Reviewerly/exHarmony huggingface dataset:
FOVQA: Blind Foveated Video Quality Assessment

Previous blind or No Reference (NR) Image / video quality assessment (IQA/VQA) models largely rely on features drawn from natural scene statistics (NSS), but under the assumption that the image statistics are stationary in the spatial domain. Several of these models are quite successful on standard pictures. However, in Virtual Reality (VR) applications, foveated video compression is regaining attention, and the concept of space-variant quality assessment is of interest, given the availability of increasingly high spatial and temporal resolution contents and practical ways of measuring gaze direction. Distortions from foveated video compression increase with increased eccentricity, implying that the natural scene statistics are space-variant. Towards advancing the development of foveated compression / streaming algorithms, we have devised a no-reference (NR) foveated video quality assessment model, called FOVQA, which is based on new models of space-variant natural scene statistics (NSS) and natural video statistics (NVS). Specifically, we deploy a space-variant generalized Gaussian distribution (SV-GGD) model and a space-variant asynchronous generalized Gaussian distribution (SV-AGGD) model of mean subtracted contrast normalized (MSCN) coefficients and products of neighboring MSCN coefficients, respectively. We devise a foveated video quality predictor that extracts radial basis features, and other features that capture perceptually annoying rapid quality fall-offs. We find that FOVQA achieves state-of-the-art (SOTA) performance on the new 2D LIVE-FBT-FCVR database, as compared with other leading Foveated IQA / VQA models. we have made our implementation of FOVQA available at: https://live.ece.utexas.edu/research/Quality/FOVQA.zip.
Querying in the Age of Graph Databases and Knowledge Graphs

Graphs have become the best way we know of representing knowledge. The computing community has investigated and developed the support for managing graphs by means of digital technology. Graph databases and knowledge graphs surface as the most successful solutions to this program. This tutorial will provide a conceptual map of the data management tasks underlying these developments, paying particular attention to data models and query languages for graphs
The Benefits of Using Design Workbooks with Speculative Design Proposals in Information Communication Technology for Development (ICTD)

This article argues that design workbooks can benefit the field of Information Communication Technology and Development (ICTD). To demonstrate this, I present a workbook comprised of 12 speculative design proposals. I then present findings from interviews conducted with 22 participants in Bungoma, Kenya; I used the workbook images as prompts during these sessions. My findings suggest that the design workbook method supports a participant-driven interview process. The workbook images prompted rich responses from participants about the contexts where the ideas would exist. These responses draw attention to the practical problems that might accompany the introduction of the ideas into their communities. Significantly, these responses also included critical feedback. Important information was gleaned from comparing and contrasting the multiple ideas in the workbook; these insights include novel understandings about surveillance and participant/researcher relations. These findings motivate a discussion about how design workbooks support different ways for people to participate in the design process, and encourage different outcomes in ICTD.
Detection and Elimination of Systematic Labeling Bias in Code Reviewer Recommendation Systems

Reviewer selection in modern code review is crucial for effective code reviews. Several techniques exist for recommending reviewers appropriate for a given pull request (PR). Most code reviewer recommendation techniques in the literature build and evaluate their models based on datasets collected from real projects using open-source or industrial practices. The techniques invariably presume that these datasets reliably represent the “ground truth.”
Hierarchical Encoder-Decoder With Soft Label-Decomposition for Mitochondria Segmentation in EM Images

Semantic segmentation of mitochondria from electron microscopy (EM) images is an essential step to obtain reliable morphological statistics about mitochondria. However, automatically delineating plenty of mitochondria of varied shapes from complex backgrounds with sufficient accuracy is challenging. To address these challenges, we develop a hierarchical encoder-decoder network (HED-Net), which has a three-level nested U-shape architecture to capture rich contextual information. Given the irregular shape of mitochondria, we introduce a novel soft label-decomposition strategy to exploit shape knowledge in manual labels. Rather than simply using the ground truth label maps as the unique supervision in the model training, we introduce additional subcategory-aware supervision by softly decomposing each manual label map into two complementary label maps according to mitochondria's ovality. The three label maps are integrated with our HED-Net to supervise the model training. While the original label map guides the network to segment all the mitochondria of varied shapes, the auxiliary label maps guide the network to segment subcategories of mitochondria of circular shape and elliptic shape, respectively, which are much more manageable tasks. Extensive experiments on two public benchmarks show that our HED-Net performs favorably against state-of-the-art methods.
Industrial Forecasting with Exponentially Smoothed Recurrent Neural Networks

