Reviewerly/exHarmony huggingface dataset:
A Fast Search System for Remote Sensing Imagery Based on Bag of Visual Words and Latent Dirichlet Allocation

In this paper, we present our image search system for remote sensing imagery leveraging the capabilities of Elasticsearch, a well-known full-text search engine. We use bag of visual words and bag of visual topics model to represent the earth observation images in a text- equivalent format. The image files are stored in Elasticsearch in their text-equivalent format, making it feasible to apply some sophisticated text-based search functionalities of Elasticsearch on the data. This gives great flexibility to the search clauses allowing the user to match chosen fragments of images. We also implement visual topic search which enables search by higher level semantics. The proposed methods are simple and fast as they do not require any complex image feature computation.
The Impact of Activation Sparsity on Overfitting in Convolutional Neural Networks

Overfitting is one of the fundamental challenges when training convolutional neural networks and is usually identified by a diverging training and test loss. The underlying dynamics of how the flow of activations induce overfitting is however poorly understood. In this study we introduce a perplexity-based sparsity definition to derive and visualise layer-wise activation measures. These novel explainable AI strategies reveal a surprising relationship between activation sparsity and overfitting, namely an increase in sparsity in the feature extraction layers shortly before the test loss starts rising. This tendency is preserved across network architectures and reguralisation strategies so that our measures can be used as a reliable indicator for overfitting while decoupling the network’s generalisation capabilities from its loss-based definition. Moreover, our differentiable sparsity formulation can be used to explicitly penalise the emergence of sparsity during training so that the impact of reduced sparsity on overfitting can be studied in real-time. Applying this penalty and analysing activation sparsity for well known regularisers and in common network architectures supports the hypothesis that reduced activation sparsity can effectively improve the generalisation and classification performance. In line with other recent work on this topic, our methods reveal novel insights into the contradicting concepts of activation sparsity and network capacity by demonstrating that dense activations can enable discriminative feature learning while efficiently exploiting the capacity of deep models without suffering from overfitting, even when trained excessively.
Specific Congruence Classes of Integer Parameters for Generating BLS Curves for Fast Pairings

Pairings are widely used for innovative protocols such as ID-based encryption and group signature authentication. According to the recent works, the Barreto-Lynn-Scott (BLS) family of pairing-friendly elliptic curves are suggested for the pairings at the various security levels. The BLS family has specific polynomial parameters in terms of an integer x <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> for generating the pairing-friendly elliptic curves with various embedding degrees k, which are called BLS curves. The important fact is that one can find congruence classes of x <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> which give rise to the BLS curves having a benefit of an efficient performing field arithmetics. However, except for the BLS curves with k = 24, such the practical usable congruence classes of x <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> have not been provided at this time. In this manuscript, the authors provide the specific congruence classes generating the practical subfamilies of the BLS curves with k = 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sup> = 3 and 3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">j</sup> with arbitrary positive integers i and j.
Discovery of Analogical Knowledge for the Transfer of Workflow Tasks

Analogical knowledge considers functional properties of objects in contrast to literal similarity which compares the degree of featural overlap. A classical example from Gentner's structure mapping theory is "An electric battery is like a reservoir" [1]. Acquiring analogical knowledge in a computational approach is a challenging task. In this paper, we present a solution that combines learning with knowledge engineering. The proposed knowledge discovery approach uses word embeddings to learn analogy on workflow tasks. The resulting knowledge is integrated with an ontology for the purpose of workflow transfer across application domains. A case study is conducted on the two example domains 'passenger and baggage handling at the airport' and 'SAP warehouse management'. The experimental results on comparing the computational analogy with a golden standard from a knowledge engineering expert are quite promising and provide a proof-of-concept for the feasibility of the approach.
A Sign of Things to Come: Predicting the Perception of Above-the-Fold Time in Web Browsing

