Reviewerly/exHarmony huggingface dataset:
Automated Brand Color Accuracy for Realtime Video

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">The perception of color in sports teams and branding alike is a highly valued asset in broadcasting and marketing that is worth protecting. The appearance of color is impacted by many factors including capture source, ambient light, and the limitations of screen technology. The current industry practice involves manual adjustment by a technician, who corrects the footage from a dozen or more camera feeds in realtime. This method adjusts the entire video frame based on the producer’s instructions. This paper presents a -pending, machine-learning approach titled “ColorNet” that targets brand color areas of a frame to output a color-corrected feed in realtime. Initial tests of ColorNet demonstrate the ability to reproduce manually created correction masks with high accuracy, while also being computationally efficient. Discussion includes system training and development and plans for a beta test where the system will be applied during a live broadcast of a sporting event in partnership with Clemson University Athletics</i> .
The Role of Virtual Reality in Criminal Justice Pedagogy: An Examination of Mental Illness Occurring in Corrections

There is an absence of empirical studies that document the efficacy of interactive technology within a criminal justice pedagogy. The current study examines qualitative responses from a general criminal justice course and a special topics mental health and criminal justice course that included virtual reality (VR). This includes five classes taught in two states between 2019–2020, with a total sample of two-hundred and forty-two (n = 242) undergraduate students. Participant responses were coded by changes in perceptions, emotional content, and overall satisfaction with the VR experience. Findings reveal the presence of crime myths that were altered by the VR experience. The VR experience produced an emotional response that facilitated learning. Qualitative themes include concepts of realism, motivated learning, increased memory recall, deeper meaning in learning, the inclusion of visual learning techniques and validation of overall student satisfaction. This suggests that virtual reality technologies may be an effective though rarely used technology in criminal justice education.
ExKaldi-RT: A Real-Time Automatic Speech Recognition Extension Toolkit of Kaldi

This paper describes the ExKaldi-RT online automatic speech recognition (ASR) toolkit that is implemented based on the Kaldi ASR toolkit and Python language. ExKaldi-RT provides tools for building online recognition pipelines. While similar tools are available built on Kaldi, a key feature of ExKaldi-RT that it works on Python, which has an easy-to-use interface that allows online ASR system developers to develop original research, such as by applying neural network-based signal processing and by decoding model trained with deep learning frameworks. We performed benchmark experiments on the minimum LibriSpeech corpus, and it showed that ExKaldi-RT could achieve competitive ASR performance in real-time recognition.
Blockchain Enabled and Changeable Threshold-Based Group Specific Multiple Keys’ Negotiation

The increase in networking along with communications taking place through the open network have resulted in multifarious intensification of rancorous and vengeful blitzkrieg. Our sensitive data, hence, is always at high risk of getting misused. In this paper, we present our proposed method of changeable threshold-based secrecy scheme, where, whenever required, the threshold can be easily changed. For this, we modify the original Shamir’s (t, n) scheme because it has some inherent flaws of cheating and also because it was not possible to change the threshold there. The transpiring blockchain technology has favorable properties like it being very secure, and so, we incorporate it in our technique. We make use of the high computation dependent proof-of-work to provide reliable security to our mechanism. Our method is capable to procure multiple secure keys for an individual group. However, all the participants must collaborate together. All different groups will have unique secret keys. Not only adversaries are prevented from cheating, but also fairness of dealer is ensured by preventing from getting involved in partiality.
Advertising and Spams: How to Recognize the Lie and Reality in Social Media

Spamming has become a developing issue wherever all through the world. As the internet develops so does the issue of spamming. With the headway of the internet this issue has developed to such a phase, that cybercrime has become a typical thing between us. For this kind of cybercrime, fake accounts are favored approaches. Henceforth, the priority should be to detect and destroy these malicious identities as quick as possible to make sure that the genuine person can be saved from these spammers and to keep up the unwavering trustworthiness of the network. In any case any individual phony record may have all the stores of being authentic on first assessment, for instance by having a genuine sounding name or a satisfactory profile. With the implementation of three different machine learning algorithms this paper presents a comparative study that analyses which algorithm is best for the identifying these fake accounts. The key features utilized in the model are experiences on fields of customer made substance, for example, name, email address, affiliation, or school.
Design of distance sensing and distance using quadcopter drone based on face recognition method

