Reviewerly/exHarmony huggingface dataset:
Moonlight: A Push-based API for Tracking Data Lineage in Modern ETL processes

This paper presents Moonlight - a novel solution for tracking data lineage in Extract Transform Load (ETL) data processes. It gives a brief overview of the reasons behind the need for such a solution, yielding its expected requirements - the ability to easily store, update and view ETL metadata with a clear way of representing the data lineage. To comply with these requirements, Moonlight uses a push mechanism in the form of REST API to track the metadata (which contains the information about the inputs and outputs, pipeline owners and involved components, reasons behind transformations being made, etc.), but most importantly for providing the digestible presentation of all information in the form of JSON, Markdown and HTML output. Its main intuition is to be a tool that is easy to set up, use, and integrate, and that allows a better understanding of data flows and their faster debugging.
Feature Selection for Hierarchical Multi-label Classification

In this work we study how conventional feature selection methods can be applied to Hierarchical Multi-label Classification Problems. In Hierarchical Multi-label Classification, instances can belong to two or more classes (labels) simultaneously, where such classes are hierarchically structured. Feature selection plays an important role in Machine Learning classification tasks, once it can effectively reduce the dataset dimensionality by removing irrelevant and/or redundant features, improving classification accuracy. Although many relevant real-world problems are from the hierarchical and multi-label domains, the majority of the related researches address the feature selection task focusing on single-label problems. In many works, even when the proposal deals with multi-label problems, the classes are not associated with a hierarchical structure. Therefore, in this work we study how feature selection can be applied in the Hierarchical Multi-label Classification context. For this, we propose four hierarchical strategies combining the Binary Relevance (BR) and Label Powerset (LP) multi-label transformations with the attribute evaluators ReliefF (RF) and Information Gain (IG). We tested our strategies on 10 real-world datasets from the functional genomic field, commonly used in Hierarchical Multi-label Classification works. As main results, three of the four proposed strategies produced some relevant subsets of features, while keeping predictive performances in comparison to the use of the complete set of features.
New Reverse Conversion for Four-Moduli Set and Five-Moduli Set

Most reverse conversions in Residue Number Systems (RNS) are based on the Chinese Remainder Theorem (CRT) and the Mixed Radix Conversion (MRC).The complexity of the circuitry of the CRT is high due to the large modulo-M operation.The MRC has a simple circuitry but it's a sequential process in nature.The purpose of this research is to obtain an efficient reverse conversion method to reduce the computational overhead found in the conventional reverse conversion algorithms.In this paper, new algorithms for reverse conversion in RNS for four-moduli set and five-moduli set have been proposed and their correctness evaluated.Numerical evaluations to ascertain the correctness and simplicity of the algorithm have been presented.These algorithms have fewer multiplicative index operations than those in the conventional CRT and MRC.The large modulo-M operation has been eliminated which reduces the computational overhead.
Large Feature Mining and Deep Learning in Multimedia Forensics

As one of the most interesting areas in cyber forensics, multimedia forensics faces many challenges as users are generating a humongous amount of data with different operations. Forgery detection and steganography detection are two hotspots in multimedia forensics. To solve some highly challenging problems in multimedia forensics, especially in image forensics, we will introduce in this tutorial large feature mining-based approaches with ensemble learning in image forgery detection, including seam-carving forgery and inpainting forgery in JPEG images with the subsequent anti-forensics' operations. We will also introduce deep learning and apply the well-known deep learning models that were transferred and used for image forgery detection and image steganalysis, which considerably improve the detection accuracy.
Accurate Recognition of Leukemia Sub-types by Utilizing a Transfer Learned Deep Convolutional Neural Network

Leukemia has been causing more than 350,000 deaths per year despite holding a notable number of studies on the prognosis, diagnosis, and therapy for Leukemia. Computerized Leukemia disclosure may change the circumstances, as steps can be obtained instantly, hence, precise identification of Leukemia has been a domain of concern for researchers for nearly a decade now. Lately, many contributions have been bestowed to the scientific society concerning the identification of Leukemia. But with the expansion and development of the datasets, the demand for accurate identification of Leukemia is becoming more demanding every day. In this research, we examined a Leukemia sub-types dataset that consists of three kinds of Leukemia individuals. We introduced and implemented a modified DenseNet-201 design and produced an overall accuracy of 99.56% which exceeded all the previous investigations.
Experience spaces for critical co-reflection on artefact use

