Reviewerly/exHarmony huggingface dataset:
Nearest Neighbor Non-autoregressive Text Generation

Non-autoregressive (NAR) models can generate sentences with less computation than autoregressive models but sacrifice generation quality. Previous studies addressed this issue through iterative decoding. This study proposes using nearest neighbors as the initial state of an NAR decoder and editing them iteratively. We present a novel training strategy to learn the edit operations on neighbors to improve NAR text generation. Experimental results show that the proposed method (NeighborEdit) achieves higher translation quality (1.69 points higher than the vanilla Transformer) with fewer decoding iterations (one-eighteenth fewer iterations) on the JRC-Acquis En-De dataset, the common benchmark dataset for machine translation using nearest neighbors. We also confirm the effectiveness of the proposed method on a data-to-text task (WikiBio). In addition, the proposed method outperforms an NAR baseline on the WMT'14 En-De dataset. We also report analysis on neighbor examples used in the proposed method.
TINTO: Converting Tidy Data into image for classification with 2-Dimensional Convolutional Neural Networks

The growing interest in the use of algorithms-based machine learning for predictive tasks has generated a large and diverse development of algorithms. However, it is widely known that not all of these algorithms are adapted to efficient solutions in certain tidy data format datasets. For this reason, novel techniques are currently being developed to convert tidy data into images with the aim of using Convolutional Neural Networks (CNNs). TINTO offers the opportunity to convert tidy data into images through the representation of characteristic pixels by implementing two dimensional reduction algorithms: Principal Component Analysis (PCA) and t-distributed Stochastic Neighbour Embedding (t-SNE). Our proposal also includes a blurring technique, which adds more ordered information to the image and can improve the classification task in CNNs.
Development of Traffic Delay and Queue Metamodel on Landslide Zone Based on Simulation Using VISSIM

Abstract Landslides occur due to instability in the soil triggered by vibrations, high rainfall and topographical conditions of an area. Landslide is a natural disaster that often occurs in West Sumatra. This landslide certainly has an impact on traffic around the area. Congestion is the impact caused by landslides due to falling chunks of material onto the road. Lack of traffic control during landslides makes travel ineffective and inefficient. Therefore, the traffic control and its impact on possible landslides were tested using a VISSIM microscopic simulation software by setting the queue and delay parameters as the outputs. Based on this output of simulations, the metamodel was developed using regression analysis. This model could be used to predict the queue and delay of traffic because of landslide at road network. Two traffic control conditions that were simulated, open and close lane is used if the width of the landslides covering the road is more than one lane. Two-way traffic control in landslide zone is used when the width of the landslides covering the road is less than one lane.
Synthesising Programs with Non-trivial Constants

Program synthesis is the mechanised construction of software. One of the main difficulties is the efficient exploration of the very large solution space, and tools often require a user-provided syntactic restriction of the search space. While useful in general, such syntactic restrictions provide little help for the generation of programs that contain non-trivial constants, unless the user is able to provide the constants in advance. This is a fundamentally difficult task for state-of-the-art synthesisers. We propose a new approach to the synthesis of programs with non-trivial constants that combines the strengths of a counterexample-guided inductive synthesiser with those of a theory solver, exploring the solution space more efficiently without relying on user guidance. We call this approach CEGIS(T), where T is a first-order theory. We present two exemplars, one based on Fourier-Motzkin (FM) variable elimination and one based on first-order satisfiability. We demonstrate the practical value of CEGIS(T) by automatically synthesising programs for a set of intricate benchmarks. Additionally, we present a case study where we integrate CEGIS(T) within the mature synthesiser CVC4 and show that CEGIS(T) improves CVC4's results.
WASMOD: Detecting vulnerabilities in Wasm smart contracts

