Reviewerly/exHarmony huggingface dataset:
Dealing with (some of) the fallout from meltdown

The meltdown vulnerability allows users to read kernel memory by exploiting a hardware flaw in speculative execution. Processor vendors recommend "page table isolation" (PTI) as a software fix, but PTI can significantly degrade the performance of system-call-heavy programs. Leveraging the fact that 32-bit pointers cannot access 64-bit kernel memory, we propose "Shrink", a safe alternative to PTI, which is applicable to programs capable of running in 32-bit address spaces. We show that Shrink can restore the performance of some workloads, suggest additional potential alternatives, and argue that vendors must be more open about hardware flaws to allow developers to design protection schemes that are safe and performant.
A Study on Virtual Reality Technology for College Oral English Online Self-help Learning System

In order to improve the online self-help learning ability of college oral English, a virtual reality-based online self-help learning system for college oral English is proposed. The Internet of Things networking technology is used to design the network of online self-help learning system for college oral English. VR technology is used to simulate the virtual reality and reconstruct the virtual scene of online self-help learning for college oral English. The image processing of online self-help learning for college oral English is carried out in the data processing module. Combined with ISA/EISA framework, the network design of online self-help learning system for college oral English is carried out. The 32-bit embedded design method is used for the integrated information collection and fuzzy scheduling of college oral English online self-help learning system, and the core controller is used for college oral English online self-help learning and information sampling. VR technology is used to simulate the virtual reality and reconstruct the virtual scene of online self-help learning of college oral English, and the online self-help learning of college oral English is carried out in A/D and D/A. The simulation results show that the designed online self-help learning system for college oral English is stable and reliable.
Disaster Management and Detection System

Dealing with disaster could be a human process that requires public support for coming up with. It needs to be property within the lightweight of challenges expose by non-renewable resource utilization, global climate change, growth and imbalances of wealth. Emergency, not only includes nephrotoxic spills and industrial explosions however conjointly the floods, storms and earthquakes. Disaster management essentially deals with the management of resources and knowledge towards a disastrous event and is measured by however efficiently, effectively and seamlessly one coordinates these resources. the power to effectively traumatize disasters has become a challenge to fashionable technology. Disasters maybe divided into fast disasters and continuing disasters. Earthquakes, floods and tsunamis are thought of as fast onset disasters whereas civil wars, droughts, famines and epidemics are the continued or future disasters.
Automated Testing of Graphics Units by Deep-Learning Detection of Visual Anomalies

We present a novel system for performing real-time detection of diverse visual corruptions in videos, for validating the quality of graphics units in our company. The system is used for several types of content, including movies and 3D graphics, with strict constraints on low false alert rates and real-time processing of millions of video frames per day. These constraints required novel solutions involving both hardware and software, including new supervised and weakly-supervised methods we developed. Our deployed system has enabled a ~20X reduction of human effort and discovering new corruptions missed by humans and existing approaches.
Truncated total variation image denoising model based on fractional B-spline wavelet

Abstract In order to suppress the noise and keep the clear structure, edges and details of the image under noisy images, a truncated total variation (truncated TV) image denoising model based on the fractional wavelet domain is proposed. The fractional B-spline wavelet can well describe the features of fractional singularity such as fine textures in the image. The truncated TV model is based on the TV model, which solves the shortcomings of the TV model’s large penalty amplitude and reduced image contrast. The model first performs the fractional B-spline wavelet transform on the image, applies truncated TV to the fractional wavelet domain to process the image, and then performs the inverse transform of the fractional B-spline wavelet transform to obtain the denoised image. The experimental results of image noise reduction show that compared with some existing image noise reduction methods, the proposed noise reduction model can effectively suppress noise and maintain the structure and details of the image. The application of the model to other fields in the future remains to be studied.
Keeping track of UWP application changes for digital forensic purposes

Digital forensics aims to collect and interpret artifacts that can help in the understanding of the action performed by a given individual or organization. The forensic artifacts are highly dependent on the inner working of the operating system (OS) and applications. This is the case for applications of the Universal Windows Platform (UWP), with digital forensic practitioners often depending on the data kept in the private database(s) of the application. However, some of these applications evolve rapidly, with new versions bringing changes that sometimes involve their inner databases and thus might impact digital forensic artifacts. In this paper, we present UWPscanner, an open source system that we have developed. The UWPscanner system allows to track the changes of internal databases used by UWP applications, aiming to ease the task of not only digital practitioners, but also of digital forensic tool developers. The paper is complemented with the case-study of tracking Microsoft Skype (SkypeApp) and Your Phone evolution with UWPscanner.
Multi-Session Multicasting for 360-Degree Video Multicast over OFDMA Systems

