Reviewerly/exHarmony huggingface dataset:
Content, Operation, Laboratory: Application and Innovation of Artificial Intelligence in Broadcast and TV Industry

In recent years, with the rapid development of big data, cloud, IoT and other technologies, artificial intelligence has attracted more and more attention all over the world, and has developed rapidly in various industries. The broadcast and TV industry has been seeking media convergence, but also in the direction of intelligent transformation. Artificial intelligence technology plays an extremely important role in the transformation and development of the broadcast and TV industry. This paper introduces the application and innovation of artificial intelligence of broadcast and TV media from the perspective of content, operation, as well as the exploration of representative broadcast and TV institutions in the field of artificial intelligence laboratory. The research of artificial intelligence technology will be a major task for the industry for a long time to build a more intelligent broadcast and TV system with artificial intelligence technology, which is also a huge systematic project.
Application and Analysis of Phishing Website Detection in Machine Learning and Neural Networks

Web Phishing asks the user to connect to a malicious website. The number one goal of this assault is to steal crucial statistics from the user. Intruder builds websites similar to those that look like the original website. It allows attackers to access Sensitive data such as username, password, details of debit cards etc. This paper aims to review many of the phishing detection strategies recently suggested for the website. This will also provide a high-level description of various forms of phishing detection techniques.
COSTA: Communication-Optimal Shuffle and Transpose Algorithm with Process Relabeling

Communication-avoiding algorithms for Linear Algebra have become increasingly popular, in particular for distributed memory architectures. In practice, these algorithms assume that the data is already distributed in a specific way, thus making data reshuffling a key to use them. For performance reasons, a straightforward all-to-all exchange must be avoided. Here, we show that process relabeling (i.e. permuting processes in the final layout) can be used to obtain communication optimality for data reshuffling, and that it can be efficiently found by solving a Linear Assignment Problem (Maximum Weight Bipartite Perfect Matching). Based on this, we have developed a Communication-Optimal Shuffle and Transpose Algorithm (COSTA): this highly-optimised algorithm implements $$A=\alpha \cdot {\text {op}}(B) + \beta \cdot A,\ {\text {op}} \in \{{\text {transpose}}, {\text {conjugate-transpose}}, {\text {identity}}\}$$ on distributed systems, where A, B are matrices with potentially different (distributed) layouts and $$\alpha , \beta $$ are scalars. COSTA can take advantage of the communication-optimal process relabeling even for heterogeneous network topologies, where latency and bandwidth differ among nodes. Moreover, our algorithm can be easily generalized to even more generic problems, making it suitable for distributed Machine Learning applications. The implementation not only outperforms the best available ScaLAPACK redistribute and transpose routines multiple times, but is also able to deal with more general matrix layouts, in particular it is not limited to block-cyclic layouts. Finally, we use COSTA to integrate a communication-optimal matrix multiplication algorithm into the CP2K quantum chemistry simulation package. This way, we show that COSTA can be used to unlock the full potential of recent Linear Algebra algorithms in applications by facilitating interoperability between algorithms with a wide range of data layouts, in addition to bringing significant redistribution speedups.
Wearable System for Personalized and Privacy-preserving Egocentric Visual Context Detection using On-device Deep Learning

Wearable egocentric visual context detection raises privacy concerns and is rarely personalized or on-device. We created a wearable system, called PAL, with on-device deep learning so that the user images do not have to be sent to the cloud for processing, and can be processed on-device in a real-time, offline, and privacy-preserving manner. PAL enables human-in-the-loop context labeling using wearable audio input/output and a mobile/web application. PAL uses on-device deep learning models for object and face detection, low-shot custom face recognition (~1 training image per person), low-shot custom context recognition (e.g., brushing teeth, ~10 training images per context), and custom context clustering for active learning. We tested PAL with 4 participants, 2 days each, and obtained ~1000 in-the-wild images. The participants found PAL easy-to-use and each model had gt80% accuracy. Thus, PAL supports wearable, personalized, and privacy-preserving egocentric visual context detection using human-in-the-loop, low-shot, and on-device deep learning.
Wavelet-based Spatial-temporal Feature Extraction for Gesture Recognition

Gesture recognition in human-machine interaction is a popular subject of study, but it also includes some problems. The main motivation of the proposed study is developed a novel vector-based feature extraction method for gesture recognition in an unmanned aerial vehicle (UAV) control. In this paper, the use of discrete wavelet transform on the signals acquired by multiple sensors and then, a statistical feature extraction from this transformed signals is proposed for the person-independent gesture recognition. In this way, it is aimed to get the invariant feature space according to speed and magnitude of the movement in different time slices. The success of the proposed method in the problem of person-independent gesture recognition is experimentally demonstrated with the comparative experiments.
Feature coding method based on shared weights support vector data description for face recognition

