Reviewerly/exHarmony huggingface dataset:
Unsupervised Transfer Softmax Regression

Cross-domain image classification is a challenge in numerous practical applications due to the variance between the training and testing datasets. To solve the problem, we propose a new classification method named unsupervised transfer softmax regression in this paper. It firstly introduce joint distribution adaptation to the objective function of the softmax regression to construct a new classifier for knowledge transfer. Then the new objective function is solved by gradient descent method to realize the unified optimization of classification and feature extraction. Finally, we evaluate the effectiveness of the proposed method by the classification experiments on image data sets and text data sets, and the result demonstrate the good performance of our approach.
User Response Driven Content Understanding with Causal Inference

Content understanding with many potential industrial applications, is spurring interest by researchers in many areas in artificial intelligence. We propose to revisit the content understanding problem in digital marketing from three novel perspectives. First, our problem is to explore the way how user experience is delivered with divergent key multimedia elements. Second, we treat understanding as to elucidate their causal implications in driving user responses. Third, we propose to understand content based on observational audience visit logs. To approach this problem, we measure and generate heterogeneous content features and model them as binary, multivalued or continuous genres. Multiple key performance indicators (KPIs) are introduced to quantify user responses. We then develop a flexible and adaptive doubly robust estimator to identify the causality between these features and user responses from observational data. The comprehensive experiments are performed on real-world data sets. We show that the further analysis of the experimental results can shed actionable insights on how to improve KPIs. Our work will benefit content distribution and optimization in digital marketing.
Web Service Clustering on the Basis of QoS Parameters

Due to ease of development, service oriented applications have replaced traditional web based applications. Web Services have made development easier and secure. But it is always a tough task to select the best service. Thus, web services clustering prior to selection can be useful. Before performing clustering on web services, it is desired to first determine appropriate clustering technique. In this paper, an in-depth analysis of various clustering techniques is performed. Two quality evaluation parameters, internal and stability are used. To conduct various experiments, dataset based on real world web services and dataset generated using standard available dataset generators are used.
Comparison of Decision Tree, Naïve Bayes and K-Nearest Neighbors for Predicting Thesis Graduation

Thesis is one of the evaluations of learning for students. In Universitas Budi Luhur (UBL), especially in the Informatics Department, the thesis is one of the requirements for graduating students to obtain a Bachelor of Computer degree. In each semester, the number of Informatics Department students who take thesis is around 200-300 students. The problem that is still faced is that student graduation in the thesis is not optimal. Student failures in the thesis are allegedly related to several technical and nontechnical factors. In this study, an analysis using data mining algorithms was carried out to determine the factors that influence student graduation in the thesis. The dataset obtained from the Informatics Department students who took a thesis in the 2016/2017, and 2017/2018. In order to obtain the right classification method, this research was tested with three classification methods, namely Decision Tree, Naïve Bayes, and k-Nearest Neighbors (kNN). The results of the comparison of the values of accuracy, precision, and recall indicate that the kNN algorithm has advantages, so this method is chosen to predict graduation. In this study also developed an application for predicting graduation of students' thesis by applying the kNN classification method. The test results showed an accuracy of 78.20%, precision of 80.32%, and recall of 96.49%. This research is expected to be useful for improving the service quality of student thesis.
Hearing pastness and presence: the myth of perfect fidelity and the temporality of recorded sound

This article rethinks the experience of listening to technologically reproduced sound and music by dispelling the myth of perfect fidelity or the ideal of complete similitude between originals and copies. It does so, on the basis of a media archaeological analysis of the symbolic idealisations used in mathematical acoustics and the physical processes that turn these idealisations into media technological operations. Contrasting Friedrich Kittler’s media theoretical take on the idealised sonic purity of the sine wave with Jacques Derrida’s epistemological concept of the temporal “delay” that defines all mediatic operations, the article argues that the inevitable introduction of transient elements – noise, distortion and randomness – shapes the listener’s experience of the multi-layered temporality of recorded sound and music. By no longer focussing on the input and output of a recording chain but on the transmission channels in between, it is argued that recorded sound and music simultaneously marks pastness and presence. Pastness in the sense that sound recordings resonate with the transience, temporal irreversibility and finitude of all physical phenomena; and presence in the sense that they also produce the experience of the constant flow of time through the here and now.
Research on Personalized Learning Path Discovery Based on Differential Evolution Algorithm and Knowledge Graph

