Reviewerly/exHarmony huggingface dataset:
Distance Transform Based on Weight Sequences

There is a continuous effort to develop the theory and methods for computing digital distance functions, and to lower the rotational dependency of distance functions. Working on the digital space, e.g., on the square grid, digital distance functions are defined by minimal cost-paths, which can be processed (back-tracked etc.) without any errors or approximations. Recently, digital distance functions defined by weight sequences, which is a concept allowing multiple types of weighted steps combined with neighborhood sequences, were developed. With appropriate weight sequences, the distance between points on the perimeter of a square and the center of the square (i.e., for squares of a given size the weight sequence can be easily computed) are exactly the Euclidean distance for these distances based on weight sequences. However, distances based on weight sequences may not fulfill the triangular inequality. In this paper, continuing the research, we provide a sufficient condition for weight sequences to provide metric distance. Further, we present an algorithm to compute the distance transform based on these distances. Optimization results are also shown for the approximation of the Euclidean distance inside the given square.
Detector-in-Detector: Multi-level Analysis for Human-Parts

Vision-based person, hand or face detection approaches have achieved incredible success in recent years with the development of deep convolutional neural network (CNN). In this paper, we take the inherent correlation between the body and body parts into account and propose a new framework to boost up the detection performance of the multi-level objects. In particular, we adopt region-based object detection structure with two carefully designed detectors to separately pay attention to the human body and body parts in a coarse-to-fine manner, which we call Detector-in-Detector network (DID-Net). The first detector is designed to detect human body, hand and face. The second detector, based on the body detection results of the first detector, mainly focus on detection of small hand and face inside each body. The framework is trained in an end-to-end way by optimizing a multi-task loss. Due to the lack of human body, face and hand detection dataset, we have collected and labeled a new large dataset named Human-Parts with 14,962 images and 106,879 annotations. Experiments show that our method can achieve excellent performance on Human-Parts.
Illumination-Invariant Face Recognition by Fusing Thermal and Visual Images via Gradient Transfer

Face recognition in real life situations like low illumination condition is still an open challenge in biometric security. It is well established that the state-of-the-art methods in face recognition provide low accuracy in the case of poor illumination. In this work, we propose an algorithm for a more robust illumination invariant face recognition using a multi-modal approach. We propose a new dataset consisting of aligned faces of thermal and visual images of a hundred subjects. We then apply face detection on thermal images using the biggest blob extraction method and apply them for fusing images of different modalities for the purpose of face recognition. An algorithm is proposed to implement fusion of thermal and visual images. We reason for why relying on only one modality can give erroneous results. We use a lighter and faster CNN model called MobileNet for the purpose of face recognition with faster inferencing and to be able to use it in real time biometric systems. We test our proposed method on our own created dataset to show that real-time face recognition on fused images shows far better results than using visual or thermal images separately.
On Possibility and Impossibility of Multiclass Classification with Rejection.

We investigate the problem of multiclass classification with rejection, wherea classifier can choose not to make a prediction to avoid criticalmisclassification. We consider two approaches for this problem: a traditionalone based on confidence scores and a more recent one based on simultaneoustraining of a classifier and a rejector. An existing method in the formerapproach focuses on a specific class of losses and its empirical performance isnot very convincing. In this paper, we propose confidence-based rejectioncriteria for multiclass classification, which can handle more general lossesand guarantee calibration to the Bayes-optimal solution. The latter approach isrelatively new and has been available only for the binary case, to the best ofour knowledge. Our second contribution is to prove that calibration to theBayes-optimal solution is almost impossible by this approach in the multiclasscase. Finally, we conduct experiments to validate the relevance of ourtheoretical findings.
On Certifying Non-Uniform Bounds against Adversarial Attacks

This work studies the robustness certification problem of neural network models, which aims to find certified adversary-free regions as large as possible around data points. In contrast to the existing approaches that seek regions bounded uniformly along all input features, we consider non-uniform bounds and use it to study the decision boundary of neural network models. We formulate our target as an optimization problem with nonlinear constraints. Then, a framework applicable for general feedforward neural networks is proposed to bound the output logits so that the relaxed problem can be solved by the augmented Lagrangian method. Our experiments show the non-uniform bounds have larger volumes than uniform ones. Compared with normal models, the robust models have even larger non-uniform bounds and better interpretability. Further, the geometric similarity of the non-uniform bounds gives a quantitative, data-agnostic metric of input features’ robustness.
Specialized memory systems for learning spoken words.

