Reviewerly/exHarmony huggingface dataset:
Product Review Analysis Using Social Media Data Based on Sentiment Analysis

In this current world where everyday people are generating a large amount of data and different business organizations are becoming more and more dependent on it, it has become very important to come out of the traditional methods of data analysis and focusing on the techniques that can prepare much more accurate and valid result to make business decision more easy and simple. This thesis proposes a technique to collect and analyze Twitter posts based on a different keyword-based product searching to generate products market statistical report. Using this program, it can be determined that if any product is getting popularity or losing its market. Few types of results were generated in this project. Each of them has their own type of importance. Overall this type of application can be trusted support for a business analyst or decision makers.
Image to Video Domain Adaptation Using Web Supervision

Training deep neural networks typically requires large amounts of labeled data which may be scarce or expensive to obtain for a particular target domain. As an alternative, we can leverage webly-supervised data (i.e. results from a public search engine) which are relatively plentiful but may contain noisy results. In this work, we propose a novel two-stage approach to learn a video classifier using webly-supervised data. We argue that learning appearance features and temporal features sequentially, rather than jointly, is an easier optimization for this task. We show this by first learning an image model from web images, which is used to initialize and train a video model. Our model applies domain adaptation to account for potential domain shift present between the source domain (webly-supervised data) and target domain, and also accounts for noise by adding a novel attention component. We report results competitive with state-of-the-art for webly-supervised approaches (while simplifying the training process) on UCF-101 and also evaluate on Kinetics for comparison.
Composite Discrete Logarithm Problem and a Reconstituted ElGamal Cryptosystem Based on the Problem

In this chapter, the authors have defined a new ElGamal cryptosystem by using the power Fibonacci sequence module m. Then they have defined a new sequence module m and the other ElGamal cryptosystem by using the new sequence. In addition, they have compared that the new ElGamal cryptosystems and ElGamal cryptosystem in terms of cryptography. Then the authors have defined the third ElGamal cryptosystem. They have, particularly, called the new system as composite ElGamal cryptosystem. The authors made an application of composite ElGamal cryptosystem. Finally, the authors have compared that composite ElGamal cryptosystem and ElGamal cryptosystem in terms of cryptography and they have obtained that composite ElGamal cryptosystem is more advantageous than ElGamal cryptosystem.
TYPING EYES: A HUMAN COMPUTER INTERFACE TECHNOLOGY

The human’s beings always make use of their sensory modalities to effectively communicate or carry out their jobs. The humans use the sensory organs such as the ears, eyes mouth, including the hands to deliver a good verbal conversation. The human computer interface technology could also be made classier by involving the human voice, hands and eyes to access an application or control a device etc.; they paper puts forth the development of the human computer interface based on the eyes movement for typing the information without using the hand for typing, this could be very much useful for the patients with disabilities in the arms or affected by paralytic attacks for their regular works associated with the computers and can also be used by the normal people in security intensive applications, the evaluation results obtained for the proposed method using the MATLAB shows that the mean typing speed to be 30 characters per minute.
Visual and Auditory Impression for Virtual Reality Space Expressed by Panoramic Image and Impulse Response Signal

In late years the virtual reality (VR) technology which spread and development in many fields is used to communicate with clients smoothly in architecture field and property market. However, today, it is often shown only VR image (visual information) expressing design of an architectural space, and the case shown auditory information at the same time has little. The purpose of this report is to grasp subjective evaluation for visual and auditory impression when subjects experience VR space expressed with a panoramic image using simple VR goggles and an impulse response signal.
Improvising song writing and composition within a hybrid modular synthesis system

This chapter focuses on the use of modular synthesizer rack-units Ornament and Crime CopierMaschine. These are implementations of an Analogue Shift Register as a performative and writing/composition tool. Having covered much of the basic mechanical detail and operation, the chapter presents some performance and writing approaches to use as the basis for improvising using CopierMaschine. It is best to keep in mind that whilst the modules under discussion here are in the domain of modular synthesis, Shift Registers are also available as plugins for Ableton Live, Native Instruments Reaktor Blocks and in the free VCV Rack. Beyond scale quantization, there are also a number of features that offer additional scope to organize and prepare melodic outputs for performance. There is no initial requirement to invest in an expensive modular system to explore this process, assuming one has access to any or all of these software environments.
Facial Expression Recognition using Hahn Moment on Facial Patches

