Reviewerly/exHarmony huggingface dataset:
The Role of Cryptography in Cryptocurrency

In today's world of digital media, people are mainly sharing all of their resources via digital platforms. Today, in almost all regions across the globe, the physical exchange of money is becoming less in practice. People are more flexible with buying and paying for their necessities via digital platforms rather than exchanging physical money. Cryptocurrency, the new global money for the internet age, is also a medium of exchange similar to other currencies. Still, here, the sole purpose of the exchange is via digital means. How secure is your cryptocurrency? How anonymous are users of cryptocurrencies? Since the currency is digital, it is more prone to attacks, data theft, and money. Here comes the role of cryptography. Cryptography is an essential mechanism for securing information in computer systems. Without cryptography, cryptocurrency is just a central hub for attackers and scammers. Cryptocurrency requires cryptography for mainly two purposes; to secure the transactions and to verify these transfers. This paper discusses the types of cryptographic techniques used in cryptocurrencies, studies their characteristics, and explores the working of these techniques.
Unsupervised Method for Text Summarization Using Content Based Approach

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Edge Based Method for Kidney Segmentation in MRI Scans

The precise and proficient detection of the kidney boundary in low-contrast images is considered as the main difficulty in the detection of kidney boundary in MRI image. The exact identification of a kidney shape in medical images with decreased non-kidney components to acquire insignificant false edge detection is adequately vital for several applications in surgical planning and diagnosis. Low illumination, poor-contrast, image close to the non-uniform state of organs with missing lines, shapes, and edges are considered fundamental difficulties in kidney boundary detection in MRI images. Kidney image edge detection is a significant step in the segmentation procedure because the final appearance and nature of the segmented image depend greatly on the edge detection technique utilized. This study presented a new method of extracting kidney edges from low quality MRI images. The proposed method extracted the unique information of the pixels, which represent the contours of the kidney for segmenting the region. The experimental results on different low-quality kidney MR images showed that the proposed model be able to carry out the effective segmentation of kidney MRI images based on the use of kidney edge components while preserving kidney-segmented edge information from low-contrast MRI images.
Development and Maintenance of Mobile Forensic Investigation Software Modules

Nowadays, mobile devices are indispensable for social and business activities. Therefore, mobile device forensic technologies are critical for forensics practitioners. While using these technologies, they can investigate and analyze mobile phone application artifacts that contain data useful metadata, such as geographical location and timestamp. However, developing a complete forensic investigation software for mobile devices is challenging. While developing or maintaining forensic investigation software, project team must consider new trends such as emerging IT technologies, increases of popular mobile applications, new features added to existing mobile applications, and security constraints constantly. In this study, we have defined and applied a process based on agile methodology to develop forensic investigation modules. Moreover, we have presented the implementation details of modules for 9 popular Android social media and instant messaging applications, including wireless communication and system information. Finally, we have summarized the difficulties encountered in this study.
Implementation of Touch-Less Input Recognition Using Convex Hull Segmentation and Bitwise AND Approach

Pandemic situations lead to a heightened risk of viral transmission, particularly in public spaces, that hampers humans' daily lives. Some of the general public's utilities such as vending machines, ATMs, etc. have the potential to quickly become viral hotspots and transmit diseases at unmatched and uncontrollable levels. A possible solution to solving such issue is to utilise the computer vision technique, to convert visual cues into the input to the device using hand gestures. The proposed technique can be implemented using webcams since it is a cost-effective and easy way to integrate hand gestures. The process of gesture recognition includes segregating the videos into frames. An effort has been taken to propose a hand gesture recognition using Convex Hull Segmentation and Bitwise AND approach. Finally, the proposed method demonstrate the viability of the proposed system based on its memory usage in different test environments.
User Interface for Vehicle Theft Recovery System

We present a proposal for a generic vehicle anti-theft system. The system has two parts – a platform located in the protected object and a user interface on a user’s phone or computer. A specific system is described in an XML fie, which is made by an anti-theft system designer using an interface described in this paper. High-level code to run on the protected object portion of the system is automatically generated from the XML description. After a system is deployed to a protected object, the user (owner, police, etc.) interacts with the protected device through a user interface described in this paper and can read status of the protected object as well as control devices on the object. The user interface is also generated automatically from the XML description. The system is generic and could be used to describe anti-theft systems for a variety of types of objects such as cars, trucks, boats, ATM machines, shipping containers, and others.
Simulation of the Field of View in AR and VR Headsets

