Reviewerly/exHarmony huggingface dataset:
Nyström-SGD: Fast Learning of Kernel-Classifiers with Conditioned Stochastic Gradient Descent

Kernel methods are a popular choice for classification problems, but when solving large-scale learning tasks computing the quadratic kernel matrix quickly becomes infeasible. To circumvent this problem, the Nyström method that approximates the kernel matrix using only a smaller sample of the kernel matrix has been proposed. Other techniques to speed up kernel learning include stochastic first order optimization and conditioning. We introduce Nyström-SGD, a learning algorithm that trains kernel classifiers by minimizing a convex loss function with conditioned stochastic gradient descent while exploiting the low-rank structure of a Nyström kernel approximation. Our experiments suggest that the Nyström-SGD enables us to rapidly train high-accuracy classifiers for large-scale classification tasks. Code related to this paper is available at: https://bitbucket.org/Whadup/kernelmachine/ .
Criminal-Justice Apps

In recent years, lawyers, activists, and policymakers have introduced cell phone applications -- “criminal justice apps” -- that are very slowly beginning to democratize the criminal justice system. Some of these apps -- such as DWI apps -- teach citizens about the law, while also connecting them with lawyers and bail bondsman. Policymakers have created apps to help defendants navigate confusing court systems. Still other apps are grander in purpose and seek to bring systemic changes to the criminal justice system. For instance, the ACLU has designed an app to fight wrongful seizures and police brutality by recording police interactions. A non-profit has created an app to aggregate spare change and use it to post bail for the indigent. This essay explores the universe of criminal justice apps and considers how they are likely to result in modest improvements to the criminal justice system.
Unsupervised Automatic Keyphrases Extraction Algorithms

Massive volumes of images of museums or art collections, or made available by artists and photographers, more and more often, are available on the web, along with some metadata, essential for their characterization and retrieval. A set of (scored) keywords/keyphrases that characterize the semantic content of the documents should be, automatically or manually, extracted and/or associated. We present here a work-in-progress to evaluate different methods for the unsupervised keyword extraction to Italian and English datasets. In the paper datasets, algorithms and approaches are presented and discussed together with some preliminary results referred to relatedness of terms.
Research on the Construction of Three Level Customer Service Knowledge Graph in Electric Marketing

With the advent of the information explosion era, more and more enterprises realize that knowledge management and application requirements are the key points that enterprises attach importance to. This paper solved existing problems by building customer service of power industry marketing and relevant theories of Knowledge Graph, and explored the three-level customer Knowledge Graph framework of power system marketing. Neo4j was used to realize knowledge reasoning, and relatively good results were obtained in practice.
Elimination of Impulse Noise using Mean Median filter for Image Enhancement

In this method, we've got introduced a new technique for the improvement of gray scale images, when images are corrupted by salt and pepper noise that's additionally referred to as an impulse noise. Our suggested phenomena show a better output for Medium density impulse noise as compare to the opposite renowned filters like standard Median Filter (SMF), a decision based mostly Median Filter (DBMF) and modified decision based Median Filter (MDBMF), Nonlinear filter (NLF) and so on. Our projected technique worked on two steps, within the beginning is that the detection of noisy pixels and within the second step is that the removal of noisy pixels. For detection of noisy constituent apply condition pixels values lies in between 0 to 255 it noisy it's noisy free pixels. In our second step that's the removal of noisy pixel recommended technique that's replaces the noisy pixel by alpha trimmed mean median value. Different grayscale pictures are tested via proposed technique. The experimental result shows higher Peak Signal to Noise ratio (PSNR) values and with higher visual and human perception.
Solar Radiation Spatio-Temporal Analysis and Its Implications for Power Grid Management

Photovoltaic power has the temporal and spatial variability associated with the natural intermittency of solar resource. This variability results in potential threats to the grid since it must be able to accommodate safely the foreseeing variability. This chapter gives an overview of the variability scales and its quantification. The smoothing effect and other tools in power grid management are also described.
Wireless mesh network data communications and reliability analysis for anti-theft application deployment in educational institutions

