Reviewerly/exHarmony huggingface dataset:
Multi-teacher knowledge distillation for compressed video action recognition based on deep learning

Recently, Convolutional Networks have great progress in classifying images. While action recognition is different from still image classification, video data contains temporal information which plays an important role in video understanding. Currently most CNN-based approaches for action recognition has excessive computational costs, an explosion of parameters and computation time. The most efficient method currently trained a deep network directly on the compressed video contains the motion information. However, this method has a large number of parameters. We propose a multi-teacher knowledge distillation framework for compressed video action recognition to compress this model. With this framework, the model is compressed by transferring the knowledge from multiple teachers to a single small student model. With multi-teacher knowledge distillation, students learn better than single-teacher knowledge distillation. Experiments show that we can reach a 2.4 × compression rate in number of parameters and 1.2 × computation reduction with 1.79% loss of accuracy on the UCF-101 dataset and 0.35% loss of accuracy on the HMDB51 dataset.
Performance Impact of Simulation-Based Virtual Laboratory on Engineering Students: A Case Study of Australia Virtual System

Practitioners of virtual laboratory confront issues on how to fulfill individuals' needs, motivate them to participate and use the tools, and to enhance their performance using virtual tools.Therefore, this study aims at examining the effects of usability and learning objective factors in evaluating students' performance impact from using virtual laboratory.The study proposes a theoretical model based on usability factors of technology acceptance model (perceived ease of use and perceived usefulness) and laboratory learning objectives (instruments, creativity and innovation) to capture the entire patterns of students' perceptions and use outcomes from using the simulation-based virtual laboratory.The study collected survey data from 116 first year Electrical Engineering students from the University of Queensland in Australia, reflecting their personal experience in using virtual laboratory tools.Partial least square approach using structural equation modeling technique (PLS-SEM) was used for statistical analysis and model testing.The results confirm that the proposed model provides a comprehensive understanding of students' perceptions and the understudy factors were truly significant in reflecting their performance impact from using such laboratory tools.More specifically, instrumentation and perceived usefulness of virtual laboratory were found to be the most significant influencing factors that have impacts on students' performance.Also, the findings shed light on the mediation roles of laboratory learning objectives between usability factors and use outcomes.Overall, this study contributes to literature by demonstrating the beneficial use of laboratory learning objectives in creating realistic and credible simulation tool, that can expedite the learning process and foster students' learning outcomes.
Can a Conversation Paint a Picture? Mining Requirements In Software Forums

The modern software landscape is highly competitive.Software companies need to quickly fix reported bugs and release requested new features, or they risk negative reviews and reduced market share.The amount of online user feedback prevents manual analysis.Past research has investigated automated requirement mining techniques on online platforms like App Stores and Twitter, but online product forums have not been studied.In this paper, we show that online product forums are a rich source of user feedback that may be used to elicit product requirements.The information contained in forum questions is different from what has been described in the related work on App Stores or Twitter.Users often provide detailed context to specific problems they encounter with a software product and other users respond with workarounds or to confirm the problem.Through the analysis of two large forums, we identify 18 different types of information (classifications) contained in forums that can be relevant to maintenance and evolution tasks.We show that a state-of-the-art App Store tool is unable to accurately classify forum data, which may be due to the differences in content.Thus, specific techniques are likely needed to mine requirements from product forums.In an exploratory study, we developed classifiers with forum specific features.Promising results are achieved for all classifiers with f-measure scores ranging from 70.3% to 89.8%.
Knowledge graph based natural language generation with adapted pointer-generator networks

Pointer-generator networks have recently shown superior performance in Natural Language Generation (NLG) tasks, such as automatic generating descriptions for entities in Knowledge Graph (KG). In the absence of the introductory description about an entity we intend to know, the natural language text automatically generated by neural network model can greatly help people to better understand the entity. Entities in KG always have multiple property fields and corresponding values. The target generated description sentences should consist of these slot types and slot values in a consistent and high coverage manner. In order to cover the facts in an input KG, pointer-generator networks always copy certain segments from the input sequences via softmax pointing while generating novel words through the generator. But the challenges that when and where to integrate the copied information with the generated one, how to prevent duplicate generation and information loss are hard to conquer. In this paper, the KG2TEXT model based on adapted pointer-generator networks is proposed. In it, a varied coverage loss function is devised to cover attribute-value pairs as many as possible when generating natural language descriptions for entities in KG. Secondly, an attention mechanism named supervised attention mechanism is added to the model which aims to guide the soft switch transformation process (to generate or to copy). With empirical experiments on two kinds of realistic datasets, the KG2TEXT model achieves promising results and outperforms the state-of-the-art approaches.
Improved DWT-SVD-Based Medical Image Watermarking Through Hamming Code and Chaotic Encryption

