Reviewerly/exHarmony huggingface dataset:
Hybridization of Chaotic Maps and Gravitational Search Algorithm for Constrained Mechanical and Civil Engineering Design Frameworks

The Chaotic Gravitational Search Algorithm (CGSA) is a physics-based heuristic algorithm inspired by Newton's law of universal gravitation. It uses 10 chaotic maps for optimal global search and fast convergence rate. The advantages of CGSA has been incorporated in various Mechanical and Civil engineering design frameworks which include Speed Reducer Design (SRD), Gear Train Design (GTD), Three Bar Truss Design (TBTD), Stepped Cantilever Beam Design (SCBD), Multiple Disc Clutch Brake Design (MDCBD), and Hydrodynamic Thrust Bearing Design (HTBD). The CGSA has been compared with eleven state of the art stochastic algorithms. In addition, a non-parametric statistical test namely the Signed Wilcoxon Rank-Sum test has been carried out at a 5% significance level to statistically validate the results. The simulation results indicate that CGSA shows efficient performance in terms of high convergence speed and minimization of the design parameter values as compared to other heuristic algorithms. The source codes are publicly available on Github i.e. https://github.com/SajadAHMAD1.
Selective mixing and voting network for semi-supervised domain generalization

Domain generalization (DG) addresses the problem of generalizing classification performance across any unknown domain, by leveraging training samples from multiple source domains. Currently, the training process of the state-of-the-art DG-methods is dependent on a large amount of labeled data. This restricts the application of the models in many real-world scenarios, where collecting and annotating a large dataset is an expensive and difficult task. Thus, in this paper, we address the problem of Semi-supervised Domain Generalization (SSDG), where the training set contains only a few labeled data, in addition to a large number of unlabeled data from multiple domains. This is relatively unexplored in literature and poses a considerable challenge to the state-of-the-art DG models, since their performance degrades under such condition. To address this scenario, we propose a novel Selective Mixing and Voting Network (SMV-Net), which effectively extracts useful knowledge from the set of unlabeled training data, available to the model. Specifically, we propose a mixing strategy on selected unlabeled samples on which the model is confident about their predicted class labels to achieve a domain-invariant representation of the data, which generalizes effectively across any unseen domain. Secondly, we also propose a voting module, which not only improves the generalization capability of the classifier, but can also comment on the prediction of the test samples, using references from a few labeled training examples, despite of their domain gap. Finally, we introduce a test-time mixing strategy to re-look at the top class-predictions and re-order them if required to further boost the classification performance. Extensive experiments on two popular DG-datasets demonstrate the usefulness of the proposed framework.
Semantic-Consistent Deep Quantization for Cross-modal Retrieval

With making up for the deficiency of the constraint representation capability of hashing codes for high-dimensional data, the quantization method has been found to generally perform better in cross-modal similarity retrieval research. However, in current quantization approaches, the codebook, as the most critical basis for quantization, is still in a passive status and detached from the learning framework. To improve the initiative of codebook, we propose a semantic-consistent deep quantization (SCDQ), which is the first scheme to integrate quantization into deep network learning in an end-to-end fashion. Specifically, two classifiers following the deep representation learning networks are formulated to produce the class-wise abstract patterns with the help of label alignment. Meanwhile, our approach learns a collaborative codebook for both modalities, which embeds bimodality semantic consistent information in codewords and bridges the relationship between the patterns in classifiers and codewords in codebook. By designing a novel algorithm architecture and codebook update strategy, SCDQ enables effective and efficient cross-modal retrieval in an asymmetric way. Extensive experiments on two benchmark datasets demonstrate that SCDQ yields optimal cross-modal retrieval performance and outperforms several state of-the-art cross-modal retrieval methods.
Development of the Estimation Model for Intentions to Move Based on Gaze and Face Information with 1DCNN-LSTM and Evaluation of Electric Wheelchair Driving

Gaze interaction depicts a lightweight and fast method for hands-free control of machines, as it is not susceptible to paralysis, has been made to use it as an interface for electric wheelchairs. However, there is a problem called the Midas Touch Problem, in which unconscious eye movements are also treated as input operations. In this paper, we propose the estimating model for Intentions to move based on gaze and face information to develop an electric wheelchair that can be operated by the natural gaze and face movements of users. The proposed model is consisted of 1D-CNN and LSTM layers and uses time-series data of gaze and face movements as input. As a result of the experiment, the highest estimation accuracy was obtained when input the time-series data of 2 seconds to the proposed model.
Assessing Harm from Cyber Crime

