Reviewerly/exHarmony huggingface dataset:
Demystifying the concept of IoT enabled gamification in retail marketing: An exploratory study

The retail landscape is evolving rapidly as firms embrace innovative technologies in an attempt to stay ahead of the aggressive competition prevalent within the industry. Gamification is one such innovative technology that has been gaining popularity in recent times. This study aims to explore the application of Gamification in the context of Retail Marketing in Sri Lanka and ultimately propose a concept for a Gamified application that can be used by customers of moderntrade retailers. The study took an exploratory qualitative approach where intensive surveys of literature and in-depth interviews with a judgmental purposive sample of seven marketing professionals in the modern-trade retail industry were conducted to determine the current play of technology in retail marketing as well as the drivers & challenges of Gamification adoption. Further, in-depth interviews with the customers of such organizations were conducted in gathering user preferences and design recommendations for a Gamified app. Thematic analysis was carried out in deriving insights. Findings show that the retail firms currently employ several technologies in line with those discussed in existing literature such as loyalty card systems, digital signage, VR technologies, online Gamification amidst others in carrying out their marketing efforts. Gamification is predominantly applied in the online context as opposed to the offline (in-store) context. Furthermore, the key drivers that propel firms to implement novel technology like Gamification are to generate customer insights, enhance customer experience and achieve marketing related KPI targets. Conversely, inadequate technology infrastructure, justifying the focus on a niche crowd of tech-oriented customers and slow ROI pose as challenges in the process of Gamification adoption. Three main themes emerged upon exploring user preferences and design recommendations for a Gamified app and are identified as information at the touch of a fingerprint, automation & integration and use of game mechanics. Ultimately by incorporating these insights gathered, a concept for a Gamified app was proposed.
Univariate Time Series Anomaly Labelling Algorithm

Unsupervised anomaly detection in an n-length univariate time series often comes with high risk. Anomaly contextual dependencies limit the application of binary classification methods. Analyzing the statistical features of data may help enrich the context of anomaly detection. This article proposes a quadratic time algorithm for analyzing possible anomalies in the context of unsupervised learning. Detection of possible anomalies uses Median Absolute Deviation on the residual of a univariate time series. Computation of residuals uses robust STL (Seasonal and Trend decomposition using Loess). Experiments on three datasets (Yahoo, NUMENTA NAB and district-heating substation power profiles) show the ability of the algorithm to enrich anomalies by associating labels such as Certainty, Uncertainty, and Probable, with the probable class indicating a need to further process the anomalies.
Colombian Women’s Life Patterns: A Multivariate Density Regression Approach

Women in Colombia face difficulties related to the patriarchal traits of their societies and well-known conflict afflicting the country since 1948.In this critical context, our aim is to study the relationship between baseline sociodemographic factors and variables associated to fertility, partnership patterns, and work activity.To best exploit the explanatory structure, we propose a Bayesian multivariate density regression model, which can accommodate mixed responses with censored, constrained, and binary traits.The flexible nature of the models allows for nonlinear regression functions and non-standard features in the errors, such as asymmetry or multi-modality.The model has interpretable covariatedependent weights constructed through normalization, allowing for combinations of categorical and continuous covariates.Computational difficulties for inference are overcome through an adaptive truncation algorithm combining adaptive Metropolis-Hastings and sequential Monte Carlo to create a sequence of automatically truncated posterior mixtures.For our study on Colombian women's life patterns, a variety of quantities are visualised and described, and in particular, our findings highlight the detrimental impact of family violence on women's choices and behaviors.
A Data Analysis Graphical user Interface for RoboCup 2D Soccer Simulation League

Spatial and temporal data from a robotics soccer game could enable an in-depth analysis of the game. However, the amount and nature of the data make it challenging for analysts to uncover insights from it. This paper presents a user experience-oriented approach that aims to solve a data visualization problem in the context of RoboCup Soccer 2D Simulation League matches. At each cycle of a match, a set of data containing information about the ball, players, and overall game status is provided by the server and later compiled into a dense log file. This log file is a highly valuable resource for understanding the performance of both teams in a given match. However, the considerable amount of data in a raw format makes it challenging to gather and interpret useful information by manual means. In this sense, we propose a data analysis tool that, through the use of data manipulation and data visualization libraries, offers a graphical interface from which the user can get refined information such as statistics and performance diagnoses directly from a specific log file, allowing more in-depth understanding and accurate observations on both teams play actions.
Digital Fluency and the Construction of Pedagogical Strategies for Distance Learning

