Reviewerly/exHarmony huggingface dataset:
The Impact of Black Edge Artifact on User Experience for the Interactive Cloud VR Services

When user turns head in an interactive Cloud VR service, such as the Cloud VR games, the black edge artifact often appears at the boundary of the viewport due to the inevitable latency, which significantly affects the user experience. In this paper, we focused on the interactive Cloud VR gaming services and investigated the influence of the black edge artifact on the user experience. The appearance of the black edge artifact during the playing period was regarded as a series of black edge events. The user experience on a single black edge event was first evaluated combining the influential factors of the area ratio and duration of the black edge artifact. Then, a pooling strategy was proposed to evaluate the user’s overall experience. Experimental results showed that the proposed model can accurately predict the user experience that influenced by the black edge artifact for the Cloud VR services. This work can be served as a guideline for service providers and network operators to improve their services.
Design of Blockchain based e-Voting System for Vote Requirements

Abstract Voting refers to the submission of an election or an opinion on a specific matter by expressing an intention on the ballot to a certain place. The existing voting method has the burden of counting time and cost. For this reason, research has been conducted to introduce an e-voting system. However, despite the advantages of e-voting, it is not widely used due to the risk of manipulation of voting results and various requirements. Recently, in order to reduce the risk of data manipulation, research is being conducted to apply the blockchain, a technology that guarantees data integrity, to e-voting. Blockchain guarantees the integrity of data, but has a weakness in secrecy. This paper applies the critical encryption technique to the blockchain and satisfies the requirements for voting such as verifiability, anonymity, fairness, non-reusability, competence, safety, transparency, and non-ticketing. We propose a system design and implementation method. The proposed blockchain-based e-voting system provides voter anonymity by issuing a voter certificate based on a blockchain address. The e-voting election monitoring committee generates a threshold group encryption key, and the proposed blockchain-based e-voting system guarantees confidentiality by a threshold group encryption algorithm during the voting process. The voting result is encrypted through a homomorphic encryption algorithm and stored in the blockchain. Thus, the released voting results ensure safety, confidentiality, transparency, and non-vote ticketing. In addition, the proposed blockchain-based e-voting system guarantees the unity and competence of voting through the blockchain’s smart contract.
ASH: A New Tool for Automated and Full-Text Search in Systematic Literature Reviews

Context: Although there are many tools for performing Systematic Literature Reviews (SLRs), none allows searching for articles using their full text across multiple digital libraries. Goal: This study aimed to show that searching the full text of articles is important for SLRs, and to provide a way to perform such searches in an automated and unified way. Method: The authors created a tool that allows users to download the full text of articles and perform a full-text search. Results: The tool, named ASH, provides a meta-search interface that allows users to obtain much higher search completeness, unifies the search process across all digital libraries, and can overcome the limitations of individual search engines. We use a practical example to identify the potential value of the tool and the limitations of some of the existing digital library search facilities. Conclusions: Our example confirms both that it is important to create such tools and how they can potentially improve the SLR search process. Although the tool does not support all stages of SLR, our example confirms its value for supporting the SLR search process.
An Efficient Technique to Extricate Keypoints from a digital image

Abstract With the immense technology emerging in this period, the transmitted image information across the network can be confined easily, maneuver, and then disseminated. For any labor done on processing images, it is mandatory to detect suitable meaningful keypoints. There are existing methods that perceive key points from an image but they fail to detect key points from a smooth textured area or they do not detect enough keypoints. A method is proposed using an improved version of the Scale Invariant Feature Transform (SIFT) technique that is capable of diagnosing key points even in smooth areas as well as administer key points towards the whole image evenly.
Modeling Human Perception of 3D Scenes

The ultimate goal of computer graphics is to create images for viewing by people. The artificial intelligence-based methods should consider what is known about human visual perception in vision science literature. Modeling visual perception of 3D scenes requires the representation of several complex processes. In this chapter, we survey artificial intelligence and machine learning-based solutions for modeling human perception of 3D scenes. We also suggest future research directions. The topics that we cover include are modeling human visual attention, 3D object quality perception, and material recognition.
Affect Classification in Tweets using Multitask Deep Neural Networks

We propose a multitask deep neural network for detecting affect-retweet pairs for Twitter tweets. Each task given to our network jointly learns a given affect, e.g. hate, sarcasm etc., along with learning retweeting behaviour as an auxiliary task, from a given tweet corpus. On test data, this model allows us to predict retweet behaviour in the absence of any further meta-data, along with identifying affect. This allows us also to predict whether a tweet with affect would go viral or not. Our model delivers F1-scores of 0.93 and 0.91 for hate and sarcasm detection respectively, and predicts retweets with the accuracy of 71% and 60% respectively, delivering state-of-the-art performance on benchmark data.
Analysis and Design of the Integrated Management Information System of the Company (SIMANTAP)

