Reviewerly/exHarmony huggingface dataset:
An Online Stochastic Kernel Machine for Robust Signal Classification

We present a novel variation of online kernel machines in which we exploit a consensus based optimization mechanism to guide the evolution of decision functions drawn from a reproducing kernel Hilbert space (RKHS) such that the entire stationary process observed can be efficiently modeled. We derive an efficient classification algorithm based on these principles such that our algorithm reduces to traditional online kernel machines for the special case in which the consensus based optimization mechanism is switched off. We illustrate the inherent label and input noise resistance of our algorithm for the case of online classification; and derive relevant mistake bounds. The resulting algorithm can find numerous applications such as, for example, in Automatic Target Recognition (ATR) by remote sensing platforms wherein the target being classified tends to typically be persistent over the observation interval.
SS-DPKI: Self-Signed Certificate Based Decentralized Public Key Infrastructure for Secure Communication

Currently, the most commonly used scheme for identity authentication on the Internet is based on asymmetric cryptography and the use of a centralized model. The centralized model needs a Certificate Authority (CA) as a trusted third party and a trust chain of CA. However, CA-based PKI is weak in the single point of failure and certificate transparency. Our system, called SS-DPKI, propose a public and decentralized PKI system model. We describe a detailed scheme as well as application to use decentralized PKI based secure communication. Our proposal prevents storage overhead on the data size of transactions and provide reasonable certificate verification time.
An Integrative Approach to Robust Hand Detection Using CPM-YOLOv3 and RGBD Camera in Real Time

Innovative methods to interact with electronic devices are attracting researchers attentions, and many applications of Human Computer Interaction have been developed in recent years. Hand detection under different illuminations and backgrounds is crucial for tasks such as, actions recognition, gestures recognition and hand-based interaction with wearable devices. Many existing methods are able to detect the human hand precisely, but the task still remains challenging due to environment complexity, different invariants, processing time, and accuracy. It is required for an application to recognize the human hand precisely for proper interaction with the wearable devices. This paper proposed a robust and a compact method for hand detection by employing you only look once (YOLO) and convolutional pos machines (CPM). This paper consists of two parts, network training and testing. In the first part, Oxford hand dataset is used to train a neural network. This dataset has various hand images under different invariants, and backgrounds. Afterwards, images are captured using a digital camera in real-time and then human hands are detected using a trained neural network. In addition, detected hands, positions and orientations are calculated accordance with defined active zone parameters and a depth sensor. Our trained neural network is dubbed as HHDNet, an abbreviation for human hand detection network. The HHDNet is tested under different environment conditions, and experimental results showed that the accuracy and the frame rate of the proposed model are superior than others state of the art methods.
Perceptual Video Coding using Deep Neural Network Based JND Model

We propose a perceptual video coding (PVC) method that uses the deep neural network (DNN) based just noticeable difference (JND) suppression model. The proposed JND suppression model's goal is to reduce the perceptual redundancy of the input video prior to the encoding process through a DNN, and further improve the compression efficiency while minimally affecting the perceptual quality.
Understanding the Performance of In-Network Computing: A Case Study

Numerous distributed applications, including machine learning and big data analysis, have suffered performance degradation from network bottleneck. To solve this problem, researchers have proposed In-Network Computing (INC), which commits extra on-path packet interpretation to help improve system performance. Although preliminary results have pointed out that INC can reduce network traffic and lighten the load of endpoint CPU, the question that how INC affects the overall performance of the system is left untouched. Existing performance models and tools fall short when dealing with this problem since they treat the network as a dump pipe and take no consideration of the architectural characteristics of INC. To fill this gap, in this paper, we build a performance model of INC abstracted from the state-of-art network techniques. Based on this model, we reveal the main factors which affect the effectiveness of INC. An important observation is that INC can be surprisingly harmful in some cases. Then we propose some optimization strategies and architectural improvement to overcome the drawbacks and improve the system performance. Our experiments have shown that our strategy can gain nearly 2× system performance improvements.
A Performance Comparison between Deep Learning Network and Haar Cascade on an IoT Device

This paper seeks to present the development, operation, and comparison of two object recognition methods trained for the classification of different objects ranging from simple geometric forms to traffic signs or car registration plates. There are many techniques, which have been proposed for object detection but this paper focuses on two of them: Haar Cascade Classifier (a classic method for face detection) and Deep Neural Network (a modern approach). Furthermore, different methodologies and training methods are tested on different devices including an IoT device and comparing both performances. In the end, the authors propose several accuracy enhancements of one of the methods using another one, and the drawbacks between methods on low-power device.
Testing Neural Network Classifiers Based on Metamorphic Relations

