Reviewerly/exHarmony huggingface dataset:
Political Sentiment Mining

Investigations on sentiment mining are mostly ensued in the English language. Due to the characteristics of the Indian languages tools and techniques used for sentiment mining in the English language cannot be applied directly to text in Hindi languages. The objective of this paper is to extract the political sentiment at the document-level from Hindi blogs. The authors could not find any literature about extracting sentiments at the document-level from Hindi blogs. They extracted opinion about one of India's very famous leaders who was a prominent face in the national election of 2014. They prepared the datasets from Hindi blogs reviews. They purposed the lexicon and machine learning technique to classify the sentiment. Their purposed method used four steps: (1) Crawling and preprocessing the blog reviews; (2) Extracting reviews relevant to the query using the Vector Space Model (VSM); (3) Identifying sentiment at the document level using the Lexicon method, and (4) Measuring the result using the Machine learning technique. Their experimental result demonstrates the effectiveness of our algorithms.
HaptoBOX:

This study proposes an interface device for augmenting multi-sensory reality based on a visually unified experience with a high level of consistency between real and virtual worlds using video see-through type mixed reality (MR). By putting on an MR head-mounted display (HMD) and holding a box-shaped device, virtual objects are displayed within the box, and both vibrations and reactions are presented in a synchronized way based on the dynamics of objects. Inside the device, multiple built-in actuators using solenoids and eccentric motors enable actions to be controlled in synchronicity with the motion of objects. Furthermore, one can also hear the sound emitted from virtual objects using 3D sound localization.
Protecting the Ownership of Deep Learning Models with An End-to-End Watermarking Framework

Deep neural network (DNN), as a key component of deep learning technology, plays a vital role in its development. Most major technology companies use deep neural network as a key component to build their artificial intelligence products and service. Building a deep neural network model requires us to pay a huge price: large-scale labeled data sets, a large number of computing resources, and highly specialized domain knowledge. Therefore, we believe that the model owner owns the intellectual property rights of the model, and it is very important to design a technology that protects the intellectual property rights of the deep neural network model and allows the owner to externally verify its copyright. Through statistical analysis of a large number of pre-trained network parameters, we propose an end-to-end network model protection framework-Deep Water based on the distribution of network model parameters. First, we propose a new research problem: embedding watermarks into deep neural networks. We also define the requirements for watermarking in deep neural networks, the embedding situation, and the types of attacks. Secondly, we propose a general framework for embedding the watermark into the parameter distribution function of each layer of the convolutional network. Our method does not harm the performance of the network where the watermark is placed, because the watermark is embedded when the host network is trained. Finally, we conducted a comprehensive experiment to reveal the potential of watermarking deep neural networks as the basis for this new research work. We proved that our framework can embed watermarks in the process of training deep neural networks from scratch and in the process of fine-tuning and distillation without compromising its performance. Even after migration learning and watermark overlay operations, the embedded watermark will not disappear. Even if 65% of the parameters are trimmed, the watermark remains intact.
Use of Algorithm Linear Congruential Generator (LCG) on the TOEFL Test Application

Abstract English as one of the languages most used by the world’s population to communicate. The English language proficiency testing model for one of them is the TOEFL (Test of English as Foreign Language). TOEFL is intended as a measuring tool or evaluation of a person’s ability to understand English. The use of internet technology advances in developing TOEFL learning media can provide other alternatives in studying the TOEFL. The existence of this website is expected to provide various facilities for the public, especially internet users in studying the TOEFL, so that the TOEFL is no longer an obstacle and can encourage someone’s interest in learning English. The web-based TOEFL application system can provide all types of information needed as well as the process of listing examinees, recapitating participant data and printing certificates in a structured manner. The method used in this application to scramble the problem is the Linear Congruential Generator (LCG) method which can produce a series of pseudo random numbers based on several adjusted repetitions.
State of the Art

In this chapter, different types of available information technology solutions supporting customer relationship management as well as collecting the customer feedback, are discussed, with the focus on the new generation on intelligent decision support and recommender systems.
Transmission Method of the Rendered Image based on Cube Environment Mapping