Technical parameters of today’s Optical See-Through Head Mounted Displays (OST-HMDs) do not fully match the industrial requirements yet: Especially the small field of view (FoV) of current OST-HMDs is a hindrance for industrial use. The FoV is a technical parameter the user is always confronted with while the immersive experience of Augmented Reality takes place: It defines the extent of the observable augmented world where virtual objects can be perceived. This experience is limited by the augmented objects being cut off at the screen boundaries. This paper describes a scientific approach to simulate the FoV of OST-HMDs with the help of AR and VR devices. It aims at enabling tests and validation of necessary FoV-specifications of HMDs. Therefore, a study to simulate different FoVs and evaluate the necessary FoV size for manual two-handed automotive assembly tasks is presented. Results show significant differences in ratings between AR and VR but nearly no differences between the participant groups “AR/VR experts” and “assembly line workers”.
Comparison of Modified Object Detected Graph Cut and Hybrid Ant Colony Optimization - k-means for MRI Images Segmentation

Image segmentation is the process of subdividing an image into regions that are consistent and homogeneous in some characteristics. There is neither a single segmentation model for all medical image modalities nor all methods are efficient for a specific medical image modality. In image processing and computer vision, segmentation is still a challenging problem in many real time applications and hence more research work is required. The Hybrid Ant Colony Optimization (ACO) – k-means and Modified object detected Graph Cut image segmentation algorithms for MRI images segmentation are considered in this paper. The proposed algorithms and sub-system for the medical image segmentation have been implemented. The experimental results show that the proposed algorithm has good accuracy in comparison to modified Graph cut algorithm.
RakshaNet: URL - Aware Malicious Website Classifier

Software revolution has resulted in a hyperbolic increase in internet browsing, making internet security a vital issue. Most naive users typically visit unknown websites, and due to their lack of awareness and software proficiency, malicious websites pose a significant threat to their data and security. This paper proposes a classification algorithm that determines whether a website is malicious or benign based on its application layer and network layer features. These features are extracted from the header and body of the HTTP/HTTPS request/response of a website, upon which the ML algorithm acts to determine whether the website is malicious. The client-server data can be intercepted using a proxy service, such as the Squid proxy, and the ML classifier runs as part of the Internet Content Adaptation Protocol (ICAP). If a website is determined as potentially malicious, the user shall be notified immediately and redirected back to the previous benign webpage. The URL parameters extracted include the Server name, DNS query time, TCP details, etc., which are chosen after extensive study of the contribution of these features (importance) to the classification. The study is performed on the decision tree and random forest supervised machine learning algorithms, and it is observed that the random forest algorithm is the most suitable ML classification methodology, achieving a test accuracy of 92%. The classification performance is visualized with the help of the confusion matrix and receiver operating characteristics curve (ROC), with an area under the curve (AUC) of 84%. Thus, this paper proposes an end-to-end software that utilises the random forest algorithm for the classification of websites as malicious or benign, and preempt users from accessing harmful websites.
Self-directed Mobile Robot Path Finding in Static Indoor Environment by Explicit Group Modified AOR Iteration

The main concerns in handling a self-directed path finding is that we must address the obstacle avoidance issue in which the mobile robot has to create a bump-free route in order to surpass the efficiency of its movement from any departure position to the destination position in the areas concerned. This study seeks to solve the problem by elucidating it iteratively through a numerical approach. The solution builds on the potential field approach, that uses the equation of Laplace to restrict the formation of potential functions across regions in which the mobile robot operates. This article proposes an iterative method for the resolution of mobile robot path finding problem, namely Explicit Group Modified Accelerated Over-Relaxation (EGMAOR). The experiment demonstrates that, by applying a finite difference method, the mobile robot is competent to produce a collision-free trail from any start to specific target point. In addition, the findings of the model verified that numerical techniques could provide an accelerated solution and have produced smoother path than earlier work on the same issue.
Defining the elements of the communication paradigm in higher education under the influence of social media: Literature review

Although social media provide many opportunities to improve teaching processes, and today’s students do not know the world without them, there is still a trend of non-adaptation of new technologies in higher education institutions. The existing communication paradigm of social media is multidimensional, and thus complex and covers a wide theoretical and practical area. The main objective of this paper, based on the literature overview, is the conceptual foundation of social media and their main characteristics; to determine general elements of the existing communication paradigm in higher education under the influence of social media, and to identify gaps which can serve as an impetus for future research aimed at a holistic approach to changing the communication paradigm in higher education under the influence of social media.
Automated Contact Tracing using Person Tracking and Re-Identification