Abstract With the development of communication and automation technology at this time, the Robot is widely used as a tool to assist the task of long-distance operations in the army combat unit. Especially for tasks that require high mobilization and tasks that have a relatively high level of danger, therefore the use of Unmanned Aerial Vehicles for remote sensing is developing very rapidly both in the field of civilian and military defense equipment. So in this study the researchers will develop the use of UAVs, especially in the type of quadcopter drones for remote sensing enemy areas. In this case, enemy surveillance still uses a lot of personnel, so monitoring the area requires a large amount of personnel. Conditions like this that can require high concentration and will drain personnel and logistics. The use of this remote sensing robot can minimize the use of TNI AD personnel directly, because in the sensing it is replaced by the role of visualization on the drone and has the ability to determine recognize the enemy by adding a face recognition system, so that later the tool is able to recognize enemies by detecting captured faces by the drone’s camera.
A Deep Multi-Frame Super-Resolution Network for Dynamic Scenes

The multi-frame super-resolution techniques have been prosperous over the past two decades. However, little attention has been paid to the combination of deep learning and multi-frame super-resolution. One reason is that most deep learning-based super-resolution methods cannot handle variant numbers of input frames. Another reason is that it is hard to capture accurate temporal and spatial information because of the misalignment of input images. To solve these problems, we propose an optical-flow-based multi-frame super-resolution framework, which is capable of dealing with various numbers of input frames. This framework enables to make full use of the input frames, allowing it to obtain better performance. In addition, we use a spatial subpixel alignment module for more accurate subpixel-wise spatial alignment and introduce a dual weighting module to generate weights for temporal fusion. Both two modules lead to more effective and accurate temporal fusion. We compare our method with other state-of-the-art methods and conduct ablation studies on our method. The results of qualitative and quantitative analyses show that our method achieves state-of-the-art performances, demonstrating the advantage of the designed framework and the necessity of proposed modules.
Cricket Stroke Recognition Using Hard and Soft Assignment Based Bag of Visual Words

In this work, we deal with the problem of activity recognition in Cricket telecast videos. We present a supervised approach for recognizing Cricket stroke categories using two variants of Bag of Visual words (BoV) model applied on dense optical flow based motion feature i.e., grid-based flattened vectors and orientation histograms, 3D ResNet extracted features and 2D ResNet extracted spatial features. These globally extracted features, in spite of the noise due to camera motion, give good results on the Cricket strokes dataset having 562 trimmed stroke videos. We independently labeled the strokes based on the direction of stroke play and the direction of camera motion, into five and three categories. respectively, and provide experimental analysis on Hard Assignment (HA) and Soft Assignment (SA) based BoV methods. Our contribution lies in showing the effectiveness of the unordered BoV representation for direction based action recognition. The experimental analysis presented in our work provides an insight into the BoV HA and SA models applied to global frame-level feature descriptors. Moreover, our experiments suggest the orientation histograms to be simple yet effective for direction based recognition tasks. The best accuracy achieved on the Cricket strokes test partition was 85.85% with orientation histograms and 82.08% with grid features, for 3 category and 5 category labels, respectively.
Functions of Imagined Interactions With AI Among Alexa-Users

Humans and agents of artificial intelligence (AI) participate in human-machine communication (HMC) more frequently now than ever before – especially in the U.S. Voice powered assistants (VPAs) are widely accessible software agents that enact various social roles, such as personal assistants, and are increasingly packaged with AI-devices to complete simple-tasks, such as sending texts, more efficiently. VPAs are designed to mimic human-human interactions (HHIs) to facilitate more natural human-VPA interactions (HVPAIs). The focus of this study is on the psychological effects of HVPAIs with Amazon’s VPA, Alexa, to identify predictors of frequent Alexa-use through six functions of imagined interactions (IIs) – rehearsal, self-understanding, relational maintenance, conflict linkage, compensation and catharsis. A modified survey of imagined interaction was distributed to 810 self-reported Alexa-users recruited through Amazon’s Mechanical Turk (MTurk). Results suggest that HVPAIs with Alexa impacts the imagination of participants similarly to HHIs, and that use of specific functions of IIs are significant negative predictors of Alexa-use. Moreover, the inclusion of machine-interlocutors as part of imagined interaction theory appears to be compelling as humans and machine interactions evolve in the 21st century.
AI-Based Culture Independent Pervasive M-Learning Prototype Using UI Plasticity Design

This paper explains the development of a culturally inclusive ubiquitous M-Learning platform ("Mobile Academy") with an AI-based adaptive user interface. The rationale and need for this research and development are justif... | Find, read and cite all the research you need on Tech Science Press
Near-optimal estimation of the unseen under regularly varying tail populations