The communication between server and browser is the basic problem of remote rendering under B / S architecture. This paper first describes the difficulties of rendered image transmission, then analyzes the model characteristics in virtual reality environment, and summarizes the user action law based on the model characteristics. Because there are a large number of view rotation operations and view movement operations, and the view rotation operations are more frequent than the view movement operations, a rendered image transmission method based on cube environment mapping is proposed for view rotation; When the user rotates the view angle, it does not rely on the server rendering, but directly re renders the picture under the current view angle from the existing data. Compared with the direct transmission of 3D scene data and direct rendering results, the method proposed in this paper has nothing to do with the complexity of 3D scene and the computing power of the terminal. It can realize remote rendering with zero bandwidth pressure.
Information Security Policy in Large Public Organizations

The aim of this chapter is to study the success factors of the ISO 27002 framework related to the implementation of information security in organizations, with particular emphasis on the different maturity controls of ISO 27002 in the implementation of information security policies in organizations. The purpose of this chapter is to investigate what controls are commonly used and how they are selected to the implementation of an information security in large public organizations in Middle East and North Africa (MENA) through ISO27002, with a specific focus on practical framework for the implementation of an effective information security policy through ISO27002. The finding will help organizations to assess organizations to implement an effective information security policy.
Fall Detection Depth-Based Using Tilt Angle and Shape Deformation

The population of elderly people is in growth. Falls risk their life, to disabilities, and to fears. Automatic fall detection systems provide them secure living; helping them to be independent at home. Computer vision offers efficient systems over many developed systems. In this article, the authors propose a new vision-based fall detection using depth camera. It combines human shape analysis, centroid detection and motion where it exploits the 3D information provided by a Kinect to compute the tilt angle to discriminate falls. Experimental tests were done with SDUFall dataset that contains 20 subjects performing five daily activities and falls, demonstrate the efficiency of the proposed system, and show that our method is promising achieving satisfactory results up to 84.66%.
A Large-scale Detection Algorithm and Application Based on YOLOv4

In this paper, we addressed the problems of occlusion and crowding in large-scale object detection. First, large-scale detection is more complex and diverse than traditional object detection. The number of targets to be detected is larger and often clustered together. This will produce occlusion and dense detection problems, which brings a serious challenge to object detection. Secondly, current dominant object detection is rarely trained and inferred on large-scale labeled dataset, so it is unable to evaluate the performance of these detection models on large dataset. To solve the above problems, we propose L-YOLO large-scale object detection algorithm. We modified the structure of feature pyramid network, then the receptive field was increased by using four-scale detection. Next, we propose a new loss function designed specifically for large-scale scenarios, which keeps the prediction box that is not the target as far away from the target as possible. It prevents the fusion of adjacent boundary boxes in the inference process and improves the detection performance in the case of occlusion effectively. At last, we use a new non-maximum suppression rule to prevent suppression of the correct detection box during infer. We annotated new dataset for large-scale detection, retrained and evaluated our model. Experiments on our dataset show the superiority of our model. Compared to the original YOLOv4, our improved model increases 1.8% mAP.
Wandala

Abstract Wandala (Central Chadic) is spoken by about 45,000 people in Cameroon and northern Nigeria. The language has grammaticalized phonological means marking types of connections between the elements of the utterance, indicating an expected follow-up, a less expected follow-up, and the absence of a follow-up. The coding of some grammatical relations, such as subject and object, is distributed over a wide range of morphemes. Wandala has two tones. While in nouns both tones can be part of the underlying structure, the tones are not part of the underlying structure of verbs and carry only grammatical function. The language has a rich verbal morphology coding syntactic and semantic relations within the clause. Subject suffixes to the verb mark aspectual and modal functions different from those marked by subject pronouns preceding the verb. Inflectional markers on the verb indicate the grammatical relation of the noun phrase following the verb.
Contemporary Media for Library Users' Instruction in Academic Libraries in South-West Nigeria

This chapter focused on contemporary media for library users' instruction in academic libraries in South-west of Nigeria. Library user instruction is a complex activity that entails educating the users how to utilize library information resources and enlightening them on the benefits of using information resources in the library. Prior to the use of contemporary media in library instruction, several other methods were predominantly used including teaching and library tour, etc. Advancement in information communication and technology (ICT) has augmented those instructional methods. Some of the contemporary methods are articulated in this chapter and its application in Federal University libraries in South-west, Nigeria is examined. Findings showed that contemporary media such as OPAC, ask a librarian, FAQ and Blog, etc. are important in library instruction and were used for instructing users in academic libraries. Some challenges facing contemporary media in academic libraries are inadequate Internet access, inadequate training of librarians, lack of skill and unreliable power supply.
Factors of Stability and Sustainable Development in Jordan in Its First Centenary 1921 – 2021 (An Analytical Descriptive Study)