Learning new words entails, inter alia, encoding of novel sound patterns and transferring those patterns from short-term to long-term memory. We report a series of 5 experiments that investigated whether the memory systems engaged in word learning are specialized for speech and whether utilization of these systems results in a benefit for word learning. Sine-wave synthesis (SWS) was applied to spoken nonwords, and listeners were or were not informed (through instruction and familiarization) that the SWS stimuli were derived from actual utterances. This allowed us to manipulate whether listeners would process sound sequences as speech or as nonspeech. In a sound-picture association learning task, listeners who processed the SWS stimuli as speech consistently learned faster and remembered more associations than listeners who processed the same stimuli as nonspeech. The advantage of listening in "speech mode" was stable over the course of 7 days. These results provide causal evidence that access to a specialized, phonological short-term memory system is important for word learning. More generally, this study supports the notion that subsystems of auditory short-term memory are specialized for processing different types of acoustic information. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
Using the Game Engine in the Animation Production Process

The latestBąk, Artur animated filmsWojciechowska, Marzena from major studios, such as Pixar and Disney, make a great impression, but not everyone is aware that the production process of such films absorbs the work of hundreds of specialists, and their budgets often exceed hundreds of millions of dollars. The way of creating these films has not changed for decades, and the quality and realism, both constantly improving as the audience needs, are achieved by constantly increasing the resources of both human and computer power. Thanks to the recent advances in hardware technology, in particular graphics cards, as well as the development of game engines oriented not only on programmers, but also on artists, ideas appeared for their use to optimize the traditional pipeline in animated film production. This paper describes the traditional production pipeline with factors causing its high costs, the concept of its optimization by game engine together with the latest practical examples and describes the most important challenges of this new trend.
Video loss prediction model in wireless networks

This work discusses video communications over wireless networks (IEEE 802.11ac standard). The videos are in three different resolutions: 720p, 1080p, and 2160p. It is essential to study the performance of these media in access technologies to enhance the current coding and communications techniques. This study sets out a video quality prediction model that includes the different resolutions that are based on wireless network terms and conditions, an approach that has not previously been adopted in the literature. The model involves obtaining Service and Experience Quality Metrics, such as PSNR (Peak Signal-to-Noise Ratio) and packet loss. This article outlines a methodology and mathematical model for video quality loss in the wireless network from simulated data and its accuracy is ensured through the use of performance metrics (RMSE and Standard Deviation). The methodology is based on two mathematical functions, (logarithmic and exponential), and their parameters are defined by linear regression. The model obtained RMSE values and standard deviation of 2.32 dB and 2.2 dB for the predicted values, respectively. The results should lead to a CODEC (Coder-Decoder) improvement and contribute to a better wireless networks design.
A NOVEL APPROACH BASED ON ELEPHANT HERDING OPTIMIZATION FOR CONSTRAINED OPTIMIZATION PROBLEMS

Many real-world problems can be formulated as an optimization problem and they have some constraints generally. To overcome these constraints, bio-inspired algorithms are adapted to constrained optimization using constraint handling methods and some modifications. In this study, a new approach is developed to solve constrained optimization problems with elephant herding optimization algorithm which is a newly-emerging optimization technique. Besides the basic EHO, two EHO variants (EHO-NoB and GL-EHO) are adapted to constrained optimization with this approach. The well-known thirteen constrained benchmark functions are used to analysis the performances of algorithms. Experimental results show that the GL-EHO has a better performance than the basic EHO and other algorithms. In addition, the results of GL-EHO are comparable level with the result of another EHO variant in the literature.
The Effect of EMDR Versus Guided Imagery on Insomnia Severity in Patients With Rheumatoid Arthritis

This study compared the effect of eye movement desensitization and reprocessing (EMDR) therapy versus guided imagery on insomnia severity in patients with rheumatoid arthritis (RA). In this randomized controlled trial, 75 patients with RA were selected via convenience sampling before using block randomization to assign patients into three groups comprised of (a) six sessions of EMDR, (b) six sessions of guided imagery, and (c) a control group. The Persian version of the Insomnia Severity Index was implemented at preintervention and 2 weeks' postintervention as the outcome measure. The EMDR group obtained respective pre-and postintervention mean scores of 23.5 ± 5.2 and 11±2.1, whereas the guided imagery group obtained scores of 24 ± 3 and 15.3 ± 2.3, and the control group obtained scores of 24.2 ± 3.3 and 23.6 ± 3. Pairwise comparisons showed statistically significant differences in insomnia severity between patients from each group, with the EMDR group experiencing a greater reduction in insomnia severity than guided imagery. EMDR and guided imagery were both effective in reducing insomnia severity in RA patients, although the degree of insomnia reduction for patients from the EMDR group was greater than that of the guided imagery group.
Utilization of Imprecise Rules for Privacy Protection