Discovering the most adaptive learning path and content is an urgent issue for nowadays e-learning system, to achieve learning goals. The main challenge of building this system is to provide appropriate educational resources for different learners with different interests and background knowledge. The system should be efficient and adaptable. In addition, the best learning path to adapt learners can help reduce cognitive overload and disorientation. This paper proposes a framework for learning path discovery based on differential evolutionary algorithm and Knowledge graph. In the first stage, learners are investigated to form learners’ records according to their cognitive models, knowledge backgrounds, learning interests and abilities. In the second step, learners’ model database is generated, based on the classification of learners’ examination results. In the third stage, the knowledge graph based on disciplinary domain knowledge, is established. The differential evolution algorithm is then introduced as a method in the fourth stage. The framework is applied to learning path discovery based on differential evolution algorithm and disciplinary knowledge graph. The output of the system is a learning path adapted to learner’s needs and learning resource recommendation referring to the learning path.
Study of β,ρ and Q<sub>0</sub> parameters for Shortest Path Estimation using Ant Colony Optimization

The potential of Ant Colony Optimization (ACO) is massively dependent on its initialization parameters namely β, ρ and Q <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> . In ACO, β is the weight of the heuristic function, ρ is a parameter on which the pheromone update depends and Q <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> is responsible for exploration of the graph. The presented article deals with a methodology to auto tune the ACO parameters for extracting the optimal performance from ACO. An extensive study has been presented considering different dataset to validate the approach. As a result offered by auto tuned ACO, an error of 0.21% from optimum value for estimating shortest path was achieved. The method presented is organized, systematic and promises to extract the best out of ACO.
Design and Development of Networked Multiple FPGA Components for Autonomous Tiny Robot Car

This article presents the design and the development of a system composed of multiple FPGA components for autonomous robot cars. In the FPT'19 FPGA design competition, there are several tasks for image recognition such as driving lane, traffic signal, human or object on the road. To promote team development, component-oriented FPGA development is employed. In this paper, we describe the whole system design by integrating FPGA components for autonomous driving, the design of each FPGA component and its development process.
Image patch prior learning based on random neighbourhood resampling for image denoising

Image patch priors become a popular tool for image denoising. The Gaussian mixture model (GMM) is remarkably effective in modelling natural image patches. However, GMM prior learning using the expectation maximisation (EM) algorithm is sensitive to the initialisation, often leading to low convergence rate of parameter estimation. In this study, a novel sampling method called random neighbourhood resampling (RNR) is proposed to improve the accuracy and efficiency of parameter estimation. An enhanced GMM (EGMM) learning algorithm is further developed by incorporating RNR into the EM algorithm to initialise and update the GMM prior. The learned EGMM prior is applied in the expected patch log-likelihood (EPLL) framework for image denoising. The effectiveness and performance of the proposed RNR and EGMM algorithm are demonstrated via extensive experimental results comparing with the state-of-the-art image denoising methods, the experimental results show the higher PSNR result of the denoised images using the proposed method. Meanwhile, the authors verified that the proposed method can efficiently reduce the time of image denoising compared with the basic EPLL method.
Designing A Multi-Agent System For Improving Supply Chain Performance

Nowadays, the dynamically changing environment requires a timely response to changes in the supply chain. The authors focus on a multi-agent system as an approach to improving supply chain performance. Software agents are successfully applied in supply chain management tasks. Agent behaviour is defined according to the purpose of its development. Multi-agent systems can be used for domain areas that include negotiations between different firms or people with different goals and confidential information available. Supply chain can be represented by an intelligent agent set, where each agent is responsible for one or more functions of the supply chain. Existing multi-agent systems have shown successful results, but these systems have always been developed for each case individually, there is no any universal multi-agent system. The paper discusses the development of a new multi-agent system for supply chain management. The paper provides a sequence of actions for the system development starting from discussing supply chain processes and tasks that represent behaviour of agents. The authors also propose to develop a multi-agent system that can improve supply chain performance and present performance measurement for the system. The proposed multi-agent system provides demand forecasting, inventory management and production scheduling aimed at improving the supply chain performance.
Ternary Code of Melody and Reliable Audio Watermarking

In this paper, we offer a new type of reliable non-blind digital watermark that is suitable for audio files. A feature of the approach is the need to use a well-known melody as a watermark. We encode the melody utilizing a ternary sequence and insert the sequence into the container by modulating the fragments of the file. The advantage of the approach is its resistance to attacks since the well-known melody can be recognized by the listener when removing a watermark from a damaged container.
Transaction Finality through Ledger Checkpoints

