Reviewerly/exHarmony huggingface dataset:
Strengthening Security of Images Using Dynamic S-Boxes for Cryptographic Applications

Security plays a paradigmatic role in the area of networking. The main goal of security is to protect these networks which contains confidential data against various kinds of attacks. By changing parameters like key size, increasing the rounds of iteration and finally using confusion box as the S-box, the strength of the cryptographic algorithms can be incremented. By using the Data Encryption Standard (DES), the images can be secured with the help of Dynamic S-boxes. Each of these 8 S-boxes contain 64 elements. Each row contains elements in the range 0–15 and are unique. Our proposed system generates these S-boxes dynamically depending on the key. The evaluation of this Dynamic S-box and DES shows much fruitful results over factors like Non-linearity, Strict Avalanche criterion, Balance, memory and time required for implementation using images.
Industrial Control System Anomaly Detection Using Convolutional Neural Network Consensus

Industrial control systems provide transportation, essential utilities, and the manufacturing of goods to the masses. It is critical that controlled processes are executed correctly and according to schedule. Monitoring the system&#x2019;s performance during its operation is an important approach for maintaining high levels of reliability and availability. We present a system monitoring capability that implements parallel multi-view neural networks to detect anomalous behavior in an industrial control system by predicting operational states. By deploying the prediction capability within the system, system operation can be monitored in a semi-supervised manner to ensure the actual system state lies within an appropriate region of the state space that was previously predicted by the neural networks. Furthermore, if the two predictive models diverge in their classification of state (breaking consensus), it is likely that system operation has been compromised due to faulty equipment, communication errors, or some other source of malfunction. To achieve different &#x201C;views&#x201D; of the system, one predictive model is trained to analyze the data flow of system control packets and the other model is trained to analyze gyrometric signals obtained from physical sensors in the control system. We demonstrate that this methodology can detect anomalous behavior of an example industrial control system by emulating its operation in the presence of injected anomalies. Results indicate highly accurate anomaly detection during system operation.
Building Graphs at a Large Scale: Union Find Shuffle

Large scale graph processing using distributed computing frameworks is becoming pervasive and efficient in the industry. In this work, we present a highly scalable and configurable distributed algorithm for building connected components, called Union Find Shuffle (UFS) with Path Compression. The scale and complexity of the algorithm are a function of the number of partitions into which the data is initially partitioned, and the size of the connected components. We discuss the complexity and the benchmarks compared to similar approaches. We also present current benchmarks of our production system, running on commodity out-of-the-box cloud Hadoop infrastructure, where the algorithm was deployed over a year ago, scaled to around 75 Billion nodes and 60 Billions linkages (and growing). We highlight the key aspects of our algorithm which enable seamless scaling and performance even in the presence of skewed data with large connected components in the size of 10 Billion nodes each.
DXML: Distributed Extreme Multilabel Classification

As a big data application, extreme multilabel classification has emerged as an important research topic with applications in ranking and recommendation of products and items. A scalable hybrid distributed and shared memory implementation of extreme classification for large scale ranking and recommendation is proposed. In particular, the implementation is a mix of message passing using MPI across nodes and using multithreading on the nodes using OpenMP. The expression for communication latency and communication volume is derived. Parallelism using work-span model is derived for shared memory architecture. This throws light on the expected scalability of similar extreme classification methods. Experiments show that the implementation is relatively faster to train and test on some large datasets. In some cases, model size is relatively small.
Automated Recognition of Chemical Molecule Images Based on an Improved TNT Model

The automated recognition of optical chemical structures, with the help of machine learning, could speed up research and development efforts. However, historical sources often have some level of image corruption, which reduces the performance to near zero. To solve this downside, we need a dependable algorithmic program to help chemists to further expand their research. This paper reports the results of research conducted for the Bristol-Myers Squibb-Molecular Translation competition, which was held on Kaggle and which invited participants to convert old chemical images to their underlying chemical structures, annotated as InChI text; we define this work as molecular translation. We proposed a model based on a transformer, which can be utilized in molecular translation. To better capture the details of the chemical structure, the image features we want to extract need to be accurate at the pixel level. TNT is one of the existing transformer models that can meet this requirement. This model was originally used for image classification, and is essentially a transformer-encoder, which cannot be utilized for generation tasks. On the other hand, we believe that TNT cannot integrate the local information of images well, so we improve the core module of TNT—TNT block—and propose a novel module—Deep TNT block—by stacking the module to form an encoder structure, and then use the vanilla transformer-decoder as a decoder, forming a chemical formula generation model based on the encoder–decoder structure. Since molecular translation is an image-captioning task, we named it the Image Captioning Model based on Deep TNT (ICMDT). A comparison with different models shows that our model has benefits in each convergence speed and final description accuracy. We have designed a complete process in the model inference and fusion phase to further enhance the final results.
An ARSandplay System for People with Down Syndrome