Current design approaches mainly focus on co-design activities for creating (novel) digital artefacts. Less attention is paid on implications of new technologies on people and their experiences. This paper proposes a conceptual framework aimed at describing and supporting systematic and sustained critical co-reflection on artefact use. It employs and expands ideas from design research and user-oriented design approaches to describe sources and nature of critical co-reflection and to discuss appropriate analytical lenses. Experience spaces are introduced as a complementary concept to design spaces. An experience space is a conceptual space emerging from the collaborative creation, manipulation, merging and discarding of experience-related descriptions of technology appropriation. From a critical reflective perspective, designers not only care about rich design spaces with diverse design proposals but also about rich experience spaces displaying diverse appropriation processes and a broad spectrum of users' perspectives on effects of artefact use. The paper suggests that artefact ecologies constitute the elements of experience spaces.
Authenticating resilience of RTL codes against Power Side Channel leakages

Electronic consumables in the digital era realises smart technology to the very core of innovation and challenges stereotypical livelihood. Within a few years the number of IoT devices will surpass the population of this world. Data shared among them is enormous which needs protection on the network. With such power, the hardware industry, specifically the Electronic Design Automation tool industrial sector plays a significant role, by making available the necessary fabricated chips embedded on the IoT devices. With the emergence of power analysis for side channel attacks, among various ongoing linear and differential cryptanalysis, a RTL code needs to be resilient to all of them prior to fabrication, before it is accepted as a design in the EDA tool process and delivered at the market as a viable chip. This work acts as an aid to the ongoing work on the different testing methods namely Six Sigma, Pearson's chi-squared test and Welch's t-test for tracing leaks in a RTL code with a conceptual method for testing furthermore.
A Study of Password Security Factors among Bangladeshi Government Websites

The Government of Bangladesh is aggressively transforming its public service landscape by transforming public services into online services via a number of websites.The motivation is that this would be a catalyst for a transformative change in every aspect of citizen life.Some web services must be protected from any unauthorised usages and passwords remain the most widely used credential mechanism for this purpose.However, if passwords are not adopted properly, they can be a cause for security breach.That is why it is important to study different aspects of password security on different websites.In this paper, we present a study of password security among 36 different Bangladeshi government websites against six carefullychosen password security heuristics.This study is the first of its kind in this domain and offers interesting insights.For example, many websites have not adopted proper security measures with respect to security.There is no password construction guideline adopted by many websites, thus creating a barrier for users to select a strong password.Some of them allow supposedly weak passwords and still do not utilise a secure HTTPS channel to transmit information over the Internet.
Evaluation of Anonymous Streaming Possibility in Tor Network

Tor is a volunteer-operated network of application-layer relays designed to enable low-latency anonymous communication. One of the used scenarios in modern internet is streaming of audio or video. We suggest that traffic flows of streaming services have their distinguishable signatures. This potentially can ease traffic correlation attacks.In this paper, it is shown that streaming traffic has its own unique signature. A hidden service and dedicated guard node was deployed to confirm the idea. Through experiments conducted on the live Tor network, we verify our hypothesis about traffic flows in different streaming scenarios and show that streaming traffic can be distinguished in real-world tor networks. Later we discuss possible points of passive adversary attacks and evaluate potential impact of using discovered signatures.
Study of Ruangguru application based on human computer interaction principles and paradigm

Abstract This Ruangguru application study aims to determine the relevance of application functions based on the principles and paradigms of Human and computer Interaction (HCI). In this study, the aspects evaluated included aspects of human, technology, usability, and ergonomics. The interest in studying the Ruangguru application is based on several sources stating that Ruangguru is believed to be the “Best Education Platform”. So from that sentences, it was examined several aspects to determine the extent of the relationship between the Ruangguru application and HCI principle. After the study was conducted, the Ruangguru application was assessed to have fulfilled these aspects. So it’s no wonder the Ruangguru application is used as an online learning centre among Indonesian students.
Simulating Tardos Finger Printing Codes under Randomized Bits Collusion Attacks

In today's world digital right protection is an important issue. Finger printing codes are used in this digital right protection. When digital media have to be distributed each digital media is embedded with a unique code. The digital content received by each recipient has a unique digital finger printing code. Illegal distribution of digital media is identified by the unique finger printing codes and the traitors distributing these illegal copies are caught. These finger printing codes can be attacked by colluders. In these attacks a combination of users to whom the digital media is distributed collude together to attack the finger printing codes. The collusion attacks are threats to prevention of piracy of the digital content. The work here simulates Tardos Finger Printing Codes with Randomized Bits Collusion Attacks and evaluates the performance of Tardos Finger Printing Codes under this attack.
Design of Library User Profile System Based on Dynamic Density Clustering Algorithm and Stream Computing

