Reviewerly/exHarmony huggingface dataset:
Part-Guided Attention Learning for Vehicle Instance Retrieval

Vehicle instance retrieval often requires one to recognize the fine-grained visual differences between vehicles. Besides the holistic appearance of vehicles which is easily affected by the viewpoint variation and distortion, vehicle parts also provide crucial cues to differentiate near-identical vehicles. Motivated by these observations, we introduce a Part-Guided Attention Network (PGAN) to pinpoint the prominent part regions and effectively combine the global and part information for discriminative feature learning. PGAN first detects the locations of different part components and salient regions regardless of the vehicle identity, which serve as the bottom-up attention to narrow down the possible searching regions. To estimate the importance of detected parts, we propose a Part Attention Module (PAM) to adaptively locate the most discriminative regions with high-attention weights and suppress the distraction of irrelevant parts with relatively low weights. The PAM is guided by the instance retrieval loss and therefore provides top-down attention that enables attention to be calculated at the level of car parts and other salient regions. Finally, we aggregate the global appearance and part features to improve the feature performance further. The PGAN combines part-guided bottom-up and top-down attention, global and part visual features in an end-to-end framework. Extensive experiments demonstrate that the proposed method achieves new state-of-the-art vehicle instance retrieval performance on four large-scale benchmark datasets.
PEMBELAJARAN KOLABORATIF PADA MATA PELAJARAN BAHASA INDONESIA DI SMK NEGERI 1 KOTABUMI

The problem that will be examined in this article is collaborative learning in Indonesian language subjects at SMK Negeri 1 Kotabumi. The purpose of writingis to describe the application of collaborative learning in Indonesian language subjects at SMK Negeri 1 Kotabumi. Based on the results of reflection, obtainedsome lessons learned such as: a) collaborative learning tools development resulting in more directed learning plan designs, interactive learning media, and more interesting teaching materials, b) the application of teaching collaboration between teachers and lecturers makes the evaluation process learning is better covered, c) the use of collaborative learning methods opens space for teachers and lecturers to discuss so as to improve cooperative skills and improve the quality of learning in schools.
MinTL: Minimalist Transfer Learning for Task-Oriented Dialogue Systems

In this paper, we propose Minimalist Transfer Learning (MinTL) to simplify the system design process of task-oriented dialogue systems and alleviate the over-dependency on annotated data. MinTL is a simple yet effective transfer learning framework, which allows us to plug-and-play pre-trained seq2seq models, and jointly learn dialogue state tracking and dialogue response generation. Unlike previous approaches, which use a copy mechanism to "carryover" the old dialogue states to the new one, we introduce Levenshtein belief spans (Lev), that allows efficient dialogue state tracking with a minimal generation length. We instantiate our learning framework with two pre-trained backbones: T5 and BART, and evaluate them on MultiWOZ. Extensive experiments demonstrate that: 1) our systems establish new state-of-the-art results on end-to-end response generation, 2) MinTL-based systems are more robust than baseline methods in the low resource setting, and they achieve competitive results with only 20\% training data, and 3) Lev greatly improves the inference efficiency.
Real-time visualization of electrical circuit schematics: An augmented reality experiment setup to foster representational knowledge in introductory physics education

Empirical research has shown that augmented reality (AR) has the potential to promote learning in different contexts. In particular, this has been shown for AR-supported physics experiments, where virtual elements (e.g., measurement data) were integrated into the learners’ visual reality in real time: compared to traditional experimentation, AR reduced cognitive load and promoted conceptual learning. Drawing upon previous work from this column, we present an AR-supported experiment on simple electrical circuits that allows for real-time visualization including highlighting of electrical circuit schematics using either smartglasses or tablet computers. The experiment addresses students in introductory physics education and holds potential to provide visual assistance for complex electrical circuits in secondary or higher physics education.
Towards the Heisenberg limit in microwave photon detection by a qubit array

Using an analytically solvable model, we show that a qubit array-based detector allows to achieve the fundamental Heisenberg limit in detecting single photons. In case of superconducting qubits, this opens new opportunities for quantum sensing and communications in the important microwave range.
Analysis of Fuzzy Logic Modification for Student Assessment in e-Learning