In general, people with Down syndrome have cognitive limitations and motor difficulties that may compromise the individual's development throughout life. That situation is due to the genetic condition that imposes peculiarities, such as low muscle tone and difficulty in learning, making it difficult to understand and interact with and around the environment. However, the use of new technologies proves to be a promising route because it offers new stimuli that can be used as reinforcers in rehabilitation, training, and entertainment activities. This work describes the development and evaluation of a computational system with a tangible interface, called 3D Sandplay. This system offers a sand box, which enables user interaction with the virtual world, amplified by Augmented Reality, for creating scenarios and understanding abstract concepts. A group of people with Down syndrome evaluated 3d Sandplay in three distinct activities. Also, the interest, attention, and resistance to the execution of the activities were assessed. Results have shown that 3D Sandplay is a tool that can contribute to the cognitive and motor development of children, adolescents, and adults with Down syndrome.
Incorporating Transformer Models for Sentiment Analysis and News Classification in Khmer

In recent years, natural language modeling has achieved a major breakthrough with its sophisticated theoretical and technical advancements. Leveraging the power of deep learning, transformer models have created a disrupting impact in the domain of natural language processing. However, the benefits of such advancements are still inscribed between few highly resourced languages such as English, German, and French. Low-resourced language such as Khmer is still deprived of utilizing these advancements due to lack of technical support for this language. In this study, our objective is to apply the state-of-the-art language models within two empirical use cases such as Sentiment Analysis and News Classification in the Khmer language. To perform the classification tasks, we have employed FastText and BERT for extracting word embeddings and carried out three different type of experiments such as FastText, BERT feature-based, and BERT fine-tuning-based. A large text corpus including over 100,000 news articles has been used for pre-training the transformer model, BERT. The outcome of our experiment shows that in both of the use cases, a pre-trained and fine-tuned BERT model produces the outperforming results.
A Novel Multi-scale Key-Point Detector Using Residual Dense Block and Coordinate Attention

Object detection, one of the core missions in computer vision, plays a significant role in various real-life scenarios. To address the limitations of pre-defined anchor boxes in object detection, a novel multi-scale key-point detector is proposed to achieve rapid detection of natural scenes with high accuracy. Compared with the method based on key-point detection, our proposed method has fewer detection points which are the sum of pixels on four-layer compared to one-layer. Furthermore, we use feature pyramids to avoid ambiguous samples. Besides, in order to generate feature maps with high quality, a novel residual dense block with coordinate attention is proposed. In addition to reducing gradient explosion and gradient disappearance, it can reduce the number of parameters by 5.3 times compared to the original feature pyramid network. Moreover, a non-key-point suppression branch is proposed to restrain the score of bounding boxes far away from the center of the target. We conduct numerous experiments to comprehensively verify the real-time, effectiveness, and robustness of our proposed algorithm. The proposed method with ResNet-18 and resolution of \(384\times 384\) achieves \(77.3\%\) mean average precision at a speed of 87 FPS on the VOC2007 test, better than CenterNet under the same settings.
A Collaborative Task Offloading Framework for Smart TV Applications in a Household Computing Environment

Smart TV can perform interactive computing while also providing video content services. However, this leads to a high delay during interactive computing because of the lack of computing capability, thus smart TVs are unable to undertake large scenes and complex interactive computing tasks. This article proposes a collaborative task offloading framework (CTOF) for interactive computing of smart TV video applications. The main contributions of this article are as follows: 1) a computing offloading mechanism is proposed for interactive computing with video content. A part of the interactive computing task is offloaded to the user’s high-computing mobile device in a household video service environment via a Wi-Fi Direct channel and 2) a collaborative computing offloading algorithm is proposed for complex interactive computing tasks. According to the computing complexity and the computing expansion coefficient, a parallel and serial collaborative smart offloading framework is established to minimize the delay. We conduct extensive experiments to indicate that in the existing experimental network environment, the video-based complex interactive service operation efficiency is improved by 20%. With a smart offloading framework, we can further achieve a satisfactory experience in terms of the interactive computing delay. Consequently, the smart TV can quickly respond to the complex video interactive service and improve the business interaction capability of the smart TV.
Path Planning Method Based on Multi-Layer ELM Optimized A*

