Reviewerly/exHarmony huggingface dataset:
The Effect of High-Performance Work System with Behavioural Factors in Jordanian Hospitals: A Literature Review

People are our most valuable asset, and Human Resource Management (HRM) plays a critical role in organizations that influences performance quality. Adopting a high-performance work system is gaining popularity since data shows that organizations that implement such systems, particularly in hospitals, have had great success. The effects of high-performance work systems (HPWS) on nurses' performance are investigated in this paper. This paper suggests that (HPWS) can improve performance by moderating the role of job stress. Since high-performance work systems, job stress, and nurse performance in Jordanian hospitals, it will be fascinating to see if the outstanding results can be translated to the service industry. The research findings of high-performance work systems in hospitals are explored in this conceptual study. For research reasons, this paper may be useful to people interested in studying the phenomenon of high-performance work systems, job stress, and nurses' performance in hospitals. Also, by expanding and combining explanations and predictions of HPWS and work stress, two topics that have generally been examined independently of one another, this work opens up new research options. There are implications for practice as well as directions for future research.
Professional Communication Strategies in the Design Process of an Open Education Tutor

The article describes the professional communication strategies in the design activities of an open education tutor and the process of mastering these strategies. This process is based on the close relationship between the tutor's professional-oriented goals and his/her design levels in open education. Communication strategies allow the tutor to implement successfully professional activities in open education. Nowadays the problem of tutors’ mastering the professional communication strategies is very relevant due to the increasing opportunities of open education. The article presents the progress and results of the experimental work on the testing of the effectiveness of the proposed model of mastering the professional communication strategies by open education tutors. This model is aimed at developing communication skills in the process of their professional activity. In particular, the model is thought to achieve the necessary level of motivation for mastering the professional communication strategies in open education. The article presents the results of experimental work processed by methods of mathematical statistics. The results presented in the paper confirm the proposed provisions.
Classification and Detection of Malarial Parasite in Blood Samples Using K-Means Clustering Algorithm and Support Vector Machine Classifier

The natural history of malaria involves cyclical infection of humans and female Anopheles mosquitoes. In humans, the parasites grow and multiply first in the liver cells and then in the red cells of the blood. The early diagnosis of malaria is required; otherwise, it leads to death. In this study, an effective method for the classification and segmentation of malaria parasite using k-means clustering (KMC) segmentation algorithm and support vector machine (SVM) classifier is presented. Initially, the input blood sample images are given to KMC segmentation technique for segmentation. Then the segmented image is given to statistical features like mean and standard deviation for feature extraction. Then the extracted features are saved in the feature database and used for classification using SVM classifier. The classification of healthy and affected cells of malaria parasite in blood sample images is made by using SVM classifier. Experimental result shows the performance of the proposed system.
Real-Time Video Surveillance System for Detecting Malicious Actions and Weapons in Public Spaces

In today’s world, thousands of surveillance cameras have been working round the clock. These cameras are installed at railway stations, ATMs, streets and all the public spaces that one must have come across in their day-to-day life. A wide variety of malicious events take place even in these locations. Security personnel has to monitor these footages continuously round the clock. But humans are not perfect; they do not have a perfect vision nor the ability to concentrate for a longer span. Hence, it is very obvious that at some point, something abnormal might happen that the security personnel might fail to notice. The definition of the abnormal event differs from one situation to another. An automated system is being proposed using deep learning and pose detection to detect unusual events in such scenarios. Such a system has wide applications in many industries. There is an increasing demand for developing an automated surveillance technique that is fast and accurate in real-world applications. The main aim is to alert the authorities while the crime is being committed to a certain premise. Currently, very few techniques are available which lack accuracy. But using pose estimation techniques for human action recognition fast and accurate results can be observed.
BASE Layers: Simplifying Training of Large, Sparse Models

We introduce a new balanced assignment of experts (BASE) layer for large language models that greatly simplifies existing high capacity sparse layers. Sparse layers can dramatically improve the efficiency of training and inference by routing each token to specialized expert modules that contain only a small fraction of the model parameters. However, it can be difficult to learn balanced routing functions that make full use of the available experts; existing approaches typically use routing heuristics or auxiliary expert-balancing loss functions. In contrast, we formulate token-to-expert allocation as a linear assignment problem, allowing an optimal assignment in which each expert receives an equal number of tokens. This optimal assignment scheme improves efficiency by guaranteeing balanced compute loads, and also simplifies training by not requiring any new hyperparameters or auxiliary losses. Code is publicly released at this https URL
Blockchain-based secured data transmission of IoT sensors using thingspeak