The objective of the study is to identify Pedagogical Strategies (PS) that can contribute to the construction of Digital Fluency in the Distance Learning (DL) context. Technological changes in society include their own set of knowledge, skills, and attitudes called Digital Competences (DC). Specifically, in distance learning, digital fluency is considered paramount since it is related to the use of technologies where the subject feels digitally active, especially with regard to the production of content/materials for the virtual environment. This can be divided into five specific competences: Content Production, Data Protection, Networking, Virtual Resilience, and Teamwork. Thus, PS were created from the analysis of competences in order to assist the instructors to build them with their students in the DL environment. This study used a qualitative methodology based on an interpretative approach. The instrument used for data collection was an online questionnaire evaluating the Pedagogical Strategies for the Digital Fluency Competence. The target audience of the research was 90 specialists in the area of ​​distance learning who responded and suggested changes to the PS. The results enabled the development of a framework with 46 Pedagogical Strategies divided into the 5 specific competences of Digital Fluency to aid teachers in meeting students’ needs.
Connecting The Dots To Combat Collective Fraud

Modern fraudsters write malicious programs to coordinate a group of accounts to commit collective fraud for illegal profits in online platforms. These programs have access to a set of finite resources - a set of IPs, devices, and accounts etc. and sometime manipulate fake accounts to collaboratively attack the target system. Inspired by these observations, we share our experience in building two real-time risk control systems to detect collective fraud. We show that with TigerGraph, a powerful graph database, and its innovative query language - GSQL, data scientists and fraud experts can conveniently implement and deploy an end-to-end risk control system as a graph database application.
Image splicing detection based on noise level approach

The rapid development in technology and the spread of editing image software has led to spread forgery in digital media. It is now not easy by just looking at an image to know whether the image is original or has been tampered. This article describes a new image splicing detection method based on noise level as a major feature to detect the tempered region. Principal Component Analysis (PCA) is exploited to estimate the noise of image and the K-means clustering for authentic and forged region classification. The proposed method adopts Columbia Uncompressed Image Splicing Dataset for evaluation and effectiveness. The experimental results for 360 images demonstrate that the method achieved an 83.33% for detecting tampered region this percentage represent a promising result competed with Stat-of-art splicing detection methods.
Leveraging a Music Streaming Platform in Establishing a Novel Storage Covert Channel

Covert communication enables the hidden transfer of data. Unlike encrypted communication, where the goal is to make the transmitted data unreadable, covert communication aims to hide the existence of the communication. There have been research efforts on developing and preventing numerous types of channels. We present a novel storage covert channel by leveraging a music streaming platform and take an in depth look into its inner workings. The channel aims to enable data transmission between one sender and multiple receivers. The channel, presented in two different methods of encoding, utilizes a public music streaming platform playlists and encodes data by selectively adding music tracks. The first method of encoding leverages song names while the second uses data embedded within the song track id. We were successful in implementing both encoding schemes, thus demonstrating the covert channels' feasibility.
Development of a two-step LDA based aspect extraction technique for review summarization

ABSTRACTSummarization of online reviews by customers is a popular practice for evaluation of products or services. As the reviews accumulate, the large size and the unstructured nature of the reviews hinder manual summarization. Automatic categorization of the reviews as a whole into only positive and negative group cannot represent a clear picture. An aspect based automatic summarization technique can provide better visualization.  However, automatic extraction of proper aspects from the huge reviews of any product is not very easy. There are some research works in this direction, but any definite method is yet to come. In this work, a two-step Latent Dirichlet Allocation (LDA) technique, which is popularly used for topic modelling has been developed for efficient aspect extraction. The method has been evaluated by simulation experiments on Amazon product reviews and Yelp restaurant and hotel reviews. The results have been found quite matching with human annotated results.
Feature Fusion based Efficient Convolution Network for Real-time Table Tennis Ball Detection

Ball detection is an important technique for the athlete training and technical analysis of the sport like table tennis, especially for the ball trajectory real-time analysis for the table tennis robot or real-time technical guidance system. How to detect the flying ball accurately and timely are two main challenges in this area. Since the table tennis ball is relatively small compared with the playing court, it belongs to the category of small object detection in large scenes. In this study, an efficient one-stage based detection algorithm structure is proposed to speed up the detection and achieves promising accuracy. Specifically, a network pruning strategy is applied to accelerate the detection. Furthermore, in order to alleviate the performance drop caused by network pruning, a novel context fusion component is introduced to the network which is a specified design for small object detection. The experimental results show the proposed feature fusion based efficient convolution network achieves real-time table tennis ball detection of 3.4 ms on the Nvidia 1050Ti hardware with 0.971 (mAP) accuracy.
Language Acquisition of Children Age 4-5 Years Old in TK Dhinukum Zholtan Deli Serdang