In this paper, we utilize imprecise rules for privacy protection in the publication of data sets. We assume that data sets show the classification results with more than two classes. First k-anonymous imprecise rules are induced. Using several k-anonymous imprecise rules explaining an object, the object is replaced with several imprecise patterns corresponding to the k-anonymous imprecise rules which are common in at least k objects. In this way, privacy protected data tables are composed. The proposed data table is investigated by numerical experiments from its usefulness in rule induction as well as from its privacy protection ability. The results show that the proposed method will be satisfactorily useful.
Predictive Analytics and Deep Learning Techniques in Electronic Medical Records: Recent Advancements and Future Direction

The demands on medical services are increasing rapidly in the global context. Therefore, handling beds availability, identifying and managing the length of stay (LOS) is creating persistent needs for the physicians, nurses, clinicians, hospital management, and caregivers in the public hospital admissions and the private hospital admissions. Health analytics provides unprecedented ways to predict trends, patients’ future outcomes, knowledge discovery, and improving the decision making in the clinical settings. This paper reviews the state-of-the-art machine learning, deep learning techniques and the related work in relation to the length of stay common hospital admissions. Research trends and future direction for the forecasting LOS in medical admissions are discussed in this paper.
Complementarity-Preserving Fracture Morphology for Archaeological Fragments

We propose to employ scale spaces of mathematical morphology to hierarchically simplify fracture surfaces of complementarily fitting archaeological fragments. This representation preserves complementarity and is insensitive to different kinds of abrasion affecting the exact fitting of the original fragments. We present a pipeline for morphologically simplifying fracture surfaces, based on their Lipschitz nature; its core is a new embedding of fracture surfaces to simultaneously compute both closing and opening morphological operations, using distance transforms.
Digital Cultural Heritage: From Educational Experience to the Artefact in Augmented Reality

Several national and international research studies have highlighted important aspects linked to the application of augmented reality in the educational contexts, with particular reference to education concerning the cultural patrimony. The augmented reality ambients indeed offer different training opportunities: some with cognitive finalities others in terms of active participation, others, again by means of the realisation of experiences of an expressive-creative kind with high coefficients of imagination. Specifically speaking, this contribution aims to propose the results of research carried out on three case studies, relating to school projects that are situated in the field of a process of knowledge, participation and recreation of the cultural patrimony in ambients of augmented reality through the construction of new digital artefacts.
Technical note: Next generation identification — A powerful tool in cold case investigations

Ever changing technology is allowing law enforcement/crime laboratories the ability to use new advances to solve old cases. Exonerations through advances in DNA testing have become increasingly common. However, other forensic disciplines are also utilizing new technologies, which are providing investigative leads and case closures. One advancement is the Federal Bureau of Investigation’s Next Generation Identification (NGI) biometric system, which has enhanced the field of print identification. The Oklahoma State Bureau of Investigation (OSBI) Latent Evidence Unit is utilizing NGI to assist in solving “Cold Cases.” While the OSBI cold case project has been successful, reviewing old evidence and case files, while keeping up with current casework, can be challenging. However, the recognition and application of such powerful technology is warranted, as demonstrated by the success that has been achieved.
High-capacity and Transparent Digital Audio Watermarking using Rounding Reduced-arc MPSK and a Genetic Algorithm

In this paper, a novel, high-capacity and transparent blind audio watermarking system based on rounding reduced-arc M-ary phase shift keying (MPSK) is presented. Signal samples of the audio file are divided into several frames and then transformed into the frequency domain using fast Fourier transform. The watermark is embedded in the phase element of the selected samples based on several criteria. Two modified modulation schemes (256-PSK and binary phase shift keying) are used for the embedding process to compare the effects of the M-ary number used in the system. To improve the system, a genetic algorithm is utilized to obtain the best embedding parameters, which produces the optimal output based on the perceptual quality of the watermarked audio and the robustness of the extracted watermark. Experimental results show that the proposed audio watermarking system produces high-quality watermarked audio while loading a huge amount of data.
Classical algorithm for quantum SU(2) Schur sampling