Given n samples from a population of individuals belonging to different species, what is the number U of hitherto unseen species that would be observed if λn new samples were collected? This is the celebrated unseen-species problem, which has been the subject of recent breakthrough studies introducing non-parametric estimators of U that are minimax near-optimal and consistent all the way up to λ≍logn. These works do not rely on assumptions on the underlying unknown distribution p of the population, and therefore, while providing a theory in its greatest generality, worst-case distributions may hamper the estimation of U in concrete settings. In this paper, we strengthen the non-parametric framework for estimating U, making use of suitable assumptions on p. Inspired by the estimation of rare probabilities in extreme value theory, and motivated by the ubiquitous power-law type distributions in many natural and social phenomena, we make use of a semi-parametric assumption of regular variation of index α∈(0,1) for the tail behaviour of p. Under this assumption, we introduce an estimator of U that is simple, linear in the sampling information, computationally efficient, and scalable to massive datasets. Then, uniformly over our class of regularly varying tail distributions, we show that the proposed estimator has provable guarantees: i) it is minimax near-optimal, up to a power of logn factor; ii) it is consistent all of the way up to logλ≍nα∕2∕ logn, and this range is the best possible. This is the first study on the estimation of the unseen under regularly varying tail distributions p. Our results rely on a novel approach, of independent interest, which combines the renowned method of the two fuzzy hypotheses for minimax estimation of discrete functionals, with Bayesian arguments under Poisson-Kingman priors for p. An illustration of our method is presented for synthetic and real data.
Web usage Mining: A Comparison of WUM Category Web Mining Algorithms

Web mining is emerging as one of the most demanding streams for researchers as it deals with the mining of information on the internet. It is especially useful in the application domains of e-commerce. In order to understand and better serve the needs of Web-based applications, web use mining (WUM) is used as the application of data mining techniques to discover use mining. Other techniques like web content mine (WCM), web structure mine (WCM) are also discussed in the paper. It has seen a brisk extension in interest from both the assessment and practice of organizational data patterns. In depth, this paper discusses each of these steps. Web use mining has seen a quick expansion in interest from both the examination and practice perspective in networks, considering its application potential. This paper provides a comprehensive taxonomy, counting research endeavors of the work in the same field. An up-to-date survey is also given on the ongoing work.
SigVer—A Deep Learning Based Writer Independent Bangla Signature Verification System

Handwritten signature is one of the most popular biometric traits which have been addressed by researchers since the last few decades. Scientists have proposed disparate handcrafted feature-based techniques for signature verification in the past. Presently there has been an interest towards deep learning-based approaches which has demonstrated promising results across different avenues of pattern recognition problems. In this paper, light-weight CNN architecture named SigVer is proposed for Bangla offline writer independent signature verification. The proposed network was built with only six layers with no pooling in between convolution layers to minimize information loss. Experiments were performed on 2956 signatures from 57 writers and a highest accuracy of 99.49% was obtained. Further, comparative analysis was performed with established CNN architectures as well as handcrafted feature-based techniques and SigVer produced better results.
Shape-Adaptive RBF Neural Network for Model-Based Nonlinear Controlling Applications

Radial basis function neural networks (RBF-NNs) are simple in structure and popular among other NNs. RBF-NNs are capable of fast learning proving their applicability in developing deep learning applications. Its basic form with center states or the means and standard deviations with weight adaptation makes limited variability and complex in tuning when such embedded to the model. Dynamics systems are nonlinear, especially behavior is uncertain and unpredictable, and complete mathematical modeling or model-based controlling has limited applicability for stability and accurate control. Shape-adaptive RBF-NN presented in the paper theoretically proved for stability control using the Lyapunov analysis. The autonomous surface vessel controlling selected for the numerical simulation consists of a mathematical model developed using marine hydrodynamics for a prototype vessel and classical proportional-derivative (PD) controller. Results indicated that shape adaptive RBF-NN blended controlling is more accurate and has a fast learning ability in intelligent transportation vessel development.
A faster and less aggressive algorithm for correcting conservativity violations in ontology alignments

Ontologies are computational artifacts that model consensual aspects of reality. In distributed contexts, applications often need to utilize information from several distinct ontologies. In order to integrate multiple ontologies, entities modeled in each ontology must be matched through an ontology alignment. However, imperfect alignments may introduce inconsistencies. One kind of inconsistency, which is often introduced, is the violation of the conservativity principle, that states that the alignment should not introduce new subsumption relations between entities from the same source ontology. We propose a two-step quadratic-time algorithm for automatically correcting such violations, and evaluate it against datasets from the Ontology Alignment Evaluation Initiative 2019, comparing the results to a state-of-the-art approach. The proposed algorithm was significantly faster and less aggressive; that is, it performed fewer modifications over the original alignment when compared to the state-of-the-art algorithm.
Secure mitigation and migration of virtual machines over hybrid cloud hypervisors infrastructure