The Internet of Things (IoT) is the next paradigm in the communication information age. Through using IoT, smart devices can be activated to automatically build, collect, and share the data. Existing and potential IoT technologies bring additional consumers’ degree of comfort, productivity, and functionality. So, for this, it needs high data protection, anonymity, and restoration from breaches to be able to incorporate such an environment. Blockchain, a distributed ledger technology, capable of maintaining data privacy in a decentralized system, is widely implemented in various fields especially in the field of IoT. Blockchain has recently been gaining recognition because of its conjunction with the Internet of Things (IoT) technology. Therefore, in this paper, we analyzed the various security threats in IoT architecture and its possible Blockchain solution is presented. The core functionality of our system is to monitor the real-time values of Humidity 78and temperature of remote locations through Internet after securing it using blockchain-based technology. The live values of Humidity, the temperature can be seen through the channels of ThingSpeak Cloud. We can easily get any malicious activity in the infrastructure as with the help of Blockchain, a Secured Hash Algorithm can be applied on each block, and changing something inside a block will change its hash value. So, our paper aims at Secured data transmission by storing sensor data in the blockchain and transmitting the blocks onto the cloud. At last, we examine the challenges and future scope for research in the protection safeguarding of blockchain-based IoT frameworks.
Development E-Commerce Applications

Buying and selling activities carried out electronically have now become delightful activities for many people. Also called online shopping, this is considered to make it easier for sellers and buyers and seems more practical. Not only making online shops in various types of shopping applications, but many business actors also create e-commerce themed websites to expand their sales network of products and services. For website creation, many templates can be used, one of which is Prestashop CMS, which is an e-commerce platform used to build online store sites. In Prestashop, there are already many features that can be used, and it also makes it easier for users to build the website they want to create. Even though many people have switched to online shop users. But there are too many shops or malls that they still visit directly to see first hand the quality of the desired goods.
FPGA implementation of a lightweight simple encryption scheme to secure IoT using novel key scheduling technique

ABSTRACTInternet of things (IoT), being the technology of this generation and several billions of electronic devices exchanging a huge secure information. With the low resource devices like the sensors, RFIDs etc. to the super computers and the clouds the security and privacy issues remain a concern. While the conventional cryptographic algorithms approved by the National Institute of Standards and Technology, could be embedded into the Low-resource devices their performance may be reduced, then the design of Ciphers for such resource constrained devices become a challenge with the security principles confidentiality, Integrity and Availability remains the same.This paper proposes, simple encryption schemes based on arithmetic operations Addition-Modulo/Multiplication Modulo, Rotation and XOR hence the name ARX/MRX. The cipher schemes have been implemented using reversible logic and Vedic Mathematics. The adders have been implanted using reversible logic and multipliers and the modular algorithms have been implemented using the combination of Vedic maths and Reversible logic. The software and hardware implementations are presented. The Histogram, Correlation coefficient and Entropy are found for the grayscale plaintext image using MATLAB to evaluate the security, and the hardware implementation is done writing Verilog code using Xilinx-Vivado and is verified using Nexys-4 Artix-7 FPGA the performance of the encryption schemes are analysed and compared with the existing literature.
Efficient hearing aid algorithm using DCT with uniformly re-sampled and recursively modified audiogram values

People with the hearing problems have different listening preferences and characteristics in hearing loss. So, hearing aids need algorithms that provide amplification based on frequency, so that the hearing-impaired persons can use hearing aids comfortably for a long duration. In this paper, a new algorithm is proposed for hearing aids in order to compensate for sensorineural and conductive hearing loss using discrete cosine transform (DCT). DCT coefficients of the input audio signal are multiplied with uniformly resampled and recursively modified audiogram values to compensate for hearing loss. This algorithm comprised of 4 stages namely precomputation to calculate gain values from audiogram, DCT, gain adjustment, and inverse DCT. In the above stated stages except precomputation, each stage requires only one matrix multiplication, which makes the proposed algorithm computational efficient. Performance of the proposed algorithm is compared with uniform filter banks, non-uniform filter banks, variable filter bank and reconfigurable filter banks. The algorithm is tested using audiograms with four different hearing loss cases. It is proved that the proposed algorithm provides less complexity, minimized delay and better matching with all types of audiograms, further, it also avoids degradation of audio signal due to sampling rate conversions in variable and reconfigurable filter banks.
Syntopical Graphs for Computational Argumentation Tasks