The study aimed to identify the factors that contributed to the stability of the political system in Jordan, despite the challenges and conditions experienced by the Jordanian state in a region known as the tensest region in the world for more than a hundred years. The study started from a hypothesis; there is a correlative relationship between the stability of Jordan and the legitimacy of the regime, based on voluntary consent between the leadership and the people, and based on loyalty to the leadership, belonging to the homeland, the rule of law and respect for the constitution. The study showed the factors of stability in Jordan, the stages of development, and the position of Jordan today on world's political map in its first centenary.
Haematococcus pluvialis cell based on support vector machine Classification research

The classification of Haematococcus pluvialis cells, a support vector machine was used to construct a model, and 150 groups of Haematococcus pluvialis data were used to fit the model. The data set and test set of Haematococcus pluvialis were used separately. For classification prediction, different kernel functions are selected to compare the classification accuracy of support vector machines, and learning curve are used to adjust the parameters of part of the eucalyptus function. Finally, the model measurement standards of confusion moment and ROC curve are introduced. It is concluded that the support vector machine classifier can classify Haematococcus pluvialis in the proliferation and induction stages, and the classification accuracy under the multi-directional kernel function and sigmoid is the highest, with a classification accuracy of 91.11%.
Smartfit - Artificial Intelligence Based Virtual Dressing Room

In the current existing busy and modern lifestyle had led many people to purchase ready-made clothes from e-commerce stores or retail stores without actually knowing the fitting of the clothes, in the expectation of perfect fit. To improve the accessibility and increase the time efficiency of clothes trying on we have created a virtual clothes trial room environment. In this, we have introduced a virtual try-on strategy which is based on generative adversarial networks(GAN).Our technique will firstly obtain masking of cloth region of a person with the help of human parsing model. Next, the cloth region will be removed from the person's image and another desired cloth image is added on the region. This application will also be more useful for the shops which requires to show every clothes to customer and hence time consuming and requires more Human Effort.
Copyright Pluralism and Human Rights of the Visually Impaired Persons

This paper explores the creation of a human rights framework for copyright law to the extent that it affects the rights of the blind and visually impaired person to access copyright works.
Horn loudspeakers

The chapter begins by treating the drive units for horn loudspeakers, including circuits, frequency response, and examples. The properties of horns come next, including acoustic impedances at the throat. Then, other types of horns are treated: parabolic, hyperbolic, and conical, both infinite and finite in length and Klipsch type. The details include effects of bends, cross-sectional shapes, materials, and examples.
Solutions for Securing End User Data over the Cloud Deployed Applications

With more and more organizations working on the cloud over unsecure internet, sharing files and emails and saving them on cloud storage imperative. Securing the end user sensitive data in transit has thus started to get maximum priority to protect it from Cloud company staff, hackers and data thieves. In this study, an attempt is made to review the research of end user data security. There is an urgent need for solutions for end users' data protection, privacy and during the times when migrating from one Cloud service provider to other. This chapter identifies end user data challenges and issues on cloud and presents use of Public Key Cryptography, Multi Factor Authentication and use of Cloud Aware applications as possible solutions.
A Topic-level Sentiment Classification Model Based on Deep Learning

The key point of topic-level sentiment classification is how to construct a contextual sentence representation related to the topic word according to the given topic word and context sentence. The method based on attention and recurrent neural network can be calculated end-to-end according to the topic word representation. This type of method has achieved excellent performance on the relevance of the given subject words and the parts of the context sentence. This paper improves the mainstream neural network work based on the attention mechanism, and combines the AEN (Attention Encoder Networks) model and the AOA (Attention Over Attention) model to propose a new AEN-AOA model, which is based on the BDCI2018-Automotive Industry User View Theme and Good results have been achieved on emotion recognition tasks. This model can effectively mine the emotional tendency based on the topic level, and has a good application prospect and use value.
A Clinical Approach to the Assessment of Selected Three-Year-Old Pupils’ Renditions