Facial expression recognition involves the identification and extraction of facial features that have maximum discriminative ability between the different expression classes. It has been found that most of the information that discriminates between expressions is contained in very specific regions of the face. This paper attempts to extract facial features from very localized regions of the face image by first isolating specific regions and then evaluating localized Hahn moments at different points in these regions. A Point distribution model (PDM) is trained in the proposed method to get the best fit of the landmark pattern for any face image. These landmark points are then used to isolate various regions of the face (facial patches). Then, Hahn moments are evaluated in a targeted manner to specific locations of these facial patches. Hahn moments with controlled region of emphasis and degree of localization gives exceptionally good features for facial expression recognition. The final feature vector for a face image contains Hahn moment features from all the facial patches. These feature vectors are used to train support vector machine (SVM) classifiers that use a one-against-one (OAO) classification technique. These classifiers are used to test the effectiveness of the proposed method. Experiments were done on the extended Cohn-Kannade (CK+) dataset as well as JAFFE dataset.
An Efficient Approach to Analyse Sentiment Using Deep Learning

Recently a huge rise regarding the amount of data in the form of texts is observed and it is equally important to understand the emotion behind these texts. The sentiment analysis of a conversation is very important to judge the mind setup of a person in this era. Sentiment analysis is a technique to explain a view regarding a discussion about a topic either related to verbal or drafted form. It is a very difficult task to analyse the sentiment of a conversation. Deep Learning is the latest process to help analyse near-perfect sentiments. It feeds a lot of data to the algorithm and in the process adjusts itself to constantly progress in the prediction process. In this paper, an attempt has been made to judge the sentiment of a conversation using deep learning techniques. The aim of this work is to establish a model for selecting sentiments from sentences. After the “sentiment” word has been identified, we plan to devise a method that will select a few prior words so that the aspect can also be determined directly from the sentence itself.
Data Storage Security Issues and Solutions in Cloud Computing

Cloud computing is the backbone of most of the modern technological functioning employed across the globe. It‘s primary concern is to store data in an efficient and secured manner. In cloud computing, users can access their data from server on which their data is stored. User access data from servers when required via the cloud service providers. The server is most vulnerable to threats as it is the main hub where the data is stored, not only server is vulnerable to attacks but also the channels via which the user access data hence everywhere precautions has to be taken. In order to provide a secure access of data to the users various measures have to be made. Cloud computing is vulnerable to various threats as the data is accessed from remote server that are away from users, this also make users doubtful to prefer cloud over traditional storage. This paper reviews various security issues being faced with data storage in cloud computing.
Wavelength-Space Domain High-Throughput Artificial Neural Networks by Parallel Photoelectric Matrix Multiplier

We propose a massively parallel neural network architecture with photonic matrix-vector multiplication in the wavelength and space domains with balanced photodetectors and nonlinear transfer functions in MZI modulators. An experimental proof-of-principle demonstration is also discussed.
Information Security Cost Reduction Through Social Means

As data breaches in mid-sized to large organizations become more frequent and more public, there is a need to focus less on technological solutions to information security management and more on sociological solutions. In this paper cost saving information security initiatives are identified and a framework is proposed for organizational and behavioral change in technical human resources, to better address information security concerns.
Typological Aspects of System Representation of Mass Media

The article applies the system approach to the conceptual image of mass media uniting analysis and synthesis, qualitative and quantitative methods of research, heuristic and logical-mathematical ways of revealing the content of the phenomenon under study. Special attention is paid to the basic concepts. A homeostatic model of mass media is provided. Typical relations between the media components are identified and analyzed with the help of mathematical and graphical models and interpretation of their meaning. A model representation reduces lack of knowledge on the essence of a phenomenon and allows one to demonstrate a mechanism of producing a new property of any system with a certain degree of adequacy. It is important to take into account that interactions between properties of the components manifest themselves in an uneven way with some of them increasing and some of them, on the contrary, decreasing. Methodological grounds for the system approach and homeostasis methods and models developed at the same time for formal describing complex social-economic systems have formed a background for building mathematical models of mass media systems. The article aims at defining the basic principles of relations between the media components for their further application to the insight into functioning of mass media systems. The article is up-to-date as the classification provided as well as systematization of relations in the system of mass media contribute to increasing methodological basis of the research into processes of communication.
Visualization Analysis of Multidimensional Data for Online Transaction Log