Content is a key influencing factor in Web Quality of Experience (QoE) estimation. A web user’s satisfaction can be influenced by how long it takes to render and visualize the visible parts of the web page in the browser. This is referred to as the Above-the-fold (ATF) time. SpeedIndex (SI) has been widely used to estimate perceived web page loading speed of ATF content and a proxy metric for Web QoE estimation. Web application developers have been actively introducing innovative interactive features, such as animated and multimedia content, aiming to capture the users’ attention and improve the functionality and utility of the web applications. However, the literature shows that, for the websites with animated content, the estimated ATF time using the state-of-the-art metrics may not accurately match completed ATF time as perceived by users. This study introduces a new metric, Plausibly Complete Time (PCT), that estimates ATF time for a user’s perception of websites with and without animations. PCT can be integrated with SI and web QoE models. The accuracy of the proposed metric is evaluated based on two publicly available datasets. The proposed metric holds a high positive Spearman’s correlation (rs=0.89) with the Perceived ATF reported by the users for websites with and without animated content. This study demonstrates that using PCT as a KPI in QoE estimation models can improve the robustness of QoE estimation in comparison to using the state-of-the-art ATF time metric. Furthermore, experimental result showed that the estimation of SI using PCT improves the robustness of SI for websites with animated content. The PCT estimation allows web application designers to identify where poor design has significantly increased ATF time and refactor their implementation before it impacts end-user experience.
Linked Data Effectiveness in Neural Machine Translation

Quality of data-driven Machine Translation (MT) systems depends on large volumes of data from which models can be constructed to leverage patterns and knowledge from these datasets. In corpus-based MT systems, Out-Of-Vocabulary (OOV) words and ambiguous translations are the most common sources of error. In this paper, JRC-Names and DBpedia have been employed as Linked Data (LD) to minimize the aforementioned types of errors on top of a Neural MT (NMT) model. Three strategies have been evaluated for exploiting knowledge from LD in translating named entities; 1) Dictionaries, 2) Pre-decoding, and 3) Post-editing. Based on the experimental results, these strategies optimize the benefit of the multilingual LD to NMT application. The experiments on English-Spanish translation as well as English-French translation evaluate the validity of the proposed idea.
Cache-based Optimization for Block Commit of Hyperledger Fabric

Blockchain technology has been applied to various domains in recent years, and Hyperledger Fabric is one of the most popular blockchain systems. This paper conducts a fine analysis on the block commit of Hyperledger Fabric through latency evaluation as well as flame graph. To overcome the bottlenecks pointed out by the analysts, this paper proposes two cache-based schemata. In particular, we add a cache for index and state storage to accelerate commit. We also cache deserialized block globally for block delivery to avoid repeat operations. The experiments reveal performance improvement of 2×. Notably, our optimizations can also be implemented easily in the future release of Hyperledger Fabric.
Designing Auditory Experiences for Technology Imagination

This paper explores how auditory experiences evoke future scenarios and further foster future applications of emerging technologies in an IoT research center. Taking an experiential approach in design fiction research, we designed audio clips that mixed personal voices with sci-fi soundscapes and explored the participants’ experiences of digital encounters. What they imagined and experienced contributed to new understandings of how to better design auditory experiences for evoking alternative possibilities.
Android-Based Learning Environment To Enhance Creative Thinking Mathematics and Self-Efficacy

Abstract The ability to think mathematics and self-efficacy is a combination of skills needed to face a bright future. But the present conditions force the learning process to take place online which is caused by the spread of the pandemic COVID 19. The innovation of the learning process as a result of technological transformation enables innovation based on Android learning. The purpose of this study is to impact the Android-based pursuit of mathematical creative thinking skills. This research is pre-experimental research with one-group pretest-posttest design. The research sample is junior high school students in Aceh Province. The sampling technique used was purposive sampling to get samples with equal ability. The instruments in this study were tests of mathematical creative thinking ability and self-efficacy scale. Test analysis using paired sample t-test with one way ANOVA test. The results of the study, in general, prove that android-based learning has a positive impact on students’ mathematical creative thinking abilities and self-efficacy has a positive impact on mathematics creative thinking abilities.
Ship Detection on Single-Band Grayscale Imagery Using Deep Learning and AIS Signal Matching Using Non-Rigid Transformations

The potential of hazardous situations between mariners in conventional manned craft in proximity to other vessels and operating ASVs requires new solutions to improve utilisation of shared waterspace. This paper explores the possibility of combining AIS data and optical satellite imagery to provide exhaustive and near-real-time information to ASVs about surrounding ships.
Online Evaluation System of Students' Daily Emotional Intervention Effect Based on Human Computer Interaction Platform