Abstract Production and distribution activities are carried out by combining various factors of production, namely humans, nature, and capital. From this management, of course, humans cannot manage existing data in the company manually continuously because it could be that data that has been recapitalized can be lost resulting in the company’s strategy to achieve success is a little hampered. This study proposes a design of an integrated management information system company that can manage human and financial resources in one application. The proposed application is called SIMANTAP, where the purpose and function of this application are to create, view, and manage all company data starting from employee data, inventory items, seeing company expenses and income in one media to make it easier for employees, admins, and managers to see information company. This application was developed by the waterfall method, with a case study of a company in Surabaya. This application is expected to support the company’s business, especially related to human resources and financial management.
Challenges of Digitalization: Smart Pedagogy for Smart University

In the study smart pedagogy is considered as the key element of educational management system at the university which is targeted at designing students’ competences and skills including their professional self-determination and social self-organization, self-motivation. The analysis of the key tools of smart-pedagogy and their implementation according to the changing requirements of digitalization age is carried out. Smart education (SmE) is constantly developing today and smart learning is increasingly correlated with the notion of distance education which takes place according to the creed: “at any point, with any educational resource, in a comfortable time and for evolutionary purposes”. The study reveals the key notes of smart pedagogy for svart university and describes the examples of its implementation in two universities of the RF: Rosdystant project in Togliatti State University and Kabardino-Balkaria State University has been considered.
Text Classification for Embedded FPGA Devices using Character-Level CNN

In the past few years, tremendous growth has been observed in the Internet of Things (IoT) and mobile technologies. At the same time, deep learning technologies have also grown significantly. Thus, it is essential to investigate the hardware acceleration and deployment of deep learning algorithms on edge devices. Two types of deep neural networks RNN and CNN are widely used for NLP (Natural Language Processing). Recent exploration of CNN based methods for NLP has proven that they are fast and effective. In this paper, we discuss multi-class text classification using 1D Temporal convolutional neural networks. We have explored the acceleration and deployment of our model on the Xilinx PYNQ (Python Productivity for ZYNQ) FPGA board. In this paper, we explored the features of character-level text classification which proves to be a perfect fit for edge applications owing to adaptivity to misspelled, infrequent words or informal text, lightweight models, and low latency. We have accelerated our text classification model using coarse pruning and quantization. The deployment exploits the strong relationship of a sequential processor with a PL (programmable logic) as an accelerator, to implement power efficient and low latency inference machine learning architecture. The model is evaluated on a dataset of reviews for several brands. A method of evaluation robustness of text classifications model, for informal language and misspellings, is also proposed. Compared to GPU, after proposed hardware acceleration a 50% drop in latency and a 1500% decrease in power has been observed on an FPGA.
Enhanced Hash Based Image Steganography Technique to Increase Data Integrity and Confidentiality

In the current world, providing secure data with confidentiality and integrity has become a big challenge mainly in the networking field. In order to improve the security of these existing parameters, in this paper, we have proposed a methodology through which integrity of the data could be maintained and extended the existing steganography technique that can increase confidentiality and integrity by utilizing various encryption and decryption methods to provide a collective result. In this work, we have used AES128, SHA256, and Image Steganography Cryptographic Methods. In order to provide secure data before sending it to the end-user. In this approach, we have concatenated Encrypted plain text and Hash code in an Image. Even though if there are any attacks happened the attacker will get only a RAW message but not plain text. Our Observation can enhance further development in various research areas.
3D face reconstruction with RGB-D camera: A practical approach

The continuous development of the RGB-D camera technology and it reduce price, have helped them to be used in different fields of applications, including research tasks. In this context, this paper presents a 3D face reconstruction methodology based on a RGB image and its associated depth map, acquired by a RGB-D camera Orbec Astra Pro. A preliminary stage based on face detection with the Haar Cascade classifier is performed in order to find the face area (2D) to be analyzed. These 2D pixel points found are then matched with its corresponding depth values in order to generate a 3D RAW data represented as a point cloud with values in the X,Y,Z coordinates. Several filters are then applied to the point cloud in order to eliminate the noise on the 3D image and smoothing the final 3D face representation. Finally, several tests are performed in order to evaluate the performance of the system, related with parameters as: face distance to the camera, filter tuning, point cloud resolution and the effect of the background scene. The results shown that the appropriate calibration of the filters and the face's distance to the camera affects directly to the final 3D representation.
Classification of Ultrasound Thyroid Nodule Images by Computer-Aided Diagnosis: A Technical Review