This study aims to determine the language acquisition of children aged 4-5 years. This research was conducted at TK Dhinukum Zholtan Medan which was conducted on March 4, 2020 with 4 students, 2 students aged 4 years and 2 students aged 5 years. In this study, researchers used the method of observation and also a questionnaire to collect data. Based on the description above, it can be concluded that children aged 5 and 4 years have different mastery. In addition to clear pronunciation, 5 year olds have mastery of more vocabulary and are able to describe things. Meanwhile, children aged 4 years have less vocabulary, have not been able to describe something and are not able to pronounce it clearly. This difference occurs because 5 year olds have already taken part in learning compared to 4 year olds. So that children aged 5 years are better able to communicate well, because they often interact with teachers and peers at school.
The successful use of volunteers to enhance NHS Test and Trace contact tracing of in-patients with Covid-19: a Pilot Study

Abstract Contact tracing in the UK for Covid-19 is performed by NHS Test and Trace (NHSTT) via telephone or email. This study estimates how many patients who have been admitted to hospital are not reached by NHSTT and the number of their contacts who were not advised to self-isolate. Medical Student volunteers conducted face to face interviews with patients diagnosed with Covid-19 on an infectious diseases ward. Data on their close contacts were sent to NHSTT. 20 cases were enrolled. 13(65%) did not engage with NHSTT, 4(20%) because they had no positive PCR, 9(45%) because of severity of illness, language or intellectual difficulties. 49 close contacts were identified of whom 33(67%) were from cases who had not engaged with NHSTT. “Backwards” contacts tracing information was collected from 11(55%) cases and 8(40%) gave detailed information. These data suggest that NHSTT fails to engage nearly two thirds of Covid-19 in-patients and fails to advise two thirds of their close contacts to self isolate.Volunteers used face to face interviews to overcome false negative tests, illness and communication problems to identify both close contacts and data on sources of infection.
VideoGen: Generative Modeling of Videos using VQ-VAE and Transformers

We present VideoGen: a conceptually simple architecture for scaling likelihood based generative modeling to natural videos. VideoGen uses VQ-VAE that learns learns downsampled discrete latent representations of a video by employing 3D convolutions and axial self-attention. A simple GPT-like architecture is then used to autoregressively model the discrete latents using spatio-temporal position encodings. Despite the simplicity in formulation, ease of training and a light compute requirement, our architecture is able to generate samples competitive with state-of-the-art GAN models for video generation on the BAIR Robot dataset, and generate coherent action-conditioned samples based on experiences gathered from the VizDoom simulator. We hope our proposed architecture serves as a reproducible reference for a minimalistic implementation of transformer based video generation models without requiring industry scale compute resources. Samples are available at https://sites.google.com/view/videogen
Horizon: A Gas-Efficient, Trustless Bridge for Cross-Chain Transactions

With the rise of digital currency systems that rely on blockchain to ensure ledger security, the ability to perform cross-chain transactions is becoming a crucial interoperability requirement. Such transactions allow not only funds to be transferred from one blockchain to another (as done in atomic swaps), but also a blockchain to verify the inclusion of any event on another blockchain. Cross-chain bridges are protocols that allow on-chain exchange of cryptocurrencies, on-chain transfer of assets to sidechains, and cross-shard verification of events in sharded blockchains, many of which rely on Byzantine fault tolerance (BFT) for scalability. Unfortunately, existing bridge protocols that can transfer funds from a BFT blockchain incur significant computation overhead on the destination blockchain, resulting in a high gas cost for smart contract verification of events. In this paper, we propose Horizon, a gas-efficient, cross-chain bridge protocol to transfer assets from a BFT blockchain to another blockchain (e.g., Ethereum) that supports basic smart contract execution.
Efficient Multi-objective Reinforcement Learning via Multiple-gradient Descent with Iteratively Discovered Weight-Vector Sets

