Reviewerly/exHarmony huggingface dataset:
A Multi-resolution Face Verification System for Crowd Images Across Varying Illuminations

Human face detection and verification in crowd images is an active area of interest for researchers due to their roles in real-world applications. Most of the real-world photo images are taken under varying illumination and the scale of the face is not the same always. In this paper, a system to verify a specific identity of an individual from crowd images is presented. This system contains two main phases, face detection and verification. The process of face detection is performed by utilizing the Viola and Jones face detection algorithm combined with a skin color based face detection scheme. Face verification is carried out by using an illumination insensitive measure over image pyramids. The hierarchical information obtained at different levels of the pyramid naturally provides better representation at different scales. Experimental results show that the proposed method performs considerably well across varying illumination conditions and scales.
Motorized Spindle Fault Diagnosis Technology Based on Deep Learning

Motorized spindle is a key functional component of machine tool. Its reliability and accuracy directly affect the operational reliability and machining accuracy of the entire CNC machine tool, and are even directly related to the production efficiency of the entire production line. The fault analysis of motorized spindle belongs to the category of reliability physics, and is the study of the fault phenomenon, mechanism, mode and hazard of motorized spindle. It is a necessary measure to realize the reliability growth of motorized spindle, and is a basic work ensuring the reliability of motorized spindle. The fault diagnosis of motorized spindle can provide a basis for subsequent reliability model establishment, reliability analysis and reliability design.
BVLCs and BDIPs in Covariance Descriptor with a Statistical Measure of Divergence for Image Retrieval

This paper proposes BVLC (block variation off local correlation coefficients) and BDIP (block difference of inverse probabilities) into covariance descriptors for image recognition and retrieval. The images in RGB is partitioned to R,G and B then BVLCs and BDIPs are computed for each channel then BVLCs and BDIPs in covariance among R,G and B is computed. The proposed BVLCs and BDIPs in covariance descriptor is extremely precious to reflect the degree of linear association among identified textures, edges and valleys. Degree of similarity among query and target images is analyzed using statistical measures of divergence namely Chernoff and Bhattacharya and results shown that Chernoff is noticeably outperform Bhattacharya measure. Comprehensive experiments on Corel- 1k, Corel- 5k and Corel-10k databases illustrates that proposed BVLCs and BDIPs in covariance descriptor is satisfactory and can attains significantly enhanced retrieval accuracies than traditional techniques.
Maintaining Diversity in an SVM integrated Case Based GA for Solar Flare Prediction

Unusually intense solar flares may cause serious calamities such as damages of electric/nuclear power plants. It is thereupon highly demanded, but is quite difficult, to predict intense solar flares due to the imbalanced character of the available data. To cope with this problem, we have heretofore developed and applied a Case Based Genetic Algorithm (CBGALO) that contains a Local Optimizer, which is a Support Vector Machine (SVM). However, the prediction performance significantly depends on input data for learning. Hereupon, CBGALO is further extended by a Case Based automatically restartable Good combination searching GA for both learning features and input data (CBRsGcmbGA). Even the powerful but computationally expensive Deep Learning cannot automatically (evolutionarily, in our approach) search the learning data. Our approach solved this problem a little better by the case-based approach. However, it became obvious that even this work suffers from the typical GA effect in falling into local optima. To improve the results, we hence developed newly a diversity maintenance approach that inserts good individuals with large Hamming distance into the case base as elite individuals in GA's population. In 2 out of 3 classes of solar flares, the performance of our new approach became as high as the best ones among the conventional world top records. Namely, even in those ≥ C class solar flares, our approach applying the Hamming distance to increase diversity had as high a performance 0.662 as compared with the conventional world top record 0.650.
Analysis of Global Motion Compensation and Polar Vector Median for Object Tracking Using ST-MRF in Video Surveillance

Paper presents tracking a single moving region in the compressed domain using a spatiotemporal Markov random field (ST-MRF) model. An ST-MRF model naturally integrates the spatial and temporal aspects of the region’s motion. Built upon such a model, the proposed method uses only the motion vectors (MVs) and block coding modes (BCMs) from the compressed bitstream to perform tracking. First, the MVs are pre-processed through intra-coded block motion approximation and global motion compensation (GMC). It is for region tracking in H.264/AVC-compressed video, which could be used to enhance important region analysis and, more generally, scene interpretation, in the compressed domain. It is well known that motion has a strong impact on saliency in dynamic scenes.
Tunable geometries from a sparse quantum spin network

