Reviewerly/exHarmony huggingface dataset:
Extending Deep Neural Categorisation Models for Recommendations by Applying Gradient Based Learning

A large number of websites hosted on the Internet use categorizations systems for the organization and storage of their big data. Big data management is performed by the design of these systems extensively ranges from using probability models to deep neural networks. Deep learning categorization models optimize the learned activation weights stored in their system for all their tasks, to reduce loss and increase the accuracy of predictions, which can be extracted and used for building recommendation systems for users on their platform for personalization and ease of use. In this paper, we have used the popular MovieLens 100k dataset for building a movie recommendation model by using the weighted class activation maps obtained from a neural classification model using cosine similarity and visualized using k-NN algorithm. The model performed with a precision and recall of 0.70 and 0.89 while giving 25 recommendations to the user for their selected movie.
Behind the Chain Coffee Shop: Design of Utilizing Virtual Reality for Coffee-Making Training

Training new employees is the most vital part of any vocational industry. Along with the development of digitization, new methods of employee qualification arise. Virtual Reality (VR) can create immersive situations, have great potential in terms of repetitive, high retention rate learning and multi-sensory interaction, and are gradually being applied to training in various fields. Chain coffee shop is an industry with a relatively high demand for manpower. New employee training requires a lot of human resources and costs. Furthermore, the coffee-making process is a more complex training project that requires professionals to guide details and a lot of time to practice. The study chooses Starbucks as a case and proposes a design combining virtual reality and coffee-making training. As an initial attempt to provide insight into utilizing a new technology for better coffee-making training of chain coffee shops’ new employees.
Interactive methods in teaching psychology of professional education

This article explores the implementation of interactive methods in teaching psychology of professional education. The object of this research is the process of teaching the discipline &amp;ldquo;Psychology of Professional Education&amp;rdquo;, while the subject is the interactive methods of teaching this discipline. The article employs the method of theoretical analysis of scientific and methodological literature, methods of generalization, synthesis, comparison, and classification. The author discusses the essence of interactive teaching methods and their role within system of modern teaching techniques. Special attention It is emphasized that each class can be conducted using interactive teaching methods, selected by the pedagogue based on the desire to improve the effectiveness of educational process and develop professional competencies of students. The scientific novelty consists in determination of specificity of using interactive teaching methods within the framework of the discipline under review. It is established that heuristic conversation, psychological diagnostics, method of projects, role games, and problem-based learning are considered as most effective interactive teaching techniques. The acquired results can be applied in organizing educational process with regards to psychology of professional education and other disciplines of psychological and pedagogical orientation. The conclusion is made that in teaching psychological-pedagogical disciplines and future pedagogues, the students learn how to implement these methods in their future pedagogical activity.
ExtraFerns: Fully Parallel Ensemble Learning Technique with Random Projection and Non-Greedy yet Minimal Memory Access Training

Training machine learning models on edge devices is always a conflict with power consumption and computing cost. This paper introduces a hardware-oriented training method called ExtraFerns for a unique subset of decision tree ensembles, which significantly decreases memory access and optimizes each tree in parallel. ExtraFerns benefits from the advantages of both extraTrees and randomFerns. As extraTrees does, it generates nodes by randomly selecting attributes and generating thresholds. Then, as randomFerns does, it builds ferns, which are decision trees that share identical nodes at each depth. In contrast to other ensemble methods using greedy optimization, ExtraFerns attempts global optimization of each fern. Experimental results show that ExtraFerns requires only 4.3% and 4.1% memory access for training models with 3.0% and 1.2% accuracy drops compared with randomForest and extraTrees, respectively. This paper also proposes applying lightweight random projection to ExtraFerns as a preprocessing step, which achieved a further accuracy improvement of up to 2.0% for image datasets.
Practitioner Perception of Vulnerability Discovery Strategies

The fourth industrial revolution envisions industry manufacturing systems to be software driven where mundane manufacturing tasks can be automated. As software is perceived as an integral part of this vision, discovering vulnerabilities is of paramount of importance so that manufacturing systems are secure. A categorization of vulnerability discovery strategies can inform practitioners on how to identify undiscovered vulnerabilities in software. Recently researchers have investigated and identified vulnerability discovery strategies used in open source software (OSS) projects. The efficacy of the derived strategy needs to be validated by obtaining feedback from practitioners. Such feedback can be helpful to assess if identified strategies are useful for practitioners and possible directions the derived vulnerability discovery strategies can be improvised. We survey 51 practitioners to assess if four vulnerability discovery strategies: diagnostics, malicious payload construction, misconfiguration, and pernicious execution can be used to identify undiscovered vulnerabilities. Practitioners perceive the strategies to be useful: for example, we observe 88% of the surveyed practitioners to agree that diagnostics could be used to discover vulnerabilities. Our work provides evidence of usefulness for the identified strategies.
The Effect of Smartphones Usability on High School Students' Science Literacy Ability in Physics Learning