&#x0D; &#x0D; &#x0D; Solving multi-objective optimization problems is important in various applications where users are interested in obtaining optimal policies subject to multiple (yet often conflicting) objectives. A typical approach to obtain the optimal policies is to first construct a loss function based on the scalarization of individual objectives and then derive optimal policies that minimize the scalarized loss function. Albeit simple and efficient, the typical approach provides no insights/mechanisms on the optimization of multiple objectives due to the lack of ability to quantify the inter-objective relationship. To address the issue, we propose to develop a new efficient gradient-based multi-objective reinforcement learning approach that seeks to iteratively uncover the quantitative inter-objective relationship via finding a minimum-norm point in the convex hull of the set of multiple policy gradients when the impact of one objective on others is unknown a priori. In particular, we first propose a new PAOLS algorithm that integrates pruning and approximate optimistic linear support algorithm to efficiently discover the weight-vector sets of multiple gradients that quantify the inter-objective relationship. Then we construct an actor and a multi-objective critic that can co-learn the policy and the multi-objective vector value function. Finally, the weight discovery process and the policy and vector value function learning process can be iteratively executed to yield stable weight-vector sets and policies. To validate the effectiveness of the proposed approach, we present a quantitative evaluation of the approach based on three case studies.&#x0D; &#x0D; &#x0D;
Handling Complex Multilayer Networks—An Approach Based on Visual Network Analytics

We present a visual analytics framework to aid the investigation of dynamic multilayer networks, focusing on the visual tracking of evolving communities. We were challenged by edges not persistent in time, rather hectic, inducing communities that are “volatile”. Working closely with field experts, three complementary perspectives are developed to tackle this problem: times series summarizing a community’s behavior over time; drag and drop a selected community assessing how a community splits in time; augmented Sankey diagrams to investigate the split and merge patterns among multiple communities. Coupled with expert knowledge, we efficiently support tracking communities emerging from media events.
PassEye: Sniffing Your Password from HTTP Sessions by Deep Neural Network

Abstract Passwords are the most widely used method for user authentication in HTTP websites. Password sniffing attacks are considered a common way to steal password. However, most existing methods have many deficiencies in versatility and automation, such as manual analysis, keyword matching, regular expression and SniffPass. In this paper, to better describe the problem, we propose a HTTP Sessions Password Sniffing (HSPS) attack model which is more suitable in HTTP environment. Furthermore, we propose PassEye, a novel deep neural networkbased implementation of HSPS attack. PassEye is a binary neural network classifier that learns features from the HTTP sessions and identifies Password Authentication Session (PAS). We collected 979,681 HTTP sessions from the HTTP and HTTPS websites for training the binary classifier. The results show that PassEye is effective in sniffing the passwords with an accuracy of 99.38%. In addition, several measures are provided to prevent HSPS attacks in the end.
Personality and Employees’ Information Security Behavior among Generational Cohorts

The Big Five Factors Model (FFM) of personality traits theory was tested for its ability to explain employee information security behavior (EISB), when age, measured by generational cohort (GCOHORT), moderated the relationship between the independent variables (IVs) extraversion, agreeableness, conscientiousness, emotional stability, intellect (EACESI) and the dependent variable (DV), employees&amp;rsquo; information security behavior (EISB) which is measured by file protection behavior (FPB). Three age groups defined GCOHORT: 52&amp;ndash;70 years old (1946&amp;ndash;1964, Baby Boomers), 36&amp;ndash;51 yrs old (1965&amp;ndash;1980, Generation X), and 18&amp;ndash; 35 yrs. Old (1981&amp;ndash;1998, Millennial). Results of hierarchical multiple regressions analyses revealed statistically significant relationships between overall personality traits, four individual factors of personality traits, and the DV (p &amp;lt; .05). However, contrary to expectations, GCOHORT did not moderate the relationship between any of the main IVs and the DV (p &amp;gt; .05). Recommendations for future research are offered.
Cross-Lingual Named Entity Recognition Using Parallel Corpus: A New Approach Using XLM-RoBERTa Alignment

We propose a novel approach for cross-lingual Named Entity Recognition (NER) zero-shot transfer using parallel corpora. We built an entity alignment model on top of XLM-RoBERTa to project the entities detected on the English part of the parallel data to the target language sentences, whose accuracy surpasses all previous unsupervised models. With the alignment model we can get pseudo-labeled NER data set in the target language to train task-specific model. Unlike using translation methods, this approach benefits from natural fluency and nuances in target-language original corpus. We also propose a modified loss function similar to focal loss but assigns weights in the opposite direction to further improve the model training on noisy pseudo-labeled data set. We evaluated this proposed approach over 4 target languages on benchmark data sets and got competitive F1 scores compared to most recent SOTA models. We also gave extra discussions about the impact of parallel corpus size and domain on the final transfer performance.