Abstract Over the past few years, blockchain platforms supporting WebAssembly (Wasm) smart contracts are gaining popularity. However, Wasm smart contracts are often compiled from memory‐unsafe languages (e.g. C and C++). And there is a lack of effective defense against integer overflow and stack overflow at the compiler and virtual machine (VM) layers, making Wasm smart contracts even more exploitable than native C and C++ programs. In this paper, the authors propose wasm overflow detector (WASMOD) to address the integer overflow and stack overflow vulnerabilities. The authors’ approach combines bytecode instrumentation, run‐time validation, and grey‐box fuzzing to detect these vulnerabilities. The authors applied their approach to the popular EOSIO blockchain and evaluated it on 4616 deployed Wasm smart contracts. The authors’ approach detected 13 real‐world vulnerable smart contracts.
STEP TOWARDS INTELLIGENT TRANSPORTATION SYSTEM WITH VEHICLE CLASSIFICATION AND RECOGNITION USING SPEEDED-UP ROBUST FEATURES

&amp;lt;p&amp;gt;Vehicle classification is a crucial task owing to vehicles' diverse and intricate features, such as edges, colors, shadows, corners, and textures. The accurate classification of vehicles enables their detection and identification on roads and facilitates the development of an electronic tollcollection system for smart cities. Furthermore, vehicle classification is useful for traffic signal control strategy. However, achieving accurate vehicle classification poses significant challenges due to the limited processing time for real-time applications, image resolution, illumination variations in the video, and other interferences. This study proposes a method for automated automobile detection, recognition, and classification using statistics derived from approximately 11,000 images. We employ SURF-based detection and different classifiers to categorize vehicles into three groups. The Traffic Management System (TMS) is crucial for studying mobility and smart cities. Our study achieves a high automobile classification rate of 91% with the medium Gaussian Support Vector Machine (SVM) classifier. The paper's main objective is to analyze five object classifiers for vehicle recognition: Decision Tree, Discriminant Analysis, SVM, K-Nearest Neighbor Classifier (KNN), and Ensemble Classifier. In the discussion section, we present the limitations of our work and provide insights into future research directions.&amp;lt;/p&amp;gt;
Analysis Of Spearman Rank Correlation &amp; Linear Regression Of Atmospheric Stability And Cloud Tops Temperature Of Himawari-8 IR Satellite Images (Case Study Of Hail On May 22, 2022)

This research is based on cases of hail that caused flooding in Binjai City on May 22, 2022. This study aims to see the correlation and regression of the Atmospheric Stability index value on cloud top temperature data from the Himawari-8 satellite imagery. So that the ERA-5 reanalysis data can be used to obtain cloud top temperatures in predicting hail events. The indices used in this study are CAPE, KI and TTI. The data used is located in Binjai City with correlation testing from 00UTC – 24UTC on May 22, 2022. Supported data is used in the form of sea surface temperature and wind conditions and looks at the causes of hail at the research location. The method used is Spearman's rank correlation with the results obtained in the form of a correlation value at the K index of -0.651, a correlation of the TT index of -0.563, and a correlation of the CAPE index of -0.348. The simple linear regression results also show a negative gradient line. This shows that the increase in the value of the stability index is in line with the decrease in cloud top temperature. So that the stability index data can be used as a benchmark in estimating cloud top temperatures in the case of convective cloud formation which causes hail.
Dynamic Virtual Machine Scheduling Using Residual Optimum Power-Efficiency In The Cloud Data Center