Global path planning is considered as a basic problem of mobile robot. In this paper, an optimized A* algorithm based on multi-layer Extreme Learning Machine (named as ELM-A *) is proposed to solve the problem of path planning. Taking the environment and the path point information as input, the direction of the next search node is predicted quickly by multi-layer ELM. Then the output direction of multi-layer ELM is used to calculate the cost function of A* algorithm. This method can determine accurate search direction by estimating the influence of obstacles. Simulation results show that compared with RD, RA*and Greedy algorithm, the proposed algorithm reduces the number of traversed nodes and shows better time efficiency stability.
Game-Ready Inventory Systems for Virtual Reality

In this paper we present a requirement analysis of inventory systems for virtual reality (VR) games as well as three according, integrative prototypes, and we compare them to state-of-the-art approaches. Our key contribution integrates solutions for selection and manipulation of inventory items as required by various categories of adventure games. Therefore, we follow a user-centered engineering approach to VR inventory systems design. We further improved the prototypes implementing proprioceptive interactions and adapting established user interface design practices such as the application of layout patterns.
Machine Learning Algorithms for the Prediction of Daily Mean Solar Power

Daily Mean Solar Power is the main factor that is taken into consideration while a solar panel is being installed. The average solar irradiance data which is collected from the NASA satellite is being used as input to the model. The results obtained from the model showed the amount of solar energy that can be generated before any installation of solar panel at a given location by using the Machine Learning(ML) Algorithms. To predict the mean solar irradiance three regression algorithms are being used along with its parameters that are linked to solar irradiance. In the given work there is representation of a comparative analysis among these machine learning algorithms. Furthermore, the data is divided into four parts i.e. prediction of solar irradiance for one week, one month, one year and fifteen years data. This data is applied for three algorithms namely Decision Tree Regression, Linear Regression, Support Vector Regression. And the algorithm which produces least root mean square error is selected for prediction.
Facial expression recognition Using Machine Learning

Facial expression recognition (FER) plays an important role in computer vision. In this paper, we compare the result of two proposed methods of representing Facial landmarks detection and Feature Extraction. The first method is based on image processing (for example, histogram equalization, thresholding, color conversion, morphological operations, etc.) and the second one used the Dlib library to detect facial landmarks. We examine each feature descriptor by considering two classifications methods such as Support Vector Machine (SVM) and the Multi-layer Perceptron (MLP) with three facial expression databases(10k US Adult Faces Database, the MUG Facial Expression and personal database) to classified three different facial expressions: happiness, surprise and neutrality.The Experimental results demonstrate that the first proposed method shows 91.5% accuracy and more than 96% accuracy for the second method.
Investigating the Changes in Software Metrics after Vulnerability is Fixed

Preventing software vulnerabilities while writing code is one of the most effective ways for avoiding cyber attacks on any developed system. Although developers follow some standard guiding principles for ensuring secure code, the code can still have security bottlenecks and be compromised by an attacker. Therefore, assessing software security while developing code can help developers in writing vulnerability free code. Researchers have already focused on metrics-based and text mining based software vulnerability prediction models. The metrics based models showed higher precision in predicting vulnerabilities although the recall rate is low. In addition, current research did not investigate the impact of individual software metric on the occurrences of vulnerabilities. The main objective of this paper is to track the changes in every software metric after the developer fixes a particular vulnerability. The results of our research will potentially motivate further research on building more accurate vulnerability prediction models based on the appropriate software metrics. In particular, we have compared a total of 250 files from Apache Tomcat and Apache CXF. These files were extracted from the Apache database and were chosen because Apache released these files as vulnerable in their publicly available security advisories. Using a static analysis tool, metrics of the targeted vulnerable files and relevant fixed files (files where vulnerable code is removed by the developers) were extracted and compared. We show that eight of the 40 metrics have an average increase of 2% from vulnerable to fixed files. These metrics include CountDeclClass, CountDeclClassMethod, CountDeclClassVariable, CountDeclInstanceVariable, CountDeclMethodDefault, CountLineCode, MaxCyclomaticStrict, MaxNesting. This study will help developers to assess software security through utilizing software metrics in secure coding practices.
Probabilistic Inverse Velocity Obstacle for Free Flying Quadrotors