The online transaction data presents significant multi-dimensional and spatiotemporal attributes. The existing visualization methods cannot fully combine the time series, hierarchical, geospatial and multi-dimensional features of online transaction log to perform multi-aspect analysis on commodity sales. Therefore, this paper proposes a collaborative visualization method for multi-dimensional and spatiotemporal data. The attribute collaborative view is designed to present the multi-dimensional properties and their relationships of spatial objects of interest. By means of multi-dimensional scaling, the original dataset is degraded to one-dimensional space in a chronological order, which further enable the construction of the sequence parallel coordinates. In order to enhance the visual perception of the parallel coordinates, the coordinate axes are scaled, so the sparse areas are compressed and the dense areas are stretched. Hierarchical clustering is used to further analyze the display results of parallel coordinates, and the temporal features of different categories can be found more clearly. It can be concluded that the proposed method is able to help users quickly explore the characteristic patterns hidden in the multi-dimensional and spatiotemporal dataset through the actual case study of online transaction data.
On-the-fly DICOM-RTV metadata pseudonymization during a real-time streaming

With the current advancement of the information technologies and the growing demand for data sharing, the risk of private data leakages increases. Moreover, the aggregation of multiple data, required for improving quality, may favor the prediction of unrevealed private and sensitive information, in particular in the medical data. Thus, with the current laws and regulations (GDPR, CNIL, HIPAA, etc.), it becomes required to protect the privacy of the patients when dealing with their sensitive data, and in particular when sending them outside the clinical site. Inline with this objective, the data anonymization and pseudonymization emerged among the solutions for safely sharing private data with remote peers. In this work, a particular interest is given to the pseudonymization of the DICOM Real-Time Video (DICOM-RTV) associated metadata that can be generated inside the operating room (OR), and shared via a streaming technology. The pseudonymization, by definition, includes both the notion of de-identification and re-identification, necessary for ensuring patient safety. Thus, the main objective of this paper is to define and conceptualize an architecture for de-identifying DICOM-RTV metadata on-the-fly before streaming them together with the DICOM-RTV video of the ongoing surgery outside the medical facility in real-time.
An Lipreading Modle with DenseNet and E3D-LSTM

For Chinese lip reading, the paper proposes an improved DenseNet network structure to strengthen the ability of short-term dependence of the model. At the backend,E3D-LSTM is adopted to conduct time modeling of features extracted from CNN. The loss function of CTC is used to solve different speech habits of speakers, which will lead to different time dependence of the same word. LRW-1000 datasets are used for training, and experiments show that the Chinese recognition rate is better than the traditional method. In the diffferent length of sequences, our work arrives 38.96 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> in easy,38.49% in medium and 37.92% in hard, respectively.
Evaluation of Dimensionality Reduction Techniques-Principal Feature Analysis in case of Text Classification Problems

One of the commonly observed phenomena in text classification problems is sparsity of the generated feature set. So far, different dimensionality reduction techniques have been developed to reduce feature spaces into a convenient size that a learner algorithm can infer. Among these, Principal Component Analysis (PCA) is one of the well-established techniques which is capable of generating an undistorted view of the data. As a result, variants of the algorithm have been developed and applied in several domains, including text mining. However, PCA does not provide backward traceability to the original features once it projected the initial features to a new space. Also, it needs a relatively large computational space since it uses all features when generating the final features. These drawbacks especially pose a problem in text classification problems where high dimensionality and sparsity are common phenomena. This paper presents a modified version PCA, Principal Feature Analysis (PFA), which enables backward traceability by choosing a subset of optimal features in the original space using the same criteria PCA uses, without involving the initial features into final computation. The proposed technique is tested against benchmark corpora and produced a comparable result as PCA while maintaining traceability to the original feature space.
From a Common Chair to a Device that Issues Reminders to Seniors

Over the years, people tend to fail to recall different activities of everyday life, especially those that are performed less frequently. Forgetting a medical appointment, a family member’s birthday, or to take a medicine, is a common problem for many older adults. Although Information and Communication Technologies provide several options to help older adults remember activities like these, many of them could feel discouraged by not being able to properly use these tools and take full advantage of them. In other cases, elderly people may feel intimidated and even refuse to interact with technological devices. For these reasons, this paper proposes the use of a conventional object, which can be found in any home, to help older adults remember certain activities. Specifically, an ordinary chair has been selected to be employed as a device to provide the necessary reminders. The reminders implemented in the designed prototype are provided by audio, lights and vibrations, things that users do not notice at first sight the presence of technology in the chair. This prototype was evaluated through a user study with the collaboration of older adults. The results of the evaluation were positive, which concludes that the proposal has a favorable reception. Thus, this proposal could provide an important contribution to the major goal of helping to improve the quality of life of the elderly population.
Performance Evaluation of Two-layer lossless HDR Coding using Histogram Packing Technique under Various Tone-mapping Operators