Nonlocal light-mediated interactions between cold atoms coupled to the mode of an optical cavity present unique prospects for simulating the quantum dynamics of strongly-interacting many-body systems. In a recent publication, we introduced a tunable, nonlocal sparse spin network that can be engineered in near-term single-mode cavity QED platforms.<sup>1</sup> In this companion paper, we study this spin network in detail and pedagogically review its basic dynamical properties, providing theoretical details and calculations that expand on the statements made in our original publication. We show that the network exhibits two distinct notions of emergent geometry - linear and treelike - that can be accessed using a single tunable parameter. In either of these two extreme limits, we find a succinct description of the resulting dynamics in terms of two distinct metrics on the network, encoding a notion of either linear or treelike distance between spins. We also show that the network can be mapped in these two extreme limits onto exactly solvable models: a linear Heisenberg spin chain in one limit, and a Dyson hierarchical model in the other. These observations highlight the essential role played by the geometry of the interaction structure in determining a system's dynamics, and raise prospects for novel studies of nonlocal and highly chaotic quantum dynamics in near-term experiments.
Intelligent Exploration of Large-Scale Data: What Can We Learn in Two Passes?

Exploring large-scale data to determine an analysis approach is often made difficult by the sheer size of the data. If the characteristics of the data, including any variations within the data set, are not taken into account, our choice of algorithms and associated parameters may not be optimal, resulting in possibly inaccurate conclusions drawn from the data. Iteratively refining the analysis approach, as is normally done with smaller data sets, becomes prohibitively expensive for large-scale data. A typical solution is to randomly subsample the data set and determine the analysis algorithms using the characteristics of this subsample. In this paper, we propose the use of an improved sampling algorithm that is modified to identify well-distributed samples in a single pass through the data set. We then describe how we can use this subsample to probe the data in a second pass. Using very simple, low-cost algorithms, we demonstrate that the additional insight gained in this second pass can improve the process of analyzing large-scale data sets.
State Parameter Prediction of Fire Based on Extreme Learning Machine

Fire spread process can be simulated through Fire Dynamics Simulation (FDS) accurately. However, it is hard to assist fire rescue scheduling and real-time guidance of crowd evacuation due to its long computational time. To alleviate this problem, a fire surrogate model using extreme learning machine (ELM) is proposed to predict key state parameters of fire in the current study. The data obtained by FDS are used to train and test the fire surrogate model. The results indicate that the fire surrogate model has good prediction performance and short calculation time. Therefore, the proposed fire surrogate model can be used to help fire rescue and crowd evacuation. Finally, the number of hidden neurons and active function in ELM are analyzed.
Optimal Mounting Configuration of Fixed Bifacial PV Systems

In this work, the effects of configuration parameters upon the performance of a bifacial photovoltaic (PV) array is investigated. The relationships between different configuration parameters, including the tilt angle and height of modules, the distance between PV rows, the albedo of ground, and the total available solar radiation energy of the array are studied. A model is first developed for evaluating the total available solar radiation energy of a bifacial PV module, where the front-side irradiance of the module is calculated based on the Perez diffuse model, and the backside irradiance of the module is estimated based on the AOI (angle of incidence) correction factors. The effects of the configuration parameters on the solar radiation energy incident on the modules are investigated in detail. Based on the developed model, an optimal set of configuration parameters can be obtained for a given array in any location to achieve maximum energy generation.
Despeckling of Synthetic Aperture Radar (SAR) Images Using Local Statistics-Based Adaptive Filtering

Synthetic Aperture Radar (SAR) images are speckle corrupted right at the initial stage of their acquisition due to repeated backscattering signals from the uniformly distributed target sites. Addition of speckle noise in the images causes poor visualization, image analysis and image translation. For this reason, despeckling of the image data is important to not only remove the speckle but also preserve the finer details without losing them. In this paper, a method of despeckling using local statistics-based adaptive filtering has been proposed which can be used in all kinds of speckle environment especially in higher speckle levels. The filter is adaptive in nature as the size of the filtering window which is being used in this method can be varied to despeckle those regions with fully developed speckle and protect minute details. The filtering competency of the proposed filter is then scrutinized and compared using various metrics like Peak Signal-to-Noise Ratio (PSNR) and Speckle Suppression Index (SSI) on different speckle variances.
Analysis of the Problems of Industrial Enterprises Information Security Audit

