Reviewerly/exHarmony huggingface dataset:
Assessment of the usage of cloud-based library service in Dr. Ambedkar Institute of Technology (Dr. AIT) Library among Post Graduate Students: A case study

Cloud computing is another marvel in the administrations which are presented over the web. It has changed the method of utilization of the extent of PCs independent of any geographic area. The greatest advantage for associations and organizations that offer Cloud computing, is that it offers administrations in utilizing equipment or programming or foundation of outsider sources. It is exceptionally practical as it saves cost and support. Cloud computing comes in a few distinct structures. To limit the cost and keep away from duplication of assets, framework, programming, equipment, labour utilization of arising innovations an endeavour has additionally been made to outline the regions in libraries where this innovation can be sent to give better library administrations and the efficiency of library staff can be increased.
Opinion mining hybrid technique to classify people’s emotions in text using Kansei and lexicon-based approach for national security domain

Current information sharing networks today offer a valuable medium to express emotions, feelings, ideas and opinions, as well as interactions between individuals from different walks of life. Nowadays, there are currently 2.62 billion social networks for active users. Social networks are used to exchange ideas and knowledge allowing individuals, groups and organisations to connect. This provides a large and rich pool of knowledge that can play a critical role for corporations, governments, political campaigns as well as administrative management and welfare in decision making. Most posts on the Internet are in the form of text, which can be processed to mine people’s sentiments and emotional feelings. People do not realise that text on digital media platforms can be a threat to society’s peace when judged with excessive emotions. Excessive emotions from citizens can bring unwanted risks including riots and civil war. Any threat to society’s peace needs to be addressed as it can weaken national security. For this purpose, this paper explores a hybrid technique to classify people’s emotions in text from online news that can affect national security using the lexicon-based approach and Kansei to determine sentiment polarity and emotions in text. The findings from this study can be utilised as a foundation for opinion mining in the national security domain.
An Automated Tool to Remove Flash Light Spots using Image Processing Techniques

An image is captured using a flash list on reflecting surfaces or glass objects, and it causes a bright light on a particular spot in an image. It is because of the direct reflection of flash resulting from glassy objects and results in loss of data over the reflected part of an image. To overcome these problems, an application is developed to capture and process the images. This application can capture the images in a single click in both with flash and without flash condition. The captured images are divided into a small number of blocks and calculate Peak Signal to Noise Ratio (PSNR) value for each block. Based on these PSNR values the blocks of a flash image is replaced with blocks without flash image and while replacement image intensity of an image will change to the same as the flash image. The resultant image is free from a flashlight spot and lost information. This technique is tested on different images and tabulated the corresponding values for comparing the result. The proposed method provides intuitively better results than the pixel-wise method. This application will be used in the photography to remove the reflected objects' flashlight spot.
Arrest Records Management System (ARMS) using Fingerprint and Face Recognition: A Criminal Record Management System for the City of Makati, Philippines

This research seeks to design and build an Arrest Record Management System (ARMS) using Fingerprint and Face Recognition: A Criminal Record Management System for the City of Makati in the Philippines. An android mobile application, a web-based application, and a desktop application extension were developed as part of the prototype. This study used a systematic and descriptive approach to investigate, design, and implement the Makati ARMS. The Input-Process-Output with Feedback research paradigm was explored in building the system. At the same time, the V-Model Software Development Life Cycle (SDLC) was employed for the system design because of its verification and validation features. Different phases of the SDLC were carefully observed and were meticulously monitored throughout the entire system's concept, design, and schedule. A set of performance and speed tests were conducted using industry-available methods to confirm the consistency of the established framework. Based on the results of the tests, the system recorded an average of 90/100 from the tool analyzed. Loading performance averages 1.725s, interactivity averages 35ms, and visual stability averages at 0, better than the ideal values. Having an overall mean of 1.48, ARMS has adequately captured users' perceived satisfaction, which corresponds to highly acceptable. In general, the Makati ARMS was successfully implemented using the developed system. Interestingly, all the functional requirements were met.
Enterprise dynamics and employment creation in Africa

This book intends to provide a continuous assessment of the crisis in governance in Africa. As it is, there are huge deficits in the capacity of African states to harness vast human and material resources to promote good governance. This manifests in pervasive corruption, collapsed service delivery, collapsed state-owned enterprises, eroded social trust, capital flight, escalating levels of poverty and wars, human insecurity, and stunted growth. The public sector is the pulse of service delivery because the entire governance system revolves around the sourcing of materials and services, mostly from the private sector, in order to achieve its public policy intents. The procurement process, therefore, ordinarily ought to yield positive economic outcomes and an efficiency-driven system in favour of the government itself and its service recipients. However, this more often than not is not the case. Despite its enormous wealth, the African continent is in an economic quagmire, a dilemma that requires multi-facet research activities. This is the motivation for this book.
A comparative analysis of copy-move forgery detection algorithms