&lt;p style="text-align:justify"&gt;Learning in high schools has used a lot of smartphone assistance to make it easier for students to understand the material explained by the teacher. However, with the many uses of smartphones in learning, of course, it must provide positive benefits to the ability of students, especially the science literacy ability. In this study, the objectives to be achieved are to examine how much the smartphone's usability in physics learning, examine students' science literacy and examine the effect of the smartphone’s usability on students' science literacy in physics learning. The method used in this study is a quantitative method with a research design used is a one-shot case study. The data on smartphone usability is obtained using a questionnaire, and science literacy of students is obtained through on physics science literacy test. The results of this study indicate that the smartphones usability and students' science literacy in physics learning are respectively in the high and medium categories with a respective percentage of 57.20% and 36.36%. The students' science literacy is influenced by the smartphone's usability in physics learning by 34.30%. These results indicate that smartphone usability by most students is very high, but has not been able to contribute optimally to students' science literacy. Therefore, special treatment is needed in utilizing the use of smartphones in physics learning so that students' science literacy can be optimized.&lt;/p&gt;
Flora Generation and Evolution Algorithm for Virtual Environments

A crucial aspect to game development is the ability to establish the player emersion in a simulated and virtual world, presenting engaging content and maximizing the gaming experience. The demand for new and hand-customized content keeps increasing, in playable maps that are ever expanding in size, without compromising quality or differentiation. The usage of Procedural Content Generation (PCG) allows computers to generate game content and produce distinguishable and unique instances amongst the ones generated allowing to better replicate reality and deliver intricate systems without the costs and time consumption of human intervention. As a key element for the appeal of many games, natural life exhibits both complex and distinguishable behaviors, from species to species as well as from individual to individual. Biodiversity is maintained by processes operating over broader spatial and temporal scales, and as such, simulating plant diversity is a process better suited to operate under PCG algorithms. In this study, we describe a procedural flora evolution algorithm that can replicate a flora species life cycle for a practical 3D game setting (Universe 51), using scientific knowledge to better reproduce such intricate behaviors. We based some part our approach on complex intelligence algorithms, as well as in some original processes targeted at our project specificities. The evolution algorithm starts with the end results of a flora generation algorithm that can create millions of different flora species based on selected biological parameters. This data is then interpreted by the evolutionary algorithm, that uses these values to determine each species survival chances in the surrounding environment. Our results were able to replicate and autonomously manage plant species life cycle, under a dynamic environment with potentially varying conditions.
Building Facades to Normal Maps: Adversarial Learning from Single View Images

Surface normal estimation is an essential component of several computer and robot vision pipelines. While this problem has been extensively studied, most approaches are geared towards indoor scenes and often rely on multiple modalities (depth, multiple views) for accurate estimation of normal maps. Outdoor scenes pose a greater challenge as they exhibit significant lighting variation, often contain occluders, and structures like building facades are often ridden with numerous windows and protrusions. Conventional supervised learning schemes excel in indoor scenes, but do not exhibit competitive performance when trained and deployed in outdoor environments. Furthermore, they involve complex network architectures and require many more trainable parameters. To tackle these challenges, we present an adversarial learning scheme that regularizes the output normal maps from a neural network to appear more realistic, by using a small number of precisely annotated examples. Our method presents a lightweight and simpler architecture, while improving performance by at least 1.5x across most metrics. We evaluate our approaches against the state-of-the-art on normal map estimation, on a synthetic and a real outdoor dataset, and observe significant performance enhancements.
An Application of Nano Topological Spaces with Decision Making Problem in Medical Field

In this article, a distinct approach in nano topology is used as the perception to reduce conditional attributes. It also studies the hazardous factors for the inception of Dengue fever. It can also be worn on any kind of situation to avoid problems in various field
User-assisted video reflection removal