The article analyzes the statistics of cyber attacks on industrial enterprises in 2018. The relevance of the information security audit is determined by the increased dependence of the success of the enterprise’s activities on the corporate information protection system as a whole. In order to protect from such attacks risks in the future, management of enterprises needs to take measures to ensure information security of production. This article describes the basic principles, goals and objectives of the audit of enterprises information security. In particular, it describes the general approach to conducting of IT audit at an enterprise. It is prove the importance of correct analysis of the enterprise’s belonging to the objects of critical information. In the course of the experiment, a security audit was conducted of one of the industrial enterprises of our region. The main problems arising during the audit of information security are listed on the example of the enterprise “Machine-Building Plant”. The results of the information security audit were presented and deficiencies in the provision of information security were shown.
Fast Face Recognition Model without Pruning

This paper proposes a real-time high accuracy face recognition model which can be deployed on performance-constrained embedded platforms or mobile terminals without pruning. In the proposed model, we optimize the network structure by balancing the memory access cost (MAC) and Floating point Operations (FLOPs) and propose a new factor to measure the efficiency of learning parameters. Based on these two metrics, we designed two structures called Simple SqueezeFaceNet (SSN) and Channel-Split Network (CSN) to achieve a good balance between the high precision and high FPS (Frames per Second). After being trained with the refined MS1M-refine-v2 dataset, our architectures achieve 0.992 (or 0.990) accuracy on the Labeled Faces in the Wild (LFW) with 155 (or 180) FPS. This indicates that our models can also be applied to real-time multi-face recognition in video.
호텔관광영어의 텍스트성 분석

The Journal of Studies in Language 34.4, 629-638. Language is communicated through discourse which consists of texts between a speaker and a hearer (receiver). Linguists have defined a text as the unit beyond a sentence. A text can be described in terms of textuality which identifies the properties and characteristics of a text. This paper aims to describe textuality which is uttered in hospitality English on the basis of seven criteria (cohesion, coherence, intentionality, acceptability, informativity, situationality, and intertextuality) suggested by Beaugrande and Dressler(1981). This paper shows all seven kinds of the textuality mentioned above are used in hospitality English discourse. We furthermore suggest that the seven criteria of textuality must be taught to the learners in the hospitality English. (Chungwoon University)
Forward Index Compression for Instance Retrieval in an Augmented Reality Application

Instance retrieval systems are widely used in applications such as robot navigation, medical diagnosis, and augmented reality. Blippar is a company that creates compelling augmented reality experiences or provides you with the tools to build your own. In this paper we focus on one of the company's augmented-reality applications, with which users are able to point their phone cameras at different objects in order to receive information about the objects in real time. In this paper, we provide what we believe to be the first study of forward index compression techniques for such instance retrieval systems. First, we perform an analysis of real-world data from a large-scale commercial instance retrieval system, run by Blippar focusing on augmented reality. Then we propose an entropy-based lossless compression strategy. Experiments show that our proposed Huffman-based approach outperforms a variety of other compression techniques, while also increasing overall system efficiency slightly.
Captioning Images on Mobile Devices Using Semi-Statistical Extraction

In this work, we propose a semi-statistical Image Extracting Image Semantic Framework using TensorFlow for Mobile Devices. We used datasets for training and generate extraction from other datasets of sentences using the N-Best algorithm selecting the closest sentence associated to images. In addition, we test our experiment with two models of Object Recognition and make comparisons between the different datasets of images and sentences.
Robust Features for Activities Recognition

When developing smart home system techniques for predicting a person's activities most are trained and evaluated by the same group of people. However, in practice, it is difficult and expensive to manually label each activity the participants perform. In this poster, we describe the findings of our initial investigations on simple features that we can use to predict an inhabitant's activities based on the machine learning model that is trained on activities done by different residents. Our study indicates that ultrasonic sensors' readings, duration and on/off states can accurately predict (86%) of the other resident's activities, even when the activities are interleaved with each other.
Philosophy of Information Technology and Its Contemporary Value

Information technologyInformation technology (IT) is the technology that determines the social characteristics of today, so the philosophy of informationPhilosophy of information technologyInformation technology (PIT) which takes information technologyInformation technology as the research object is undoubtedly the most important branch of philosophy nowadays, and also the contemporary form of the philosophy of technology. The main research contents of the PIT can be constructed based on the main research fields of philosophy, so as to form the ontology of ITPhilosophy of information technology ontology of, epistemology of ITPhilosophy of information technology epistemology of, and axiology of IT. Each part contains new philosophical issues and a new paradigm shift. Modern information technologyInformation technology is expanding the new frontier and bringing new challenges of philosophy, including the Internet of things, artificial emotionArtificial artificial emotion, artificial experience, brain-machine interface and so on. In this article, we take the latest development of these four areas and to explore their philosophical implication and value, thus presenting that the PIT is an open system, and a kind of philosophical research driven by frontier questions.
Improving Query Results in Ontology-Based Case-Based Reasoning by Dynamic Assignment of Feature Weights

