Reviewerly/exHarmony huggingface dataset:
INTELLIGENT DATA ANALYSIS IN BIOMEDICAL RESEARCH: CONVOLUTIONAL ARTIFICIAL NEURAL NETWORKS

Convolutional neural networks are one of the modern tools that allow medical research to analyze and recognize not just a set of data about the objects under study or patients, but to use an image as the object under study. Due to the significant role of visual diagnostic methods in the provision of medical care, the use of intelligent recognition of the results of these methods becomes essential. At the moment, convolutional neural networks become widespread in research on the quality of diagnostics in various fields of medicine. However, complex mathematical apparatus behind convolutional neural networks function, and the tools for their construction limit implementation of these models into medical research and practice. This paper provides a gentle introduction to the methodology and application possibilities of convolutional neural networks in medical research. In this paper the reader will find methodological foundations behind convolutional neural networks, a description of a data set for building such models, an example of construction of a convolutional neural network model for classification of dermatoscopic images using TensorFlow and Keras libraries in Python as well as recommendations on how to present the results of building convolutional neural networks.
An Optimal and Stable Algorithm for Clustering Numerical Data

In the conventional k-means framework, seeding is the first step toward optimization before the objects are clustered. In random seeding, two main issues arise: the clustering results may be less than optimal and different clustering results may be obtained for every run. In real-world applications, optimal and stable clustering is highly desirable. This report introduces a new clustering algorithm called the zero k-approximate modal haplotype (Zk-AMH) algorithm that uses a simple and novel seeding mechanism known as zero-point multidimensional spaces. The Zk-AMH provides cluster optimality and stability, therefore resolving the aforementioned issues. Notably, the Zk-AMH algorithm yielded identical mean scores to maximum, and minimum scores in 100 runs, producing zero standard deviation to show its stability. Additionally, when the Zk-AMH algorithm was applied to eight datasets, it achieved the highest mean scores for four datasets, produced an approximately equal score for one dataset, and yielded marginally lower scores for the other three datasets. With its optimality and stability, the Zk-AMH algorithm could be a suitable alternative for developing future clustering tools.
A Real Haze Video Database for Haze Level Evaluation

Current video quality assessment methods mainly deal with videos with distortions generated in compression or capture process, few researchers focus on perceptual degradation caused by heavy haze in real outdoor scenes. However, assessment of haze level in such application scenario is important. For instance, in the advanced driver assistance system the accurate evaluation of haze level helps to properly release warning signals to drivers, which may avoid latent traffic accidents and save people's lives. It is necessary to develop studies on haze video quality assessment for real outdoor scenes, and to build benchmark dataset for haze videos. To fill the gap of this benchmark lack, a real haze video database called RHVD was designed and constructed. The dataset contains 403 haze videos captured in real outdoor scenes, which were collected from the Flickr video sharing platform and post-edited in our lab. We conducted subjective quality assessment experiments on the RHVD dataset. Forty non-experts were recruited and asked to rate the haze levels for each of the video in the dataset. The original ratings were screened and normalized to calculate the final subjective scores. The RHVD dataset is free to download from the following website: https://drive.google.com/file/d/1zY0LwJyNB8u1JTAJU2X7ZkiYXsBX7BF/view?usp=sharing, or https://pan.baidu.com/s/11p78zwMXmUHMGDj8WDanPA with password RHVD.
The Caucal hierarchy: Interpretations in the (W)MSO+<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:mi mathvariant="sans-serif">U</mml:mi></mml:math> logic

The Caucal hierarchy contains graphs that can be obtained from finite directed graphs by alternately applying the unfolding operation and inverse rational mappings. The goal of this work is to check whether the hierarchy is closed under interpretations in logics extending the monadic second-order logic by the unbounding quantifier, U (saying that a subformula holds for arbitrarily large finite sets). We prove that by applying interpretations described in the MSO+Ufin logic (hence also in its fragment WMSO+U) to graphs of the Caucal hierarchy we can only obtain graphs on the same level of the hierarchy. Conversely, interpretations described in the more powerful MSO+U logic can give us graphs with an undecidable MSO theory, hence outside of the Caucal hierarchy.
In-Air 3D Dynamic Signature Recognition using Haptic Devices