The study of book recommendation system based on user profile is of great significance in accurately grasping the potential reading needs of readers, improving the quality of book recommendation and promoting the development of personalized service in libraries, and it is an indispensable part of the future construction of smart libraries. The existing book recommendation system based on user profile has a series of problems, such as incomplete summary of various information of users, inaccurate grasp of users' real reading needs, and time lag in user information analysis, etc. This paper describes the architecture principle, label system and algorithm flow of library user profile system, and uses dynamic density clustering method and stream computing based on time series analysis to give the label system a time dimension by combining the big data characteristics of users. Combined with the needs of the development of high-quality personalized information services in future smart libraries, this paper explores the application of this system in the effective study of library reading promotion activities, readers' sharing sessions, readers' personalized reading list design and so on.
New Models for Solving Time-Varying LU Decomposition by Using ZNN Method and ZeaD Formulas

In this paper, by employing the Zhang neural network (ZNN) method, an effective continuous-time LU decomposition (CTLUD) model is firstly proposed, analyzed, and investigated for solving the time-varying LU decomposition problem. Then, for the convenience of digital hardware realization, this paper proposes three discrete-time models by using Euler, 4-instant Zhang et al. discretization (ZeaD), and 8-instant ZeaD formulas to discretize the proposed CTLUD model, respectively. Furthermore, the proposed models are used to perform the LU decomposition of three time-varying matrices with different dimensions. Results indicate that the proposed models are effective for solving the time-varying LU decomposition problem, and the 8-instant ZeaD LU decomposition model has the highest precision among the three discrete-time models.
Virtual Reality to Improve Human Computer Interaction for Art

The integration of technology, namely virtual reality, with arts is implemented in this paper. An observational study is performed by replicating an actual museum by a Swiss-Lebanese artist and having user experience it in a virtual immersive environment. Data is then collected to monitor the user experience and its effects on his emotional and psychological state. This information is used to reflect on future developments and enhancements to elevate the user experience and potentially upgrade it with further advanced technologies.
Semi-automatic Labelling of Scientific Articles using Deep Learning to Enlarge Benchmark Data for Scientific Summarization

Scientific article summarization is a challenging task not least due to the lack of large annotated corpora. In this research proposal, we present an approach to construct a large annotated corpus for scientific articles using semi-supervised/automatic annotation approaches. We intend to apply deep learning methods to increase a small seed of annotated corpus. Then, we will measure the quality of the annotated corpus on down stream informative summaries using various evaluation techniques.
Tracking eye-gaze in smart home systems (SHS): first insights from eye-tracking and self-report measures

Abstract Human–computer interaction (HCI) is particularly interesting because full-immersive technology may be approached differently by users, depending on the complexity of the interaction, users’ personality traits, and their motivational systems inclination. Therefore, this study investigated the relationship between psychological factors and attention towards specific tech-interactions in a smart home system (SHS). The relation between personal psychological traits and eye-tracking metrics is investigated through self-report measures [locus of control (LoC), user experience (UX), behavioral inhibition system (BIS) and behavioral activation system (BAS)] and a wearable and wireless near-infrared illumination based eye-tracking system applied to an Italian sample (n = 19). Participants were asked to activate and interact with five different tech-interaction areas with different levels of complexity (entrance, kitchen, living room, bathroom, and bedroom) in a smart home system (SHS), while their eye-gaze behavior was recorded. Data showed significant differences between a simpler interaction (entrance) and a more complex one (living room), in terms of number of fixation. Moreover, slower time to first fixation in a multifaceted interaction (bathroom), compared to simpler ones (kitchen and living room) was found. Additionally, in two interaction conditions (living room and bathroom), negative correlations were found between external LoC and fixation count, and between BAS reward responsiveness scores and fixation duration. Findings led to the identification of a two-way process, where both the complexity of the tech-interaction and subjects’ personality traits are important impacting factors on the user’s visual exploration behavior. This research contributes to understand the user responsiveness adding first insights that may help to create more human-centered technology.
A content-based recommendation algorithm for agricultural science popularization