Ontology-Based Case-Based Reasoning combines the effectiveness of memory-based problem-solving technique and semantic reasoning capabilities by integrating with domain ontologies. The knowledge base consists of cases where we search based on the given user query for case retrieval. The accuracy of the ranked results depends on the global similarity of the cases which in turn depends on the local similarity of case features. We transform user input into a query and conduct k-nearest neighbor search. However, the challenge is the default search mechanism relies on the static weights assigned to the various features of the case. This paper will focus on building a model to dynamically determine features weights and similarity functions based on the query provided by the user. With a camera features data set used for illustration, the results showed significant improvement in the ranking of results obtained by using dynamic weights as against the static ones. Finally, we discuss prospective techniques to further improve the ranking of the results.
Researching technologies for implementing physicality in digital multimedia system

The paper reports the exploratory search and development of the implementing technology for a type of multimedia prototype that solves the problem of children learning gap in digital multimedia learning, namely physical-digital multimedia system. The learning process for young children is different from adults. Their cognition and learning abilities are highly reliant on concrete stimuli. However, concrete stimuli that manifests the sense of physicality is never put forth in any circumstances of digital multimedia system, causing children to face various difficulties in learning. The scarce deployment of physical objects into multimedia may be ascribed to the immense challenges in terms of technicality in implementation. We therefore explore various devices to materialise the physical-digital multimedia. Our finding discovers that sensors are the technology that allows physical-digital multimedia implementable. At the end of this paper, a succinct discussion of potential research works in future is provided.
FADIT: Fast Document Image Thresholding

We propose a fast document image thresholding method (FADIT) and evaluations of the two classic methods for demonstrating the effectiveness of FADIT. We put forward two assumptions: (1) the probability of the occurrence of grayscale text and background is ideally two constants, and (2) a pixel with a low grayscale has a high probability of being classified as text and a pixel with a high grayscale has a high probability of being classified as background. With the two assumptions, a new criterion function is applied to document image thresholding in the Bayesian framework. The effectiveness of the method has been borne of a quantitative metric as well as qualitative comparisons with the state-of-the-art methods.
Real Full Binary Neural Network for Image Classification and Object Detection

We propose Real Full Binary Neural Network (RFBNN), a method that can reduce the memory and compute power of Deep Neural Networks. This method has similar performance to other BNNs in image classification and object detection, while reducing computation power and memory size. In RFBNN, the weight filters are approximated as a binary value by applying a sign function; we apply real binary weight to the whole layer. Therefore, RFBNN can be efficiently implemented on CPU, FPGA and GPU. Results of the all experiments show that the proposed method works successfully on various task such as image classification and object detection. All layers in our networks are composed of only {1, −1}, and unlike the other BNN, there is no scaling factor. Compared to recent network binarization methods, BC, BWN and BWRN, we have reduced the memory size and computation costs, but the performance is the same or better.
Eirene: Improving Short Job Latency Performance with Coordinated Cold Data Migration and Scheduler-Aware Task Cloning

In large-scale enterprise data centers for big data analytics, long batched jobs and short interactive jobs are usually mixed. Hybrid job schedulers, consisting of one centralized scheduler for long jobs and multiple distributed schedulers for short jobs, have become a promising alternative because they can significantly shorten latencies of short jobs via independent and parallelized assignment of short tasks by distributed schedulers and lower chances of head-of-line blocking via a number of performance optimization techniques.However, short jobs are still facing long job latencies under hybrid job schedulers due to workload fluctuation and straggler task problem. In this paper, we propose Eirene to optimize the latency performance of short jobs via two schemes tightly coupled into the general architecture of hybrid job schedulers. Coordinated Cold Data Migration leverages high task waiting time of short jobs under heavily-loaded periods and migrates cold data from disks to local memory for the initial phase of reading input so as to shorten task runtime and queueing time. On the other hand, Scheduler-Aware Task Cloning exploits spare computing resources under lightly-loaded periods and performs proactive task cloning for short jobs to mitigate the straggler problem.We implement a prototype of Eirene based on Eagle, a state-of-the-art hybrid job scheduler. Experimental results show that, under heavy loads, Eirene is able to improve 50-percentile (P50), 75-percentile (P75), 90-percentile (P90) latency performance of short jobs by up to 44.4%, 80.3%, 84.1% respectively compared with Eagle under the Facebook trace with a cluster of 50000 nodes.