Reviewerly/exHarmony huggingface dataset:
Evaluation of Azimuth Angle Profile for Solar Photovoltaic System in Humid Subtropical Climate of Varanasi City

This research work focuses on the prognosis of energy exploration opportunity due to geographical coordinates and celestial positioning of Sun at Varanasi city. As per the Köppen classification, the city has its humid subtropical climate with pretty higher temperature and scattered precipitation all over the year. The prime objective of this research work is to predict the solar energy security of a future smart city like Varanasi with its geographical circumstances. Computer program with MATLAB coding is used for mathematical computation of solar azimuth angle profile assessment of Varanasi city. The mathematical computation of azimuth angle profile relates to the mathematical expression of altitude angle, longitude angle and angular measurement of Sun position. The summary of experimental results shows significant variation of azimuth angle profile corresponding to different seasons of the year of the city.
ExVis: Explainable Visual Decision Support System for Risk Management

Managing and communicating risk has become a crucial task in today's economy and society, where data visualization can play a vital role. Existing researchers have applied various methods to assess the risks and have shown traditional ways of visualization such as maps, charts, diagrams, etc. However, designs, developments, evaluations, and lack of proper explanations have decreased their reliability in decision-making. In this regard, we propose a solution named “Explainable visual decision support system (ExVis)” to present all the relevant information concisely and provide a visual recommendation for risk management. ExVis combines the state-of-the-art interactive visualizations blending with the Bayesian network (BN) model. Our approach is used to analyze insurance data as a case study. In this study, we have three main implications: (i) insurance data are potentially useful for understanding business risk; (ii) a visual explanation dashboard enhances stakeholder decision-making; and (iii) some critical insights into the underwriting of policyholders have been deduced. Finally, we evaluate the usefulness of our visual interactive system (VIS) by a user study.
University Philosophy Course: Reboot is Required

The philosophical training of tertiary-level students is designed to perform a number of functions assigned to it by society and the state. However, in recent years, it is no longer in demand by the majority of the educational process participants, since the university philosophy does not respond well to modern challenges from education, primarily to the need of digitalization and competence approach implementation. To preserve the disciplinary status of philosophy, the lecturers have to modernize the content, methods and forms of implementation of the discipline in order to increase its attractiveness in students’ minds, as well as the demand by society and the state. One of the possible ways to update the training course of philosophy is seen in its reorientation towards the development of students’ universal competencies prescribed in the state educational standards. In this case, the philosophical content becomes a tool to achieve this goal, and the philosophical heritage is used as a theoretical basis for mastering the technologies of mental action. The article presents the experience of Kazan University, where the philosophy course has recently become a tool for “system and critical thinking” competence training. Although a digital format of philosophical training has a number of cognitive advantages, nevertheless it is fraught with the risk that the philosophical value potential will not be fully realized. This risk can be minimized through a mixed format of teaching philosophy. Тo increase the demand for philosophy in higher education, the author offers a number of ways to enhance the role and importance of philosophy in students’ minds.
METHODS OF DISSERTATION RESEARCH IN PHILOSOPHICAL SCIENCES: MODERN PROBLEMS AND FEATURES

Mastering the methodology of dissertation research is an important and necessary component of obtaining the third scientific and educational level. These skills are necessary for modern graduate students to successfully write and defend a dissertation. Mastering the methods involves understanding the features of modern research processes in the humanities / philosophical sciences, knowledge of methodology and rules of organization of scientific activity. Mastering the methods of modern scientific research orients young scientists / graduate students to produce new ideas and perform original research, to achieve high scientific results and create new knowledge in the field. Understanding the methodology of scientific research provides the formation of applicants’ holistic view of the research process, logic of its construction; forms the ability to conduct research on the basis of academic integrity and critical thinking; generate new idea; formulate and test scientific hypotheses, as well as to publish the results of scientific research in leading scientific journals, conference and seminar proceedings, and to present and defend the results of the dissertation in a qualified manner.
GIST Strategy Effect on Reading Descriptive Text

The purpose of this study to find out whether there is a significant effect of GIST strategy to teach reading comprehension in descriptive text of seven grade students. This study uses quantitative method. The study usedtrue experimental which took two classes randomly, VII I as experimental class and VII H as control class. The instrument used a test. The result showed from a mean of the pretest in experimental class is 75.13 and posttest is 86.94. The researchers used the ttest formula to calculate the results of the data and hypothesis. The result of tvalue is higher than a ttable,ttest is 3.415 higher than ttable 1.994 with degree freedom 70 and level significance 5%. It means that the Ha is accepted and H0is rejected. The concluded there is a significant effect of GIST strategy to teach reading comprehension of seven grade students at SMPN 4 Sidoarjo.
Heuristic Scheduling Method with the Importance of Earlier Tasks for Deadline Constrained Workflows in Clouds