With the development of agricultural science, agricultural science knowledge overload widespread. In order to allow agricultural workers to discover new agricultural science and technology knowledge that suits their preferences, and to enable the agricultural science popularization service system to locate agricultural knowledge to appropriate users. The recommended scheme applied to agricultural science popularization needs to be solved urgently. In this paper, we propose a content-based recommendation algorithm for agricultural science popularization based on convolutional neural network (CNN). Considering the structure and professional fields of agricultural science information, a language model based on labeled latent Dirichlet allocation and a latent factor model with inherent features are constructed in the CNN model. The experimental results on the public database show that compared with the classical methods, it has a better improvement in recommendation evaluation, and it performs well in solving the problem of cold start and data sparsity.
A mobile survey for collecting data from Deaf people who use sign language for communication

Hearing researchers and practitioners might have encountered challenges in collecting data from Deaf people who mainly rely on sign language for communication. This could be due to language barriers and the non-user-friendly interface of paper and text-based questionnaires. In an attempt to overcome such challenges, this paper proposes a solution using a mobile questionnaire that operates on smartphones useable by relevant researchers and practitioners to utilize for data collections in Deaf communities. This mobile survey was a result of a design participatory method that involved a multidisciplinary team in Design, Development, Testing, and Deployment stages. This paper provides a brief description of the high-level view of the first three stages. The paper further elaborates on the Deployment stage to address the use of this mobile survey when collecting data from Deaf communities in Western Cape, South Africa. The mobile usage patterns of Deaf people were implemented as a use case to implement the proposed data collection instrument.
Quality-Oriented Task Allocation and Scheduling in Transcoding Servers With Heterogeneous Processors

Dynamically adaptive streaming over HTTP requires a large-scale server to transcode various bitrate versions in which different preset parameters can be used to provide different video qualities at each resolution. When transcoding servers contain a heterogeneous mix of CPUs and GPUs, the task scheduler must choose a processor and preset parameter for each transcoding task to meet the transcoding deadlines while achieving the best possible video quality. We apply regression analysis to sample variable-bit-rate videos to provide accurate (mean absolute percentage error values from 1.3% to 13.9%) model for predicting bitrate, transcoding time and video quality at each resolution on different processors. We build this into a greedy allocation and scheduling algorithm which first satisfies deadlines with low video quality, and then redistributes the workload to improve that quality while continuing to meet the deadlines. This scheme was both simulated and implemented on a testbed server. It satisfies all deadlines while outperforming standard algorithms by between 3.12% and 15.59% in terms of popularity-weighted video quality divided by bitrate.
Designing and Building Context-Aware Services: The ContextServ Project

In the era of Web of Things and services, context-aware services (CASs) are emerging as an important technology for building innovative smart applications. CASs enable the information integration from both the physical and virtual world, which affects the way human live. However, it is still challenging to build CASs, due to lack of context provisioning management approach and lack of generic approach for formalizing the development process. In this paper, we briefly introduce a large research project, ContextServ, which provides a platform for model-driven development of CASs based on a UML-based modelling language. We discuss the literature and also highlight several future research opportunities for context-aware service research and development.
Sustainability Considerations in the Product Design Using System Dynamics and Fuzzy Cognitive Maps

Globally, sustainable product design is an issue that has been receiving more academic attention around the world. Understanding the sustainability-related design decisions seems to be important to ensuring sustainable design. Based on a stance, this study attempted to quantify sustainability factors in the product design through a hybrid method which combines the fuzzy cognitive mapping (FCM) and the system dynamics (SD) approaches. Designer behavior with regard to sustainability criteria was simulated in Vensim and sourced through sessions of two groups who deal with sustainable design. The results provide an easy-to-understand model of sustainable design to help designers analyze the dynamics of the cause-and-effect relationships between factors of sustainable design and long term scenarios, therefore, different scenarios at the inter and intra-cluster levels were built to determine the impacts of variable changes on the model developed.
Cyclic codes of length <i>p<sup>n</sup></i> over (ℤ<i><sub>p</sub></i>)<i><sup>m</sup></i>

The purpose of this paper is describing cyclic codes of length pn over (ℤp)m. We partition ideals of into two general forms and calculate the annihilator of these two forms. In this way, we can introduce an explanation of cyclic codes and the dual code of an arbitrary cyclic code of length pn over (ℤp)m.
Blockchain-Based Access Control System

Access control assures protection of resources against illegal access. This is implemented using policies, which enforce stringent mechanisms to protect highly confidential information. Blockchain is an apt technology for providing access control. Blockchain-based access control will offer security and transparency. It would also be helpful in avoiding a third party involvement. Blockchain-based access control system, in spite of providing confidentiality to the data, also ensures the integrity of the same. Blockchain technology is tamper-proof by its default implementation and access control increases the security of the data stored in the devices.