The thyroid is an indispensable gland of the human endocrine system that secretes hormones which have a significant effect on the metabolic rate and protein synthesis. The thyroid is susceptible to a variety of disorders. One among them is the formation of a thyroid nodule, an extraneous mass formed at the thyroid gland requiring medical attention and diagnosis. About 5–10 nodules out of 100 are malignant (cancerous). When a formation of nodules is discernible to the doctors, they call for a diagnostic blood test, often perfunctory, and do not differentiate malignant and benign tumours. This is where ultrasonography comes across as a better option. Automation of ultrasonography diagnosis results in a decrease in reporting time as well as provides a provisional diagnosis before the doctors’ expert opinion. Thus, deep learning methods were suggested and produced better results. Initially, region of interest (ROI) and feature extraction were done before applying machine learning models like support vector machines and multilayer perceptrons. Nevertheless, the feature selection required in machine learning methods was a long-drawn process, often involving the elements of trial and error. Deep convolutional neural networks along with histogram of gradients (HOG)-aided feature extraction which was used along classifiers have yielded high specificity and sensitivity values along with the accuracy. In this paper, we have studied and compared the efficacy of the application of various deep convolutional networks for the diagnosis of malignancy in thyroid nodules.
Topology-Aware Load Balancing in Datacenter Networks

Datacenter networks play a vital role in accommodating massive data transmission, such as in Giga/Terabytes on multiple links. With this inevitable data transmission, some uncertainties like dynamic traffic behavior, topology asymmetry, and link failure are major issues that data center faces, resulting in poor performance. The load balancing scheme should be accurate, especially path sensing and timely responses to mitigate the data center network's fallouts. Different solutions have been proposed to overcome these issues. Some of the proposed techniques are unaware of path conditions. They reroute at a fixed data rate blindly. Others could sense the path condition but reroute and adjust the load, balancing very slowly, degrading the performance. A few research studies demand hardware changes, particularly for acquiring accurate congestion feedback from the end host and adopting load balancing. Thus they can not react timely to these uncertainties, which results in reasonable performance degradation, especially in the worst situation to detect some issues like random packets drop and throughput of the channel. In this paper, a proposed solution focuses on two things (1) accurate timely acknowledgment of packet delivery, (2) Current load on each link. In our proposed work, there is no need for hardware changes. In our proposed scheme, the accurate response of acknowledgment of each packet delivery is achieved. The proposed scheme has been implemented using the NS3 simulator. The results of our proposed scheme have been compared with the other existing state of the art solutions.
The Notion of “Sound” in Popular Music

The present study aims at three main goals. Firstly, there will be an effort to define and describe the notion of “sound” in popular music, not in its denotative sense (as in “the sensation produced by stimulation of the organs of hearing by vibrations transmitted through the air or other medium”—or similar definitions), but within the complex semantic field of discourses and practices occurring among musicians and music listeners. Far from simply addressing the idea of sound as “physical” phenomenon, such field tends to incorporate a varied range of musical and extra-musical elements, including production, performance, arrangement, sensorial perceptions, taste, cultural conventions and much more. Secondly, an investigation will be performed on the relationship between sound and human subject, in terms of impact, conducts and, so to speak, “familiarity” between the two parties. In other words, the research will attempt to understand how and how much the sound (in that eclectic and heterogeneous sense that is here discussed) manages to impose itself on the listener's attention (also in relation to other fundamental musical traits, such as rhythm and melody), if it is possible to speak reasonably of a specifically “sonic” musical conduct, and finally of what nature is the relationship existing between sound and subject, in terms of importance and, in a way that shall be clarified later, of ‘priority’. Both this and the previous goal will be highly informed by the theories of the late Gino Stefani, the first (and, to some extent, the only) musical semiotician who has devoted attention to this particular acceptation of the term “sound”, and, at the same time, to its relationship with the subject, within his well-known theoretical framework of the “musical competence”. Finally, the above reflections will be empirically applied in a traditional “Stefanian” sense (that is, by using his methodologies), through the transcription and the analysis of a full “From experience to theory” workshop (performed at Helsinki University in 2015) focused on sound.
Professional motivation of employees of internal affairs bodies as a factor of personnel potential management in the system of the Ministry of internal affairs of Russia