Abstract This chapter discusses a harm concept that enables a more comprehensive assessment of the consequences of cyber crime. Even though harm resulting from cyber crime is not fundamentally different from harm that is caused by other forms of crime or crime in general, the reach, scope, and volume of crime facilitated by information and communications technology have transformed risks posed to individuals, organizations, and nations, and challenge conventional approaches of crime detection and prevention. Assessments of the impact of cyber crime have been focusing on estimating the cost in monetary value. However, most significant harm might not be experienced as a loss of money, but as a disruption or destabilization of systems that are built on trust. This article advocates for a human-centric approach to cyber security, which emphasizes harm mitigation strategies.
A Framework to Achieve Cybersecurity Accountability of Critical Infrastructure Providers – A Design Science Research Approach

Today’s pervasive use of information systems (IS) not only comes with many opportunities but also with considerable risks especially in relation to cyberattacks, which become increasingly sophisticated and dangerous. Especially organizations providing critical infrastructures are at risk, which are held to account by governments to ensure sufficient protection. Governments request information to monitor cybersecurity levels of critical infra-structure providers over time, which are today subject to respective nation-wide legislation in developed economies. Following guidelines of design science research, this study offers a generic framework that supports continuous monitoring and benchmarking of an organization’s cybersecurity status. It is generic allowing application by different critical infrastructure providers and usage by government institutions to help achieve oversight of the nation-al cybersecurity status. Our design proposition is supported by an extensive review of academic literature, the consultation of relevant industry standards, and two main rounds of field interactions. The framework includes 15 major risk areas, and a collection of associated metrics and controls, which cover material and social mechanisms. We would like to note that our domain of study would require more design work that targets knowledge accumulation spanning academic research and industry practice.
Pair-Coding as a Method to Support Intercoder Agreement in Qualitative Research

The goal of this WiP paper is to provide an overview of our adaptation of pair programming from agile software development to support intercoder agreement in qualitative analysis. In pair programming, two programmers work together on the same program: one writes while the other observes and guides. Pair programming interleaves development and inspection activities and has been shown to produce higher quality software in a shorter time than individuals working alone. Our adaptation of agile software development pair programming to qualitative research, which we have called pair-coding, uses a similar approach to merge the qualitative research activities of coding and consensus-building by having two team members work on the same text simultaneously. In using pair-coding in qualitative research, the active team member highlights passages and assigns codes while the other team member observes and guides. Our research team anecdotally found that pair-coding of qualitative data provided benefits that were similar to the benefits of pair programming. Specifically, consensus and consistency were built continuously rather than at discrete stages. As well, differences in biases and worldviews were overtly revealed in the act of coding, thus improving the bracketing of biases. Finally, we found that consensus was better understood when built in the moment of coding rather than in comparison after the fact. We believe pair-coding could be effective in supporting the trustworthiness and credibility of qualitative analysis.
Image compression and encryption based on integer wavelet transform and hybrid hyperchaotic system

Compression and encryption of images are emerging as recent topics in the area of research to improve the performance of data security. A joint lossless image compression and encryption algorithm based on Integer Wavelet Transform (IWT) and the Hybrid Hyperchaotic system is proposed to enhance the security of data transmission. Initially, IWT is used to compress the digital images and then the encryption is accomplished using the Hybrid Hyperchaotic system. A Hybrid Hyperchaotic system; Fractional Order Hyperchaotic Cellular Neural Network (FOHCNN) and Fractional Order Four-Dimensional Modified Chua’s Circuit (FOFDMCC) is used to generate the pseudorandom sequences. The pixel substitution and scrambling are realized simultaneously using Global Bit Scrambling (GBS) that improves the cipher unpredictability and efficiency. In this study, Deoxyribonucleic Acid (DNA) sequence is adopted instead of a binary operation, which provides high resistance to the cipher image against crop attack and salt-and-pepper noise. It was observed from the simulation outcome that the proposed Hybrid Hyperchaotic system with IWT demonstrated more effective performance in image compression and encryption compared with the existing models in terms of parameters such as unified averaged changed intensity, a number of changing pixels rate, and correlation coefficient.
High-Precision Micro-motion Parameter Estimation of Sparse Aperture Ballistic Target