Abstract Cloud data center serves tremendous workload demand due to the ever-increasing usage of internet services. Scheduling of these workloads over the physical servers is the combinatorial problem that resembles to an NP-complete problem. Furthermore, the workload is dynamic and changes at each scheduling interval result in high power consumption and Service Level Agreement (SLA) violation. Virtual Machine (VM) migrations provide the opportunity to balance this dynamic workload. However, it results in additional power consumption and performance loss. Therefore, this paper aims to find the optimal VM scheduling with minimum VM migration to minimize power consumption and ensure the SLA. This paper proposes a Residual Optimum Power Efficiency (ROPE) aware Improved Clonal Selection Algorithm (ICSA) for dynamic VM scheduling. ICSA-ROPE algorithm finds optimal VM schedules at each scheduling interval guided by two optimization functions Total Datacenter Residual Optimum Power-Efficiency (TDCROPE) and VM Migration Cost (VMC). TDCROPE ensures that servers operate at optimum power efficiency as they consume less power and are less prone to SLA violation, while VMC ensures to find optimal VM schedule with minimum VM migrations. The proposed approach is implemented on a CloudSim simulator, and results show that the ICSA-ROPE is 95.54 %,90.34% and 88.49% more significant in terms of performance efficiency than the LrMmt, DthMf and VMS-MCSA.
Enabling saccadic redirection through real‐time saccade prediction

Abstract Our proposed redirected walking technique improves on current solutions by leveraging temporary visual disruption caused by saccades for redirection without requiring additional eye‐tracking hardware and enables saccadic redirection on widely used consumer‐grade hardware, like mobile VR. Using a deep neural network trained on head rotation data, we predict saccades during an apparent head rotation and apply rigid transformations to the virtual environment for redirection. This allows users to infinitely walk in a perceived straight direction from within a physical tracked space of m. The three user studies illustrated in this article validate our approach and demonstrate its efficacy.
Reconstruction of Meteorological Records by Methods Based on Dimension Reduction of the Predictor Dataset

The reconstruction or prediction of meteorological records through the Analog Ensemble (AnEn) method is very efficient when the number of predictor time series is small. Thus, in order to take advantage of the richness and diversity of information contained in a large number of predictors, it is necessary to reduce their dimensions. This study presents methods to accomplish such reduction, allowing the use of a high number of predictor variables. In particular, the techniques of Principal Component Analysis (PCA) and Partial Least Squares (PLS) are used to reduce the dimension of the predictor dataset without loss of essential information. The combination of the AnEn and PLS techniques results in a very efficient hybrid method (PLSAnEn) for reconstructing or forecasting unstable meteorological variables, such as wind speed. This hybrid method is computationally demanding but its performance can be improved via parallelization or the introduction of variants in which all possible analogs are previously clustered. The multivariate linear regression methods used on the new variables resulting from the PCA or PLS techniques also proved to be efficient, especially for the prediction of meteorological variables without local oscillations, such as the pressure.
A Wavelet Transform-Based Real-Time Filtering Algorithm for Fusion Magnet Power Signals and Its Implementation

With high real-time performance and a good filtering effect, the wavelet transform-based real-time filter is more commonly used in the fusion magnet power supply. Considering the signal characteristics of the fusion magnet power supply, the sliding time window method is widely used to satisfy the requirements of filtering efficiency. In this paper, an updated half-edge extension method is proposed to improve the boundary effect of sliding window data. Moreover, the hardware-based wavelet loopback structure is proposed to avoid the double computation of long sliding window data, thereby improving real-time performance and satisfying the demands of fusion magnet power supply filtering. The proposed half-edge extension method and the wavelet loopback structure are implemented by designed FPGA and experimented on the Experimental and Advanced Superconducting Tokamak (EAST) fusion magnet power supply system to verify the filtering efficiency. According to the experiment’s results, the proposed half-edge extension method has a better filtering effect, and the proposed wavelet loopback structure decreases the time by 67.4%, which can satisfy the requirements of the power supply system.
Triple critical feature capture network: A triple critical feature capture network for weakly supervised object detection