This paper presents an efficient watermarking technique to enhance the performance of DWT-SVD-based approach. The method uses well-known error-correcting code (ECC) and chaotic encryption to reduce the channel noise distortion and improve the security of the technique, respectively. In the proposed method, the cover image is transformed by DWT and the sub-bands are selected for embedding the watermarks. Subsequently, the selected sub-bands are further transformed by SVD. The more robust watermark “patient report” and less robust watermark “patient medical image” is embedded into singular values of the selected DWT sub-bands. The use of transform domain techniques along with hamming code ensures that the approach offers more robustness and reliability. Inclusion of chaos-based encryption carries twofold benefits such as obscuring the watermarked content and strengthening the overall security of the projected scheme. The proposed technique offers a significant value of peak signal-to-noise ratio (PSNR), normalized correlation (NC), bit error rate (BER), number of pixel change rate (NPCR), and unified average changing intensity (UCAI) in presence of major attacks. The performance comparisons of our technique with a similar approach show that the proposed technique is superior for nine considered attacks.
Analysis of solar PV glare in airport environment: Potential solutions

Solar PV plants are being installed in many airports around the globe. Reflection from the solar PV arrays is a big concern for airport stakeholders. This paper aims to assess the glare occurrence and its impact from the proposed solar PV plant installed in an airport. The prediction of glare is carried out with the help of computational software for a randomly selected area within the boundary of Kuantan airport, Malaysia. The selected zone is not suitable for solar installations as per FAA's glare policy. In the selected area (Apv), the duration of glare on ATCT from solar modules installed is 6778 min (green and yellow glare). Also, the flight path is free from any kind of glare occurrence. Glare occurs between March to mid-October, mostly from 7.00 a.m. to 8.00 a.m. Green and Yellow glare last up to 10 min and 30 min respectively in a year. In this regard, remedial steps for mitigating possible glare are discussed. Glare prediction helps in the early adoption of suitable remedial measures against glare hazard.
Modified Advanced Encryption Standard Algorithm for Information Security

The wide acceptability of Advanced Encryption Standard (AES) as the most efficient of all of the symmetric cryptographic techniques has further opened it up to more attacks. Efforts that were aimed at securing information while using AES is still being undermined by the activities of attackers This has further necessitated the need for researchers to come up with ways of enhancing the strength of AES. This article presents an enhanced AES algorithm that was achieved by modifying its SubBytes and ShiftRows transformations. The SubBytes transformation is modified to be round key dependent, while the ShiftRows transformation is randomized. The rationale behind the modification is to make the two transformations round key dependent, so that a single bit change in the key will produce a significant change in the cipher text. The conventional and modified AES algorithms are both implemented and evaluated in terms avalanche effect and execution time. The modified AES algorithm achieved an avalanche effect of 57.81% as compared to 50.78 recorded with the conventional AES. However, with 16, 32, 64, and 128 plain text bytes, the modified AES recorded an execution time of 0.18, 0.31, 0.46, and 0.59 ms, respectively. This is slightly higher than the results obtained with the conventional AES. Though a slightly higher execution time in milliseconds was recorded with the modified AES, the improved encryption and decryption strength via the avalanche effects measured is a desirable feat.
Automating Car Insurance Claims Using Deep Learning Techniques

With the number of people driving a car increasing every day, there has been a proliferation in the number of cars insurance claims being registered. The life cycle of registering, processing and making a decision for each claim involves the manual examination by the service engineer who creates the damage report followed by the physical inspection by a surveyor from the insurance company which makes it a long drawn out process. We propose an end to end system to automate this process, which would be beneficial for both the company and the customer. This system takes images of the damaged car as input and gives relevant information like the damaged parts and provides an estimate of the extent of damage (no damage, mild or severe) to each part. This serves as a cue to then estimate the cost of repair which would be used in deciding insurance claim amount. We have experimented with popular instance segmentation models like the Mask R-CNN, PANet and an ensemble of these two along with a transfer learning [1] based VGG16 network to perform different tasks of localizing and detecting various classes of parts and damages found in the car. Additionally, the proposed system achieves good mAP scores for parts localization and damage localization (0.38 and 0.40 respectively).
Attention boosted bilinear pooling for remote sensing image retrieval