This paper presents an implementation of contact tracing (a protocol followed to curtail the spread of viruses like Covid-19) using CCTV video footage from multiple cameras. The proposed system gives immediate insights about all the possible people at risk when a person under surveillance is clinically tested positive. This system automates the process of manual contact tracing by detecting contact between people under surveillance by estimating the distance between them, identifying them, and tracking their interactions. This data is then stored, filtered, and analyzed. A confusion matrix was derived from the videos that took into account true contacts, false contacts between people in the input video and whether the contacts in the video were considered as true contacts or false contacts according to the algorithm. This gave us insights about the algorithm's accuracy, precision and recall which were found to be 91%, 94% and 71% respectively.
Cloud System for the Management of Neuropsychological Test in Mexico

In last years, the population growth rate is higher than the growth rate of health personnel in Mexico. This phenomenon has generated a health gap problem. The e-Health systems can help mitigate this problem because rehabilitation can be done from home, and the specialist doctors can review the test results remotely. In particular, for older adults with neuropsychological problems, it is very feasible to use this type of tool and the neuropsychological tests can be programmed on mobile devices such as tablets or smartphones.
Modeling the Impact of Desert Aerosols on the Solar Radiation of a Mini Solar Central Photovoltaic (PV)

This work focuses on modeling the impact of desert aerosols on a mini central solar photovoltaic (PV).Our studied physical model is comparable to a multilayer.We have described and discretized the mathematical equations which govern the physical model.Also, we analyzed the influence of the pasults show that desert aerosols significantly influence the global solar radiation received.Unfortunately, this influence lowers the productivity of the central solar PV in general.
DHLBT: Efficient Cross-Modal Hashing Retrieval Method Based on Deep Learning Using Large Batch Training

Cross-modal hashing has attracted considerable attention as it can implement rapid cross-modal retrieval through mapping data of different modalities into a common Hamming space. With the development of deep learning, more and more cross-modal hashing methods based on deep learning are proposed. However, most of these methods use a small batch to train a model. The large batch training can get better gradients and can improve training efficiency. In this paper, we propose the DHLBT method, which uses the large batch training and introduces orthogonal regularization to improve the generalization ability of the DHLBT model. Moreover, we consider the discreteness of hash codes and add the distance between hash codes and features to the objective function. Extensive experiments on three benchmarks show that our method achieves better performance than several existing hashing methods.
Service Design of Stray Cat Feeding Based on the Theory of Sustainable Development

With the rapid advancement of information technology, the increasing pressure of life and the accelerated pace of life, more and more people become addicted to keeping cats. Then the pet industry can be seen everywhere, such as pet feeding, pet health, pet beauty, pet sharing platform. At the same time, more and more people are willing to join in the rescue of stray cats. In this context, there are various problems in feeding stray cats, such as inconvenience to buy food, unhealthy cats, fear of feeders, and the growing ecological pressure on society caused by the rapid reproduction of stray cats. Given the above problems, the innovative design of cat feeding is carried out. The main innovative contents include the following: The design can become a goal and direction of cat rescuing and provide ideas and solutions for integrating pet public welfare project design.
Mobile Applications vs. Chat-based Applications : A Comparative Study based on Academic Library Domain

Mobile applications and chat-based applications (aka. chatbots) have become the main channel for organizations/companies to provide transactions, operations and services to their users via online platform. Understanding the trade-off between these two types of applications can help organizations to better plan their resources and choose the most appropriate technology/platform to serve their users. This research, therefore, presents a comparative study between the two types of applications and proposes important comparative metrics, comprising the development cost and time metrics and the usability metrics. The study uses an academic library domain as a case study. First, the application requirements were collected and analyzed. The system development was then carried out for both a mobile application and a chatbot. The corresponding development cost and time metrics as well as the usability metrics were investigated and compared.
An Improved Lion Swarm Algorithm Based on Reinforcement Learning

Lion swarm algorithm is a swarm intelligence algorithm. By simulating the swarm composition and behavior pattern of the lion swarm in wild world, the agents cooperate with each other to complete the optimization mission. In this paper, we propose an improved lion swarm optimization algorithm based on reinforcement learning. In the early stage, the algorithm has strong exploration ability and is able to find local optimum effectively. In the later stage, the idea of reinforcement learning is introduced to enable individuals with high optimization efficiency to gain reward from the environment and further improve the optimization efficiency. According to test results on 13 benchmark functions, the proposed algorithm outperforms several state-of-the-art algorithms on both unimodal and multimodal functions.
Surveillance and Disease Control in COVID-19: Big Data Application in Public Health