Signature recognition is one of the most widespread and legally accepted methodology to authenticate a person's identity. In this work, we show how a haptic device can be used to acquire in-air 3D signatures, and provide the time-dependent position and orientation characteristics needed to effectively perform user verification. Dynamic time warping and hidden Markov models are here employed to compare samples acquired during the enrolment and verification stages. The recognition performance achieved when testing the proposed system on samples captured from 52 subjects testify the effectiveness of the proposed approach. Furthermore, a longitudinal analysis carried out on data from a subset of 21 subjects, for which two recording sessions have been taken at an average distance of four months, demonstrates that effective recognition can be performed even at long time distances from the enrolment.
Viewing Direction Based LSB Data Hiding in 360° Videos

In this paper, we propose a viewing direction based least significant bit (LSB) data hiding method for 360° videos. The distributions of viewing direction frequency for latitude and longitude are used to control the amount of secret data to be hidden at the latitude, longitude, or both latitude and longitude of 360° videos. Normalized Gaussian mixture models mimicking the viewing behavior of humans are formulated to define data hiding weight functions for latitude, longitude, and both latitude and longitude. On this basis, analytical expressions for the capacity offered by the proposed method to hide secret data in 360° cover videos are derived. Numerical results for the capacity using different numbers of bit planes and popular 360° video resolutions for data hiding are provided. The fidelity of the proposed method is assessed in terms of the peak signal-to-noise ratio (PSNR), weighted-to-spherically uniform PSNR (WS-PSNR), and non-content-based perceptual PSNR (NCP-PSNR). The experimental results illustrate that NCP-PSNR returns the highest fidelity because it gives lower weights to the impact of LSB data hiding on fidelity outside the front regions near the equator. The visual quality of the proposed method as perceived by humans is assessed using the structural similarity (SSIM) index and the non-content-based perceptual SSIM (NCP-SSIM) index. The experimental results show that both SSIM-based metrics are able to account for the spatial perceptual information of different scenes while the PSNR-based fidelity metrics cannot exploit this information. Furthermore, NCP-SSIM reflects much better the impact of the proposed method on visual quality with respect to viewing directions compared to SSIM.
Efficient Enumeration of Regular Expressions for Faster Regular Expression Synthesis

We study the problem of synthesizing regular expressions from a set of positive and negative strings. The previous synthesis algorithm proposed by Lee et al. [12] relies on the best-first enumeration of regular expressions. To improve the performance of the enumeration process, we define a new normal form of regular expressions called the concise normal form which allows us to significantly reduce the search space by pruning those not in the normal form while still capturing the whole class of regular languages. We conduct experiments with two benchmark datasets and demonstrate that our synthesis algorithm based on the proposed normal form outperforms the previous algorithm in terms of runtime complexity and scalability.
Scalable All-pairs Shortest Paths for Huge Graphs on Multi-GPU Clusters

We present an optimized Floyd-Warshall (Floyd-Warshall) algorithm that computes the All-pairs shortest path (APSP) for GPU accelerated clusters. The Floyd-Warshall algorithm due to its structural similarities to matrix-multiplication is well suited for highly parallel GPU architectures. To achieve high parallel efficiency, we address two key algorithmic challenges: reducing high communication overhead and addressing limited GPU memory. To reduce high communication costs, we redesign the parallel (a) to expose more parallelism, (b) aggressively overlap communication and computation with pipelined and asynchronous scheduling of operations, and (c) tailored MPI-collective. To cope with limited GPU memory, we employ an offload model, where the data resides on the host and is transferred to GPU on-demand. The proposed optimizations are supported with detailed performance models for tuning. Our optimized parallel Floyd-Warshall implementation is up to 5x faster than a strong baseline and achieves 8.1 PetaFLOPS/sec on 256~nodes of the Summit supercomputer at Oak Ridge National Laboratory. This performance represents 70% of the theoretical peak and 80% parallel efficiency. The offload algorithm can handle 2.5x larger graphs with a 20% increase in overall running time.
Chemotactic Lion Swarm Optimization Based on SVM for Breast Disease Detection

In this paper, we propose a novel breast disease detection algorithm, Chemotactic Lion Swarm Optimization based on SVM (CLSO-SVM), which combines the improved Lion Swarm Optimization Algorithm (LSO) with Support Vector Machine (SVM). Firstly, we add chemotactic mechanism and difference mechanism to LSO algorithm, and propose a Chemotactic Lion Swarm Optimization (CLSO) Algorithm, which significantly improve the efficiency and precision on local optimization. Later, CLSO algorithm is used as a parameter selector for hyper-parameters in kernel function of SVM. Finally, CLSO-SVM algorithm overcomes the problem on random selection of hyper-parameters, and is applied to the diagnosis and classification for breast diseases. According to the experimental results, CLSO-SVM has good performance on classification prediction compared with several typical algorithms. At the same time, it provides a new method for parameter selection, and is more advantageous with fast convergence and high precision.
Efficient Adaptive Resource Management for Spot Workers in Cloud Computing Environment