The discontinuity of target echo data under sparse aperture (SA) observation will make it difficult to extract micromotion parameters. To address this problem, this paper proposes a novel method for high-precision micro-motion curve reconstruction of ballistic targets with sparse aperture. For the complex phase modulation of ballistic target echo, the proposed method adopts the idea of piecewise, and a simplified echo signal model is established through the quantitative analysis. Combined with the modified CLEAN algorithm, the phase information of high-resolution range profile (HRRP) is used to estimate the micro-motion curve parameters of each scattering center. The estimation accuracy can reach the level of half a wavelength, and the phase unwrapping can be avoided, so as to the high-precision micro-motion curve can be obtained. Extensive experimental results are provided to verify the effectiveness of the proposed method.
Monocular Depth Estimation for Mobile Device

Depth estimation is an important work in inferring scene geometry from 2D images applied in robotics, AR, autonomous driving, etc. Especially, monocular depth estimation is required for wide application in mobile devices. In this paper, a lightweight monocular depth estimation method is proposed for mobile devices and applied to mobile devices by changing the loss function and using network slimming. The result shows that our model requires low memory and reduces computational complexity that is suitable for mobile devices. The mobile device that we deploy is iPhone 11 Pro and it operates at over 30fps.
Sarcasm Detection of Tweets in Indonesian Language Using Long Short-Term Memory

Twitter is a massive source of information that can potentially be used to obtain valuable insights about public opinions, public ideas, and public circumstances. Extracting accurate information from tweets, however, is often challenging due to the use of informal, non-standard, and figurative languages including sarcasm. Sarcasm itself conveys messages using words with opposite literal meaning. Sarcasm detection, therefore, becomes an important task during information extraction from public tweets. This research proposes the use of LSTMs to detect sarcastic tweets in Indonesian language through the extraction of sentence-embedding features. LSTMs have been known to be able to learn sequential patterns in input data so that features extracted by LSTMs are more representative than those manually hand-crafted by human. The proposed LSTMs are combined with the Word2Vec model that serves as a word encoder that preserves semantic meaning. The proposed method is evaluated on tweets that are scrapped from the Web using some keywords related to popular topics. The experimental results demonstrate that the proposed method is able to achieve an accuracy of 82.13% and an f1-score of 61.31% outperforming the conventional TF-IDF + naïve Bayes sarcasm detector. These results thus prove that sentence-embedding is able to extract features that are more accurate and more discriminative for sarcasm detection.
Application of Spatial Data Warehouse for Agriculture: Challenge and Future Trends

Spatial Data Warehouse (SDW) used for agricultural data is considered capable of responding to user requests in a timely manner by integrating two or more databases which contain geo-spatial data from several different data sources with analytical processing properties. Several studies have linked agriculture with technologies that have the ability to provide updated data, high performance, data availability, predictive capabilities and integrated information in supporting tactical decisions. In agriculture, data exploration using location coordinates for visualization are still challenge to be solved, because geo-spatial and agricultural environmental data do not only include maps of land-use locations, but also other characteristic such as production, population, farmers socio-economic data, etc. Trends in Spatial DW for agriculture discussed usage of SDW with Cloud, Big Data and Real time technology so that high-speed and large-volume agricultural data paths are clear, development and implementation can also be carried out efficiently. The resulting visualization dashboard using SOLAP makes it easy for users to get reliable, real-time data-oriented analysis results and reports. This study aims to present an overview and trends of SDW in agriculture, also support from GIS tools that are capable of generating various types of spatial data as a solution to increase stakeholder satisfaction.
Cloud Native Data Platform for Network Telemetry and Analytics

In this manuscript, we present a prototype of a modular data platform that is able to continuously ingest, process, retain, and analyse large amounts of network telemetry data in a scalable and straightforward manner. It follows a recently proposed Data Lakehouse architectural pattern, which is an evolution of two well-known approaches used in this area – data warehouses and data lakes. The platform is based on open standards and open-source components, and it follows cloud native principles in order to be able to run in modern computing environments such as public, private, and hybrid clouds. The primary focus of the prototype is network telemetry and analytics over traffic flows and infrastructure logs for the purposes of cyber-security digital forensics and incident response. During the demonstration part, we will further describe internal workings of the presented data platform and showcase its capabilities and possible applications on a public dataset.
Hardware Speculation Vulnerabilities and Mitigations