Copy-paste/copy-move image forgery is also known as image cloning, in which a portion of an image or entity is copied and pasted to another region of a certain picture. This category of image manipulation has the intent either to conceal the entity or to fabricate the image details. Thus, the authenticity of the photographs in different real-world implementations becomes challenging. The number of cases of image tampering is raising with the simple accessibility of image manipulation tools. Therefore, robust, precise and effective approaches to digital image forgery detection are increasingly required. A study on copy-move forgery detection (CMFD) is performed in this paper using three common schemes and their efficiency is checked and compared on images with rotation and scaling in the copied region. First, we will cover DCT-based CMFD, then adaptive over-segmentation and matching feature point. Finally, CMFD, based on PatchMatch, is addressed. The findings show the very good performance of each system.
Inclusive Technical Terms for the Deaf

Understanding the meaning of technical terms is essential when using technical and scientific documentation, whether directed to education, research, or labour. In education settings, there is a need to provide clear definitions of terms, to use a glossary explaining the meaning of each technical term when introducing a new topic, and to align terminology and communication channels to the abilities of the target audience. It is exactly at this last point that equity and inclusion issues originate. If we provide definitions or explanations of new concepts using spoken languages in writing, as we commonly do, we leave apart all those who cannot fluently read them. Deaf people cannot read fluently. Sign languages and spoken languages are distinct languages, each one on its own. Sign and spoken languages in the same country use distinct channels, different phonology and morphology, different grammar and arise from different cultures. When we force deaf students to study via written/spoken languages, we are placing them at a clear disadvantage and seriously compromising equity. Inclusive education is about assuring that all students have the conditions and the resources they need to succeed; this does not happen when we force students to study using a language they do not master. There is a need for a tool that can introduce and explain to deaf students technical and scientific concepts from specific areas of knowledge in sign language. TechWhiz is a glossary of scientific and technic concepts, described in sign language, aiming to assist deaf students in gaining access to education in their first language and enhance their learning achievements.
Realization of 2D and 3D SLAM for Automated Driving of Raspberry Pi Mouse

A laser scanner is mounted on a wheeled robot to measure the distance to obstacles and walls based on the time it takes for the laser to bounce back from its surroundings. Authors aim to make the robot run autonomously by SLAM, which corrects its own position and creates map from the data.
Hashing the Hypertrie: Space- and Time-Efficient Indexing for SPARQL in Tensors

Abstract Time-efficient solutions for querying RDF knowledge graphs depend on indexing structures with low response times to answer SPARQL queries rapidly. Hypertries—an indexing structure we recently developed for tensor-based triple stores—have achieved significant runtime improvements over several mainstream storage solutions for RDF knowledge graphs. However, the space footprint of this novel data structure is still often larger than that of many mainstream solutions. In this work, we detail means to reduce the memory footprint of hypertries and thereby further speed up query processing in hypertrie-based RDF storage solutions. Our approach relies on three strategies: (1) the elimination of duplicate nodes via hashing, (2) the compression of non-branching paths, and (3) the storage of single-entry leaf nodes in their parent nodes. We evaluate these strategies by comparing them with baseline hypertries as well as popular triple stores such as Virtuoso, Fuseki, GraphDB, Blazegraph and gStore. We rely on four datasets/benchmark generators in our evaluation: SWDF, DBpedia, WatDiv, and WikiData. Our results suggest that our modifications significantly reduce the memory footprint of hypertries by up to 70% while leading to a relative improvement of up to 39% with respect to average Queries per Second and up to 740% with respect to Query Mixes per Hour.
Real-Time Hand Gestures recognition using Skin Masking and CNN

This paper aims at analyzing and recognizing American Sign Language (ASL) that can be converted to text in order to facilitate communication with differently-abled people. This has been a key challenge for communication with/between differently-abled people. Many different approaches have been formulated trying to solve this problem including Principal Component Analysis (PCA), Finger Peak and Angle Calculation, and Support Vector Machine (SVM) to name a few. Here we have proposed a method for recognizing hand gestures and signs using Background Subtraction, Skin Masking, and Convolutional Neural Network (CNN) for segmentation and classification of gestures into text. The method provides a training accuracy of approx 98% in recognition under ideal conditions (simple background and good light intensity).
"Deep Learning-based Classification of Materials into Biodegradable and Non-Biodegradable"