The reversal of transactions due to blockchain ledger reorganization has become a major hindrance for public blockchain technologies' adoption in real-world business and financial applications. Since a typical real-world product, service, or agreement cannot be reversed; associated transactions in the blockchain ledger must also be final. This paper describes and analyzes the transaction finality solution for our proof of work (PoW) mining based blockchain network, the Kona Blockchain Platform. The solution works even if 49% of the network's total mining power is compromised due to malicious attacks. Although designed for our specific platform, the ideas from the solution can be easily adapted to achieve transaction finality in existing public blockchain networks. This paper also discusses how this can be done. To the best of our knowledge, ours is the first solution for deterministic transaction finality for blockchain networks incentivized exclusively by PoW mining.
Decentralized Platform for Investments and Operation of Energy Communities

With the rise of blockchain technology and distributed ledger application platforms in general, it became possible to create new forms of digital assets and implement financial mechanisms in a transparent and secure way. Many companies use these features to create crowdfunding campaigns to raise funds for projects and offer digital assets in form of tokens to investors in return for their investments.This paper aims to propose and elaborate a platform based on distributed ledger technology for investing in flexible assets of an energy community. In addition to the investment platform, complex management mechanisms would be implemented to enable the investors to freeze transactions or stop fund withdrawals if they find the investment wrong or unnecessary. In this system, tokens modeled on a distributed ledger technology (DLT) platform would be issued to investors as proofs of their investment, and as voting stakes for managing the collected funds. Tokens would also be used as tradeable assets in complex market mechanisms implemented on top of the investment platform.The investment platform is complemented with community energy management system, where the community manager (CM) is an electricity market entity and optimizes the operation of the energy community assets in order to minimize the investment return period. Benefits gained by smart positioning of the energy community in the market are shared among micro-loan investors. The concept is demonstrated for energy community investments in different distributed energy sources.
Sensor Fusion and Compensation Algorithm for Vehicle Tracking with Front Camera and Corner Radar Sensors

Autonomous vehicle encounters numerous difficulties which need to be solved. Precise detection and perception of surrounding obstacles are the first step required to overcome the problems. Therefore, it is important to properly fuse the sensor data. In this paper, one front camera and four corner radars are used to percept the surrounding obstacles. Two algorithms are introduced; a sensor fusion algorithm and a compensating algorithm which improve the performance of the sensor fusion algorithm. Nearest neighbor (NN) algorithm and linear Kalman filter are used for data association and tracking respectively. Moreover, the obstacles' boundary information which is given by camera and radars is used to estimate the free space. By applying Occupancy Grid Map (OGM) with boundary information, previous fused data can be compensated.
Adopting the use of a legacy digital artefact in formal educational settings: opportunities and challenges

This paper explores how a legacy digital artefact can be adopted within formal education settings and looks at the pedagogical and other opportunities created by its use within three primary and secondary classrooms in the UK. Through a comparative case study, the paper investigates the potential of using a virtual 3D model of the 1938 British Empire Exhibition in formal educational settings and highlights a number of salient issues and challenges. As the data suggests, engagement with the model created opportunities for playful learning, facilitated motivation and collaboration, and enhanced student interest. However, participation was often compromised by technical inhibitors as well as the wider pressures of the curriculum, teacher time and assessment regimes. Last, the paper considers the range of actors and factors that influenced the experiences of teachers and students who engaged with the use of the 3D model and concludes by considering the implications of these findings.
Framework for Pedestrian Detection, Tracking and Re-identification in Video Surveillance System

In this work, we present a framework for implementing pedestrian re-identification in real world surveillance camera systems. Pedestrian re-identification has seen a lot of attention as a stand-alone identifier that takes in cropped images of pedestrians and matches their IDs. The focus of this work is to produce the input needed for the re-identifier network form a raw video output from the surveillance cameras. The integrated system utilizes Tensorflow object detection API with a Faster-RCNN pre-trained model, for pedestrian detection, a discriminative correlation filter from [1], for the purpose of short-term tracking of subjects in each camera and a pedestrian re-identification network from [2]. This configuration resulted in an accuracy above 93% on our dataset, that was automatically produced using pedestrian detection and tracking algorithms.
A Novel Slip-Kalman Filter to Track the Progression of Reading Through Eye-Gaze Measurements

In this paper, we propose an approach to track the progression of eye-gaze while reading a block of text on computer screen. The proposed approach will help to accurately quantify reading, e.g., identifying the lines of text that were read/skipped and estimating the time spent on each line, based on commercially available inexpensive eye-tracking devices. The proposed approach is based on a novel slip Kalman filter that is custom designed to track the progression of reading. The performance of the proposed method is demonstrated using 25 pages eye-tracking data collected using a commercial desk-mounted eye-tracking device.
FPGA Implentation of JPEG Image Compression