The application of machine learning programs is becoming increasingly more widespread. Neural networks, which are among the most popular machine learning programs, play important roles in people's daily lives, such as by controlling cars in autonomous driving systems. However, neural networks still lack effective testing methods. To address this problem, this paper proposes a testing method for neural network classifiers based on metamorphic relations. Firstly, it designs metamorphic relations to transform the original data set into derivative data sets. Then, it uses the data before and after the transformation to train and test the neural network classifier, respectively. Finally, it checks whether the output conforms to the metamorphic relations. The neural network classifier is defective if conflicts are detected. Experiments are conducted on a neural network classifier from Stanford's cs231n course to verify the effectiveness of the method. The results show that the defect detection capability of the proposed method is accurate, and 87.5% of the mutants are successfully detected.
p-Bits for Probabilistic Computing

Digital computing is based on deterministic bits that take on one of two values 0 and 1. At the other extreme is the field of quantum computing based on q-bits which are delicate superpositions of 0 and 1, and have to be entangled using coherent coupling techniques requiring novel technologies operating at cryogenic temperatures.
A Meta-level Annotation Language for Legal Texts

There are many legal texts which can greatly benefit from the support of automated reasoning. Such support depends on the existence of a logical formalization of the legal text. Among the methods used for the creation of these knowledge bases, annotation tools attempt to abstract over the logical language and support non-logicians in their efforts to formalize documents. Nevertheless, legal documents use a rich language which is not easy to annotate. In this paper, an existing annotation tool is being extended in order to support the formalization of a complex example - the GDPR’s article 13. The complexity of the article prevents a direct annotation using logical and deontical operators. This is overcome by the implementation of several macros. We demonstrate the automated reasoning over the formalized article and argue that macros can be used to formalize complex legal texts.
Design Thinking Approach for Mobile Application Design of Disaster Mitigation Management

Effective disaster management lead to useful and reliable mitigation program. Mitigation program is an action to reduce the risk to life, property, social and economic activities, etc. from natural disasters. One of the most often disasters that occurred in Indonesia is a tsunami. Tsunami also had a lot of victims, including life victims, house, property, etc. This article focuses on using design thinking method as an approach to developing a mobile application for tsunami disaster management in Indonesia. The first step of the design thinking is empathizing with the user, which we have done by with the victims of the tsunami by interviewing one of the survivors. The second step is defining the user's needs. In the third step, ideation, we are doing brainstorming by using an affinity diagram. After that, we are developing the prototype by developing the beta version of the application to visualize the interfaces. Lastly, in the testing step in validating our mobile application prototype, we visited one of the schools in Banten which had experienced a tsunami disaster. In the end, we are discussing the results including from the pre-test and post-test results from students who had tried our application.
Building an Automatic Image Tagger with DenseNet and Transfer Learning

Automatic tagging for digital images, a.k.a. multi-label image classification has long been a prevalent topic in computer vision, as it can be applied to a variety of other fields. An automatic tagging model has the ability to automatically put one or more appropriate tags which realistically reflect the content of an image. Successfully building a good tagging model would, in turn, aid a lot in the development of software and automated systems that implements image searching, image retrieving, and visual storytelling. Based on DenseNet - an advanced deep learning model used for solving the classification problem, we build models to tackle the tagging problem. Training on the ILSVRC 2016 dataset and experimenting on the test dataset we have made, our BW_CE model achieves an 0.5975 on the micro-average F_1 score metric which surpassed 0.4259 of the Microsoft Computer Vision, and reaches an 0.8565 on the micro-average recall compared to 0.8139 of the Google Cloud Vision.
A Computational Face Alignment Method for Improved Facial Age Estimation

Facial age estimation has increasingly gained attention in the Computer Vision and Image Processing research community due to its numerous applications in several domains. Thus, research efforts are still on to improve facial age estimation accuracy. One of the ways to improve facial age estimation is to strengthen the associated preprocessing stages, two of which are face detection and face alignment. In this paper, a computational method of aligning the face prior to facial age estimation is proposed. Without any form of learning, the proposed face alignment method uses trigonometric and set operations to align a given facial image and to enhance the face detection process prior to age estimation. The impact of the proposed face alignment method on facial age estimation was evaluated via experiments on two publicly available facial ageing datasets FG-NET and Lifespan datasets.
Noise Reduction in Distant Supervision for Relation Extraction Using Probabilistic Soft Logic