Reflections in videos are obstructions that often occur when videos are taken behind reflective surfaces like glass. These reflections reduce the quality of such videos, lead to information loss and degrade the accuracy of many computer vision algorithms. A video containing reflections is a combination of background and reflection layers. Thus, reflection removal is equivalent to decomposing the video into two layers. This, however, is a challenging and ill-posed problem as there is an infinite number of valid decompositions. To address this problem, we propose a user-assisted method for video reflection removal. We rely on both spatial and temporal information and utilize sparse user hints to help improve separation. The proposed method removes complex reflections in videos by including the user in the loop. The method is flexible and can accept various levels of user annotations, within each frame and in the number of frames being annotated. The user provides some strokes in some of the frames in the video, and our method propagates these strokes within the frame using a random walk computation as well as across frames using a point-based motion tracking method. We implement and evaluate the proposed method through quantitative and qualitative results on real and synthetic videos. Our experiments show that the proposed method successfully removes reflection from video sequences, does not introduce visual distortions, and significantly outperforms the state-of-the-art reflection removal methods in the literature.
MongoDB: Analysis of Performance with Data from the National High School Exam (Enem)

It is increasingly common for public and private organizations to deal with the challenges of data management in Big Data environments, tipycally characterized by high volume, great variety, fast generation velocity, in addition to intrinsic issues of data veracity and value. This context gave rise to NoSQL database technologies, with MongoDB as one of its prominent representatives. This paper aims to evaluate the performance of MongoDB in its cluster architecture against the standalone mode, with massive data from the National High School Exam (Enem), in addition to performing a brief interpretation of the results from the perspective of the educational domain. The initial results of query, update, and delete operations demonstrate a set of advantages and disadvantages of the experiments' architectures. The Information Technology (IT) professional's role in defining the scope of the database project for a more bold choice of the NoSQL architecture is highlighted in a cost-benefit perspective for organizations.
An implementation methodology for Neural Network on a Low-end FPGA Board

Artificial Intelligence(AI) has achieved unprecedented success in various fields that include image, speech, or even video recognition. Most systems are implemented on power-hungry devices like CPU, GPU, or even TPU to process data due to the models' high computation and storage complexity. CPU platforms do weak in computation capacity, while energy budgets and expense of GPU and TPU are often not affordable to edge computing in the industrial business. Recently, the FPGA-based Neural Network (NN) accelerator has been a trendy topic in the research field. It is regarded as a promising solution to suppress GPU in both speed and energy efficiency with its specifically designed architecture. Our work performs on a low-end FPGA board, a more desirable platform in meeting the restrictions of energy efficiency and computational resource on an autonomous driving car. We propose a methodology that integrates a NN model into the board using HLS description in this paper. The whole design consists of algorithm-level downscaling and hardware optimization. The former emphasizes the model downscale through model pruning and binarization, which balance the model size and accuracy. The latter applies various HLS design techniques on each NN component, like loop unrolling, inter- /intra- level pipelining, and so on, to speed-up the application running on the target board. In the case study of tiny YOLO (You Only Look Once) v3, the model running on PYNQ-Z1 presents up to 22x acceleration comparing with the PYNQ's ARM CPU. Energy efficiency also achieves 3x better than Xeon E5-2667. To verify the flexibility of our methodology, we extend our work to the BinaryConnect and DoReFaNet. It is worth mentioning that the BinaryConnect even achieves around 100x acceleration comparing with it purely running on the PYNQ-Z1 ARM core.
User Experience in Previewing Interactive Mode of Live Streaming News

With the advancement of internet technology, the rise of live streaming and user interaction, traditional TV news producers have also joined the web live streaming platform, such as international epidemic information exchanges, election online political opinions, etc., in an attempt to attract users’ attention. There are multiple interactions, including traditional text, static emoji, dynamic pictures, and donations. This study focuses on the cross-screen live streaming news interaction, with TV as the first screen and handheld devices as the second screen. The preview mode (between groups) x dialogue mode (within groups) is designed for a two-way mixed experiment. The participants perform three tasks, and the researcher will count the seconds during the experiment. After the task, they were asked to fill in the subjective preference and System Usability Scale (SUS) questionnaires, and a short interview was conducted at the end. The research results show that: (1) The participants prefer the marquee dialogue. Bullet and vertical dialogues that are more likely to cause visual interference. (2) Cross-screen involves attention conversion, and live streaming news has an unretrospectable time characteristic. (3) Too much complicated dynamic information should be avoided so that users can obtain the content they want and intuitive operation gestures instantly. (4) The participants expect news and dialogue to appear simultaneously across screens in the preview mode so that important information will not be missed.
Automated Image Processing Module for Images Captured by Earth Observation Microsatellite Diwata-1 as Support for Ground Station Operations