Abstract Weakly supervised object detection (WSOD) is becoming increasingly important for computer vision tasks, as it alleviates the burden of manual annotation. Most WSOD techniques rely on multiple instance learning (MIL), which tends to localise the discriminative parts of salient objects instead of the whole object. In addition, network training is often supervised using simple image‐level annotations, without including object quantities or location information. However, this can lead to ambiguous differentiation of object instances, both in terms of location and semantics. To address these issues, propose an end‐to‐end triple critical feature capture network (TCFCNet) for WSOD is proposed. Specifically, a multi‐task branch, which can perform fully supervised classification and regression task, was integrated with a PCL in an end‐to‐end network for refining object locations in an online method. A cyclic parametric dropblock module (CPDM) was then designed to help the detector focus on the contextual information by using cyclic masking techniques to maximise the removal of the discriminative components of an object instance to alleviate the part domination problem. Finally, a feature decoupling module (FDM) is proposed to further reduce the ambiguous distinction of object instances by adaptively constructing robust critical features that adapt to multi‐task branch for classification and regression tasks, which contains a feature enhancement module and task‐specific polarisation functions. Comprehensive experiments are carried out on the challenging Pascal VOC 2007 and VOC 2012 datasets. The proposed method achieves a 54.6% mAP and a 44.3% mAP on the Pascal VOC 2007 and VOC 2012 datasets respectively, showed that our method outperformed existing mainstream techniques by a considerable margin.
Few‐shot logo detection

Abstract The proliferation of deep learning has driven research into deep learning‐based logo detection, which usually needs a large number of annotated data to train the model. However, due to the occasional appearance of new brands or the high cost of annotation, the number of training data is limited. Against this backdrop, the authors adapt the few‐shot object detection into logo detection, and thus present a cutting‐edge method called Double Classification Head (DCH) for Few‐Shot Logo Detection (DCH‐FSLogo), which aims at detecting the unseen logo classes using few annotated data. Unlike the traditional few‐shot detection, some logo objects are similar to their backgrounds and have diverse shapes as well. For this reason, the authors adopt balanced feature pyramid and deformable Region of Interest pooling in DCH‐FSLogo, this enhances the feature extraction capability and adapts to the different logo shapes. In addition, we introduce the DCH for few‐shot logo detection to detect logo objects using few annotated data. Specifically, we use an extra classification head for the base classes to ease the influence from the novel classes. The experimental results on four datasets, namely: FlickrLogos‐32, FoodLogoDet‐1500‐100, LogoDet‐3K‐100 and QMUL‐OpenLogo‐100, demonstrate that our method achieves better performance.
Vector model-based robot-assisted control system for a wheeled mobile robot

A robot-assisted control system based on the vector model is proposed for a wheeled mobile robot. According to the closed-loop control structure, the system is constituted of two parts – localization and path planning. The localization algorithm, which is enhanced from Monte Carlo localization, is more effective, stable, and robust than the traditional algorithm because of using many strengthening mechanisms, such as using a vector model, re-initialization, and reverse convergence. The path planning algorithm includes three stages to obtain a path with a motion plan. Firstly, a path from the current position to the goal is planned by an enhanced A* algorithm. Secondly, a smooth mechanism is applied to the path to obtain the continuity of orientation. Finally, a motion design based on the trapezoidal-curve velocity profile is implemented to the smoothed path in both linear and angular velocities to obtain the estimated moving time, position schedule, and velocity schedule. With the assisted control system, the robot knows its current position, the path with motion planning to the destination and its estimated arrival time. If the robot deviates from the move plan, the system will reschedule based on the current state. The experimental results show the great performance of our proposed method.
Automated Subject Identification using the Universal Decimal Classification: The ANN Approach

Universal Decimal Classification (UDC) is a popular controlled vocabulary that is used to represent subjects of documents. Text categorization determines a text's category, as evident from the notation-text label format of the Universal Decimal Classification. With the help of machine learning techniques and the Universal Decimal Classification (UDC), the present work aims to develop an end-user (library professional) based recommender system for automatically classifying documents using the UDC scheme. The proposed work is conceived for determining and constructing a complex class number using the syntax of Universal Decimal Classification (UDC). A corpus of documents classified with the UDC scheme is used as a training dataset. The classification of the documents is done with human mediation having proficiency in classificatory approaches. The BERT model and the KNIME software are used for the study. This study uses the classified dataset to fine-tune the pre-trained BERT model to construct the semi-automatic classification model. The results show that the model is constructed with high accuracy and Area Under Curve (AUC) value, although the prediction represented a low accuracy rate. This study reflected that if the model is explicitly trained by annotating each concept and if the full licensed version of UDC class numbers becomes available, there is a greater potency of developing an automated, freely faceted classification scheme for practical use.
Consociationalism as a power-sharing solution in Pakistan. Obstacles and prospects for a democratic transformation