360-degree video content provides a rich and immersive multimedia experience to viewers by allowing viewers to the video from any angle. However, 360-degree videos require much higher bandwidth to be delivered over mobile networks compared to conventional videos. Multicasting of the videos is one of the solutions to efficiently utilize the limited bandwidth since many viewers share the wireless spectrum resource for popular videos, such as sports events or musical concerts. LTE eMBMS assigns the videos to the video sessions, and multiple viewers can subscribe to the same video allocated to the video sessions. Moreover, the tiling of the 360-degree video makes it possible to control the regional quality of the video. The tiles that are likely to be seen by many viewers should have higher quality than other tiles to satisfy more viewers. In this paper, we proposed the Multi-Session Multicast (MSM) system to optimally allocate the wireless resources to tiles with different qualities to maximize the expected user experience. The experimental results show that the proposed MSM system provides higher quality videos to viewers using limited wireless resources.
Survey on Techniques in Improving Quality of Underwater Imaging

The quality and appearance of underwater images perform a relevant role in the underwater computer vision paradigm. Wherein, the underwater images are useful in various applications to make a detailed study on underwater life. Despite the hype, the images caprited underwater undergoes various challenges like light attenuation, color absorption, type of water, etc. To address these types of issues, many algorithms are proposed. This paper provides a comprehensive study of frequent methods used to intensify the visual nature of underwater images and different underwater image datasets that are used to perform tasks regarding underwater imaging. The different quality assessment measures are also summarized in this research work. The presented methods and their shortcomings are studied to enable the in-depth comprehension of underwater image enhancement. In the end, possible future research directions are also implied.
Automated Rocker Rover for Terrain Surface Surveillance

The methodology used in the Mars exploration and curiosity rover by Mars Science Laboratory is rocker bogie. The term rocker derives from the suspension system with large forward leg on each side of the vehicle chassis on either side which exhibits automated wheel movement, henceforth when one side of the vehicle rocker goes up, the other side goes down and maintain an average pitch level. Earlier research has envisaged that the Rocker-Rovers are much suitable for conducting scientific experiments like travelling to many meters to tens of kilometres. However, the recent mobility designs are complex, using many wheels or legs. Moreover, such rocker bogies can achieve only minimum distance transverse on field like agriculture land, rough land, inclined, stairs and obstacle surfaces which concludes that greater mobility experiments need to be conducted. There are most cases noted that mechanical failures were caused due to harsh environment. Hence, for surveillance in rough terrains, a robust design of rover is required. This paper focused on development of an efficient high mobility suspension system that is capable of travelling through rough terrain using a four wheeled rover. The primary mechanical feature of the rocker bogie design is arrived by using only two motors for mobility and drive line easiness. Motors are located inside the body for increased reliability and efficiency, while considering the thermal variation to be minimum. Increased stability requires both front wheels to be climb first, put forth the necessity of four wheels for driving the rover. The Rocker-Rover suspension system has a vigorous capability to overcome the uneven terrain because of its equal distribution of the pressure over its six wheels. Moreover, this article has implemented a rover with TSM (Total Surveillance Manager) which is an intelligent surveillance and security robot system, designed for monitoring behaviours and activities in the region of interest. The proposed prototype utilizes multiple types of surveillance devices including robots, CCTV cameras, and sensors
Reviews of using solar energy to cover the energy deficit after the recent war in Mosul city

This study provides a review of solar energy in Iraq, as Iraq is one of the oil-rich countries that are considered more intelligent in the field of alternative energy for the post-oil era, especially as it is located near the solar belt, which makes the solar radiation of high intensity and brightness a period of the year. We must replace non-renewable energy resources (traditional or fossil fuels) with renewable (sustainable) energy resources. This renewable energy (solar energy) is possible, clean, unlimited and environmentally friendly, and it can be used in many applications of lighting, water heating and heating in the winter, and this Reduces the electricity needed during winter for this application. And if we talk about Mosul, the climate of Mosul It’s marked by high summer and cold temperatures .degrees Celsius in the middle of the day, and in December, January and February, temperatures range from -1 degrees Celsius to 8 degrees Celsius. As Iraq is suffering from electricity shortages during this time, so the Iraqi government and the local government of Mosul in particular must take serious decisions and steps to confront and overcome these challenges, and develop developed strategies, and programs of specialized and expert people to meet the increases in demands on electric power. Renewable energies such as solar and wind energy which could play a significant role in Iraq’s future , especially the solar energy covered in this study.
A Combination of Mathematics, Statistics and Machine Learning To Detect Fraud