This paper will discuss speculation vulnerabilities, which arise from hardware speculation, an optimization technique. Unlike many other types of vulnerabilities, these are very difficult to patch completely, and there are techniques developed to mitigate them. We will look at many of the variants of this type of vulnerability. We will look at the techniques mitigating those vulnerabilities and the effectiveness and scope of each. Finally, we will compare and evaluate different vulnerabilities and mitigation techniques and recommend how various mitigation techniques apply to different situations.
A Form Understanding Approach to Printed and Structured Engineering Documentation

A significant amount of companies still depends on printed documents, such as healthcare reports, engineering specifications, or historical documents. Those documents are diverse in terms of layout and content, thereby it requires different approaches for each document structure, which makes information extraction a costly and inefficient task. We classify documents into three categories, non-structured, semi-structured, and structured documents. The last one being the focus of the present work. We propose a pattern recognition method for structured documents with an anchoring relationship between question-answer objects through a system of hypotheses and a probability distribution in order to identify which predefined model the document belongs to. Therefore, acting as a system for both identification and content extraction to structured documents. The method has promising results for pattern recognition from all document models, with 78% to 97% objects extracted correctly.
PyDash - A Framework Based Educational Tool for Adaptive Streaming Video Algorithms Study

Full Paper in the Innovative Practice track - The pandemics caused by the spreading of the COVID 19 virus cornered the educational system worldwide, changing the classroom into remote class activities. This change in our social behavior has directly impacted the volume and shape of the Internet traffic data. A recent study shows 15% to 30% increases in Internet traffic caused, among other reasons, by educational video streaming traffic during few weeks in the 2020 lockdown period in Europe. To give some perspective, network providers usually work with a 30% data traffic increase per year. In 2021, it is expected that almost 82% of all Internet traffic will be video, according to CISCO annual forecast report. This scenario has a tremendous impact on the Internet bandwidth capacity, demanding optimized video streaming solutions, such as adaptive bitrate algorithms (ABR). On the other hand, considering the educational challenges in computer network courses, the core activities must be executed using specialized infrastructure to develop students' capabilities with networking equipment. As these types of equipment are costly to be obtained and forwarded to in-home students or simply e-students, a remote platform capable of reproducing an environment for networking applications is required. This is the scenario where PyDash was built. PyDash is a framework for the development of adaptive streaming video algorithms. It is a learning tool designed to abstract the networking communication details, allowing e-students to focus exclusively on developing and evaluating ABR protocols. This paper presents our practical experience developing and using PyDash as an educational tool for teaching ABR protocols at Computing Networking courses at the Department of Computer Science at the University of Brasilia, Brazil. Last semester, over 120 students, divided into four different undergraduate courses, had their first contact with PyDash. Even though this was their first contact with ABR concepts and the pyDash tool, they were able to perform the design, implementation, validation, and analysis of some state-of-the-art algorithms used by Netflix and Youtube.
Global Path and Local Motion Planning for Humanoid Climbing Robot Using Kinect Sensor

This paper presents a study on improving the adaptability of humanoid climbing robots in vertical environments. Although the robot is intended to be used for rescue and load-bearing in mountains or other hazardous areas in the future, this paper focuses on a path planning and local planning algorithm for humanoid robot wall climbing as the initial phase of our development. The first step is to acquire a depth map to extract accurate climbing holds on the vertical wall. Secondly, we propose a global planning algorithm for the humanoid robot using data from Kinect. During climbing, the humanoid robot utilizes the local planning algorithm, based on quasi-static equilibrium, to adjust its body posture to remain in an equilibrium state. Finally, all algorithms are evaluated with a simple, practical example for a humanoid climbing robot system, and its effectiveness is demonstrated experimentally in a real environment.
Student Perceptions of the Fairness of Video Presentation Grading in the Online EFL Classroom

This qualitative case study, which employed thematic analysis, explores student perceptions about the fairness of grading video presentations in an online EFL Korean classroom. The study surveyed 145 students about their attitudes towards the fairness of grading for video presentations using three open-ended questions. To better understand the students’ thoughts, they were asked what most affected the fairness of grading live versus video presentations. It found that students believed that the use of video presentations in the required classroom was fair. The students cited a clear, understandable rubric, hard work resulting in the deserved grade, the best material could be submitted, the video could demonstrate ability without nerves, and the teachers’ grading as reasons the grading was deemed fair. There were also some negative perceptions related to the fairness of grading. The two registered were using cheats and not having a fair playing field in terms of technology. The study also found that the main factors affecting video versus live presentations were the fear of live presentations, preparation time, technology use, and the teachers’ grading. The results from the survey indicated that student-produced video presentations should be considered in the future.