Cloud has gradually become a more popular platform for executing scientific workflow applications due to its unique features. However, how to effectively schedule scientific workflows in clouds to minimize execution cost under a deadline constraint remains a critical problem. And in this problem, deadline distribution strategy plays a key role. In this paper, a new deadline distribution strategy with level importance rate is proposed. Based on this, we propose a heuristic scheduling method, named LIRHM. LIRHM prioritizes tasks by their upward ranks and distributes the deadline to each task as its sub-deadline using the proposed deadline distribution strategy, then maps each task to the best VM instance which meets its sub-deadline and minimizes workflow execution cost. Compared to four methods with different deadline distribution strategies, experiments on five real-word scientific workflows show the effectiveness of LIRHM and the importance of earlier tasks on deadline distribution strategy. In addition, we analyze the performance difference of different deadline distribution strategies on each workflow.
The challenges and opportunities of blockchain systems

Blockchain technology is another technological innovation under the background of the new epoch. It boosts the development of sharing economy and leads to the continuous reformation of technology and industry. As brand-new information technology, blockchain plays an unparalleled role in promoting individuals, society, and the nation. It is significant to study the social popularization of blockchain technology in this context. This paper first reviews the birth and the development of blockchain technology, including Bitcoin, the application prospect, and the value of blockchain technology. And then, this paper discusses the risk factors that constrain the social popularization of blockchain technology. Finally, this paper puts forward some practical strategies to regulate them and makes countermeasures and prospects in the future regarding blockchain technology.
VQMG: Hierarchical Vector Quantised and Multi-hops Graph Reasoning for Explicit Representation Learning

Vector Quantized Variational AutoEncoder (VQ-VAE) models realize fast image generation by encoding and quantifying the raw input in the single-level or hierarchical compressed latent space. However, the learned representations are not expert in capturing complex relations existed, while one usually adopts domain-specific autoregressive models to fit a prior distribution for two stages of learning. In this work, we propose VQMG, a novel and unified framework for multi-hops relational reasoning and explicit representation learning. By introducing Multi-hops Graph Convolution Networks (MGCN), complicated relations from hierarchical latent space are effectively captured by Inner Graph, while the fitting of autoregressive prior are performed coherently by Outer Graph to promote the performance. Experiments on multimedia tasks including Point cloud segementation, Stroke-level text detection and Image generation verify the efficiency and applicability of our approach.
Word Estimation in Continuous Colloquial Bengali Speech

Word segmentation is a crucial part in any speech to text conversion. Many works have been done on popular languages, especially on English, but a very few work has been carried out on Bengali language, especially on colloquial speech. In our work, we present a simple pitch profile-based technique to find the words in a Bengali speech. We extract the feature of the existence of words based on the pitch profile of a speech. To find the pitch profile, we have used the state phase technique. A simple deviation of a 20 ms window is studied to find the pitch. In order to reshape the pitch, power profile of the speech is used. Then apply morphology to make the profile more robust. Finally, we cluster the data and use silhouette index to select the number clusters present in the data which in turn estimate the word boundaries. The algorithm is tested over continuous colloquial Bengali speech.
An Efficient Hybrid De-Noising Method for Remote Sensing Images

In recent years, analysis of remote sensing imagery has become significant due to its good representation of data in region of interest (ROI) in the world. However, the noise-corrupted satellite image data may not be capable to recognize and analyze it accurately. It necessitates the usage of suitable image de-noising that aids in improving the clarity, sharpness of image for further processing like feature extraction and enhanced analysis of the image data. In this paper, an efficient hybrid bilateral-guided (HBG) filter has been proposed that can filter noise faithfully from the satellite digital image. The attractive feature of the HBG filter is its focus on preserving edges of the original image while removing noises, thereby supporting edge detection and other image processing in a better way. Weiner filtering, median filtering, bilateral filtering, guided filtering and HBG are implemented using MATLAB R2016a on Intel core i3 system. These filtering techniques are carried out on Gaussian noise, salt-and-pepper noise and speckle noise-corrupted satellite images. The performance measures used are mean square error (MSE) and peak signal-to-noise ratio (PSNR). PSNR of HBG is 83%, 76% and 81% more than that of conventional bilateral filter, and MSE of HBG is 49%, 50% and 49% lesser than that of conventional guided filter for 50% salt-and-pepper noise, speckle noise and Gaussian noise-corrupted image, respectively. It is also proved that PSNR of proposed HBG is 74, 71 and 69% more than that of existing hybrid median Wiener filter (HMW) and MSE of HBG is 85%, 95% and 84% lesser than that of hybrid median Wiener filter (HMW) for 50% Gaussian noise, salt-and-pepper noise and speckle noise-corrupted image, respectively. Thus, the simulation outcomes show that the HBG filtering is suitable for removal of all types of noise from satellite images
Are Cold Boot Attacks Still Feasible: A Case Study on Raspberry Pi With Stacked Memory