Sound perception is pivotal to language acquisition and usage, and it is the bedrock for the display of linguistic knowledge in every individual. However, misperception of sounds and sound production anomalies can be language-based or cognitive oriented. The aim of this study was to assess the utterances of selected three-year-old pupils from a clinical perspective. The study basically adopts a survey research approach. Using the purposive sampling technique and the participatory observation method, twenty utterances of kindergarten pupils were recorded, transcribed and analysed both perceptually and acoustically. This study adapted a blend of the clinical phonological and clinical psycholinguistic approaches for the analysis of the selected pupils’ utterances. The results of the assessment were that even though speech disturbance characterise the language of the pupils, gender difference plays a role in cognitive and linguistic development. The female pupils are found to be less deficient than their male counterparts as their word-realisations are more appropriate and correspond more with the superstrate transcriptions. Also, in spite of the differences in the cognitive abilities of the pupils, they unconsciously adopt simplification procedures to cover up their speech deficiencies. It has been recommended that teachers have a key role to play to facilitate learning by both genders of learners in the classroom by varying their teaching methods and selecting instructional materials carefully.
Neural Network Model for Semantic Analysis of Sanskrit Text

Semantic analysis is the process of extracting meaning of the sentence, from a given language. From the perspective of computer processing, challenge lies in making computer understand the meaning of the given sentence. Understandability depends upon the grammar, syntactic and semantic representation of the language and methods employed for extracting these parameters. Semantics interpretation methods of natural language varies from language to language, as grammatical structure and morphological representation of one language may be different from another. One ancient Indian language, Sanskrit, has its own unique way of embedding syntactic information within words of relevance in a sentence. Sanskrit grammar is defined in 4000 rules by PaninI reveals the mechanism of adding suffixes to words according to its use in sentence. Through this article, a method of extracting meaningful information through suffixes and classifying the word into a defined semantic category is presented. The application of NN-based classification has improved the processing of text.
A Hybrid Approach for Shape Retrieval Using Genetic Algorithms and Approximate Distance

This article describes how the classical algorithm of shape context (SC) is still unable to capture the part structure of some complex shapes. To overcome this insufficiency, the authors propose a novel shape-based retrieval approach that is called HybMAS-GA using a multi-agent system (MAS) and a genetic algorithm (GA). They define a new distance called approximate distance (AD) to define a SC method by AD, which called approximate distance shape context (ADSC) descriptor. Furthermore, the authors' proposed HybMAS-GA is a star architecture where all shape context agents, N, are directly linked to a coordinator agent. Each retrieval agent must perform either a SC or an ADSC method to obtain a similar shape, started from its own initial configuration of sample points. This combination increases the efficiency of the proposed HybMAS-GA algorithm and ensures its convergence to an optimal images retrieval as it is shown through experimental results.
Video Summarization using Machine Learning Mechanism: A Comprehensive Review

In the present era, information plays a major role for an application of video, image processing, indexing of videos and video searching. Digital processing of videos, and editing of videos are trending now days due to advancement in technology but the same time having some limitations such as slow retrieval of video and content delivery. The process of creating synopsis through video by extracting key element such as frame, clips known as video summarization technique. Video summarization having tendency to provide effective and advance information and can process huge amount of video data without any interruption. The process of video summarization is as similar to process of subtraction of foreground and background from the video. This paper presents a review of literature focused on video summarization and their comparative analysis.
Channel Key Extraction Scheme Using Asymmetric Intelligent Reflecting Surface Transformation

In physical layer secret key generation (PLSKG), the threshold-based quantization scheme is usually used to convert the channel detection value into the initial key bits. However, the data near the threshold is easily disturbed by noise fluctuation, which will lead to an increase in the key disagreement rate(KDR). We proposed an IRS-assisted key generation scheme, which uses the asymmetric change of IRS to ameliorate the downlink channel environment, to reduce the key inconsistency after quantization. Monte Carlo simulation and numerical results showed that our proposed scheme is feasible.
Multi-Agents Based Safe Path Planning of Autonomous Mobile Robots in Smart Factories

In the smart factory, multiple autonomous mobile robots (AMR) need to work together, which brings the problem of path conflicts. In addition, the smart factory should be able to handle the collaborative work with people, in order to plan the path reasonably without stopping the robot and ensure the safety of people. Hence, real-time control should be guaranteed, and the control algorithm needs to be robust and fast enough in order to ensure the safety of the planned path of AMR. Based on the characteristics of the smart factory, this paper presents a novel approach based on the avoidance vector field, which can realize the collision avoidance between each AMR and the dynamic intruder. A digital simulation platform is constructed to valid the effect of the proposed approach.