Reviewerly/exHarmony huggingface dataset:
Preparatory Manipulation Planning Using Automatically Determined Single and Dual Arm

This paper presents a manipulation planning algorithm for robots to reorient objects. It automatically finds a sequence of robot motion that manipulates and prepares an object for specific tasks. Examples of the preparatory manipulation planning problems include reorienting an electric drill to cut holes, reorienting workpieces for assembly, and reorienting cargo for packing, etc. The proposed algorithm could plan single- and dual-arm manipulation sequences to solve the problems. The mechanism under the planner is a regrasp graph, which encodes grasp configurations and object poses. The algorithms search the graph to find a sequence of robot motion to reorient objects. The planner is able to plan both single- and dual-arm manipulation. It could also automatically determine whether to use a single arm, dual arms, or their combinations to finish given tasks. The planner is examined by various humanoid robots like Nextage, HRP2Kai, HRP5P, etc., using both simulation and real-world experiments.
Egocentric Visitors Localization in Cultural Sites

We consider the problem of localizing visitors in a cultural site from egocentric (first-person) images. Localization information can be useful both to assist the user during his visit (e.g., by suggesting where to go and what to see next) and to provide behavioral information to the manager of the cultural site (e.g., how much time has been spent by visitors at a given location? What has been liked most?). To tackle the problem, we collected a large dataset of egocentric videos using two cameras: a head-mounted HoloLens device and a chest-mounted GoPro. Each frame has been labeled according to the location of the visitor and to what he was looking at. The dataset is freely available in order to encourage research in this domain. The dataset is complemented with baseline experiments performed considering a state-of-the-art method for location-based temporal segmentation of egocentric videos. Experiments show that compelling results can be achieved to extract useful information for both the visitor and the site-manager.
Metadata accounts: Achieving data and evidence in scientific research

'Metadata' has received a fraction of the attention that 'data' has received in sociological studies of scientific research. A neglect of 'metadata' reduces the attention on a number of critical aspects of scientific work processes, including documentary work, accountability relations, and collaboration routines. Metadata processes and products are essential components of the work needed to practically accomplish day-to-day scientific research tasks, and are central to ensuring that research findings and products meet externally driven standards or requirements. This article is an attempt to open up the discussion on and conceptualization of metadata within the sociology of science and the sociology of data. It presents ethnographic research of metadata creation within everyday scientific practice, focusing on how researchers document, describe, annotate, organize and manage their data, both for their own use and the use of researchers outside of their project. In particular, this article argues that the role and significance of metadata within scientific research contexts are intimately tied to the nature of evidence and accountability within particular social situations. Studying metadata can (1) provide insight into the production of evidence, that is, how something we might call 'data' becomes able to serve an evidentiary role, and (2) provide a mechanism for revealing what people in research contexts are held accountable for, and what they achieve accountability with.
Blockchain Based Metering and Billing System Proposal with Privacy Protection for the Electric Network

Measurement of energy especially electricity consumption becomes an issue in big cities. Electricity usage monitoring is becoming more crucial and there is a need for the instant view of active energy usage. Solutions like smart grids are possible. Smart grids give a view in macro-level, there is also a need of a micro-level view. We mean a small region's or customer's usage when we mean micro-level view. The privacy of the personal data and the user's trust in the system should also be considered in these scenarios. This study aims to propose such an alternative system which uses blockchain technology and Internet of Things (IoT) devices for the metering and billing of the customer for the electric network. The trust and privacy issues are aimed to be solved. Blockchain can provide safer and more transparent solutions with its decentralized structure. Raspberry Pi is used to simulate metering, Hyperledger Fabric is selected as a blockchain system. A scalable and energy efficient energy tracking system with blockchain and IoT devices is proposed. A prototype system is formed and the possible usage scenario is simulated on the prototype.
Spatial Encoding Strategy Theory