In this paper, we explore Probabilistic Inverse Velocity Obstacle (PIVO) as an alternative to probabilistic versions of Velocity Obstacles (PVO) for free flying quadrotor systems. Inverse Velocity Obstacles compute effective controls from a sequence of observations on other agents without the need to access ego state information. As a direct consequence of this the ego state noise is not entailed in probabilistic formulations bringing in verifiable advantages in the form of reduced path lengths, less conservative maneuvers, reduced occurrences of stopping/hovering to let others pass. These advantages are vividly tabulated in this paper, showcasing the efficacy of PIVO as an alternative to probabilistic versions of Velocity Obstacles. In particular we show the benefits of PIVO over PVO in relation to sample complexity as well as overall trajectory lengths. We also show the efficacy of our probabilistic formulation in handling non-parametric and often multimodal noise distributions.
“All-versus-nothing” proof of genuine tripartite steering and entanglement certification in the two-sided device-independent scenario

We consider the task of certification of genuine entanglement of tripartite states. For this purpose, we first present an "all-versus-nothing" proof of genuine tripartite Einstein-Podolsky-Rosen (EPR) steering by demonstrating the non-existence of a hybrid local hidden state (LHS) model in the tripartite network as a motivation to our main result. A full logical contradiction of the predictions of the hybrid LHS model with quantum mechanical outcome statistics for any three-qubit generalized Greenberger-Horne-Zeilinger (GGHZ) states and pure W-class states is shown. Using logical contradiction, we can distinguish between the GGHZ and W-class state in a two-sided device-independent (2SDI) steering scenario. We next formulate a 2SDI steering inequality which is a generalization of the fine-grained steering inequality (FGI) derived in \cite{PKM14} for the tripartite scenario. We show that the maximum quantum violation of this tripartite FGI can be used to certify genuine entanglement of three-qubit pure states.
A Proposed Solution to Build a Breast Cancer Detection Model on Confidential Patient Data using Federated Learning

Due to the increasing number of privacy breaches of personal data there is a need for the development of methods that function along with the intent of preserving user privacy. Keeping this in mind we have proposed an algorithm using a federated approach to predict whether or not a patient is suffering from breast cancer, using data from multiple hospitals. This approach ensures that the user data is protected. The federated approach provides the hospitals with a safe and secure way to train their models without having to send their data to a central server. We have compared our approach with the standard approach to evaluate the performance of the federated approach. We determined that the federated learning model was able to achieve an accuracy comparable with the conventional model. There are advantages as well as limitations of our approach, that have been discussed further. This paper discusses an overall idea of federated learning, the past works done in this field, and our approach to implement a solution.
Learning for action-based scene understanding

Action plays a central role in our lives and environments, yet most Computer Vision methods do not explicitly model action. In this chapter we outline an action-centric framework which spans multiple time scales and levels of abstraction, producing both action and scene interpretations constrained towards action consistency. At the lower level of the visual hierarchy we detail affordances – object characteristics which afford themselves to different actions. At mid-levels we model individual actions, and at higher levels we model activities through leveraging knowledge and longer term temporal relations. We emphasize the use of grasp characteristics, geometry, ontologies, and physics based constraints for generalizing to new scenes. Such explicit representations avoid overtraining on appearance characteristics. To integrate signal based perception with symbolic knowledge we align vectorized knowledge with visual features. We finish with a discussion on action and activity understanding, and discuss implications for future work.
Global trends health science libraries: Part 2

This is the second of three articles which explore trends in health science libraries. It is based on a series of articles called New Directions in Health Science Libraries published in a HILJ regular feature (International Perspectives and Initiatives) between June 2017 and March 2020. The series covered 12 countries: The United States, Canada, Australia, China, England, two countries in Africa (Uganda and Tanzania) and five in Europe (Sweden, Romania, Belgium, Germany, and Switzerland). The commissioning editor identified potential authors and invited them to write a short piece. They were given a briefing sheet which said: 'Your article should serve as a road map, describing the key changes in the field and explain the factors driving the changes'. A review of the 12 articles identified 11 trends. This is the article which explores the trends four trends, using examples provided by the authors. The trends covered are: Involvement in systematic reviews and data synthesis; Professional development for health science librarians; Providing education and training to students, researchers, and clinicians; Supporting the delivery of health literacy.
Data Selective Deep Neural Networks For Image Classification

As the volume of data keeps growing, the use of deep neural networks has been widespread in a variety of applications, including image classification. This big available data has led to an increasing interest in designing more efficient systems. Most applications use all training data without taking into account their relevance. A mini-batch gradient descent algorithm is preferable in practice, but the chosen batch size is typically based on empirical tests, and it depends on the dataset characteristics. This work proposes a data-selection strategy applied to classification problems leading to computational savings and, in most cases classification error reduction. A few examples corroborate the effectiveness of the proposed approach.