level Prediction: Models and Applications

Author(s): Kong, Shu | Advisor(s): Fowlkes, Charless | Abstract: Pixel-level prediction enables visual understanding at finer granularity, such as segmenting all the persons and vehicles and estimating their 3D shapes as well as distances from the camera. It distinguishes itself from image-level prediction, which is relatively coarse such as simply telling an image of a person from that of a car.The solutions to pixel-level prediction are core to many real-world applications, spanning a variety of vision tasks from the low-level vision like image deblurring, to the mid- and high-level such as understanding scene geometry and all the objects' 3D shape and motion. It has been greatly advanced since the past decade and fostering new challenges and opportunities, owing to the fast development of hardware, the availability of large-scale dataset, and the resurgence of deep convolutional neural networks.In this thesis, we study pixel-level prediction with new algorithms, innovative model architectures, and novel applications. We begin with training convolutional neural networks (CNN) for different pixel-level prediction tasks, and demonstrate that CNN acts as a unified framework. Within the framework, we propose novel modules to encode perceptual or cognitive principles, such as 1) objects appearing larger when closer to the camera, and 2) cognitive mechanism allowing one for perceiving the world with dynamical attention. We show the proposed modules not only achieve the state-of-the-art performance on different tasks, but also enables dynamic and parsimonious computation.As the dataset for per-pixel labeling tasks requires painstaking per-pixel annotations, we propose the Predictive Filter Flow (PFF) framework to train over simulated images for image reconstruction tasks. PFF generates per-pixel kernels for warping the input towards the output, thus has better interpretability w.r.t decision making. We further present its multigrid extension (dubbed mgPFF) to train over unconstrained videos. We show successful applications of mgPFF to visual tracking and flow learning, as well as a unique interactive application for photo editing.
Educational resource online evaluation system based on neural network dynamic feedback algorithm

This article has been retracted, and the online PDF has been watermarked “RETRACTED”. A retraction notice is available at https://doi.org/10.3233/JIFS-219218.
Penjernihan Air Bersih dengan Filter Alami dan Aerasi di Teluk Bakung, Sungai Ambawang, Kubu Raya

Fresh water is a vital requirement in human daily life. Teluk Bakung Village relies on fresh water supplies from the Ambawang River and wells made by the community. The condition of well water in Teluk Bakung Village tends to be the color of ground water which is influenced by the roots of trees, blackish brown and high iron content. The purpose of this Community Service activity is to carry out water filtration on gambut soil to improve the condition of fresh water sources at Al-Muhajirin Mosque in Mu'allaf village, Teluk Bakung Village, Sungai Ambawang District, Kubu Raya Regency, Kalimantan Barat as meeting the mosque's needs and the society. The method of applying IPTEKS is by transferring IPTEKS by improving the physical condition of the Al-Muhajirin Mosque building, the application of clean water filtration as well as training the local staffs for operations and maintenance as well as the plumbing network. It is expected that the addition of aeration system groundwater that has iron content will make it clearer than just filtration which has been carried out at that location.