We proposed a lossless two-layer HDR coding method using a histogram packing technique. The proposed method was demonstrated to outperform the normative JPEG XT encoder, under the use of the default tone-mapping operator. However, the performance under various tone-mapping operators has not been discussed. In this paper, we aim to compare the performance of the proposed method with that of the JPEG XT encoder under the use of various tone-mapping operators to clearly show the characteristic difference between them.
Deep Learning based Radial Blur Estimation and Image Enhancement

In this paper, we propose a deep learning based pipeline to estimate the radial blur and enhance the deblurred image. The radial blur is introduced in the image as an effect of ego motion in autonomous vehicle systems. The deblurring of the image with radial blur is challenging since most of the blur models do not estimate radial blur. Hence, we design a deep learning based pipeline with estimation and enhancement modules. The estimation module is designed with CuratorNet to estimate radial PSF in two stages. The estimated PSF is used for deblurring of input radial blurred images. The enhancement module is designed with convolutional autoencoder which enhances the deblurred image to remove artefacts in order to detect the traffic signs. We demonstrate the results of the proposed pipeline on synthetic and real images with traffic signs and compare the results with existing methods.
Cooperative Game Theory-Based Request Distribution Model in Federated Cloud Environment

Cloud computing has emerged as the most important paradigm of distributed computing in the last decade. Increasing demand of the cloud users and inadequacy of resources along with the cloud providers has encouraged the federation of multiple cloud providers. In federation, multiple rational providers coordinate for the execution of the user’s tasks. In federated cloud, the distribution of the user’s request for resources and revenue among the providers has evolved as the most challenging task. Existing works aim at the profit maximization of the providers while discounting the importance of fair distribution. This paper presents cooperative game theoretic model for the fair distribution of revenue and resources among the providers, and analyzes the variation of power index with the change in the user’s request. For analysis of the proposed model, comparative evaluation is performed with the existing distribution model. An experimental result demonstrates that the proposed model distributes the resource request and revenue more fairly that the existing state of art.
A Holistic System for Pre-clinical Diagnosis of Sleep Disorders in the Home Environment

The potential for mHealth solutions is steadily increasing due to an enormous growth in the area of mobile networks and the mobile Internet. However, not only the general connection is becoming faster and more stable, but also the mobile devices themselves are becoming even more advanced. Nowadays, these devices are able to acquire physiological data and transfer them to e.g. a physician or technician to be analyzed before an actual appointment. Using these technological advantages, more and more evidence could be used for diagnosis and treatment. Instead, long preparation and delay are part of everyday practice nowadays and information and data acquisition take up much time before diagnosis.This paper describes a general concept for a centralized screening / pre-diagnosis system for mobile sleep laboratories. The system is designed to have as little influence as possible on the usual sleep environment, but still allows a medically usable recording of sleep activities and health parameters. Furthermore, the concept covers the access possibilities of the attending physician as well as the back-flow of a final diagnosis. Finally, we report on the resulting challenges of such systems with respect to privacy.
VCSELs for fast neuromorphic photonic systems operating at GHz rates

We report experimentally on VCSEL-based artificial optical spiking neurons with ultrafast spiking refractory period; hence allowing operation at GHz rates. This feature is used to demonstrate all-optical digital-to-spiking information format conversion at 1.0 Gbps.
Is Deductive Program Verification Mature Enough to be Taught to Software Engineers?

Software engineers working in industry seldom try to apply formal methods to solve problems. There are various reasons for this. Sometimes these reasons are understandable---the cost of using formal methods does not make economic sense in many contexts.
Multi Person Pose Estimation with Attention

The existing pose estimation networks extract confidence maps and Part Affinity Fields (PAFs) from all pixels in images. However, we found that the recognition rate can be decreased due to the background. This limitation arises from the process to extract feature maps from the image. To overcome this limitation, we adopt the attention block in front of the backbone network. To show that our proposed network can reduce the error and increase the precision, we compared PCKh and mAP with the existing pose estimation network.