Many quantum algorithms can be represented in a form of a classical circuit positioned between quantum Fourier transformations. Motivated by the search for new quantum algorithms, we turn to circuits where the latter transformation is replaced by the SU(2) quantum Schur transform---a global transformation which maps the computational basis to a basis defined by angular momenta. We show that the output distributions of these circuits can be approximately classically sampled in polynomial time if they are sufficiently close to being sparse, thus isolating a regime in which they could lead to algorithms with exponential computational advantages. Our paper is primarily motivated by a conjecture that underpinned the hardness of permutational quantum computing, a restricted quantum computational model that has the above circuit structure in one of its computationally interesting regimes. The conjecture stated that approximating transition amplitudes of permutational quantum computing model to inverse-polynomial precision on a classical computer is computationally hard in this case. We disprove the extended version of this conjecture---even in the case when the hardness of approximation originated from a difficulty of finding the large elements in the output probability distributions. Finally, we present some evidence that output of the above permutational quantum computing circuits could be efficiently approximately sampled from on a classical computer.
Data sets for spoken conversational search

There is increasing interest in spoken conversational search—multi-turn interactions with a search engine, spoken in natural language—but until recently there was little public data to support research. We describe our experiences building two data sets for spoken conversational search: the Microsoft Information-Seeking Conversation set (“MISC”) and the Spoken Conversational Search set (“SCSdata”). Each data set contains recordings of spoken interactions between two people collaborating on web search tasks, but relatively small differences in protocol have led to observably different data. We discuss some consequences of these differences, and describe attempts to reproduce analyses from one set to the other.
Transliteration of Braille Character to Gujarati Text The Application

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Under-Sampled Phase Retrieval of Single Interference Fringe Based on Hilbert Transform

Phase retrieval from single interference fringe is important and effective method in obtaining the real phase distribution. The original phase can be retrieved by the line integral of its gradient expressed as sine and cosine components, which were gained by the Hilbert transform twice from a single interference fringe pattern. However, this method fails when the phase transformation of the interference fringe is too fast. In this paper, a novel method to recover the continuous phase of the whole field is proposed to solve the above problems. The shear interference technique is introduced into the phase retrieval method to build an exponential 2-D complex light field of natural base for the phase slope obtained by the Hilbert transform. Then, the expressions of phase slopes in x- and y-directions are constructed as a discrete Poisson equation. Therefore, the calculation of phase retrieval is equivalent to solve the discrete Poisson equation mathematically. Finally, the real phase is gotten by the weighted discrete cosine transform (WDCT) of the discrete Poisson equation. The simulation results verify the validity of this method and show that the proposed method can achieve the phase retrieval of the phase discontinuity in x- and y-directions, which leads to the under-sampled problem. It can restore the whole field phase distribution rapidly and accurately. Moreover, this method is applied to phase retrieval of interferometric synthetic aperture radar (InSAR) with the under-sampled problem in this paper. The experimental results show that this method can recover the phase of InSAR with the under-sampled problems caused by terrain abrupt change and so on. Compared with other commonly used methods, it achieved satisfactory results. This method provides a new idea for solving the under-sampled problem in the phase retrieval from a single-frame interference fringe.
Performance Evaluation of Deep Learning Frameworks over Different Architectures

We evaluate the performance of two well-known Deep Learning frameworks – Caffe and TensorFlow – on two different types of computing devices – GPU and NUMA CPU architecture – using two popular network models as benchmark – AlexNet and GoogLeNet. We variate batch sizes between trainings and estimate the average training time per iteration and per image on each configuration. Both frameworks presented similar times for the AlexNet model, and TensorFlow outperforms Caffe by presenting times up to 2 times lower than Caffe for the GoogLeNet Model. The work also presents the impact of lack of support by the frameworks for NUMA Architectures, and relates a problem stated on loss computation by the Caffe Framework.
Visualizing Attention in Transformer-Based Language models

We present an open-source tool for visualizing multi-head self-attention in Transformer-based language representation models. The tool extends earlier work by visualizing attention at three levels of granularity: the attention-head level, the model level, and the neuron level. We describe how each of these views can help to interpret the model, and we demonstrate the tool on the BERT model and the OpenAI GPT-2 model. We also present three use cases for analyzing GPT-2: detecting model bias, identifying recurring patterns, and linking neurons to model behavior.