Remote sensing image retrieval is to find the most identical or similar images to a query image in the vast archive of remote sensing images.A key process is to extract the most distinctive features.In this study, we introduce a second-order pooling named compact bilinear pooling (CBP) into convolutional neural networks (CNNs) for remote sensing image retrieval.The retrieval algorithm has three stages, pretraining, fine-tuning and retrieval.In the pretraining stage, two classic CNN structures, VGG16 and ResNet34, are pretrained respectively with the ImageNet consisting of close-range images.A CBP layer is introduced before the fully connected layers in the two networks.To extract globally consistent representations, a channel and spatial integrated attention mechanism is proposed to refine features from the last convolution layer and the features are used as the input of the CBP.In the fine-tuning stage, the new network is fine-tuned on a remote sensing dataset to train discriminable features.In the retrieval stage, the network, with fully connected layers being replaced by a PCA (principal component analysis) module, is applied to new remote sensing datasets.Our retrieval algorithm with the combination of CBP and PCA obtained the best performance and outperformed several mainstream pooling or encoding methods such as full-connected layer, IFK (Improved Fisher Kernel), BoW (Bag-of-Words) and maxpooling, etc.The channel and spatial attention mechanism contributes to the CBP based retrieval method and obtained the best performance on all the datasets, as well as outperformed several recent attention methods.
How User’s First Impression Forms on Mobile user Interface?: An ERPs Study

In an era of mobile Internet, the using of mobile interfaces is ineluctable in our daily life. To some extent, the first impression of the mobile user interfaces determines users' downloading and using behavior, even affects the overall user experience. In fact, the underlying neural mechanism of users' first impression formation of mobile user interface is worthy to be investigated. Considering the process of first impression formation is always unconscious and transient, event-related potentials (ERPs), which are used to track users' cerebral activities could be an appropriate method to explore this process. In the present study, the perceived usability and esthetics are taken as the evaluation dimensions of user' first impression formation, and ERPs were used to investigate which dimensions were automatically activated when users browsed the mobile user interfaces passively without any explicit guidance. The ERPs results showed that N2 was dominated by perceived usability and esthetics, which reflected in enhanced N2 for high esthetics than for low esthetics, and larger N2 for high-perceived usability/high esthetics compared to low-perceived usability/low esthetics. Moreover, there was a larger late positive potential (LPP) for low esthetics interfaces with respect to high esthetics ones. The findings revealed that users could make an implicit evaluation spontaneously to the mobile user interfaces with different levels of perceived usability and esthetics. Furthermore, users' first impression is significantly affected by the differences in esthetics but marginally influenced by the difference in perceived usability. The suggestions from this study for mobile application designers might be that the esthetics design of the mobile user interface should be prioritized in the early design stage.
Research on Industrial Internet of Things Security Architecture and Protection Strategy

Industrial Internet of Things (IIoT) is a fusion of industrial automation systems and IoT systems. It features comprehensive sensing, interconnected transmission, intelligent processing, self-organization and self-maintenance. Its applications span intelligent transportation, smart factories, and intelligence. Many areas such as power grid and intelligent environment detection. With the widespread application of IIoT technology, the cyber security threats to industrial IoT systems are increasing day by day, and information security issues have become a major challenge in the development process. In order to protect the industrial IoT system from network attacks, this paper aims to study the industrial IoT information security protection technology, and the typical architecture of industrial Internet of things system, and analyzes the network security threats faced by industrial Internet of things system according to the different levels of the architecture, and designs the security protection strategies applied to different levels of structures based on the specific means of network attack.
A local-gravitation-based method for the detection of outliers and boundary points

Detection of outliers and boundary points represents an effective, interesting and potentially valuable pattern, which may be more important than that of normal points. In order to detect outliers and boundary points, we propose a local-gravitation-based method in which each data point is viewed as an object with both mass and a local resultant force (LRF) generated by its neighbors. With the increase of neighbor, the LRF of outliers, boundary points and interior points varies at different rates. In this paper, the LRF changing rates of points with lower densities have higher scores, namely the changing rate of an outlier is greater than that of a boundary point and inner point. In other words, top-m ranked points can be identified as outliers, and the greater the LRF changing rate of a point is, the more likely it is a boundary point. The main advantage of our proposed method is that it does not depend on the choice of K value, which improves the detection performance. The experimental results on synthetic and real data sets show that the proposed method is better than the existing methods.
Improved RANSAC features image‐matching method based on SURF

For the speed of traditional SIFT algorithm in the feature extraction and matching is slow, the article proposes an improved RANSAC features image matching method based on speeded up robust features (SURF). First of all, detect images features and extract with SURF method, use the fast library for approximate nearest neighbours-based matcher method to perform initial matching on image feature points. Improve the RANSAC algorithm to increase the probability of correct matching points being sampled. Experimental results show that the improved RANSAC algorithm has high matching accuracy, good robustness, and short running time. It lays the foundation for the subsequent fast image stitching.
HDS-SP: A novel descriptor for skeleton-based human action recognition