The performance of modern relation extraction systems is to a great degree dependent on the size and quality of the underlying training corpus and in particular on the labels. Since generating these labels by human annotators is expensive, Distant Supervision has been proposed to automatically align entities in a knowledge base with a text corpus to generate annotations. However, this approach suffers from introducing noise, which negatively affects the performance of relation extraction systems. To tackle this problem, we propose a probabilistic graphical model which simultaneously incorporates different sources of knowledge such as domain experts knowledge about the context and linguistic knowledge about the sentence structure in a principled way. The model is defined using the declarative language provided by Probabilistic Soft Logic. Experimental results show that the proposed approach, compared to the original distantly supervised set, not only improves the quality of such generated training data sets, but also the performance of the final relation extraction model.
Feature Selection Based on Wrapper and Information Gain

The accuracy of the classification process always suffers from the high dimensionality problem due to the independent, irrelevant, redundant and not useful attributes of the dataset. In this research, feature selection techniques (wrapper selection method, and information gain method) are obtained to handle the mentioned problem by removing those features and reducing the dataset dimensions. The techniques include wrapper selection method and information gain method. This research predicates on the diabetes dataset in WEKA application, which contains checking seven models enforce wrapper selection method as an attribute evaluator, forwarding direction, backward, and bi-directional best-first search method and Naïve Bayes technique as a classifier method, checking eight models applying information gain method as attribute evaluator, as well as the ranker as a search method. Additional to demonstrate the decision tree and classification figures for the best-obtained models in each one technique. The results proved the ability of wrapper and information gain to choose a minimum number of features in order to classify the data with an accuracy of more than 76% in this work.
Velocity-Driven Particle Swarm Optimization

Particle swarm optimization (PSO) is an efficient nature-inspired optimization algorithm, which has been widely applied in many engineering fields. The performance of particle swarm optimization (PSO) has been significantly influenced by velocity update strategy. Traditionally, each particle updates its velocity based on its historical best experience and the global best experience, which may make the swarm lose its diversity and lead to premature convergence. To strengthen the performance of PSO, this paper proposes an improved PSO with the velocity-driven strategy (VD-PSO). In VD-PSO algorithm, the particles whose velocities are driven by better velocities focus on exploitation. The historical velocities, such as better velocities, may effectively characterize the landscape information of the optimization problems. In addition, the particles focus on exploration through information exchange amongst velocities. To verify the effectiveness of the proposed algorithm, experiments are conducted with CEC2014 test problems. The experimental results demonstrate the effectiveness of the proposed algorithm for solving the global optimization problems.
A Comparative Study of Heterogenous Task-based Scheduling Techniques in a Cloud Environment

Cloud computing is increasingly recognized as a new way to provide IT resources (e.g., on-demand, computing, storage, network...) in a transparent way, and effective. Because of the heterogeneity environment of the cloud computing, the scalability can be supported, which is considered a fundamental feature for big data application to achieve performance, efficiency, load balance, and energy consumption. One of the main challenges of the cloud computing is resource management to satisfy the customers' requirements (e.g., Cost, QoS of resources.. .) and resource utilization of the data center. On the other hand, the Scheduling of resources among customers' tasks is considered the main issue of resource management. Therefore, resource scheduling algorithms is a vital research area to improve the total execution time (i.e., makespan), cost, efficiency, and load balance in the case of virtual resources. In this paper, we present a synthesis of the existing algorithms concerning scheduling customers' tasks based on heuristics approach in the cloud environment. Accordingly, the significant issues that play a role in the selection of the scheduling algorithm are provided.
PolSAR image despeckling using trained models on single channel SAR images

The use of polarimetric synthetic aperture radar (PolSAR) images requires efficient PolSAR speckle filtering algorithms. Many traditional and classical PolSAR filters have been proposed during the last decade. But none of them are using deep learning to reduce the speckle of PolSAR images. In this paper, we employed the single channel SAR images to train the denoising convolutional neural network (DnCNN) model. To extend these models to PolSAR speckle reduction problem, the MuLoG (MUlti-channel LOgarithm with Gaussian denoising) framework is introduced. The experimental results show that this method is effective and efficient.
Automatic Signboard Detection and Semi-Automatic Ground Truth Generation