Fraud detection which is a discussible phenomenon to many bounds together with financial sectors, banking, insurance as well as diverse forms of industries. Nowadays fraud endeavors are amplifying with rampant pace especially via the development of technology, so building fraud discovery more significant than ever before. In this paper, we analyzed the fraud operations by managing the “Banking Fraud Detection” database by the combination of mathematical, statistical as well as machine learning ways and tried to spectacle a comparison among these ways. We narrated the elementary explorations by the combining methods such as mathematical, statistical as well as machine learning to equip the ways of insidious banking transactions.
PREPARATION OF FUTURE TEACHERS FOR USING MEDIA SPACE IN THE EDUCATIONAL PROCESS OF PRESCHOOL EDUCATION INSTITUTION

The study identifies the relevance of effective professional training of future teachers in the field of preschool education, in particular, the preparation for using media space in the educational process of preschool education institution. The content of one of the main concepts of the study — “media space”, which is defined by us as the space of a set of individual, collective entities, social institutions in the production, existence, circulation of audiovisual (electronic) information, is clarified. Modern media space includes television (terrestrial, cable, satellite, etc.), radio, printed media, movies and videos, DVDs, CD-ROMs, e-mail, computer channels and the Internet. The content of the basic concept of the study “readiness of teachers for using the media space in the educational process of preschool education institution”, which we consider as a holistic integrative, rather embedded in content personality quality, which is a set of values, motives, knowledge, skills, personal professional qualities that determine the effectiveness of the use of media space in the educational process of preschool education institution. The peculiarities of the organization of media space in the conditions of preschool education institution are outlined. On the basis of the analysis of curricula and work programs of educational disciplines of a number of Ukrainian universities, the state of formation of researched readiness of future teachers in the field of preschool education in the conditions of university pedagogical training is investigated. Based on the analysis of the educational activities of teachers of preschool education, the features of their readiness regarding the use of media space in the educational process of preschool education have been studied. The need for further improvement of the educational process to form the readiness of future teachers to use the media space in the educational process of preschool education institution and optimize their independent activities in order to effectively develop the studied professional quality.
Implementation of google single sign On (SSO) in the library management system

Abstract Almost all organizations provide web-based services as an effort to provide excellent services to users, such as a library, academic, financial, and other services. Organizations only need to provide Single Sign-on (SSO) services for their users, and users need one set of credentials (user name and password) to access all organizational services. The SSO service developed is based on the Google Authentication API. This SSO makes communication between systems more effective and efficient.
Ontology-Driven Audit Using the REA-Ontology

While blockchains are not yet ubiquitous in business practice, they are expected to serve as a platform to handle an increasing number of business transactions in a not-too-distant future. Smart contracts can be used to code and to enforce agreements between business parties. A significant difference between traditional and smart contracts is that once the actual events of the smart contract become part of a block in the blockchain, they are almost impossible to undo. Therefore, it is important that critical validity aspects of these smart contracts are explicitly represented. As smart contracts are software products too, it is therefore also critical that the coding of these critical validity aspects guarantees a faithful implementation of the validity checks. This paper suggests applying a combination of two approaches (i.e., ontology engineering and model-driven engineering) to the design and the implementation of smart contracts, in order to facilitate their audit through a clear separation of concerns. More precisely, this paper discusses the example of the REA ontology to provide the ontological commitment of the critical validity aspects of a contract, while MDE provides a tool to unambiguously translate the REA ontology’s contracting terms into a well-designed Smart Contract. This paper suggests that the resulting Smart Contract can support auditors’ assertions regarding exchanges between business partners and support the audit process.
In Big Data Era: Analysis of Hadoop Cluster Performance

In recent decades, the dependence on digital tools to assist our daily lives has been growing in almost all sectors and fields. A huge amount of data has been generated and as a result, a new era has emerged: the era of big data. To benefit from this data, several big data analysis tools have emerged, Hadoop being one of them. In this paper, we analyzed Hadoop cluster performance and investigated the effect of the cluster and dataset sizes on its performance. Furthermore, the MapReduce model was used to study and justify its benefits in the big data analysis field. The results showed that the Hadoop cluster and the MapReduce model are powerful in processing and analyzing big data. More benefits can be achieved from the analyzing time perspective when larger data sizes are used. Interestingly, we found that increasing the cluster size beyond a certain point resulted in communication issues, thereby yielding an increase in the CPU time.
Financial Engineering Course Construction under the Background of Beijing-Tianjin-Hebei Coordinated Development