3D skeleton-based human action recognition has attracted much attention due to a wide spectrum of promising applications in terms of depth sensors. This paper, based on the principle of the better viewpoint the better action recognition performance, devises a HDS-SP descriptor for skeleton-based human action, i.e., the histogram of distributed sectors based on their specific projections. The HDS-SP descriptor consists of both spatial and temporal information from specific viewpoint, in which projecting 3D trajectories on specific planes and creating reasonable histograms using proposed way are two primary contributions. Inspired by the nature of human action, the spatial information is incorporated into the histogram of the displacement of one joint between two successive frames voting for corresponding bins following weight-based rules over an undefined plane while the specific projection planes are optimized by both local search algorithm and Particle Swarm Optimization (PSO); the temporal information is captured by temporal hierarchical construction. The proposed method is evaluated in five widely researched datasets for skeleton-based human action recognition with results significantly outperforming the state-of-the-art.
Entanglement Detection beyond Measuring Fidelities

One of the most widespread methods to determine if a quantum state is entangled, or to quantify its entanglement dimensionality, is by measuring its fidelity with respect to a pure state. In this Letter we find a large class of states whose entanglement cannot be detected in this manner; we call them unfaithful. We find that unfaithful states are ubiquitous in information theory. For small dimensions, we check numerically that most bipartite states are both entangled and unfaithful. Similarly, numerical searches in low dimensions show that most pure entangled states remain entangled but become unfaithful when a certain amount of white noise is added. We also find that faithfulness can be self-activated, i.e., there exist instances of unfaithful states whose tensor powers are faithful. To explore how the fidelity approach limits the quantification of entanglement dimensionality, we generalize the notion of an unfaithful state to that of a D-unfaithful state, one that cannot be certified as D-dimensionally entangled by measuring its fidelity with respect to a pure state. For describing such states, we additionally introduce a hierarchy of semidefinite programming relaxations that fully characterizes the set of states of Schmidt rank at most D.
Incremental Algorithm for Association Rule Mining under Dynamic Threshold

Data mining is essentially applied to discover new knowledge from a database through an iterative process. The mining process may be time consuming for massive datasets. A widely used method related to knowledge discovery domain refers to association rule mining (ARM) approach, despite its shortcomings in mining large databases. As such, several approaches have been prescribed to unravel knowledge. Most of the proposed algorithms addressed data incremental issues, especially when a hefty amount of data are added to the database after the latest mining process. Three basic manipulation operations performed in a database include add, delete, and update. Any method devised in light of data incremental issues is bound to embed these three operations. The changing threshold is a long-standing problem within the data mining field. Since decision making refers to an active process, the threshold is indeed changeable. Accordingly, the present study proposes an algorithm that resolves the issue of rescanning a database that had been mined previously and allows retrieval of knowledge that satisfies several thresholds without the need to learn the process from scratch. The proposed approach displayed high accuracy in experimentation, as well as reduction in processing time by almost two-thirds of the original mining execution time.
Non-Rigid Structure from Motion: Prior-Free Factorization Method Revisited

A simple prior free factorization algorithm [9] is quite often cited work in the field of Non-Rigid Structure from Motion (NRSfM). The benefit of this work lies in its simplicity of implementation, strong theoretical justification to the motion and structure estimation, and its invincible originality. Despite this, the prevailing view is, that it performs exceedingly inferior to other methods on several benchmark datasets [14], [1]. However, our subtle investigation provides some empirical statistics which made us think against such views. The statistical results we obtained supersedes Dai et al.[9] originally reported results on the benchmark datasets by a significant margin under some elementary changes in their core algorithmic idea [9]. Now, these results not only exposes some unrevealed areas for research in NRSfM but also give rise to new mathematical challenges for NRSfM researchers. We argue that by properly utilizing the well-established assumptions about a non-rigidly deforming shape i.e, it deforms smoothly over frames [27] and it spans a low-rank space, the simple prior-free idea can provide results which is comparable to the best available algorithms. In this paper, we explore some of the hidden intricacies missed by Dai et. al. work [9] and how some elementary measures and modifications can enhance its performance, as high as approx. 18% on the benchmark dataset. The improved performance is justified and empirically verified by extensive experiments on several datasets. We believe our work has both practical and theoretical importance for the development of better NRSfM algorithms.