Cold boot attacks are semi-invasive attacks which have threatened computer systems over a decade now to leak sensitive user information passwords, keys and PIN. With internet of things (IoT) finding mass deployment, their security must be well investigated. In this work, we take a look at popular IoT device Raspberry Pi (model B+), which is already deployed in millions. Raspberry Pi features a stacked memory on top of its processor, making it impossible to physically separate the RAM from the processor. We investigate the decay model of a cold boot attack on Raspberry Pi. The results show a decay rate as low as 0.00027% which is orders of magnitude lower than previous works allowing close to perfect data recovery. We further report successful recovery of secret disk encryption key when using dm-crypt on Raspberry Pi followed by discussion on mitigation strategies.
Abnormality Diagnosis from Ambient Data: IoT Data Sequences in Real Time

Modern sensor networks are being propelled by the Internet of Things (IoT) model to track a wide variety of phenomena, including environmental control, medical care, manufacturing operations, along with intelligent metropolis. These meshwork function as pivot facilitators to a Digital Earth Nervous System by offering a constant pulse of the almost limitless events taking place in physical space. Nonetheless, the accelerated transforming for these sensor information sequences resumes posing a problem for conventional data-handling strategies, necessitating the creation of new approaches. We suggest a generalized approach to this problem that could be used to help all aspects of dispensed instantaneous inspection. For working along with multiple types of record origins, this impartial technique utilizes a brokering approach and web-based specifications to ensure interoperability. We used the technique to identify peculiarities instantaneously and related it with the field of atmospheric management as a proof of concept. The built framework is capable of identifying irregularities, carrying out alerts, and showing the user the current situation.
Personal Information Protection Based on Big Data

With the popularization and development of information technology, all walks of life have accumulated a large amount of rich data, which usually contains a lot of personal privacy information, and the direct release or analysis of it may cause the disclosure of privacy. Differential privacy, as a relatively new privacy protection model, can prevent the attack of the attacker with arbitrary background knowledge, and effectively solve the privacy threat problem in data release and analysis. This paper mainly studies the protection of personal information based on big data. In this paper, a position privacy protection scheme based on differential privacy in P2P mode is proposed, and the advantages of the scheme in performance are proved by experimental comparison and theoretical analysis.
Implementing Community-Based Participatory Design and Mixed Methods to capture and Analyze Mental Models of no/low Literate Users

One of 21 Nigerian women still die from avoidable maternal death. Yet, one of the most critical intervention for safe motherhood is ensuring that women (even in the most remote areas) have access to and are well informed of antenatal and postnatal care information and services. Previous research reveals that Ilorin women depended more on each other for antenatal and postnatal care information than on skilled birth attendants. However, when designing for historically underserved populations, the adoption and scaling of the interventions can be truncated if the target population is not adequately considered and integrated into the design process. This paper describes including no/low literate users as co-designers through community based participatory design in the development process and mixed methods to analyze collected data. Our results inform best practices for developing information architecture for these populations based on their mental models.
Reducing Noise Pixels and Metric Bias in Semantic Inpainting on Segmentation Map

Semantic Inpainting on Segmentation Map (SISM) aims to manipulate segmentation maps by semantics. Providing structural assistance, segmentation maps have been broadly used as intermediate interfaces to achieve better image manipulation. We improve the SISM by considering the unique characteristics of segmentation maps in the both training and testing processes. First, to improve SISM training process, we reduce the noise pixels, which are pixel artifacts from the generation. Because each pixel in the segmentation maps has a much smaller value range in comparison to pixels in natural images, we propose a novel denoise activation (DA) by estimating the possible pixel values for an inpainted area in advance. Second, we improve SISM testing process by reducing the metric bias. The bias is caused by the ignore of latent ground truths in the current metrics in SISM. Based on the analysis of possible latent ground truths, we then propose a novel metric, Semantic Similarity (Sem), to quantify the semantic divergence between the generated and ground-truth target objects. Sem is calculated by a pre-trained semantic classifier using object shapes as training data. Since the classifier is pre-trained on PS-COCO dataset, with a large number of training samples and relatively general classes, Sem is also applicable to other datasets. Our experiments show impressive results of DA and Sem on three datasets.
Research on Mobile Robot Platform to Assist Laboratory Management