With the application of big data, cloud computing, artificial intelligence, block chain and other technological innovations in various financial fields, the integration of finance and technology is becoming a new trend in the future development of the financial industry. In the background of financial technology development, the course designing of financial engineering is facing many opportunities and challenges. Under the background of integration of the beijing-tianjin-hebei region, colleges and universities are using this opportunity and servicing talent to different areas, and with the development of financial technology, the original financial engineering course system need adjustment and improvement, and under the background of the integration of the beijing-tianjin-hebei region, this article will promote the curriculum reform and the construction of financial engineering course program.
Noise Homogenization via Multi-Channel Wavelet Filtering for High-Fidelity Sample Generation in Gans

In a typical Generative Adversarial Network (GAN), a noise is sampled to generate fake samples via a series of convolutional operations after random initialization. However, current GANs merely rely on the pixel space to sample the noise, in-creasing the difficulty of approaching the target distribution. Fortunately, the long proven wavelet transformation is able to decompose multiple spectral information from the images. In this work, we propose a novel multi-channel wavelet-based filtering method for GANs, to cope with this problem. The proposed WaveletNet embeds a wavelet deconvolution layer in the generator to take advantage of the wavelet deconvolution. By learning a filter with multiple channels, or multiple convolutional filters, it can efficiently homogenize the sampled noise via an averaging operation, to generate high-fidelity samples. We conducted benchmark experiments on the Fashion-MNIST, KMNIST, and SVHN datasets through an open GAN bench-mark tool. The results showed that WaveletGAN has excellent performance in generating high-fidelity samples.
Addressing Non-Representative Surveys using Multiple Instance Learning

In recent years, non representative survey sampling and non response bias constitute major obstacles in obtaining a reliable population quantity estimate from finite survey samples. As such, researchers have been focusing on identifying methods to resolve these biases. In this paper, we look at this well known problem from a fresh perspective, and formulate it as a learning problem. To meet this challenge, we suggest solving the learning problem using a multiple instance learning (MIL) paradigm. We devise two different MIL based neural network topologies, each based on a different implementation of an attention pooling layer. These models are trained to accurately infer the population quantity of interest even when facing a biased sample. To the best of our knowledge, this is the first time MIL has ever been suggested as a solution to this problem. In contrast to commonly used statistical methods, this approach can be accomplished without having to collect sensitive personal data of the respondents and without having to access population level statistics of the same sensitive data. To validate the effectiveness of our approaches, we test them on a real-world movie rating dataset which is used to mimic a biased survey by experimentally contaminating it with different kinds of survey bias. We show that our suggested topologies outperform other MIL architectures, and are able to partly counter the adverse effect of biased sampling on the estimation quality. We also demonstrate how these methods can be easily adapted to perform well even when part of the survey is based on a small number of respondents.
Action classification and analysis during sports training session using fuzzy model and video surveillance

Activity recognition and classification are emerging fields of research that enable many human-centric applications in the sports domain. One of the most critical and challenged aspects of coaching is improving the performance of athletes. Hence, in this paper, the Adaptive Evolutionary Neuro-Fuzzy Inference System (AENFIS) has been proposed for sports person activity classification based on the biomedical signal, trial accelerator data and video surveillance. This paper obtains movement data and heart rate from the developed sensor module. This small sensor is patched onto the user’s chest to get physiological information. Based on the time and frequency domain features, this paper defines the fuzzy sets and assess the natural grouping of data via expectation-maximization of the probabilities. Sensor data feature selection and classification algorithms are applied, and a majority voting is utilized to choose the most representative features. The experimental results show that the proposed AENFIS model enhances accuracy ratio of 98.9%, prediction ratio of 98.5%, the precision ratio of 95.4, recall ratio of 96.7%, the performance ratio of 97.8%, an efficiency ratio of 98.1% and reduces the error rate of 10.2%, execution time 8.9% compared to other existing models.
Research on Revenue Optimization of Service Composition for Large-Scale Service Requests

Service composition is an effective way to add value in environment with large amount of services on the internet or especially in clouds. In the face of the continuous large-scale concurrent user request flow, the composition service provider needs to find the dispatching strategy to minimize the time average operating cost under the premise of buffering queue stability, so as to maximize the revenue. In this paper, this problem is modeled as a Markov decision problem. According to Lyapunov queue stability theory, LBO algorithm is proposed to solve the problem, avoiding state space explosion. Experimental results show that compared with traditional methods, composite service providers can effectively balance reducing service response time and maximizing revenue.