At present, students' psychological problems have become the focus of social concern. In the daily emotional intervention, the online evaluation system of students' daily emotional intervention effect is poor. The main reason is that the system has poor command analysis ability for users. To solve the above problems, an online evaluation system of students' daily emotional intervention effect based on human-computer interaction platform is designed. ADuC834 MCU is used as data processing chip, and FPGA core board is used as human-computer interaction chip. The two chips are set as the core processor of the system, and the power circuit is set according to the processor. The weight of influencing factors of artificial intervention effect was calculated, and the evaluation model of artificial intervention effect of students' daily emotion was constructed to complete the intervention effect evaluation. At the same time, the human-computer interaction program is set to realize the communication between the user and the system. So far, the online evaluation system of students' daily emotional intervention effect based on human-computer interaction platform has been completed. Through the comparison with other two traditional systems, the response time and user parallelism of this system are obviously better than the traditional system. In conclusion, the performance of the online evaluation system for students' daily emotional intervention based on human-computer interaction platform has been significantly improved.
Analysis and Design of Internal Information Systems of the APU-PPT Education and Training Center Using the User-Centered Design Method

PPATK established the APUPPT Training Center to increase the knowledge and competence of PPATK stakeholders and human resources through education and training in Anti Money Laundering (AML) and the Prevention of Terrorism Funding. It is able to develop and improve service quality, manage education and training through standardization of competent institutions and conduct training on certification of expertise and competence in AML and Terrorism Financing Prevention. In order to fulfill the needs of the Education and Training Center, this study design information system using the user-centered design (UCD) method that will be used internally by Education and Training Center employees called the Internal Information System of the Education and Training Center APUPPT (SINPU-PPT). SINPUT-PPT has the main goal of automating business processes and reducing the use of office stationery. The design identifies system requirements using the Unified Modeling Language (UML), designs the user interface using a Graphical User Interface (GUI) and designs the database. The results of this research is an information system design. The information system have 9 main menu tabs Home, Room Scheduling, Class Settings, Access Rights and Users, Master Data, Catering Arrangements, Training Program, Adding Letters and Receiving Letters. The results of this design can be used as a structured reference for coding programming and testing the development of the SINPU-PPT information system.
A Hybrid Loss Network for Localization of Image Manipulation

With the development of information security, localization of image manipulations havs become a hot topic. In this paper, a hybrid loss network is proposed for the manipulated image forensics. First, the patch prediction module extracts corresponding features representing the discrepancy of the tampered region boundaries. Then, these features are constrained by the Pixel Normalization, thereby improving the classification performance for the tampered patches. Finally, multi-scale patch prediction masks and semantic information are fused to segment out the tampered regions. The experiments demonstrate the proposed model can achieve high performance.
Recognition of Arabic Handwritten Text by Integrating N-gram Model

Handwritten Arabic recognition systems face several challenges, including the unlimited variation in human handwriting. In this paper, we present an adaptive method for automatic recognition of Arabic handwritten text by integrating linguistic knowledge as n-gram model. The proposed system is divided into two parts: the first part is an automatic text recognition, which based on word recognition, the second part is integrating language model as n-gram to improve the result of the system. To recognize the text, it is necessary to segment it into text lines, then the lines into words in order to recognize these words using global approach, which consist in recognizing the whole word without segmentation into letters. Before the step of segmentation, the preprocessing is applied to the input text images. The proposed method of word recognition is based on horizontal and vertical pixel distributions with the help of a k-nearest-neighbors (KNN) classifier. To make our method most optimal, we study the impact of the number of connected components and the result of the zoning technique to build the feature vectors. To improve the result of text recognition, we integrate the language model as n-gram for choosing the best sentences among them using a corpus. To assess the efficiency of the proposed work, we evaluated and compared with different configurations on a public benchmark dataset of the Arabic handwriting database for text recognition (AHDB) and show that the recognition rate can achieve 86.7%, the n-gram model improve by 6%.
Video-Based Face Recognition with New Classifiers

An exhaustive study has been conducted on face videos from YouTube video dataset for real time face recognition using the features from deep learning architectures and also the information set features.Our objective is to cash in on a plethora of deep learning architectures and information set features.The deep learning architectures dig in features from several layers of convolution and max-pooling layers though a placement of these layers is architecture dependent.On the other hand, the information set features depend on the entropy function for the generation of features.A comparative study of deep learning and information set features is made using the well-known classifiers in addition to developing Constrained Hanman Transform (CHT) and Weighted Hanman Transform (WHT) classifiers.It is demonstrated that information set features and deep learning features have comparable performance.However, sigmoid-based information set features using the new classifiers are found to outperform MobileNet features.
A depth camera-based full-body reconstruction method for body pose training in occupational safety