Due to flexible scheduling requirements for various applications, a cloud platform usually has some temporarily unleased workers. To make cost effective, such considerable amount of idle workers can be collected to perform malleable tasks. These workers, called spot workers, often cannot work unstably since they may be interrupted by an external scheduler. This paper proposes a resource management approach that employs replication to increase resource availability. In fact, lifting the replication factor can increase the reliability that a worker can finish a job. But this oppositely decreases resource-usage efficiency due to computational redundancy. We also develop an algorithm that not only controls the replication factor to adapt the changing workload, but also maintains the system performance.
Exploring Generalization Capability for Video Forgery and Detection based on Generative Adversarial Network

With the development of digital image processing technology based on deep learning, the potential risk of using related technologies to threaten the security of multimedia information is increasing. Because the generated human face effect largely depends on the completeness of the input sample set, most of the current deep forgery models have the problem of human side-face collapse. This paper has studied the deep forgery technology of Deepfacelab and Faceswap, and adjusts the original auto-encoder-based model architecture to a generative adversarial network. By using the harmonic mean of cross entropy and mean square error as the loss function, the improved model can reduce the probability of some frames being discarded during training. Meanwhile, by adjusting key characteristics and the weights of features in different frames, it further optimizes the cross-dataset detection performance. Experimental results have shown that the improved model can keep more facial details while still maintain high human face clarity. The detection performance is improved and the cross-dataset average error rate of the deep detection model is about 35%.
Analyzing the Application of Two Main Microcontrollers in Engineering Education – A Case Study of three IEEE Conferences Focusing on Education

Microcontrollers have revolutionized the field of Engineering Education.Their popularity and cost-effectiveness have opened a large door of activity for innovative projects at both the undergraduate and graduate levels.The purpose of this article is to review this activity in terms of where two of these microcontrollers have been used in Engineering Education so as to recommend further possible applications.Focus is limited to papers presented at three IEEE-based conferences over the past 10 years that mentioned the Arduino or Raspberry Pi.Documentary analysis is thus used where the abstracts of the conference papers were reviewed.Results indicate that EDUCON dominated the field of microcontroller education from 2013 to 2016, while the last three years have seen more papers dedicated to this topic being presented at the FIE series of conferences.A total of nine papers relating to microcontroller education has also been presented at TALE between 2012 and 2019.The main application of these microcontrollers has been in the field of Robotics, with general electronics and design-based learning following suit.At least 11 conference papers focusing on the use of these microcontrollers at school level were found.Overall, the Arduino outranks the Raspberry Pi by almost 4:1, with the most cited papers relating to Robotics education, to helping students at home to complete science and technology experiments and to programming.Further applications can extend to energy monitoring and academic development workshops.
Detecting Keypoints for Automated Annotation of Bounding Boxes using Keypoint Extraction

Object detection requires an enormous amount of training data annotated by bounding boxes. All bounding boxes are manually drawn, which leads to highly expensive labor costs. Therefore, this study proposes automatic bounding box annotation of training data for object detection. The keypoints to identify object regions in pictures are extracted, which can then be used for drawing bounding boxes automatically, thus, reducing manual labor requirements. When our proposed method is used for pictures of road signs, keypoints that identify road sign regions in the pictures are detected; these keypoints are found to be highly accurate for drawing bounding boxes.
A Study on the Construction of Wisdom Classroom for Oral English Course in Higher Vocational College Based on Big Data and Artificial Intelligence

With the development of the internationalization of the world economy, English is also playing a very important role in the field of today's disciplines. The innovation of artificial intelligence and information technology makes the university education change obviously. In the future English teaching, colleges and universities should integrate computer application technology and tools into the teaching process of English courses, which provides a huge material and information basis for enhancing students' English social communication ability and English subject literacy ability. Therefore, this paper takes the construction of wisdom classroom as the starting point, fully discusses the concept, construction conditions and functions of wisdom classroom. On this basis, this paper takes civil aviation professional oral English as an example to explore the construction path of oral English wisdom classroom in higher vocational colleges under the background of big data and artificial intelligence, so as to provide reform ideas for the teaching development of higher vocational English.
Flexible-width Bit-level Compressor for Convolutional Neural Network