Learners' spatial skill is a reliable and significant predictor of achievement in STEM, including computing, education. Spatial skill is also malleable, meaning it can be improved through training. Most cognitive skill training improves performance on only a narrow set of similar tasks, but researchers have found ample evidence that spatial training can broadly improve STEM achievement. We do not yet know the cognitive mechanisms that make spatial skill training broadly transferable when other cognitive training is not, but understanding these mechanisms is important for developing training and instruction that consistently benefits learners, especially those starting with low spatial skill. This paper proposes the spatial encoding strategy (SpES) theory to explain the cognitive mechanisms connecting spatial skill and STEM achievement. To motivate SpES theory, the paper reviews research from STEM education, learning sciences, and psychology. SpES theory provides compelling post hoc explanations for the findings from this literature and aligns with neuroscience models about the functions of brain structures. The paper concludes with a plan for testing the theory's validity and using it to inform future research and instruction. The paper focuses on implications for computing education, but the transferability of spatial skill to STEM performance makes the proposed theory relevant to many education communities.
Privacy-preserving computation in cyber-physical-social systems: A survey of the state-of-the-art and perspectives

Cyber-physical-social systems (CPSSs) are leading digital revolutions in academia, industry and government. Due to the rise of big data analytics, tensor computations are currently used in CPSSs. With the increasing popularity of cloud computing or fog computing, big data in CPSSs are usually sent to clouds or fogs for computations. Recently, some studies about privacy-preserving computation have been conducted to address security concerns which enable data analysis and processing in cloud or fog environments in a privacy-preserving way. To fully understand the state-of-the-art advances and discover the research directions of this field, in this survey, both previous and current privacy-preserving schemes are comprehensively reviewed and studied. In addition, a novel privacy-preserving tensor computation framework, a case study, and several future research directions are presented for CPSSs.
Integration of Blockchain and Internet of Things in a Car Supply Chain

This short paper presents a proof-of-concept prototype how distributed ledger technology can be used to ensure transparency of production: the blockchain collects data from machines and sensors, ensures integrity of information, integrates a database and enables traceability. The prototype is developed according to the design science paradigm to demonstrate the principles of blockchain and Internet of Things integration in an automotive manufacturing use case. We present an information system architecture for a traceability system based on blockchain technology and Internet of Things.
Authentication of Wireless Devices in the Internet of Things: Learning and Environmental Effects

Reaping the benefits of the Internet of Things (IoT) system is contingent upon developing IoT-specific security solutions. Conventional security and authentication solutions often fail to meet IoT security requirements due to the computationally limited and portable nature of IoT objects. In this paper, an IoT objects authentication framework is proposed. The framework uses device-specific information, called fingerprints, along with a transfer learning tool to authenticate objects in the IoT. The framework tracks the effect of changes in the physical environment on fingerprints and uses unique IoT environmental effects features to detect both cyber and cyber-physical emulation attacks. The proposed environmental effects estimation framework is proven to improve the detection rate of attackers without increasing the false positives rate. The proposed framework is also shown to be able to detect cyber-physical attackers that are capable of replicating the fingerprints of target objects which conventional methods are unable to detect. A transfer learning approach is proposed to allow the use of objects with different types and features in the environmental effects estimation process to enhance the performance of the framework while capturing practical IoT deployments with diverse object types. Simulation results using real IoT device data show that the proposed approach can yield a 40% improvement in cyber emulation attacks detection and is able to detect cyber-physical emulation attacks that conventional methods cannot detect. The results also show that the proposed framework improves the authentication accuracy while the transfer learning approach yields up to 70% additional performance gains.
Deep Learning Research and Development Platform: Characterizing and Scheduling with QoS Guarantees on GPU Clusters

Deep learning (DL) has been widely adopted in various domains of artificial intelligence (AI), achieving dramatic developments in industry and academia. Besides giant AI companies, numerous small and medium-sized enterprises, institutes, and universities (EIUs) have focused on the research and development (R&D) of DL. Considering the high cost of datacenters and high performance computing (HPC) systems, EIUs prefer adopting off-the-shelf GPU clusters as a DL R&D platform for multiple users and developers to process diverse DL workloads. In such scenarios, the scheduling of multiple DL tasks on a shared GPU cluster is both significant and challenging in terms of efficiently utilizing limited resources. Existing schedulers cannot predict the resource requirements of diverse DL workloads, leading to the under-utilization of computing resources and a decline in user satisfaction. This paper proposes GENIE, a QoS-aware dynamic scheduling framework for a shared GPU cluster, which achieves users' QoS guarantee and high system utilization. In accordance with an exhaustive characterization, GENIE analyzes the key factors that affect the performance of DL tasks and proposes a prediction model derived from lightweight profiling to estimate the processing rate and response latency for diverse DL workloads. Based on the prediction models, we propose a QoS-aware scheduling algorithm to identify the best placements for DL tasks and schedule them on the shared cluster. Experiments on a GPU cluster and large-scale simulations demonstrate that GENIE achieves a QoS-guarantee percentage improvement of up to 67.4 percent and a makespan reduction of up to 28.2 percent, compared to other baseline schedulers.
Adversarial Regularization for Attention Based End-to-End Robust Speech Recognition