Abstract In this paper, we propose a feature coding method based on shared weights support vector data description (FCM-SWSVDD). The proposed process of FCM-SWSVDD is as follows. By considering the density information of the clusters and introducing the weighting learning, we propose an improved support vector data description (SVDD), named shared weights support vector data description (SWSVDD). SWSVDD can obtain the cluster center and cluster radius more accurately. Incorporating SWSVDD and the triangle coding into the same feature coding learning process, FCM-SWSVDD is proposed. After the features of those images are extracted by using FCM-SWSVDD, a sparse representation classifier is used to classify those features. Experimental results show that the performance of the proposed method exceeds many methods.
Factors influencing collection development process at the University of Nairobi Library

Purpose The purpose of this study was to investigate the factors influencing the collection development process at the University of Nairobi library and suggest ways for improvement. Design/methodology/approach A qualitative approach was employed to collect, analyze and interpret data from collection development librarians at the University of Nairobi purposively selected. Data were collected through structured interviews and documentary review. Data were analyzed using content analysis and descriptive statistics. Findings The study established that the library has a written collection development policy which was revised in 2014 and is strictly adhered to. However, the policy has a gap on the role of faculty as stakeholders in the selection process. In addition, the criteria for selection are not explicitly stated. Findings also indicate that budget allocation, collection development policy, user demands, quality of staff, library consortium and the Public Procurement and Asset Disposal Act of 2015 influence the collection development process at the University of Nairobi Library. Research limitations/implications The study was conducted in one public university library in Kenya. However, the findings may be applicable in similar university libraries in the country. Practical implications The findings of this study may benefit students and faculty in the University of Nairobi from improved collection development process that would ensure that their information needs are met in a timely manner. This would lead to improved research output by students and faculty and ultimately improved research output by the university. University library management may also benefit in improving the collection development process, thereby making it more economical. The findings may also be useful in decision-making and policy development on matters pertaining to collection development at the University of Nairobi and other universities. Furthermore, the findings may be used by researchers to provide context and background information for future research on collection development in public universities locally and regionally. Originality/value This paper fulfills the need to make the collection development process in university libraries more efficient by proposing ways of addressing the challenges experienced.
Factors Affecting the Employees’ Loyalty in The Mobily Telecom Company, Saudi Arabia

Mobily Telecom Company is the second-largest telecom company in Saudi Arabia. Mobily is the brand name used by the company for the services it provides to its customers. The telecommunication industry is growing and a large number of competitors are entering the market. So it is becoming of crucial importance for the company that it must develop strategies for retaining the best and professional employees so that the company can obtain a sustainable competitive advantage in the industry. In this
IKAN: Interactive Knowledge-aware Attention Network for Recommendation

Knowledge graph (KG) is widely used as side information to improve recommendation performance. Among existing KG-based recommendation methods, propagation-based methods have the best performance. However, these methods are still problematic because they ignore the individual meaning of each item in the user's interaction history. So the representations of users and items learned by these methods are sub-optimal. In this paper, we proposed an end-to-end propagation-based recommendation model named Interactive Knowledge-aware Attention Network (IKAN). The proposed IKAN models each item in the user's interaction history separately, then utilize representations of these items to construct the corresponding user's representation. At the same time, our model uses knowledge-aware attention module and interaction-aware attention module to learn representations of users and items. We evaluate IKAN on three real-world datasets. Experimental results demonstrate that IKAN outperforms state-of-the-art KG-based recommendation methods.
Defeasible Reasoning via Datalog¬

Abstract We address the problem of compiling defeasible theories to Datalog¬ programs. We prove the correctness of this compilation, for the defeasible logic DL (∂ || ), but the techniques we use apply to many other defeasible logics. Structural properties of DL (∂ || ) are identified that support efficient implementation and/or approximation of the conclusions of defeasible theories in the logic, compared with other defeasible logics. We also use previously well-studied structural properties of logic programs to adapt to incomplete Datalog¬ implementations.
A likelihood-based convolution approach to estimate major health events in longitudinal health records data: an external validation study