The phenomenon of the rapid transfer of learning to online systems, such as e-Learning, has occurred massively. Institutions must ensure that student assessments run well. The characteristics of learning in e-Learning require an appropriate assessment method. The fuzzy logic method can be an option. Research shows that fuzzy logic is capable of providing flexible and objective performance evaluation. Fuzzy logic is a method that can overcome the uncertainty of transparency and objectivity of student assessments. In general, fuzzy logic applications are carried out by standards. Modification is an attempt to reveal the flexibility and to optimize the use of fuzzy logic. This study presents an analysis of fuzzy logic modification for the assessment of Algorithm and Data Structures courses held in e-Learning. These modifications include (i) modification of the parameter score with score compatibility, (ii) consequent modification of the fuzzy rules and (iii) modification of the implication process. The study results show that although the use of fuzzy logic requires more complicated procedures and tools, it can present various kinds of assessment as an option for educators to assess students in e-Learning.
AuthQX: Enabling Authenticated Query over Blockchain via Intel SGX

With the popularization of blockchain technology in traditional industries, though the desire for supporting various authenticated queries becomes more urgent, current blockchain platforms cannot offer sufficient means of achieving authenticated query for light clients, because Authenticated Data Structure (ADS) suffers from performance issues and state-of-the-art Trust Execution Environment (TEE) cannot deal with large-scale applications conveniently due to limited secure memory. In this study, we present a new query authentication scheme, named AuthQX, leveraging the commonly available trusted environment of Intel SGX. AuthQX organizes data hierarchically in trusted SGX enclave and untrusted memory to implement authenticated query cheaply.
A Novel Mobile Wallet Model for Elderly Using Fingerprint as Authentication Factor

Due to the availability of fingerprint verification technology on mobile devices, fingerprint based human identification has become the most widely used biometric technology in everyday life. The elderly find digital payment applications difficult to operate and unsecure. This work has exploited fingerprint verification availability on mobile devices to provide a user friendly and secure digital wallet payment facility to elderlies who are unable to avail this facility due to the complex infrastructure of traditional authentication mechanisms. A novel digital payment mechanism is presented which uses Bluetooth technology of mobile devices for billing at the point of sale and fingerprint verification for user authentication. A proof of the concept study is presented to validate the proposed model using state of the art usability questionnaires and attributes with the target group. The result indicates that the proposed methodology provides satisfaction and ease of use to the user.
New Approach for Industrial Training Using Virtual Reality Technology

This paper presents two case studies achieved within industrial cooperation projects between the University of Applied Sciences Karlsruhe and German manufacturers for special appliances. The aim of the case studies is development and implementation of training applications for the use and the handling of special vehicle using the virtual reality technology. Based on the experiences gathered during these cooperation projects the challenges that face the VR introduction in the industrial area is outlined in this paper. Furthermore, a best practice approach on how to transfer CAD to VR data to implement industrial VR application is presented in this contribution.
A Study on Research Trends and Knowledge System in Cultural Contents Studies Using Topic Modeling and Text Network Analysis Based on Big Data

This paper aims to analyze academic big data in the field of cultural contents studies using topic modeling and text network analysis and explore the research trends and knowledge system. To achieve concrete results, the research was conducted with following goals: first, to determine the important central theme in the research of cultural contents studies; second, to outline the major topics in the field of cultural contents studies; third, to explain how major topics and subjects have changed in the field of cultural contents studies and what their characteristics are; and fourth, how the result of the analysis is visualized on a network map and what its characteristics are. The research followed four steps—data collection, data refinement, data analysis, and integrating and interpretation. The data were collected between 2000, when the very first paper on cultural contents was published in South Korea, and 2020 from 3,685 academic papers. The collected unstructured data were refined for computer-aided analysis. First, nominal morphemes were extracted using a Korean morpheme analyzer; then, various controlling and TF-IDF analyses were applied. 18,027 words from academic papers have undergone topic modeling and text network analysis with a NetMiner program. Topic modeling is a probabilistic algorithm discovering subjects and topics hidden in a large set of documents, which extracts and classifies documents according to the topic. Text network analysis applies the network theories and analysis methods that developed out of sociology to literature analysis, analyzing the structure of connected words in the text and showing the result in the form of a network map. Recent big data analyses are evolving toward utilizing various optimized analytical techniques to enhance the reliability of the analysis result. Thus, this paper used topic modeling and network analysis to draw a result that is optimal for the purpose of our research. This paper contributes to relevant studies as it uses topic modeling and text network analysis to analyze the big data that have accumulated in the field of cultural contents studies. In addition, it makes a significant contribution as it provides a visualized knowledge map to reveal the relationship of keywords and main topics in the field of cultural contents studies, which leads to the intuitive understanding of abstract contents.
Automated Eye-in-Hand Robot-3D Scanner Calibration for Low Stitching Errors