Image compression is important in various applications such as video networking, medical images, and multimedia applications where a large amount of data transmission, storage is necessary. The main aim of the Image Compression technique is to reduce the redundant data so that it can be transmitted and stored efficiently and there is no significant change in the quality of the image. This paper introduces the design of the JPEG image compression and decompression algorithm and its simulation using the software Model Composer. Each block of the JPEG Algorithm was coded and designed as IPs. These IPs were integrated into a complete system along with some built-in blocks in Model composer. Performance of the system is measured using parameters Peak Signal to Noise ratio (PSNR), Compression Ratio (CR) based on file size and data rate. These parameters are recorded and compared with images of different sizes and formats.
An algebraic perspective on abstract and concrete domains

Abstract Looking at isomorphic constructs from an algebraic perspective, this article suggests that every concrete construct is understood by reference to an underlying abstract schema in the mind of comprehender. The complex form of every abstract schema is created by the gradual development of its elementary form. Throughout the process of cognitive development, new features are added to the elementary form of abstract schema, which leads to gradual formation of a fully-developed abstract schema. Every developed abstract schema is the underlying source for understanding an infinite number of concrete isomorphic constructs. It is suggested that the process of the mapping of base domain (base construct) unto target domain (target construct) is conducted and mediated by an abstract domain. This abstract domain, which is free from concrete features of base and target, is isomorphic to both base and target domains. To describe the mediatory role of this abstract domain, it might be argued that the chain process of understanding a less familiar domain in terms of a relatively more familiar domain (salience imbalance model) cannot continue infinitely. This chain must stop at some point. This point is the abstract domain, which is isomorphic to base and target domains.
Rural Health in Digital India: Interactive Simulations for Community Health Workers

SwastyaSIM is a simulation-based, multilingual, interactive learning environment designed to supplement the training of community health workers. It includes medical training and diagnostic tests relevant to common village ailments that are designed to be sensitive to socio-cultural perspectives. This platform supports training, reporting and assessing health workers. Findings from a pilot study with health workers are discussed.
Lost but revived. Revisiting the medieval village of Nieuw‐Roeselare (Flanders) using large‐scale frequency‐domain multi‐receiver EMI and landscape archaeological prospection

Abstract During the 10th to 13th centuries, the rural settlement landscapes of the County of Flanders underwent major changes. Complex interactions between urbanization, growing comital power, demographic changes and rural development formed the basis for intensified reclamations of the landscape. Based on historical research, the ab nihilo plantation of grouped rural settlements by the counts and other landlords played an important role in the organization and systematization of these reclamations. To date, however, archaeological attention for these grouped settlements is scarce, mainly due to Flanders' build‐up and urbanized character. This article describes the renewed cross‐disciplinary landscape archaeological research at the site of the lost village of Nieuw‐Roeselare (northern East Flanders), which was the first medieval village to be partly excavated in Flanders between 1967 and 1979. Believed to have been planted in 1241 and lost to floods in 1375, little is known historically about the site nor does the modern‐day polder landscape show any remnants of a former settlement. Now, a large‐scale cross‐disciplinary prospection comprising 35 ha of frequency‐domain multi‐receiver electromagnetic induction (EMI), oblique aerial photography, augering, pseudo‐two‐dimensional tomography and site specific artefact‐accurate field walking, allowed to define the settlement's full extent, clarify its geographical context and identify its morphology as a planted settlement in the context of the medieval landscape reclamations.
Blind Image Inpainting Using Pyramid GAN on Thyroid Ultrasound Images

Thyroid ultrasound image is an important basis for artificial intelligence assisted treatment of thyroid-related diseases, but existing images usually contain special cross symbols which represent the location of nodules marked by doctors, thus affecting the features and diagnostic results extracted by the deep learning algorithm. We propose Pyramid GAN(Py-GAN) for blind image inpainting to remove cross symbols. Py-GAN contains a generator with pyramid structure and a global discriminator. The global discriminator improves the authenticity of the corrupted regions and image consistency. The generator uses the joint context loss to get clear image restoration, which prevents the information loss of non-completion area. The inpainting results of the proposed Py-GAN not only maintains the texture and structural information of the original image, but also has the greatest advantage that there are no artifacts in the corrupted regions, achieving pixel-level realism. Both qualitative and quantitative comparisons are superior to existing learning/non-learning image inpainting works.