No AccessConsociationalism as a power-sharing solution in Pakistan. Obstacles and prospects for a democratic transformationAgnieszka Kuszewska-BohnertAgnieszka Kuszewska-Bohnerthttps://orcid.org/0000-0001-8888-8566Search for more papers by this authorhttps://doi.org/10.14220/9783737015752.133SectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinkedInRedditEmail About Previous chapter Next chapter FiguresReferencesRelatedDetails Download book cover 1st editionISBN: 978-3-8471-1575-5 eISBN: 978-3-7370-1575-2HistoryPublished online:May 2023 PDF download
Evaluation and Creation of Web-based Resource Guides using SubjectsPlus

The Resource Guide is a unified platform curated and administered by subject specialists, librarians, and liaisons, offering users access to a wide array of resources. Traditionally known by various names such as Pathfinder, bibliography, virtual reference collection, online guide, online tutorial, toolkit, or web guide, it serves as a comprehensive tool. Its primary purpose is to assist users in locating resources specific to their subject areas, staying updated on new resources, and connecting them to relevant, easily understandable information. At Central Library, IIT Delhi, diverse platforms like the library website, social media, online catalogues, Pathfinder, single search engine, and mobile app are utilized to promote library resources. However, these platforms may lack subject-level resources. This raises the question of designing and developing an online platform or resource base that consolidates and enables users to access various resources, including print resources, e-resources, professional development resources, open-access resources, and social media resources. This endeavour aims to meet the present and future needs of users in their respective disciplines. To explore this concept further, a pilot implementation of a web-based resource guide has been initiated at Central Library, IIT Delhi. This paper investigates how SubjectsPlus, an open-source tool, can facilitate the creation and management of web-based resource guides that promote the use of library and scholarly resources on the web.
Arbitrage across different Bitcoin exchange venues: Perspectives from investor base and market related events

Abstract This paper examines the impact of market related events and investor base on the spread of Bitcoin prices between two exchange platforms, Coinbase and Binance. Based on high‐frequency data samples collected from 2019 to 2021, we show how investors from different bases react differently to market related events, which create the price spreads between exchange platforms. We also identify the arbitrage opportunities these spreads create and establish arbitrage strategies for all identified events to exploit the variations in Bitcoin prices traded on both platforms. Findings indicate arbitrage offers profits that are higher overall than holding Bitcoin on either platform.
BUILDING LEARNERS’ CONCEPTUAL KNOWLEDGE AND DIGITAL LITERACY WITH PBL-BASED E-STUDENT WORKSHEET

The objective of this research was to see the role of the electronic student worksheet based on Problem-Based-Learning (PBL-based E-SW) to build conceptual knowledge and digital literacy. This research used a static pretest-posttest design. The population was students in Padangcermin Public Senior high School, in Pesawaran. Samples were taken by cluster random sampling, and they consisted of 35 students in the experiment classroom and 35 students in the control classroom. The experiment classroom was taught using PBL-based E-SW and the control classroom was taught using worksheet used to teach in the school. The result showed that there was a significant influence of using PBL-based E-SW to build conceptual knowledge and digital literacy of students. Paired sample t-test result was less than 0.05, and this indicated a significant difference in average n-gain scores between the experiment and control classrooms. The average n-gain scores on conceptual knowledge were 0.61 or belonging to a moderate category in the experiment classroom and 0.41 or belonging to a moderate category in the control classroom. The average n-gain scores on digital literacy were 0.89 or belonging to a high category in the experiment classroom and 0.67 or belonging to a moderate category in the control classroom.