A 3D measurement system consisting of a 3D scanner and an industrial robot (eye-in-hand) is commonly used to scan large object under test (OUT) from multiple fieldof-views (FOVs) for complete measurement. A data stitching process is required to align multiple FOVs into a single coordinate system. Marker-free stitching assisted by robot’s accurate positioning becomes increasingly attractive since it bypasses the cumbersome traditional fiducial marker-based method. Most existing methods directly use initial Denavit-Hartenberg (DH) parameters and hand-eye calibration to calculate the transformations between multiple FOVs. Since accuracy of DH parameters deteriorates over time, such methods suffer from high stitching errors (e.g., 0.2 mm) in long-term routine industrial use. This paper reports a new robot-scanner calibration approach to realize such measurement with low data stitching errors. During long-term continuous measurement, the robot periodically moves towards a 2D standard calibration board to optimize kinematic model’s parameters to maintain a low stitching error. This capability is enabled by several techniques including virtual arm-based robot-scanner kinematic model, trajectory-based robot-world transformation calculation, nonlinear optimization. Experimental results demonstrated a low data stitching error (< 0.1 mm) similar to the cumbersome marker-based method and a lower system downtime (< 60 seconds vs. 10-15 minutes by traditional DH and hand-eye calibration).
Client-driven Adaptive Bitrate Techniques for Media Streaming over HTTP: Initial Findings

Recently, we have seen a huge interest from academia and industry in HTTP adaptive streaming (HAS) solutions. To improve viewer experience, content providers deploy various HAS solutions that include a key element at the client side, the adaptive bitrate (ABR) logic. This client-driven technique aims to dynamically select an suitable bitrate and adapt to the available network conditions. In literature, many ABR techniques have been proposed. However, how the ABR works depends on the choice of content providers and network characteristics. In this paper, we review a set of heuristic-based client-driven ABR techniques for on-demand video delivery in HAS. We conducted a systematic mapping study to identify the primary studies related to bitrate selection in HAS. Based on the heuristic that ABR implements, we classified the client-driven ABR techniques into two categories: throughput-based, and buffer-based. A detailed analysis is given that will help researchers and practitioners for considering these ABR techniques in HAS.
Dismal Results from a Print Periodical Usage Study

Print periodicals have been a cornerstone of libraries for over 100 years. Over the past 20 years print periodicals have been eclipsed by online journal databases and individual e-journals, but most libraries still subscribe to some print journals. In 2019 Tennessee Technological University undertook a thorough study of the usage of the current print periodical subscriptions by attaching survey forms to the covers of recent issues. The results showed that most newspapers and scholarly print periodicals were not used at all and the majority could be cancelled. Popular magazines showed slightly higher usage, but many of them could be dropped as well.
Navigation on the Line: Traversability Analysis and Path Planning for Extreme-Terrain Rappelling Rovers

Many areas of scientific interest in planetary exploration, such as lunar pits, icy-moon crevasses, and Martian craters, are inaccessible to current wheeled rovers. Rappelling rovers can safely traverse these steep surfaces, but require techniques to navigate their complex terrain. This dynamic navigation is inherently time-critical and communication constraints (e.g. delays and small communication windows) will require planetary systems to have some autonomy.Autonomous navigation for Martian rovers is well studied on moderately sloped and locally planar surfaces, but these methods do not readily transfer to tethered systems in non-planar 3D environments. Rappelling rovers in these situations have additional challenges, including terrain-tether interaction and its effects on rover stability, path planning and control.This paper presents novel traversability analysis and path planning algorithms for rappelling rovers operating on steep terrains that account for terrain-tether interaction and the unique stability and reachability constraints of a rapelling system. The system is evaluated with a series of simulations and an analogue mission. In simulation, the planner was shown to reliably find safe paths down a 55 degree slope when a stable tether-terrain configuration exists and never recommended an unsafe path when one did not. In a planetary analogue mission, elements of the system were used to autonomously navigate Axel, a JPL rappelling rover, down a 30 degree slope with 95% autonomy by distance travelled over 46 meters.
Comparative Performance of Machine Learning Methods for Classification on Phishing Attack Detection

The development of computer networks today has increased rapidly.This can be shown based on the trend of every computer user around the world, whereby they need to connect their computer to the Internet.This indicates that the use of Internet is very important, such as for the access to social media accounts, namely Instagram, Facebook, and Twitter.However, with this extensive use, the Internet does not necessarily have the ability to maintain account security in mobile phones or computers.With a low level of security in a network system, it will be convenient for scammers to hack a victim's computer system and retrieve all important information of the victim for their benefit There are many methods that used by scammers to get the important information where phishing attack is the simplest and famous method to be used.Therefore, this study was conducted to develop an anti-phishing method to detect the phishing attack.Machine learning method was proposed as suitable to be used in detecting phishing attacks.In this paper, several machine learning methods were studied and applied in detecting phishing attack.Experiments of the machine learning methods were conducted to investigate which method performed better.Two benchmark datasets were used in the interest to access the ability of the methods in detecting the phishing attack.Then the results were obtained to show the performance of each methods on all dataset.
Scaling application-level dynamic taint analysis to enterprise-scale distributed systems