The collection of real-time data from people, their cars, public transit, buildings, and other urban infrastructures like the energy grid and waste management systems are at the heart of the smart city concept. The insights gathered from the data may be used by municipal authorities to manage resources and services efficiently. An important study topic at the same time is the sharp increase in environmental degradation and deterioration that causes ecological imbalance. Additionally, the development of advanced waste management systems that can categorize rubbish according to its level of biodegradability is required for the worldwide expansion of smart cities. Some of the more common ones are paper, paper boxes, food, glass, and other garbage. A costeffective technique to separate the waste from the enormous mountain of trash and garbage and classify the waste items is to use computer vision-based technologies. Recent developments in deep learning (DL) and deep reinforcement learning (DRL) have made it possible to categorize waste objects and identify and detect trash. In this sense, the research creates an intelligence model for smart cities. The technique's goal is to recognize and classify rubbish objects using the DL and DRL approaches. The two steps of the SYSTEM technique are objected classification based on DRL and object detection based on Mask Regional Convolutional Neural Network (CNN)… The CNN model uses the DenseNet model as a baseline model, and a deep learning network (DLN) is employed as the classifier. A based hyperparameter optimizer is also created to boost the efficiency of the DenseNet model.
GRAPES: semi-automatic approach for forecasting models to predict GameStop prices using cloud computing and machine learning

Since the Covid-19 pandemic, we have seen a surge of retail investors that now can easily trade anywhere in the world with just a Smartphone. Social media groups like Reddit's WallStreetBets have almost put a few hedge funds close to bankruptcy by driving GameStop share prices to the sky. In this work, we propose a framework called GRAPES which uses Cloud Computing and Machine Learning to explore various forecasting techniques in predicting GameStop prices. In addition to this, this work also provides light insight into semi-automating forecasting models using tools such as Google Cloud Platform (GCP), Airflow and Streamlit. Moreover, we monitored the investment funds from Ark Invest to provide additional insight into the market in general. Overall, the paper shows the Autoregressive Moving Average (ARMA) model gives the best accuracy based on the Mean Absolute Percentage Error (MAPE) of 1.12%. This means the predictive model is out with an average of 1.12% from the actual price.
Utilizing Machines for Education

Research fields involving “engineering” and “education” contain a specialized area called “engineering education” wherein “engineering technology” and “educational technology” are taught using technological advancements. Due to the utilization of ICT in education, “educational technology” is steadily gaining importance. In this study, from the point of view of “educational technology,” we examined research areas for machinery and mechanical systems that can be utilized for education, and proposed a new research field called “Educational Mechanical Engineering.” In addition, we examined the targeted content for this proposed research field.
High-Speed Hardware Architecture for Post-Quantum Diffie–Hellman Key Exchange Based on Residue Number System

This paper presents a hardware architecture for a post-quantum key exchange protocol, named super-singular isogeny Diffie-Hellman (SIDH). The proposed hardware employs residue number system (RNS) and is optimized to reduce the latency of $\mathbb{F}_{p^{2}}$ multiplication and RNS Montgomery reduction, which are major time-consuming procedures in SIDH. The performance of the proposed hardware is validated and evaluated through an experimental implementation on Xilinx Kintex7 Ultrascale+. As a result, we confirm that the proposed hardware can perform an SIDH computation 34% faster than the state-of-the-art existing one on the same device at a resource overhead.
Deep Q-Network Based Cloud Service Tasks Scheduling and Resource Allocation

Since the beginning of the global pandemic, users’ demand for cloud services has increased rapidly, and cloud service providers are also facing the challenge of minimizing energy costs in data centers. This paper proposes a Double Deep Q-Network based Cloud Service Tasks Scheduling and Resource Allocation algorithm (DDQN-CSTSRA), which is suitable for massive servers with lots of requests provided by users. The two-stage Resource Provisioning-Tasks Scheduling (RP-TS) process based on the DDQN algorithm is to achieve the purpose of outputting long-term optimal decisions by continuously learning the feedback from the environment corresponding to different decisions in various states. DDQN-CSTSRA achieves high cost of the energy efficiency, low uptime and fast convergence. Compared with the DQN-based algorithm, DDQN-CSTSRA has a significant advantage in the energy efficiency when the time loss does not increase, and this advantage is further expanded with the increase in the number of servers.
Fully-Secure MPC with Minimal Trust