Abstract Objective In electronic health record data, the exact time stamp of major health events, defined by significant physiologic or treatment changes, is often missing. We developed and externally validated a method that can accurately estimate these time stamps based on accurate time stamps of related data elements. Materials and Methods A novel convolution-based change detection methodology was developed and tested using data from the national deidentified clinical claims OptumLabs data warehouse, then externally validated on a single center dataset derived from the M Health Fairview system. Results We applied the methodology to estimate time to liver transplantation for waitlisted candidates. The median error between estimated date within the period of the actual true date was zero days, and median error was 92% and 84% of the transplants, in development and validation samples, respectively. Discussion The proposed method can accurately estimate missing time stamps. Successful external validation suggests that the proposed method does not need to be refit to each health system; thus, it can be applied even when training data at the health system is insufficient or unavailable. The proposed method was applied to liver transplantation but can be more generally applied to any missing event that is accompanied by multiple related events that have accurate time stamps. Conclusion Missing time stamps in electronic healthcare record data can be estimated using time stamps of related events. Since the model was developed on a nationally representative dataset, it could be successfully transferred to a local health system without substantial loss of accuracy.
Real-Time Bird’s Eye Surround View System: An Embedded Perspective

Bird’s-eye surround view is a new type of Advanced Driv er Assistance System (ADASs) that provides drivers with a real-time 360∘ top-down view of their vehicle. This paper presents an architecture for a surround view system based on a Tensilica Vision P5 embedded DSP. Fish-eye cameras mounted on each side of a vehicle are employed in the proposed model. The cameras are able to cover the entire view around the vehicle with overlapping areas between neighboring cameras. We use a morphing approach for joint lens and perspective correction of each view. For stitching frames from different cameras, we assume a fixed geometry which permits off-line calculation of frame transformations, thereby reducing the real-time processing load. To further reduce computational complexity, we employ lookup-tables in the view morphing and blending phases. The Vision P5 core combines the four frames with XGA resolution into a single XGA surround view at 30FPS.
Batching Anonymous and Non-Anonymous Membership Proofs for Blockchain Applications

Membership proof is a very useful building block for checking if an entity is in a list. This tool is widely used in many scenarios. For instance in blockchain where checking membership of an unspent coin in a huge set is necessary, or in the scenario where certain privacy-preserving property on the list or on the entity is required. When it comes to multi-user applications, the naive way that verifies the membership relations one by one is very inefficient. In this work, we utilize subvector commitment schemes and non-interactive proofs of knowledge of elliptic curve discrete logarithms to present two batched membership proofs for multiple users, i.e., batched non-anonymous membership proofs and batched anonymous membership proofs, which offer plausible anonymity assurance respectively on the organization group list and on the users when combined within the blockchain applications. The non-anonymous membership proof scheme requires a trusted setup, but its proof size is only one bilinear group element and is independent of both the size of list and the number of users. The anonymous membership proof scheme requires no trusted setup, and its proof size is linear in the size of organization group and is independent of the number of users. Their security relies respectively on the CubeDH and the discrete logarithm assumptions. Finally, as a use-case application scenario, we extend Mesh which is a blockchain based supply chain management solution to Mesh+ which supports batched anonymous membership proofs.
Theoretical Context of Cybercrime

The internet is conceivably today's most innovative development as it proceeds to change everyday life for almost everyone globally. Billions of individuals are using the internet, and thousands enter the online world each day. Not merely has the internet revolutionized the way people connect and learn; it has eternally changed the way people live across the globe. As the internet and computer advances, offenders have originated ways to utilize these innovations as intended for their criminal acts. In social science research, social theories are of great significance. Without a theoretical direction, social facts are like a snuffed-out candle that cannot determine its bearer's path. Social theories contribute to the development of sound scientific foundations for resolving issues in any social inquiry. Theories guide our observations of the world. Digital technology has an impact and has numerous challenges. The respective work has its significance in helping and exploring this dilemma via a multifaceted theoretical approach.
Long short-term cognitive networks

Abstract In this paper, we present a recurrent neural system named long short-term cognitive networks (LSTCNs) as a generalization of the short-term cognitive network (STCN) model. Such a generalization is motivated by the difficulty of forecasting very long time series efficiently. The LSTCN model can be defined as a collection of STCN blocks, each processing a specific time patch of the (multivariate) time series being modeled. In this neural ensemble, each block passes information to the subsequent one in the form of weight matrices representing the prior knowledge. As a second contribution, we propose a deterministic learning algorithm to compute the learnable weights while preserving the prior knowledge resulting from previous learning processes. As a third contribution, we introduce a feature influence score as a proxy to explain the forecasting process in multivariate time series. The simulations using three case studies show that our neural system reports small forecasting errors while being significantly faster than state-of-the-art recurrent models.
A game-theoretic approach to adversarial linear Gaussian classification