Time series modeling has entered an era of unprecedented growth in the size and complexity of data which require new modeling approaches. While many new general purpose machine learning approaches have emerged, they remain poorly understand and irreconcilable with more traditional statistical modeling approaches. We present a general class of exponential smoothed recurrent neural networks (RNNs) which are well suited to modeling nonstationary dynamical systems arising in industrial applications. In particular, we analyze their capacity to characterize the nonlinear partial autocorrelation structure of time series and directly capture dynamic effects such as seasonality and trends. Application of exponentially smoothed RNNs to forecasting electricity load, weather data, and stock prices highlight the efficacy of exponential smoothing of the hidden state for multistep time series forecasting. The results also suggest that popular, but more complicated neural network architectures originally designed for speech processing are likely over-engineered for industrial forecasting and light-weight exponentially smoothed architectures, trained in a fraction of the time, capture the salient features while being superior and more robust than simple RNNs and autoregressive models. Additionally, uncertainty quantification of Bayesian exponential smoothed RNNs is shown to provide improved coverage.
Process data based estimation of tool wear on punching machines using TCN-Autoencoder from raw time-series information

Abstract Tracking the wear states of tools on punching machines is necessary to reduce scrap rates. In this paper, we propose a method to estimate wear state of punches using Temporal Convolutional Network Autoencoder (TCN-Autoencoder), one of the deep learning techniques for learning time-series information with convolutional architecture. Approach involves inputting raw time-series information, such as sensor, vibration and audio data, into TCN-Autoencoder, and calculating the reconstruction error between the output and the input data. The reconstruction error is used as “anomaly score” and indicates the distance from the normal state. By training TCN-Autoencoder only with data annotated as “normal” state, the reconstruction error becomes larger when inputting abnormal state data, which corresponds the wear state of the punch. Performance is evaluated on experimental measurement data that spans various wear states of the punch. The results showed our model can estimate anomalies faster than the conventional machine-learning-based anomaly estimation method, while maintaining the high estimation accuracy. This is due to TCN-Autoencoder being able to learn from both frequency and time domain.
A Protecting Mechanism Against Double Spending Attack in Blockchain Systems

In this research we focus on how to prevent Double Spending Attack (also called 51 % hash rate attack), a particular security issue related to blockchain technology in the current cryptocurrency world. We describe the main idea of our proposed Block Access Restriction (BAR) mechanism, which controls the actual block requests and detects malicious behaviors while transactions have been recorded into a specific block, to protect general miner's privileges and provide fairness in the blockchain network environment. We propose an effective way to prevent this to happen (with detailed steps), discuss how to deploy BAR switch into blockchain networks and how the BAR switch can prevent DSA while the hacker bypasses it. We also present general idea of implementing BAR switch, and point out the importance of dealing with security threat at post-quantum computing era.
A Three-Level Modelling Approach for Asynchronous Speed Scaling in High-Performance Data Centres

In data centres, there exist several techniques for energy efficiency purposes. When applied, most of those techniques have impact on the quality (e.g. performance) of the underlying services. A careful study is required in order to optimise such an energy-performance trade-off. In this paper, we study the speed scaling as an energy efficiency technique within the scope of high-performance computing (HPC) data centres. We propose a methodology based on three-levels: analytic, simulation and technical. At the analytical level, the matrix-analytic method (MAM) allows one to obtain energy-performance measures explicitly for a small-scale system. At the simulation level, discrete-event simulation (DES) based on the generalised semi-Markov processes (GSMP) is used to derive the corresponding estimates. Finally, at the technical level, a real small-scaled system in a controlled environment is used. The preliminary results demonstrate that simulation and technical models go well together with the theoretical one with an accuracy of more than 95%.
Implementation of PROMETHEE-GAIA Method for Lecturer Performance Evaluation

Abstract Assessing the progress of lectures ensures the involvement of lectures in the exercise of science dissemination by research, education, and community service. That job affects the performance of lecturers at universities throughout the country. The performance of lecturers is evaluated regularly because of the vast number of evaluation attributes. Manual assessment can take time and cannot be carried out. In the evaluation of lecturer’s outcomes, a decision support model is needed to facilitate decision making. This study applies the preference ranking organization method for enrichment assessment (PROMETHEE) as a decision model in Multi-Criteria Decision Making (MCDM) and Geometric Analysis for Interactive Aid (GAIA) as a geometric analysis in determining lecturer performance. This analysis resulted in the maximal value of an alternative of 2 with Phi 0.2333, followed by 1 and 3 alternatives. These results suggest that alternative 2 is the best way to assess the performance of lecturers as the main indicator. Alternative 3 is the least recommendable alternative because of its lowest precision on the basis of visualization results.
Smart Watermark to Defend against Deepfake Image Manipulation