In this paper, a weight compression technique named Flexible-width Bit-level (FWBL) coding is proposed to compress convolutional neural networks (CNN) models without re-training. FWBL splits the weight parameters into independent size-optimized blocks and uses just-enough bits for each block. Bit-level run-length coding is employed on high bits (HBs) to further compress the redundancy due to non-uniformly distributed weights. We implemented a configurable hardware decoder and synthesize it with TSMC 28nm technology. Results show that FWBL achieves an average compression ratio of 1.6 which is close to the Huffman coding. The decoder has a throughput of 3.7GBps running at 1.1GHz, with a power dissipation of 3.55mW, which is 17.9x and 21x better in throughput and energy efficiency compared with the prior work. Implemented in FPGA, our decoder is 3.36x and 4.96x better than various Huffman decoders in throughput and area efficiency, making it a promising weight compression technique for mobile CNN applications.
A Perception-Based Brightness Segmentation Method of High Dynamic Range Infrared Images

In order to display the details from dark area to bright area in high dynamic range infrared image effectively, this paper proposes a brightness segmentation method for high dynamic range infrared image. The method considers the characteristics of human visual perception and the gray histogram distribution of high dynamic range infrared image. The proposed method is used to test the high dynamic range infrared images obtained in different scenes, and the experimental results show that the method is effective and robust. Based on the brightness region segmentation results of the proposed method, a number of different enhancement algorithms can be applied selectively to the different regions of high dynamic range infrared image to achieve a better overall enhancement than applying a single technique globally.
DISC: Density-Based Incremental Clustering by Striding over Streaming Data

Given the prevalence of mobile and IoT devices, continuous clustering against streaming data has become an essential tool of increasing importance for data analytics. Among many clustering approaches, the density-based clustering has garnered much attention due to its unique advantages. The main drawback is, however, the limited scalability attributed to its relatively high computational cost, which is further aggravated when it has to update clusters continuously along with evolving data. In this paper, we present a new incremental density-based clustering algorithm called DISC optimized for the sliding window model. DISC is capable of producing exactly the same clustering results as existing methods such as Incremental DBSCAN for streaming data much more quickly and efficiently.
An Adversarial Examples Defense Method Based on Image Low-Frequency Information

Convolutional neural networks have been applied in image recognition widely, and acquired well-known results. However, the discovery of adversarial examples in recent years has attracted many researcher's attention on the safety and reliability of convolutional neural networks. Adversarial examples are small perturbations added only to the clean image data, which could cause the trained model makes wrong identifications, while people could still recognize. In this paper, we propose a new convolutional neural network to defense the adversarial examples. It breaks into two branches after inputting an image. One branch likes a normal image recognition model, while another branch distills the low-frequency information of the image firstly, and then uses the low-frequency image to train. The two branches combine to make the output of the model. The presented model uses the image's low-frequency information, through which people often recognizes an object. The experiments show that the presented model suppresses the success rate of the attack in the face of the adversarial examples, and improves the model's recognition success rate, comparing to some other methods.
VikingBot: Towards a Hybrid Artificial Intelligence for Starcraft

The focus of this research is to develop a StarCraft bot that makes efficient use of computing resources. To this end, VikingBot employs a hybrid approach that pairs AI Planning and Machine Learning. The AI planning component handles high-level strategy management and the Machine Learning component handles individual control units in combat. In this paper, we describe our high-level approach and report initial results. This is an interim report on a work in progress.
HidSave: An Image Steganography Technique based on SudoKu Method for Smartphones

In Internet of things (IoT) era, every device in the world is generating and uploading data. These data has to reach end devices for analysis, analytics and visualization. Smartphones are part of IoT era where they can generate and manipulate data. However, the processing power and the storage capacity is limited for this devices to reduce their sizes and their power usage. However, the data generated from these devices have to be secured before transmitted to other end point. In this work, a new steganography image data hiding is proposed, named HidSave. The algorithm is proposed to increase the privacy of the data in one hand and to reduce the storage capacity of the data on the other hand. HidSave adopts SudoKu game in embedding the data in the cover images. HidSave has been implemented and compared to lest significant bit (LSB) and turtle shell hiding algorithm. HidSave has higher embedding capacity and higher PSNR.
A PRACTICAL MATURITY FOR INFORMATION SECURITY POLICY IN ORGANIZATIONS

The Information Systems Security Policy reflects the Management’s expectations and requirements concerning the Information System. It shall take account, at a minimum, of the needs relating to the availability, confidentiality, and privacy of the software and data used and exchanged on networks and systems and shall consolidate a collection of scientific, operational, legal and human security rules and standards to ensure an efficient and homogeneous level of security. The paper aims to examine what controls are widely used and how they are implemented in the Middle East and North Africa MENA area to apply a realistic information management strategy in large public organizations. The finding will assist in adopting an efficient information security policy.