Data-driven object detection techniques are widely applied to a variety of practical areas (i.e., automatic robots, self-driving vehicles, defect detection, and face detection). Nowadays, many research projects have been proposed to improve the accuracy of computer vision applications. In this paper, we propose an automatic signboard detection method and a semi-automatic ground truth generation method to help visually impaired people walk on streets in Taiwan. We consider that when visually impaired people walk down the street, they may be interested in certain stores. Therefore, we collect images of 12 kinds of the most popular stores in people's daily lives. The collected street images number over 5 million from 6 major cities in Taiwan; however, only about 1% of images contain a signboard. We propose a hierarchical object detection module to pre-label uncertain samples. Based on this module, semi-automatic ground truth generation can be achieved.
A Random Walk Through Experimental Mathematics

We describe our adventures in creating a new first-year course in Experimental Mathematics that uses active learning. We used a state-of-the-art facility, called The Western Active Learning Space, and got the students to “drive the spaceship” (at least a little bit). This paper describes some of our techniques for pedagogy, some of the vignettes of experimental mathematics that we used, and some of the outcomes. EYSC was a student in the simultaneously-taught senior sister course “Open Problems in Experimental Mathematics” the first time it was taught and an unofficial co-instructor the second time. Jon Borwein attended the Project Presentation Day (the second time) and gave thoughtful feedback to each student. This paper is dedicated to his memory.
Effect of Human Resource Management Practices on Employee Performance Mediating by Employee Job Satisfaction

Due to today economic conditions, employee performance (EP) is one of the vital foundations for the organization growth and prosperity. Employee performance increases the productivity of organizations. Also, for developing the organizations and enhancing the employee performance (EP) there is needed to develop workforce of organizations by providing proper HRM practices. The employees are satisfied in organizations if organization provides them with proper facilities. The more the employees satisfied from their work environment, the employees perform better and productivity enhance. The organization strategies related to human resource management create satisfaction among employees and employee performs well in organization and creates competitive edge for the organizations. The present study investigates the relationship among variables such as human resource management practices i.e. training and development (T&D), performance appraisal (PA), reward and compensation (R&C), employee empowerment (EE) and employee performance. Further, this study also examine that job satisfaction mediates the relationship among variables such as human resource management practices, and employee performance. The data were collected by using convince sampling technique. Self-administered questionnaire were used for data collection. For analyzing the data smart PLS were used. The findings of the study show that all the human resource management practices have significant and positive relationship with employee performance and job satisfaction mediates the relationship among variables. According to the results all the hypotheses are accepted, provide significant and positive relationship among variables expect reward and compensation on employee performance. Although, T&D and PA are more related employee performance and job satisfaction also increases more as compare to the other developing countries. Moreover, practical implication and future direction are provided at the end of the study under the discussion heading.
Unpacking Fashion Film for Digital Design

Film media have become an essential part of the symbolic production of fashion, not least because of the emergence of Internet. Fashion film uses storytelling in a way that the audience comes to experience the garments in combination with the film. The film experience then influences viewers’ eventual experience of wearing the garment. Fiction enables fashion and fashion becomes fiction. At the same time, there is an emerging interest within computer science, such as human–computer interaction (HCI), in designing fashion-related, digital services and wearable technologies, which are also termed “digital fashion.” Such technology has the potential to offer new products that are worn as garments. A digital garment shares the focus on visual appearance with traditional clothing fashion, but differs from the latter because it can change its visual expression. In developing and studying such technology, we need to understand both what constitutes fashion in a broad sense, and how to communicate these new design concepts to intended users. Both challenges motivate the production of a film on fashionable wearables. We therefore need to unpack in detail how to make such a film, as well as how to address the differences between the “functions” of fabric and of digital technology.
A Multi-Task Service Recommendation Model Considering Dynamic and Static QoS

Recommending the best Web service for users is a necessary task in the internet environment. At present, many of the proposed recommendations have yielded good results. Among them, recommendation methods based on Quality of Service (QoS) emerge endlessly. However, most service recommendation methods consider static or dynamic QoS separately and do not fully consider the impact of their combination. In this paper, we proposed a multi-task service recommendation model that not only models high-order and low-order features simultaneously but also considers the context information generated by the user invoking the service. Our model integrates Factorization Machine (FM) and Bi-directional Long Short-Term Memory (Bi-LSTM) into a deep neural network structure, leveraging their feature combination and deep mining capabilities. Furthermore, we use a pair of attention mechanisms to focus the task model on finding useful information related to the current output in the service data to improve the results of service recommendations. We have done enormous experiments on real QoS data sets, and the results prove that compared with other mainstream recommendation methods, the recommendation performance of this method is greatly improved.