Deepfake image manipulation has become a serious security threat to the social network. Currently, there are limited studies on protective methods that are against Deepfake image manipulation. To tackle this problem, we here propose an adversary attack based smart watermark model, which adds unperceptive watermarks to images so that the images become adversary examples to Deepfake models. When the Deepfake manipulates these watermarked images, the manipulated images become blur. The manipulation thus can be easily recognized by human and machines. Our experiments have shown that our model outperforms the SOTA and can be used to effectively prevent Deepfake manipulation.
Discriminative Fisher Embedding Dictionary Transfer Learning for Object Recognition

In transfer learning model, the source domain samples and target domain samples usually share the same class labels but have different distributions. In general, the existing transfer learning algorithms ignore the interclass differences and intraclass similarities across domains. To address these problems, this article proposes a transfer learning algorithm based on discriminative Fisher embedding and adaptive maximum mean discrepancy (AMMD) constraints, called discriminative Fisher embedding dictionary transfer learning (DFEDTL). First, combining the label information of source domain and part of target domain, we construct the discriminative Fisher embedding model to preserve the interclass differences and intraclass similarities of training samples in transfer learning. Second, an AMMD model is constructed using atoms and profiles, which can adaptively minimize the distribution differences between source domain and target domain. The proposed method has three advantages: 1) using the Fisher criterion, we construct the discriminative Fisher embedding model between source domain samples and target domain samples, which encourages the samples from the same class to have similar coding coefficients; 2) instead of using the training samples to design the maximum mean discrepancy (MMD), we construct the AMMD model based on the relationship between the dictionary atoms and profiles; thus, the source domain samples can be adaptive to the target domain samples; and 3) the dictionary learning is based on the combination of source and target samples which can avoid the classification error caused by the difference among samples and reduce the tedious and expensive data annotation. A large number of experiments on five public image classification datasets show that the proposed method obtains better classification performance than some state-of-the-art dictionary and transfer learning methods. The code has been available at https://github.com/shilinrui/DFEDTL.
Multimodal Container Planning: A QUBO Formulation and Implementation on a Quantum Annealer

Quantum computing is developing fast. Real world applications are within reach in the coming years. One of the most promising areas is combinatorial optimisation, where the Quadratic Unconstrained Binary Optimisation (QUBO) problem formulation is used to get good approximate solutions. Both the universal quantum computer as well as the quantum annealer can handle this kind of problems well. In this paper, we present an application on multimodal container planning. We show how to map this problem to a QUBO problem formulation and how the practical implementation can be done on the quantum annealer produced by D-Wave Systems.
Giving shape to large digital libraries through exploratory data analysis

Abstract The emergence of large multi‐institutional digital libraries has opened the door to aggregate‐level examinations of the published word. Such large‐scale analysis offers a new way to pursue traditional problems in the humanities and social sciences, using digital methods to ask routine questions of large corpora. However, inquiry into multiple centuries of books is constrained by the burdens of scale, where statistical inference is technically complex and limited by hurdles to access and flexibility. This work examines the role that exploratory data analysis and visualization tools may play in understanding large bibliographic datasets. We present one such tool, HathiTrust+Bookworm, which allows multifaceted exploration of the multimillion work HathiTrust Digital Library, and center it in the broader space of scholarly tools for exploratory data analysis.
Recent Progress on Text Summarization

Text summarization produces a summary of a document by highlighting its most important content ideas. Researchers have been developing text summarization techniques since the 1950s. Most summarization deals with summaries of single documents, but recent summarization efforts have also produced summaries from clusters of documents. This paper reviews recent approaches in three categories: Extractive, Abstractive, and Hybrid text summarization along with specific methods within each category. Most authors focus on the Extractive approach, but the generated summaries are very different from human summaries regardless of techniques used.
18‐4: Fast Image Restoration and Glare Removal for Under‐Display Camera by Guided Filter

Under‐display camera suffers from severe image blur and glare due to the structure of the display panel. Finite dynamic range of camera also causes irreversible damage on images. In this paper, we show that the blurry with glare can be rapidly restored by a neural network with guided filters.
IntelliTag: An Intelligent Cloud Customer Service System Based on Tag Recommendation