Approaches to computational argumentation tasks such as stance detection and aspect detection have largely focused on the text of individual claims, losing out on potentially valuable context from the broader collection of text.We present a general approach to these tasks motivated by syntopical reading, a reading process that emphasizes comparing and contrasting viewpoints in order to improve topic understanding.To capture collection-level context, we introduce the syntopical graph, a data structure for linking claims within a collection.A syntopical graph is a typed multi-graph where nodes represent claims and edges represent different possible pairwise relationships, such as entailment, paraphrase, or support.Experiments applying syntopical graphs to stance detection and aspect detection demonstrate stateof-the-art performance in each domain, significantly outperforming approaches that do not utilize collection-level information. Viewpoints(topic, aspect, stance) Syntopical Graph ConstructionPairwise judgements are used to as edges in a typed multigraph, where claims and documents are the nodes. . InputsThe newly created graph is then used for stance and aspect detection, to reconstruct viewpoints.
Strategies for Detecting Difference in Map Line-Up Tasks

The line-up task hides a plot of real data amongst a line-up of decoys built around some plausible null hypothesis. It has been proposed as a mechanism for lending greater reliability and confidence to statistical inferences made from data graphics. The proposition is a seductive one, but whether or not line-ups guarantee consistent interpretation of statistical structure is an open question, especially when applied to representations of geo-spatial data. We build on empirical work around the extent to which statistical structure can be reliably judged in map line-ups, paying particular attention to the strategies employed when making line-up judgements. We conducted in-depth experiments with 19 graduate students equipped with a moderate background in geovisualization. The experiments consisted of a series of map line-up tasks with two map designs: choropleth maps and a centroid-dot alternative. We chose challenging tasks in the hope of exposing participants’ sensemaking activities. Through structured qualitative analysis of think-aloud protocols, we identify six sensemaking strategies and evaluate their effects in making judgements from map line-ups. We find five sensemaking strategies applicable to most visualization types, but one that seems particular to map line-up designs. We could not identify one single successful strategy, but users adopt a mix of different strategies, depending on the circumstances. We also found that choropleth maps were easier to use than centroid-dot maps.
Efficient Technique for Removal of White and Mixed Noises in Gray Scale Images

Images are often affected by different kinds of noise while acquiring, storing and transmitting it. Even the datasets gathered by the various image acquiring devices would be contaminated by noise. Hence, there is a need for noise reduction in the image, often called Image De-noising and thereby it becomes the significant concerns and fundamental step in the area of image processing. During image de-noising, the big challenge before the researchers is removing noise from the original image in such a way that most significant properties like edges, lines, etc., of the image should be preserved. There were various published algorithms and techniques to de-noise the image and every single approach has its own limitations, benefits, and assumptions. This paper reviews the noise models and presents a comparative analysis of various de-noising filters that involve in producing a high-quality image. The metrics like PSNR (Peak Signal to Noise Ratio), Entropy, SSIM (Structured Similarity Index), MSE (Mean Squared Error), FSIM (Feature Similarity Index), and EPI (Edge Preserving Index) are considered as image quality assessment metrics.
BIM-Enabled Issue and Progress Tracking Services Using Mixed Reality

This paper reports on a case study in collaboration with an industry partner to explore the value creation potentials of Mixed Reality (MR) in construction. MR is a technology that allows for intuitive interaction with digital data through overlays on the real world, giving immediate access to the data of a construction site to quickly track or resolve possible issues. While prior research identified potential applications of MR in construction, the evaluation of MR in real contexts is lacking. Thus, this paper describes a user study that evaluates an MR-assisted inspection task with the participation of nine construction professionals. The results reveal that the average time needed to track an issue with the Building Information Modeling (BIM)-enabled MR application takes less than a minute, compared to an hour for the standard process. Furthermore, the participants reported that working with the MR application was enjoyable and they would like to use it more frequently. Finally, based on the user study’s findings, this paper develops two service concept prototypes that could be implemented by the industry partner.
DROAllocator: A Dynamic Resource-Aware Operator Allocation Framework in Distributed Streaming Processing