Occupational injuries have high incidence rates across various industries. Safety education is a key component to effectively reduce work-related injuries. Posture training for work safety is widely adopted to increase the awareness of unsafe movements at work and to evaluate workers to minimize work-related musculoskeletal stresses. However, existing one-size-fits-all pamphlet-based posture training is facing challenges in its effectiveness. In recent years, the substantial technological development in virtual reality (VR) and augmented reality (AR) has made immersive and personalized education possible. For VR/AR-assisted posture training, full-body reconstruction from multiple point clouds is the key step. In this study, we propose a fast and coarse method to reconstruct the full-body pose of safety instructors using multiple low-cost depth cameras. The reconstructed body images from depth cameras are registered through iterative closet point algorithm. The reconstructed full-body pose can be further rendered in VR/AR environments for next-generation safety education.
CaptorX: A Class-Adaptive Convolutional Neural Network Reconfiguration Framework

Nowadays, the evolution of deep learning and cloud service significantly promotes neural network-based mobile applications. Although intelligent and prolific, those applications still lack certain flexibility: for classification tasks, neural networks are generally trained with vast classification targets to cover various utilization contexts. However, only partial classes are practically inferred due to individual mobile user preference and application specificity, which causes unnecessary computation consumption. Thus, we proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CaptorX</i> —a class-adaptive convolutional neural network (CNN) reconfiguration framework to adaptively prune convolutional filters associated with unneeded classes. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CaptorX</i> can reconfigure a pretrained full-class CNN model into class-specific lightweight models based on the visualization analysis of convolutional filters’ exclusive functionality for a single class. These lightweight models can be directly deployed to mobile devices without the retraining cost of traditional pruning-based reconfiguration. Furthermore, we can apply the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CaptorX</i> framework into a distributed collaboration setting. With dedicated local training regulation and collaborative aggregation schemes, the class-adaptive models on individual mobile devices can further contribute back to the central full-class model. Experiments on representative CNNs and image classification datasets show that, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CaptorX</i> can reduce the CNN computation workload up to 50.22% and save 46.58% energy consumption for varied local devices, meanwhile improving accuracy for their targeted classes with better task focus. With our distributed collaboration paradigm, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CaptorX</i> also provides further potential to enhance the central model accuracy, while reducing up to 37.58% communication cost compared to traditional distributed learning methods.
SCAFFISD: A Scalable Framework for Fine-Grained Identification and Security Detection of Wireless Routers

The security of wireless network devices has received widespread attention, but most existing schemes cannot achieve fine-grained device identification. In practice, the security vulnerabilities of a device are heavily depending on its model and firmware version. Motivated by this issue, we propose a universal, extensible and device-independent framework called SCAFFISD, which can provide fine-grained identification of wireless routers. It can generate access rules to extract effective information from the router admin page automatically and perform quick scans for known device vulnerabilities. Meanwhile, SCAFFISD can identify rogue access points (APs) in combination with existing detection methods, with the purpose of performing a comprehensive security assessment of wireless networks. We implement the prototype of SCAFFISD and verify its effectiveness through security scans of actual products.
Light-Weight Distilled HRNet for Facial Landmark Detection

A light-weight facial landmark detection model is proposed in this paper (we named it “LDHRNet”), which can be trained in an end-to-end fashion and could perform precise facial landmark detection in various conditions including those with large pose, exaggerated expression, non-uniform lighting and occlusions. Firstly, in order to deal with these challenging cases above, a light-weight HRNet (LHRNet) structure is proposed as the backbone while the bottleneck block is used to replace the standard residual block in the original HRNet and the group convolution is used to replace the standard convolution in the original HRNet. Then in order to prevent the accuracy loss by the coordinates quantization, we use function named dual soft argmax (DSA) to map the heatmap response to final coordinates. And then we proposed Similarity-FeatureMap knowledge distillation model which guides the training of a student network such that input pairs that produce similar (dissimilar) feature maps in the pre-trained teacher network produce similar (dissimilar) feature maps in the student network. Finally, we combine the distillation loss and NME loss to train our model. The best result 79.10% for AUC is achieved on the validation set.