This paper shows the outline of an automated image processing module for the microsatellite Diwata-1 images. The downloaded images from Diwata-1 are automatically processed using thresholding and feature detection algorithms to determine the cloud cover percentage, land area percentage, and bodies of water percentage for each image. From these percentages, an image is classified as useable or not. The list of all useable images tagged by the automated image processing module is compiled and compared to the list of all usable images tagged by a manual operator. Results show that the automated process has an accuracy of 87% to 97% depending on the payload used for capturing the image.
Multiplier as a Mean for Reducing Vulnerability of Atomic Patterns to Horizontal Address-Bit Attacks

Modern communication systems rely heavily on cryptography to ensure authenticity, confidentiality and integrity of exchanged messages. Elliptic Curve Cryptography is one of the common used standard methods for encrypting and signing messages. In this paper we report on findings for our implementation of an accelerator for elliptic curve P-256 with respect to side channel analysis. First we show that our implementation despite it obeys the atomicity principle is vulnerable to horizontal address-bit differential power analysis that is a single trace attack exploiting key dependent addressing of design's blocks. We used an easy statistical analysis of the trace, the comparison to the mean. This method allows to distinguish tiny differences in shapes of the processing of the ' ‘0’ key bit values from the’ l’ bit values. We applied this method to distinguish the shapes of “point doubling” atomic patterns from the “point addition” atomic patterns. As the atomicity principle is thought to ensure the resistance against such attacks this finding is of utmost importance. Second we introduce the use of dummy operation of the multiplier used as a means to reduce the vulnerability of our hardware accelerator to horizontal attacks. Without dummy partial multiplications, 3 key candidates were revealed with the correctness of 100%,99% and 100%, when the multiplier was not active. When running dummy operations the correctness of the key candidates was reduced down to 95%, 74% and 81% respectively.
Optimizing GOST R 34.12 "Magma" Algorithms for 8-Bit Microcontrollers

The paper concentrates on development of optimizing methods for the GOST R 34.12-2015 "Magma" cipher algorithm when it is implemented on 8-bit microcontrollers. There is a number of techniques in the paper, which being used, allow you to create the specialized implementations of the algorithm: 1) focused on the operation speed; 2) focused on reducing the memory used; 3) optimal which involves the best solutions based on two previous implementations. Each optimization method is represented by description and performance indicators of the results obtained in comparison with the direct implementation of the algorithm. So, in the case of optimal algorithm implementation the enciphering process is 11 times accelerated, and an amount of the occupied memory is 1/32 of the microcontroller's memory. The built-in compiler tools were used to optimize the software code. The techniques described are applicable to any 8-bit platform.
Bio-inspired universal real time image steganalysis

Huge development in communication, innovation and utilization of web has significantly eased exchange of information and the cautioning of prohibited information access is expanding along with it. Due to huge number of the redundant bits, images have become favourable carrier for data hiding technique. The evolution in image steganography had put forward many security ventures such as unofficial data transmission, commercial spy, and terrorist activities. To address this issue, researchers begun to develop mechanisms that can detect the presence of a secret message. Image steganalysis is a counter-measure to image steganography, which starts perceiving what exists inside the transmitted source document. This paper discusses the different methods for Bio-Universal steganalysis proposed by researchers. This paper examines the numerous researchers’ contributions to the field of Universal real time steganalysis.
Digital Character CAPTCHA Recognition Using Convolution Network

Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) is a type of automatic program to determine whether the user is human or not. The most common type of CAPTCHA is a kind of message interpretation by twisting the letters and adding slight noises in the background, plays a role of verification code. In this paper, we will introduce the basis of Convolutional Neural Network first. Then based on the handwritten digit recognition using CNN, we will develop a network for CAPTCHA image recognition.
Demystifying Global Cybersecurity Threats in Financial Services

The financial sector across the globe ensures sustainable growth in the economy by mobilizing investments, funds, and savings. This chapter attempts to comprehend the current state of cybersecurity within the financial services industry worldwide. The chapter explores the different aspects of global cyber-attacks in financial sectors to elucidate the salient problems, issues, threats, safeguards, and solutions. As technology is progressing, highly technology-savvy criminals are becoming a new threat in the cybercrime space. The entire industry needs an intense transformation to create innovative, state-of-the-art information, and an up-to-date architecture of cybersecurity that is capable of confronting the continuous tides of cyber-attacks and data breaches on an everyday basis. The use of security tools like proxy servers, firewalls, multi-layered email strategy, virus security software, and effective governance strategies are necessary to protect financial sectors from cyber threats and attacks.