Purpose The purpose of this study is to locally develop low-cost wireless mesh networks for reliable data communications to devices that prevent the theft of these devices in learning institutions of South Africa. Design/methodology/approach A network test-bench was developed where millions of packets were transmitted and logged between interconnected nodes to analyze the quality of the network’s service in a harsh indoor building environment. Similar methodologies in “big data” analysis as found in particle physics were adopted to analyze the network’s performance and reliability. Findings The results from statistical analysis reveal the quality of service between multiple asynchronous transmitting nodes in the network and compared with the wireless technology routing protocol to assess coverage in large geographical areas. The mesh network provides stable data communications between nodes with the exception of reliability degradation in some multi-hopping routes. Conclusions are presented to determine whether the underlining mesh network technology will be deployed to protect devices against theft in educational institutions of South Africa. Research limitations/implications The anti-theft application will focus on proprietary firmware development with a reputable tablet manufacturer to render the device inoperable. Data communications of devices to the network will be monitored and controlled from a central management system. The electronics embedding the system-on-chip will be redesigned and developed using the guidelines stipulated by the chip manufacturer. Originality/value Design and development of low-cost wireless mesh networks to protect tablets against theft in institutions of digitized learning. The work presents performance and reliability metrics of a low-power wireless mesh wireless technology developed in a harsh indoor building environment.
Deconstructing appetitive learning in cocaine addiction using hierarchical Bayesian modelling

Extinction learning is an essential mechanism that enables constant adaptation to ever-changing environmental conditions. The underlying neural circuit is mostly studied with rodent models using auditory cued fear conditioning. In order to uncover the variant and the invariant neural properties of extinction learning, we adopted pigeons as an animal model in an appetitive sign-tracking paradigm. The animals firstly learned to respond to two conditioned stimuli in two different contexts (CS-1 in context A and CS-2 in context B), before conditioned responses to the stimuli were extinguished in the opposite contexts (CS-1 in context B and CS-2 in context A). Subsequently, responding to both stimuli was tested in both contexts. Prior to extinction training, we locally injected the N-methyl-d-aspartate receptor (NMDAR) antagonist 2-Amino-5-phosphonovaleric acid (APV) in either the amygdala or the (pre)motor arcopallium to investigate their involvement in extinction learning. Our findings suggest that the encoding of extinction memory required the activation of amygdala, as visible by an impairment of extinction acquisition by concurrent inactivation of local NMDARs. In contrast, consolidation and subsequent retrieval of extinction memory recruited the (pre)motor arcopallium. Also, the inactivation of arcopallial NMDARs induced a general motoric slowing during extinction training. Thus, our results reveal a double dissociation between arcopallium and amygdala with respect to acquisition and consolidation of extinction, respectively. Our study therefore provides new insights on the two key components of the avian extinction network and their resemblance to the data obtained from mammals, possibly indicating a shared neural mechanism underlying extinction learning shaped by evolution.
2018 Case in Point Highlights Summaries of Major 2018 Cases Affecting Biotechnology

Biotechnology Law ReportVol. 38, No. 1 2018 Review2018 Case in Point Highlights Summaries of Major 2018 Cases Affecting BiotechnologyEdited for Biotechnology Law Report by CHRISTOPHER M. HOLMANChristopher M. HolmanChristopher M. HolmanChristopher M. Holman is the Executive Editor of Biotechnology Law Report and a Professor at the University of Missouri–Kansas City School of Law.Search for more papers by this authorPublished Online:8 Feb 2019https://doi.org/10.1089/blr.2019.29107.szAboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetails Volume 38Issue 1Feb 2019 InformationCopyright 2019, Mary Ann Liebert, Inc., publishersTo cite this article:Christopher M. Holman.2018 Case in Point Highlights Summaries of Major 2018 Cases Affecting Biotechnology.Biotechnology Law Report.Feb 2019.28-37.http://doi.org/10.1089/blr.2019.29107.szPublished in Volume: 38 Issue 1: February 8, 2019PDF download
Constrained Fuzzy Modified Geometric Programming Problem

In this chapter, we have discussed modified Modified Geometric Programming (MGP) geometric programming approach in fuzzy environment.
An Overview on Mobile Cloud Computing

Mobile cloud computing is a concept that has been gaining popularity, resulting from the synergistic integration of cloud computing and mobile computing with the goal of minimizing some of the limitations inherent in mobile computing (bandwidth, storage, autonomy, etc.) and giving greater plasticity to the services of the cloud computing providers. This new paradigm of computation has similar limitations to those associated with the paradigms that are at its origin. Therefore, it is essential that the research carried out in this area is concerned about understanding its impact on audit processes, which aim to verify and evaluate the mechanisms of the internal control system implemented to minimize threats to integrity, confidentiality, and availability of the information assets (data and/or information) stored on these platforms. Based on a literature review, this chapter aims to list a set of challenges associated with the adoption of mobile cloud computing within the scope of organizational auditing.
A New FLAME Selection Method for Intrusion Detection (FLAME-ID)