To reduce the customer service pressure of small and medium-sized enterprises, we propose an intelligent cloud customer service system, called IntelliTag. Unlike traditional customer service, a cloud service based system has difficulty in collecting user personal information. Therefore, we add a tag recommendation function to quickly capture the user's question intent by clicking on the tags. Specifically, IntelliTag is elaborately designed with the consideration of the following three aspects. First, how to mine high-quality tags is a challenging problem. Second, in the tag recommendation tasks, we have multifarious data types and relations that are used to build a sequential recommendation model. Finally, system implementation and deployment also need to be carefully designed to satisfy online service requirements. In this paper, we show the details of data construction, model designs, system implementation and deployment, and the empirical results compared with several state-of-the-art methods. Nowadays, our IntelliTag has already supported hundreds of thousands of enterprises and millions of users in our industrial production environment.
A Reinforcement-Learning-Based Energy-Efficient Framework for Multi-Task Video Analytics Pipeline

Deep-learning-based video processing has yielded transformative results in recent years. However, the video analytics pipeline is energy-intensive due to high data rates and reliance on complex inference algorithms, which limits its adoption in energy-constrained applications. Motivated by the observation of high and variable spatial redundancy and temporal dynamics in video data streams, we design and evaluate an adaptive-resolution optimization framework to minimize the energy use of multi-task video analytics pipelines. Instead of heuristically tuning the input data resolution of individual tasks, our framework utilizes deep reinforcement learning to dynamically govern the input resolution and computation of the entire video analytics pipeline. By monitoring the impact of varying resolution on the quality of high-dimensional video analytics features, hence the accuracy of video analytics results, the proposed end-to-end optimization framework learns the best non-myopic policy for dynamically controlling the resolution of input video streams to globally optimize energy efficiency. Governed by reinforcement learning, optical flow is incorporated into the framework to minimize unnecessary spatio-temporal redundancy that leads to re-computation, while preserving accuracy. The proposed framework is applied to video instance segmentation which is one of the most challenging computer vision tasks, and achieves better energy efficiency than all baseline methods of similar accuracy on the YouTube-VIS dataset.
FabO: Integrating Fabrication with a Player’s Gameplay in Existing Digital Games

Fabricating objects from a player's gameplay, for example, collectibles of valuable game items, or custom game controllers shaped from game objects, expands ways to engage with digital games. Researchers currently create such integrated fabrication games from scratch, which is time-consuming and misses the potential of integrating fabrication with the myriad existing games. Integrating fabrication with the real-time gameplay of existing games, however, is challenging without access to the source files.
A New Optimized Approach to Resolve a Combinatorial Problem: CoronaVirus Optimization Algorithm and Self-organizing Maps

The optimization provides resolutions to complex combinatorial problems that generally deal within large data size and expensive operating processes. Metaheuristics target the promoting of new applied algorithms to resolve NP-Hard problems in order to improve resolutions requiring enhanced search strategies. Travelling Salesman Problem (TSP) is a common combinatorial problem, applied on several benchmark networks, namely the transportation networks and the routing vehicle problem in order to establish new intelligent computing methods as well as, to prove studies on their performances and efficiencies. Collective intelligence has proven satisfactory resolutions wherewith metaheuristics, although their algorithms complexity. The aim of this work is to solve the Euclidean TSP, classified as a NP-hard problem by means of Self-Organizing Maps (SOM) which is a Kohonen-type network. The resolution is also computed corresponding to a new bio-inspired evolutionary strategy so-called coronavirus optimization algorithm combined with SOM algorithm. The present approach is combining an unsupervised learning strategy within the new coronavirus optimization algorithm to replicate iteratively new infected individuals and to generate diversification on the search space. This new hybrid method presents a good approximative resolutions, proved by applying tests for TSPLIB instances wherein the exact optimum is defined corresponding to each TSP data. However, the present resolutions are complex specifically for large scale by means of increasing the size of input data or size parameters.
Converting Relational Database to Document-Oriented NoSQL Cloud Database

In the Internet of Things (IoT) area, the increase of data leads to the "Big data'' problem. The traditional relational database (RDB) is not being able to deal with processing big data. In contrast, the Not only SQL (NoSQL) database was created to deal with big data problems. Therefore, most organizations need to convert their data stored in RDB systems into NoSQL using flexible models and processes. Most research into the conversion of RDBs into newer database systems has concentrated on schemata transformation, RDB data publishing, whereas other little work focuses on data migration. This paper proposes an automatic approach to convert a database implemented in MySQL database management system into an equivalent database for the target MongoDB system, which is a leading NoSQL database system. This method can handle a large amount of existing data in RDBs without any loss or changes in data semantics and data instances. The solution takes an existing RDB as input, extracts its schema, and then analyzes the schema in an array to be converted with data instances according to the NoSQL target database structure. A system of the proposed approach has been designed. An experimental study was performed to evaluate the proposed approach. The experimental results show that the target database generated by the prototype and the target database generated by other methods were comparable and equivalent.