The task of achieving full security (with guaranteed output delivery) in secure multiparty computation (MPC) is a long-studied problem. Known impossibility results (Cleve, STOC 86) rule out general solutions in the dishonest majority setting. In this work, we consider solutions that use an external trusted party (TP) to bypass the impossibility results, and study the minimal requirements needed from this trusted party. In particular, we restrict ourselves to the extreme setting where the size of the TP is independent of the size of the functionality to be computed (called “small" TP) and this TP is invoked only once during the protocol execution. We present several positive and negative results for fully-secure MPC in this setting.
Management of Exhaustion in Continuous Product Development

Based on the perspective of the supply side, it is imperative to provide stable and continuous high-quality adaptation activities at an optimal frequency based on the circumstances of the development project. Nevertheless, there is also the possibility of adjustment that not only from the supply side of development resources, but also from the demand side, the development organization may take the initiative in adjusting the amount of development workload.This chapter discusses Project A from the mobile game development company X, where the development team was exhausted because the workload of bug fixes was added to the workload of new content development while the development schedule was unstable and user information processing and analysis activities continued. By changing the methods for fixing bugs, increasing the percentage of user-generated content, and improving the product architecture, the development team was able to break free from the exhaustion, while still meeting the demands of its users.
Automatic classification of document resources based on Naive Bayesian classification algorithm

World Wide Web has become big as the amount of documents collection is increasing rapidly. The automatic classification of document resources based on Naive Bayesian classification algorithm is detailed in this paper. Firstly, this paper introduces the relevant theories of naive Bayes classification and the automatic document classification system. Then, a massive network academic document automatic classification system is designed and implemented. The system uses modular design, including academic document automatic capture module, academic document word document matrix processing module, ontology integration module and semantic driven classification module. Finally, based on the Naive Bayesian classification algorithm, the training set of 12 categories preset is utilized in the professional classification directory of the Ministry of education.. Experiments show that the naive Bayesian classification algorithm can effectively complete the automatic capture, processing and classification of massive academic documents, which can not only improve the classification accuracy, but also reduce the running time of automatic classification. It solves the problems of the integration of two heterogeneous ontology libraries and also the problem that the traditional word vector space cannot meet people's needs for semantic classification.
Adding Sensors to Our Simulation

In Chapter 4 , we created our first Gazebo simulation of the rover. We manually drove the rover around the empty environment. We used URDF files to define what our rover looked like and the launch files to define the rover controls in the Gazebo environment. Now we want to add sensors so that the rover can “see” obstacles. Unfortunately, the URDF files will get very big and hard to maintain without a little help. Enter the Xacro XML language extension (Xacro stands for XML Macro). It helps add standard sensors via simple blocks of code, called plug-ins. The plug-ins support sensors such as LiDAR, radar, cameras, etc. Our LiDAR sensor develops the first distance-ranging capabilities of the rover. We will develop Python scripts that directly control the rover’s wheels through the differential drive plug-in. Finally, we will experiment with a keyboard control (Teleops) of the rover via a Python script.
Research on University-Industry Cooperation of Vocational Colleges under the Background of Artificial Intelligence

The characteristics of Artificial intelligence, such as intelligence, automation, personalization, diversification and collaboration, promote educational innovation. The essence of University-Industry cooperation is to promote the perfect integration of specialty setting and industry demand, teaching content and work practice, teaching process and work process, learning site and work place. To stimulate the efficiency release of all kinds of intelligent technology applied in the University-Industry integration of vocational education, effectively make use of the powerful resource integration ability of Artificial intelligence, empower the upgrading of the University-Industry integration system, create concrete, diversified and intelligent all kinds of new educational practice situations, and reconstruct the multi-dimensional ability of industry-education integration of vocational education.
Generalized and Simulated Architecture for Seamless Experiment Conditions in Cloud Computing

Cloud technology aims at providing platforms that are dependable, secured, fault-tolerant, long-lasting, and extensible for delivering web-based business applications. Various programs offer varied needs in terms of design, setup, and delivery. Trying to measure the efficiency of a Clouds infrastructure’s planning and assignment strategy for various applications and services types with varied loads and power consumption. To assist in this process, they present CloudSim, a new generic and extendable modeling environment for design, replication, and testing of evolving Cloud technology systems or management consulting. The following are some of the unique aspects of the modeling tool: (i) capability for designing and deploying large-scale Cloud computing environment, such as data centers on a single physical processing node and a JVM (Java Virtual Machine); (ii) a self-contained framework for predicting data centers, support brokerages, planning, and distribution guidelines; (iii) virtual machines for creating and managing numerous, self-reliant, and co-hosted virtual servers services on a network infrastructure node; and (iv) the capacity to change among both space-shared and time-shared allotment of compute nodes to virtual machines offerings.