Due to the ever growing number of cyber attacks, especially of the online systems, development and operation of adaptive Intrusion Detection Systems (IDSs) is badly needed so as to protect these systems.It remains as a goal of paramount importance to achieve and a serious challenge to address.Different selection methods have been developed and implemented in Genetic Algorithms (GAs) to enhance the rate of detection of the IDSs.In this respect, the present study employed the eXtended Classifier System (XCS) for detection of intrusions by matching the incoming environmental message (packet) with a classifiers pool to determine whether the incoming message is a normal request or an intrusion.Fuzzy Clustering by Local Approximation Membership (FLAME) represents the new selection method used in GAs.In this study, Genetic Algorithm with FLAME selection (FGA) was used as a production engine for the XCS.For comparison purposes, different selection methods were compared with FLAME selection and all experiments and evaluations were performed by using the KDD'99 dataset.
Estimation of Face Pose Orientation Using Model-Based Approach

In the domain of computer vision and pattern recognition, though there are numerous methods for face recognition, it is still remaining as a very challenging problem in real life applications. Face detection and recognition suffer from many problems which are caused by the variations in orientation, size, illumination, expression, and poses. This paper mainly revolves around face detection and oriented pose identification. The state-of-the-art Constrained Local Model (CLM) is applied to detect the face from any wild facial image. The extracted feature points are used to segregate the dominant parts of faces. From the dominant feature points, nose tip and eye points have been identified. Applying the geometrical parameters between the nose tip and eye points, the pose orientation of the wild face has been identified. This method is very simple and accurate. The performance evaluation has been done on unconstrained Essex database and internal wild database collected from internet.
Attentive models in vision: Computing saliency maps in the deep learning era

Estimating the focus of attention of a person looking at an image or a video is a crucial step which can enhance many vision-based inference mechanisms: image segmentation and annotation, video captioning, autonomous driving are some examples. The early stages of the attentive behavior are typicall y bottom-up; reproducing the same mechanism means to find the saliency embodied in the images, i.e. which parts of an image pop out of a visual scene. This process has been studied for decades both in neuroscience and in terms of computational models for reproducing the human cortical process. In the last few years, early models have been replaced by deep learning architectures, that outperform any early approach compared against public datasets. In this paper, we discuss the effectiveness of convolutional neural networks (CNNs) models in saliency prediction. We present a set of Deep Learning architectures developed by us, which can combine both bottom-up cues and higher-level semantics, and extract spatio-temporal features by means of 3D convolutions to model task-driven attentive behaviors. We will show how these deep networks closely recall the early saliency models, although improved with the semantics learned from the human ground-truth. Eventually, we will present a use-case in which saliency prediction is used to improve the automatic description of images.
Correlates of Susceptibility to Motion Sickness

The incidence of motion sickness can be affected by a number of individual factors. In this chapter, I propose to introduce you to some of the personal features that have been identified as having a bearing on the response to provocative motion, namely: age; sex of the subject; race or culture; and physical fitness. Some of these can have an impact on military personnel; as you will see, there are no hard and fast rules. More work is needed to investigate these suggested correlates both to confirm the relationship and better understand the underlying reason for these associations to exist.
Predicting Muscle Activity and Joint Angle from Skin Shape

Muscle of human body can be a clue to recognize the behavior and intention of a person. If the muscle activity is measured only by visual observation, it is useful to estimate the state of the muscle. In this paper, a method of predicting muscle activity and joint angle of human body from skin shape is proposed. Since the muscle activity and the joint angle affect the skin shape, the both factors should be considered simultaneously. The proposed method is a learning-based approach that uses the data set of the skin shape, the muscle activity and the joint angle. It trains a linear regressor for predicting muscle activity and joint angle from skin shape. The deformation of skin shape is calculated as the feature in the active regions, which are extracted from the training data and limits the regions of the skin shape that contribute to the prediction. We acquired a lower limb with simple motion to consider the small number of factors in this paper. In the experiment, the muscle activity and joint angle are predicted even in the case that the both factors change simultaneously. The skin regions that contributes to prediction are given as the result of learning, and the distribution is reasonable from the viewpoint of biomechanics.
Study of wavefront reconstruction based on four-wave lateral shear interferometer method

Shear interferometer measurement method is one of main techniques in optical interferometry metrology. A shearing interferometric detection system is presented in order to obtain the three- dimensional profile of wavefront. A aperture and chessboard grating were used to divide the measured wavefront into four wavefronts, and two coherent wavefronts form an interferogram. The theoretical model of shearing interferometric detection system based on Fourier Optics and light intensity transmission equation is structured. According to the Fourier transform algorithm, an algorithm of shear interferogram processing is presented. Finally, the measured wavefront can be reconstructed. The transient wavefront detection system was applied to reconstruct the transient wavefront automatically. Experiments show that the apparatus offers higher accuracy, which satisfies the need for the practical applications. The measured results are in good agreement with the reconstructed wavefronts from SID4 wavefront sensor. The system can be applied to transient wavefront reconsitution.
Editorial for Special issue of FGCS special issue on “Benchmarking big data systems”