With the increasing deployment of enterprise-scale distributed systems, effective and practical defenses for such systems against various security vulnerabilities such as sensitive data leaks are urgently needed. However, most existing solutions are limited to centralized programs. For real-world distributed systems which are of large scales, current solutions commonly face one or more of scalability, applicability, and portability challenges. To overcome these challenges, we develop a novel dynamic taint analysis for enterprise-scale distributed systems. To achieve scalability, we use a multi-phase analysis strategy to reduce the overall cost. We infer implicit dependencies via partial-ordering method events in distributed programs to address the applicability challenge. To achieve greater portability, the analysis is designed to work at an application level without customizing platforms. Empirical results have shown promising scalability and capabilities of our approach.
Enhanced Standard Compatible Image Compression Framework Based on Auxiliary Codec Networks

Recent deep neural network-based research to enhance image compression performance can be divided into three categories: learnable codecs, postprocessing networks, and compact representation networks. The learnable codec has been designed for end-to-end learning beyond the conventional compression modules. The postprocessing network increases the quality of decoded images using example-based learning. The compact representation network is learned to reduce the capacity of an input image, reducing the bit rate while maintaining the quality of the decoded image. However, these approaches are not compatible with existing codecs or are not optimal for increasing coding efficiency. Specifically, it is difficult to achieve optimal learning in previous studies using a compact representation network due to the inaccurate consideration of the codecs. In this paper, we propose a novel standard compatible image compression framework based on auxiliary codec networks (ACNs). In addition, ACNs are designed to imitate image degradation operations of the existing codec, which delivers more accurate gradients to the compact representation network. Therefore, compact representation and postprocessing networks can be learned effectively and optimally. We demonstrate that the proposed framework based on the JPEG and High Efficiency Video Coding standard substantially outperforms existing image compression algorithms in a standard compatible manner.
A Novel Self-Organizing Emotional CMAC Network for Robotic Control

This paper proposes a self-organizing control system for uncertain nonlinear systems. The proposed neural network is composed of a conventional brain emotional learning network (BEL) and a cerebellar model articulation controller network (CMAC). The input value of the network is feed to a BEL channel and a CMAC channel. The output of the network is generated by the comprehensive action of the two channels. The structure of the network is dynamic, using a self-organizing algorithm allows increasing or decreasing weight layers. The parameters of the proposed network are on-line tuned by the brain emotional learning rules; the updating rules of CMAC and the robust controller are derived from the Lyapunov function; in addition, stability analysis theory is used to guaranty the proposed controller's convergence. A simulated mobile robot is applied to prove the effectiveness of the proposed control system. By comparing with the performance of other neural-network-based control systems, the proposed network produces better performance.
Threat Detection in Social Media Images Using the Inception-v3 Model

Threat detection in images within social media content has become an important aspect of content monitoring. This task can be achieved using various image object detection and classification techniques. Recently, object detection has become an important task of machine learning, with significant studies dedicated to using deep learning techniques, especially the convolutional neural network (CNN), in the computer vision field. The current study involves an experiment using transfer learning technology to retrain the Inception-v3 model from TensorFlow in terms of the collected dataset related to known threat content. The novelty of this research work lies in the threat detection of images shared on social media which was not addressed before. The model achieves a high accuracy of around 96% in threat detection. The results of this research will be helpful in monitoring and tracking social media image content in terms of the detection of threats in the images shared among users, while the system can be used as a standalone system or as part of larger systems.
Towards Question-Answering as an Automatic Metric for Evaluating the Content Quality of a Summary

A desirable property of a reference-based evaluation metric that measures the content quality of a summary is that it should estimate how much information that summary has in common with a reference. Traditional text overlap based metrics such as ROUGE fail to achieve this because they are limited to matching tokens, either lexically or via embeddings. In this work, we propose a metric to evaluate the content quality of a summary using question-answering (QA). QA-based methods directly measure a summary's information overlap with a reference, making them fundamentally different than text overlap metrics. We demonstrate the experimental benefits of QA-based metrics through an analysis of our proposed metric, QAEval. QAEval out-performs current state-of-the-art metrics on most evaluations using benchmark datasets, while being competitive on others due to limitations of state-of-the-art models. Through a careful analysis of each component of QAEval, we identify its performance bottlenecks and estimate that its potential upper-bound performance surpasses all other automatic metrics, approaching that of the gold-standard Pyramid Method.