With the rapid development of Internet services and the Internet of Things (IoT), many studies focus on operator allocation to enhance the DSPAs’ (data stream processing applications) performance and resource utilization. However, the existing approaches ignore the dynamic changes of the node resources to allocate the operator instances to guarantee the performance, which increasing the number of migration leads to the waste of resources and the instability. To address these issues, we propose a framework named DROAllocator to select the appropriate nodes to allocate the operator instances. By capturing the change tendency of the node resources and the operator performance, our allocation mechanism decreases the number of migration to enhance the performance. The experimental results show the DROAllocator not only decrease the number of migrations to allocate the operator instances to ensure the end-to-end throughput and the latency, but also enhance the resource utilization.
Graphic Method As A Means Of Forming Metasubject Results Of Education

The up-to-date Federal State educational standard of basic general education suggests orientation to personal, metasubject, and subject results of education.According to the degree of significance for the student's success in life, metasubject results prevail over subject results, but currently, there are no sufficiently transparent methods for their formation, as well as means for assessing the quality of their achievement.The aim of the work is to investigate the possibilities of the graphic method of solving text tasks as a means of forming metasubject results of education.Solving text tasks plays a key role in realizing that mathematics is not a science disconnected from reality.Every formula can be filled with vital content, and, conversely, you can describe any real phenomena through mathematical models.The study of the interrelationships of different branches of mathematics, in particular, graphic illustration, geometric images, and algebraic expressions teach students to think broadly, to understand the interpenetration of various concepts not only in the field of mathematics but also in other subject areas.As experience has shown, students who have mastered the graphic method show better results in the future and not only in educational activities.They more often take part in research competitions in different subject areas; successfully pass competitive selection in educational institutions of higher education.The graphic method of solving text tasks contributes to students' intellectual and creative abilities development.This method allows a person to look more broadly at the task that arises both in educational practice and beyond school.
Mea culpa: How developers fix their own simple bugs differently from other developers

In this work, we study how the authorship of code affects bug-fixing commits using the SStuBs dataset, a collection of single-statement bug fix changes in popular Java Maven projects. More specifically, we study the differences in characteristics between simple bug fixes by the original author - that is, the developer who submitted the bug-inducing commit - and by different developers (i.e., non-authors). Our study shows that nearly half (i.e., 44.3%) of simple bugs are fixed by a different developer. We found that bug fixes by the original author and by different developers differed qualitatively and quantitatively. We observed that bug-fixing time by authors is much shorter than that of other developers. We also found that bug-fixing commits by authors tended to be larger in size and scope, and address multiple issues, whereas bug-fixing commits by other developers tended to be smaller and more focused on the bug itself. Future research can further study the different patterns in bug-fixing and create more tailored tools based on the developer's needs.
A Spatial Regulated Patch-Wise Approach for Cervical Dysplasia Diagnosis

Cervical dysplasia diagnosis via visual investigation is a challenging problem. Recent approaches use deep learning techniques to extract features and require the downsampling of high-resolution cervical screening images to smaller sizes for training. Such a reduction may result in the loss of visual details that appear weakly and locally within a cervical image. To overcome this challenge, our work divides an image into patches and then represents it from patch features. We aggregate patch patterns into an image feature in a weighted manner by considering the patch--image label relation. The weights are visualized as a heatmap to explain where the diagnosis results come from. We further introduce a spatial regulator to guide the classifier to focus on the cervix region and to adjust the weight distribution, without requiring any manual annotations of the cervix region. A novel iterative algorithm is designed to refine the regulator, which is able to capture the variations in cervix center locations and shapes. Experiments on an 18-year real-world dataset indicate a minimal of 3.47%, 4.59%, 8.54% improvements over the state-of-the-art in accuracy, F1, and recall measures, respectively.
Estimation of People Movement in Video Based on Optical Flow Block Method and Motion Maps

An algorithm for detecting and tracking moving people on video sequences using the block optical flow method and motion maps is proposed. To reduce time expenditures, a pyramidal representation of the frame and template search are used at the stage of building a preliminary map of motion vectors. The integral optical flow allows one to reduce the resulting amplitudes of the background displacement vectors and increase the resulting amplitudes of the displacement vectors of foreground objects. To improve the accuracy for localization of objects, the additive minimax similarity function is used in the analysis of motion vectors. Objects are tracked based on a modified tracing algorithm using the Kalman filter. The developed algorithm allows one not only to detect a moving object but also to show the trajectory of its movement. The results of experiments are presented that allow evaluating the effectiveness of the algorithm.