Even though several big data processing and analytics systems have been introduced with various design architectures, we are still lacking a deeper understanding of the performance characteristics for the various design architectures in addition to lacking comprehensive benchmarks for the various Big Data platforms. There is a crucial need to conduct fundamental research with a more comprehensive performance evaluation for the various Big Data processing systems and architectures. We also lack the availability of validation tools, standard benchmarks, and system performance prediction methods that can help us have a deeper and more solid understanding of the strengths and weaknesses of the various Big Data processing platforms. This special issue is dedicated to original results and achievements by active researchers, designers, and developers working on various issues and challenges related to big data research.
A Prover Dealing with Nominals, Binders, Transitivity and Relation Hierarchies

This work describes the Sibyl prover, an implementation of a tableau based proof procedure for multi-modal hybrid logic with the converse, graded and global modalities, and enriched with features largely used in description logics: transitivity and relation hierarchies. The proof procedure is provably terminating when the input problem belongs to an expressive decidable fragment of hybrid logic. After a description of the implemented proof procedure, the way how the implementation deals with the most delicate aspects of the calculus is explained. Some experimental results, run on sets of randomly generated problems as well as some hand-tailored ones, show only a moderate deterioration in the performances of the prover when the number of transitivity and inclusion axioms increase. Sibyl is compared with other provers (HTab, the hybrid logic prover whose expressive power is closer to Sibyl’s one, and the first-order prover SPASS). The obtained results show that Sibyl has reasonable performances.
External Representations and the Design of Seamless Learning Systems

Current trends in technology-enhanced learning highlight the increasing importance of mobile digital tools in learning scenarios; seamless learning, or learning that spans contexts and activities within and without the classroom, is becoming mainstream. Despite the growing body of the literature in this area, this chapter highlights a general focus on technological issues and perspectives and a lack of theoretically driven discussion. We argue that theoretically/conceptually inspired literature reviews covering pedagogy and cognitive aspects of learning are currently needed to establish a grounded framework for future research in this area. This paper contributes one such analysis—it proposes and reflects on the issues raised when considering seamless learning from the perspective of the established literature on external representations (ERs), a core concept in distributed or embodied accounts of cognition. Core issues we discuss are: (a) what are the challenges facing seamless learning from an ERs perspective? (b) how can knowledge about ERs be applied to seamless learning systems?, and (c) what methodological challenges will emerge if seamless learning systems are studied from the perspective of ERs? This discussion is intended as a bridge between practical and applied work in seamless learning and theoretical or laboratory-based work in ERs—it seeks to drive the field of seamless learning forward by highlighting best practices from an established theoretical perspective. By elaborating on a theoretically grounded lens, we seek to empower researchers to identify promising approaches for the design and evaluation of next-generation high impact seamless learning solutions.
To Monitor and Detect Suspicious Transactions in a Financial Transaction System Through Database Forensic Audit and Rule-Based Outlier Detection Model

The objective of this chapter is to monitor database transactions and provide information accountability to databases. It provides a methodology to retrieve and standardize different audit logs in a uniform XML format which are extracted from different databases. The financial transactions obtained through audit logs are then analyzed with database forensic audit. The transactions are examined, detected, and classified as per regulations and well-defined RBI antimony laundering rules to obtain outliers and suspicious transactions within audit logs. Bayesian network is used in this research to represent rule-based outlier detection model which identifies the risk level of the suspicious transactions.
The Reality of Use of WhatsApp as a Tool for Distance Education in Teaching and Learning

This research aims to investigate the reality of WhatsApp use among faculty members at the Department of Information Studies (IS) at Sultan Qaboos University (SQU) in the Sultanate of Oman as a tool for distance education (DE) and as a tool for teaching and learning. The study also focuses on the information retrieved on WhatsApp teaching groups and its use. Data was collected through the interview method. The results of the study showed that three faculty members out of nine are using WhatsApp in teaching and learning. They use it for class discussions and explanations of projects. The study showed that the three faculty members in the Department of IS who used WhatsApp in teaching and learning are using it in general to communicate with the students and also for educational purpose. In addition, the study showed a relationship between age, nationality, and specialization. The study also proved that students used WhatsApp as an open source of information.