As workloads increase in the industry so does the requirement for IT resources. IT companies are now shifting from in-house data centers to cloud-based IT services such as IAAS, PAAS, and SAAS which can offer flexibility at different levels to the developers of the applications working on different projects. Lots of researchers have contributed to migrating virtual machines migration from one cloud service provider to the other in case of backup as a service and other related services. Some of the previous works by some researchers focus on the migration of SAAS and PAAS onto different cloud service provider platforms. This research work focuses on the mitigation and migration of IAAS, PAAS, and SAAS services of the cloud. My contribution in this research will be in how to avoid loss of data during mitigation and migration of the VM from one Hypervisor environment to the other hypervisor environment on a physical machine infrastructure. This will help SME’s to provide end-user access to their data without worrying about the losses which may have incurred if the Mitigation process was not carried out carefully while migrating VM’s from one bare metal Hypervisor environment to the other bare metal Hypervisor environment.
Designing an Online Appointment System for Semiliterate Users

Information and Communications Technology (ICT) has revolutionized the healthcare leading to provision of eHealth facilities remotely. During the peak time of COVID-19, as the long queues at health care facilities can result ... | Find, read and cite all the research you need on Tech Science Press
A novel fuzzy blend scheme for image enhancement of CT scans bearing liver cancer

This paper deals with the design of an image enhancement algorithm which is capable of improving the quality of computed tomography (CT) images for liver cancer diagnosis. A novel soft computing technique namely fuzzy blend scheme (FBS) is proposed in this work. The FBS works in two stages. The first stage is the pre-processing stage in which the image is de-noised and sharpened by the application of a non-linear filter. In the second stage, the pre-processed output is blended with a novel fuzzy transformation function to give the contrast-enhanced image. The method is tested on 181 CT images from 15 patients of liver cancer named hepatocellular carcinoma. Quality metrics computed for the comparison of FBS with the existing methods include signal to noise ratio = 11.4270, enhancement measure = 12.30, structural similarity index = 91% and discrete entropy which is less than original image. The maximum computation time for FBS is 3.488 s. The proposed method can aid the radiologists and physicians in disease diagnosis. It will help in reducing the burden of ionic contrast reagents induced in patients for tumour visualisation.
Implementation of User Centered Design methodology for designing information systems introduction to culture based on mobile applications

Abstract Design and build an information system really require a method that can produce a reliable information system, this is evident from the system design process that comes from the user’s needs. The design of information systems that are not based on the needs of users directly involved will result in a software crisis. The purpose of this research is to implement an information system design method based on user needs that can produce a software requirements specification document (SRS). The research method used in making this information system design is User Centered Design (UCD) which consists of 5 stages, namely the plan the human centred Process, Specify the content of use, specify the user and organization requirements, produce design solution, and evaluating the design. The results of this study are the SRS document information system introduction to culture based on mobile applications with the UCD method, it is hoped that this research can also be used as a reference for designing information systems.
ProgressiveNN: Achieving Computational Scalability without Network Alteration by MSB-first Accumulative Computation

Computational scalability allows neural networks on embedded systems to provide desirable inference performance while satisfying severe constraints of power consumption and computing resources. This paper proposes a simple but scalable inference method called progressiveNN that consists of bitwise binary (BWB) quantization, accumulative bit-serial (ABS) inference, and batch normalization (BN) retraining. The progressiveNN does not require any network modification and obtains the network parameters from a single training. BWB quantization decomposes and transforms each parameter into a bitwise format for ABS inference. ABS inference utilizes the parameters in the MSB-first order, which enables progressive inference. The evaluation result shows that the proposed method provides computational scalability from 12.5% to 100% for ResNet18 on CIFAR-10/100 with a single set of network parameters. It also shows that BN retraining suppresses accuracy degradation at low computation cost and restores the inference accuracy to 65% at 1-bit width inference.
Visceral reality in Alejandro González Iñárritu’s Carne y arena/Virtually Present, Physically Invisible (González Iñárritu 2017)

González Iñárritu’s Carne y arena/Virtually Present, Physically Invisible (González Iñárritu 2017) is a multilayered virtual-reality installation that premiered at the Cannes Film Festival and later won a Special Achievement Oscar. This work is a notable departure from the director’s previous output, both in its use of virtual reality and in its focus on the divisive subject of migration . Carne y arena has been praised for its technological innovation, but it has also been criticized for tackling such a political subject through the medium of virtual reality. For some critics, virtual reality is an entirely inappropriate medium for exploring such a serious issue, privileging, in their view, spectacle over reflection. González Iñárritu has countered such critiques by asserting that the use of virtual reality makes the experience of visiting Carne y arena intensely personal and immersive. This article will situate the installation in relation to González Iñárritu’s previous work and examine the debates surrounding the relationship between virtual reality and empathy with reference to Carne y arena .