With the fast development of big data technology, its application in many fields is becoming more common. Big data has played an essential role in public health, and the influence of data usage expands with the rising of technology. This paper would give a brief overview of the current application of big data, specifically in the context of the COVID-19 pandemic and discuss the current issues and problems of data usage in public health related to privacy concerns. While Big Data is looking to be beneficial in many areas, especially in public health, better regulations to ensure data safety and privacy are urgently needed.
Smart Electronic Switching (ON/OFF) System Based on Real-time Detection of Hand Location in the Video Frames

Abstract Human looking forward to living in a modern and comfortable environment like smart houses. In this study, an effective user-friendly smart home prototype designed with low cost. The prototype contains eight of Light Emitting Diode (LED) considered as home appliances and controlled in real-time using eight suggested hand cases. The hand cases have different position regarded to head and shoulder levels. The hand position is detected using a new suggested algorithm programmed in Matlab software. Viola-Jones method used to detect hand in a complex background (hand with a different background) by training computer using positive (hand) and negative (non-hand) image datasets. To make computer training faster and accurate, a new idea depends on a skin detection used before computer training to determine the location and size of all positive images automatically. The LEDs in prototype switched ON/OFF using the suggested hand cases in a fast time. Where the response time of LEDs to hand cases was 0.43 second.
Quick Buttons on Map-Based Human Machine Interface in Vehicles is Better or Not: A Cross-Cultural Comparative Study Between Chinese and Germans

Map navigation is the main task and the main page of human-machine interface (HMI) in vehicles. The design of map page is paramount to improve user experience of automotive HMI. The present study was to examine whether the design of quick buttons or icons on map page has an influence on Chinese and German driver's preference and experience during using car console infotainment system. Firstly, a Chinese HMI and a German HMI was designed according to Hofstede's culture model and cross-cultural design elements. The two interfaces have the same functions, and the main difference is whether there are quick buttons on map page or not. Secondly, sixteen Chinese drivers and sixteen German drivers were recruited to use and evaluate two interfaces by user experience scale and interview in a simple driving simulator: one is with the design of quick buttons on map page, the other is without this design. Participants were asked to finish four tasks when using HMI in vehicles, respectively navigation, phone call, play music, and the return to main page. The results indicated that Chinese driver prefer to use the HMI with quick buttons on map page, but German prefer to use simple and clear HMI without quick buttons on map page.
A Conceptual Framework for Secure Cloud Migration

Abstract Cloud computing is a budding web-based technology that tends to prevail in our environment, especially in the areas of computing and information technology that require large-scale network computing. Cloud computing is a shared set of services gaining popularity due to its profitability, availability, and high production. Just on the security grounds, cloud computing’ immense potential cannot be overlooked: ongoing research and the investigation of consistent, robust, and integrated cloud computing security models may be the solitary motivational pathway. There are three service and four deployment models in cloud computing. Cloud computing, together with its several advantages, also brings a much more difficult condition concerning privacy, authenticated access, data protection, etc. Because of these problems, the acceptance of cloud computing is becoming difficult in the current era. In this study, several security concerns related to reliability and data privacy, main aspects impacting cloud computing, were tackled, and recommendations on specific areas were also discussed.
VR multi-class

VR multi-dimensional classroom is a complete VR immersive course learning system developed and launched by Zhihai Yuntian. Students can put on a helmet and have an immersive learning experience, while teachers can control the course process through the teacher's side.
Learning Ordinal Embedding from Sets

Ordinal embedding is the task of computing a meaningful multidimensional representation of objects, for which only qualitative constraints on their distance functions are known. In particular, we consider comparisons of the form “Which object from the pair (j,k) is more similar to object i?”. In this paper, we generalize this framework to the case where the ordinal constraints are not given at the level of individual points, but at the level of sets, and propose a distributional triplet embedding approach in a scalable learning framework. We show that the query complexity of our approach is on par with the single-item approach. Without having access to features of the items to be embedded, we show the applicability of our model on toy datasets for the task of reconstruction and demonstrate the validity of the obtained embeddings in experiments on synthetic and real-world datasets.