We employ a game-theoretic model to analyze the interaction between an adversary and a classifier.There are two (i.e., positive and negative) classes to which data points can belong.The adversary wants to maximize the probability of miss-detection for the positive class (i.e., false negative probability) while it does not want to significantly modify the data point so that it still maintains favourable traits of the original class.The classifier, on the other hand, wants maximize the probability of correct detection for the positive class (i.e., true positive probability) subject to a lower-bound on the probability of correct detection for the negative class (i.e., true negative probability).For conditionally Gaussian data points (conditioned on the class) and linear support vector machine classifiers, we rewrite the optimization problems of the adversary and the classifier as convex problems and use best response dynamics to learn an equilibrium of the game.This results in computing a linear support vector machine classifier that is robust against adversarial input manipulations.
Understanding Violence Against Women in Digital Space from a Data Science Perspective : Full/Regular Research Papers - CSCI-ISNA

Research has shown that Violence Against Women is a pervasive problem which has been increasing. Until a few years ago, it took place both in public and private spaces, but it has now broken into Digital Space, adopting more symbolic expressions. There has been some important related work from Data Science approaches. Mainly about cyberbullying, detection of language patterns through supervised algorithms, a few works on unsupervised learning, and some on violence against women. This article is part of a larger investigation related to the Violence Against Women in Digital Space phenomena in Mexico from Spanish-language interactions in microblogging social network, Twitter. We present the framework to address these phenomena from a Data Science perspective and go through some first stage results. The goal is to give Data Science perspectives and insights related to the understanding of Digital Violence Against Women, with the purpose of generating awareness about this problem by, modifying manners and implementing public policies that could counteract this social problem.
Improved Transmission Map for Dehazing of Natural Images

The paper proposes an efficient method of removing haze from outdoor images using dark channel prior (DCP) method. The DCP represents the statistics of outdoor images. In dark channel images, minimum of one-color channel of haze-free images from outdoor locations has pixels having very low intensity. Therefore, using imaging model of dark channel prior (DCP), we can identify the quantity of haze in an image. Moreover, the transmission map estimation and refinement using guided filter are done after the application of DCP which improves the transmission map of the image. The comparative values of PSNR and RMSE are identified for images of the transmission map obtained from DCP image and are found to have minimum value for the guided filter. The results based on the proposed method are demonstrated for generating DCP, transmission map estimation, refinement, and recovery of radiance of images. It is imperative to say that this method succeeded in its aim to transform haze images into haze-free images with high-intensity pixel values.
Using Document Embeddings for Background Linking of News Articles

This paper describes our experiments in using document embeddings to provide background links to news articles. This work was done as part of the recent TREC 2020 News Track [26] whose goal is to provide a ranked list of related news articles from a large collection, given a query article. For our participation, we explored a variety of document embedding representations and proximity measures. Experiments with the 2018 and 2019 validation sets showed that GPT2 and XLNet embeddings lead to higher performances. In addition, regardless of the embedding, higher performances were reached when mean pooling, larger models and smaller token chunks are used. However, no embedding configuration alone led to a performance that matched the classic Okapi BM25 method. For our official TREC 2020 News Track submission, we therefore combined the BM25 model with an embedding method. The augmented model led to more diverse sets of related articles with minimal decrease in performance (nDCG@5 of 0.5873 versus 0.5924 with the vanilla BM25). This result is promising as diversity is a key factor used by journalists when providing background links and contextual information to news articles [27].
A Comparative Study among Different Computer Vision Algorithms for Assisting Users in Picture Password Composition

Picture gesture authentication (PGA), utilized by millions of users worldwide, is a cued-recall graphical authentication system which requires users to select an image and subsequently draw gestures on that image to create their picture password. A crucial component for enhancing the security of PGA-like schemes is the accurate quantification of the user-chosen passwords through a picture password strength meter. Despite the huge adoption of PGA worldwide, there is rather limited knowledge on the implementation aspects of an accurate picture password strength meter that would assist users in creating secure picture passwords. In this paper, we present the implementation and evaluation of an assistive picture password strength meter system within PGA-like schemes, which is based on image analysis through computer vision techniques. Results of the evaluation study (n=34) revealed that different computer vision approaches perform different across various datasets used during training. These findings could drive the design of intelligent security mechanisms for quantifying the strength of the user-chosen passwords, and ultimately assist end-users towards making better picture password selections by providing feedback about the strength of their passwords, as well as assist service providers in terms of integration of assistive security mechanisms.