Professional motivation of employees of internal affairs bodies is a significant factor in the management of personnel potential and requires systematic monitoring of indices determining the motivational readiness for the activities of the subjects of the law enforcement sphere. It is determined by indices that reflect the essence of personnel potential, which depends largely on both the psychological parameters of employee motivation and the psychological and pedagogical competence of managers. Structured operational and service tasks are very important in the motivation management or support the importance of employee performance. The systematic monitoring of motivation level ensures its stability and creates conditions for a qualitative prediction of reliability and effectiveness of the subject. The conducted empirical research of motivational profile shows that the motivation level of subjects of professional activity of internal affairs bodies, at the initial stages of professional genesis, is significantly higher and differs by the moral and business nature of orientation from similar indices of subjects at the later stages of professional activity. At the same time, the overall motivation level, regardless of length of service, is mainly represented by average normative indices. The results of the research suggest that in order to ensure the effectiveness of motivation management one should pay attention to the identified values of motivation, reflecting the high need for employees to structure activities, getting feedback on their performance, reducing the uncertainty of the rules and directives of the job, the need to maintain long-term and stable relationships with colleagues.
Self-Supervised Feature Enhancement: Applying Internal Pretext Task to Supervised Learning

Traditional self-supervised learning requires convolutional neural networks (CNNs) using external pretext tasks (i.e., image- or video-based tasks) to encode high-level semantic visual representations. In this paper, we show that feature transformations within CNNs can also be regarded as supervisory signals to construct the self-supervised task, called internal pretext task. And such a task can be applied for the enhancement of supervised learning. Specifically, we first transform the internal feature maps by discarding different channels, and then define an additional internal pretext task to identify the discarded channels. CNNs are trained to predict the joint labels generated by the combination of self-supervised labels and original labels. By doing so, we let CNNs know which channels are missing while classifying in the hope to mine richer feature information. Extensive experiments show that our approach is effective on various models and datasets. And it’s worth noting that we only incur negligible computational overhead. Furthermore, our approach can also be compatible with other methods to get better results.
A proposal to improve information availability for seismic and volcanic monitoring systems

The seismic and volcanic monitoring systems' main information asset is "seismic data," generated by the instrumental monitoring networks. Consequently, the information's availability is a key factor in the instrumental networks and the data centers in seismological organizations for correct data interpretation and decision-making processes of early warnings and subsequent seismic-volcanic studies. This paper compares some seismic monitoring networks to recognize data acquisition systems and operating conditions in data centers. It also formulates research questions about the information availability by reviewing improvements to the seismic-volcanic network's operation. Based on our findings, we sketch a solution by developing mechanisms to recover information in instrumental networks and set up connectivity with redundant links.
Preserving Privacy for Discrete Location Information

With the development of smart mobile devices, location privacy has gained attention from both academia and industry. In recent years, a variety of location privacy definitions from different perspectives have been proposed to quantify location privacy and compare location privacy protection mechanisms (LPPMs). These definitions, however, have some drawbacks. In this paper, we propose a location privacy metric for discrete location information which improves the quantification of distance between the prior and posterior distribution of an adversary who may hold background knowledge in differential privacy. Furthermore, we develop a non-convex optimization problem and construct a near-optimal mechanism. We evaluate our proposed metric by comparing it to the state-of-the-art definitions including Shokri's incorrectness, Andrés's geo-indistinguishability and Dong's DPLO. We also evaluate our proposed mechanism with the optimal mechanisms based on the afore mentioned existing definitions. We make experiments on both simulation and realworld dataset, and the results show that our proposed metric and mechanism have the ascendant position.
Scalable Handwritten Text Recognition System for Lexicographic Sources of Under-Resourced Languages and Alphabets

The paper discusses an approach to decipher large collections of handwritten index cards of historical dictionaries. Our study provides a working solution that reads the cards, and links their lemmas to a searchable list of dictionary entries, for a large historical dictionary entitled the Dictionary of the 17 $$^{th}$$ - and 18 $$^{th}$$ -century Polish, which comprizes 2.8 million index cards. We apply a tailored handwritten text recognition (HTR) solution that involves (1) an optimized detection model; (2) a recognition model to decipher the handwritten content, designed as a spatial transformer network (STN) followed by convolutional neural network (RCNN) with a connectionist temporal classification layer (CTC), trained using a synthetic set of 500,000 generated Polish words of different length; (3) a post-processing step using constrained Word Beam Search (WBC): the predictions were matched against a list of dictionary entries known in advance. Our model achieved the accuracy of 0.881 on the word level, which outperforms the base RCNN model. Within this study we produced a set of 20,000 manually annotated index cards that can be used for future benchmarks and transfer learning HTR applications.