A laboratory service robot that significantly improves work efficiency, is presented. This solves the problem of inefficient management of laboratory equipment in universities. The robot has the ability to move around the lab freely, responding to voice commands and controlling the appliances. This helps the laboratory manager to record information that will improve laboratory management. This paper discusses innovations in the scope of application of the service robot.
Design of the Poster Image System Based on Human Vision

At present, the human visual perception system is the most effective, accurate, and fast image processing system in the world. This is because human eyes have some special visual features, but the features closely related to image enhancement include color constancy and brightness constancy. This paper presents a new image enhancement framework and computational model which can better simulate human visual features. It is based on the analysis of color constancy and luminance constancy and Retinex theory. And, this is a new image enhancement method in the compressed domain based on Retinex theory. In Retinex theory, DCT coefficients consist of incident components (DC coefficients) and reflection components (AC coefficients). By adjusting the dynamic range of DC coefficients, carefully adjusting AC coefficients, and using the threshold method for block suppression, the compressed domain image can be enhanced. On the basis of Retinex theory, the incident light and reflected light components are considered synthetically, the dynamic range (DC coefficient) of the incident light component and the details of the reflected light component (AC coefficient) are adjusted, and then the incident light component is reexamined. Moreover, it achieves a better image enhancement effect and avoids the blocking effect.
Methods to find digital footprints on a personal computer

Digital footprints remain on a personal computer no matter how hard PC users try to wipe them. This paper can be useful both as an instruction to find such footprints and as an instruction for their destruction. The paper is devoted to the overview of specific examples of the use of modern hardware and hardware-software systems to find digital footprints of activity directly on a personal computer. The options to obtain information from random access memory (RAM), hard drives and SSD devices are described in detail. For example, the following information can be obtained from a dump of the RAM of a PC: running processes, network connections, passwords, various encryption keys, clipboard, etc.
Trajectory Control Method Of UAV In Autonomous Flight Mode Using Neural Network MELM Algorithm

in our time, the use of unmanned aerial vehicles (UAVs) for communication purposes, for example in Flying Ad-Hoc Networks, often correlates with the emergence of a number of scientific and technical tasks for designing flight control systems of UAVs in the absence of GPS signals: Firstly, in connection with the requirement for the number of errors of setting the flight path. Secondly,compliance with computational accuracy requirements with power limitations of the microcomputer base. In order to solve these problems, it is proposed to use the Method of trajectory control UAV in autonomous flight mode using neural network. Multiple Hidden Layers Extreme Learning Machine (MELM) algorithm, the result of which is tested using experimental comparison with existing Extreme learning machine (ELM) algorithms - Kalman and Weight Agnostic Neural Networks (WANN) - Recurrent Neural Network (RNN) Madgwick. To solve the scientific and technical problem, for the first time it was proposed to use a unit for converting navigation data into a quaternion form to reduce the dimension of the input data, which in turn made it possible to increase the speed and accuracy of training the neural network without using the quantization process. For the first time, the MELM neural network algorithm was applied to solve autonomous navigation problems to reduce the deviation of UAVs from the target trajectory during the disappearance of GPS signals. It has been experimentally proved that the application of the developed technique has ensured a relatively slightest error in predicting the navigation parameters of the trajectory, which occurs in the event of the disappearance of signals of global satellite positioning systems.
Bayesian probabilistic models for corporate context, with an application to internal audit activities

In this article, we present a business case carried out in Poste Italiane, in the context of fair performance evaluations of human resources engaged in internal audit activities. In addition to the development of a Bayesian network supporting the goal of the Internal Audit unit of Poste Italiane, the work has led to the development of a methodological approach to advanced analytics in corporate context, whose usefulness goes well beyond the specific use case described here. We thus present the different stages of such analytical strategy, from feature selection, to model structure inference and model selection, as a general toolbox that allows a completely transparent and explainable process to support data-driven decisions in business environments.