End-to-end speech recognition, such as attention based approaches, is an emerging and attractive topic in recent years. It has achieved comparable performance with the traditional speech recognition framework. Because end-to-end approaches integrate acoustic and linguistic information into one model, the perturbation in the acoustic level such as acoustic noise, could be easily propagated to the linguistic level. Thus improving model robustness in real application environments for these end-to-end systems is crucial. In this paper, in order to make the attention based end-to-end model more robust against noises, we formulate regulation of the objective function with adversarial training examples. Particularly two adversarial regularization techniques, the fast gradient-sign method and the local distributional smoothness method, are explored to improve noise robustness. Experiments on two publicly available Chinese Mandarin corpora, AISHELL-1 and AISHELL-2, show that adversarial regularization is an effective approach to improve robustness against noises for our attention-based models. Specifically, we obtained 18.4% relative character error rate (CER) reduction on the AISHELL-1 noisy test set. Even on the clean test set, we showed 16.7% relative improvement. As the training set increases and covers more environmental varieties, our proposed methods remain effective despite that the improvement shrinks. Training on the large AISHELL-2 training corpus and testing on the various AISHELL-2 test sets, we achieved 7.0%-12.2% relative error rate reduction. To our knowledge, this is the first successful application of adversarial regularization to sequence-to-sequence speech recognition systems.
Steered Mixture-of-Experts for Light Field Images and Video: Representation and Coding

Research in light field (LF) processing has heavily increased over the last decade.This is largely driven by the desire to achieve the same level of immersion and navigational freedom for camera-captured scenes as it is currently available for CGI content.Standardization organizations such as MPEG and JPEG continue to follow conventional coding paradigms in which viewpoints are discretely represented on 2-D regular grids.These grids are then further decorrelated through hybrid DPCM/transform techniques.However, these 2-D regular grids are less suited for high-dimensional data, such as LFs.We propose a novel coding framework for higher-dimensional image modalities, called Steered Mixture-of-Experts (SMoE).Coherent areas in the higher-dimensional space are represented by single higherdimensional entities, called kernels.These kernels hold spatially localized information about light rays at any angle arriving at a certain region.The global model consists thus of a set of kernels which define a continuous approximation of the underlying plenoptic function.We introduce the theory of SMoE and illustrate its application for 2-D images, 4-D LF images, and 5-D LF video.We also propose an efficient coding strategy to convert the model parameters into a bitstream.Even without provisions for highfrequency information, the proposed method performs comparable to the state of the art for low-to-mid range bitrates with respect to subjective visual quality of 4-D LF images.In case of 5-D LF video, we observe superior decorrelation and coding performance with coding gains of a factor of 4x in bitrate for the same quality.At least equally important is the fact that our method inherently has desired functionality for LF rendering which is lacking in other state-of-the-art techniques: (1) full zero-delay random access, (2) light-weight pixel-parallel view reconstruction, and (3) intrinsic view interpolation and super-resolution.
A Novel Sentiment Polarity Detection Framework for Chinese

Nowadays, mining opinions or sentiment from online user-generated text has become a research hot spot. Although a large amount of lexicon-based Chinese polarity detection works have been done, the existing methods have one common flaw: that even the same word can have opposite polarities among different seed lexicons. This is known as polarity fuzziness. To enhance the performance of Chinese sentiment polarity detection, we start from a two-aspect lexicon expansion so that the polarity fuzziness can be avoided. Specifically, we detect sentiment polarity for new words and revise sentiment polarity for words already defined in seed lexicons. Then, we formulate a novel sentiment polarity detection framework for Chinese (SPDFC) with more attention to fine-grained sentiment processing, which is involved in symmetrical mapping, sentiment feature pruning and text representation. In this way, words’ polarity can be directly taken as features, penetrating further in the polarity detection phase. According to our experimental results, the proposed SPDFC framework can achieve the best overall performance from the perspective of Chinese polarity detection, sentiment feature pruning, and text representation compared to other classical and state-of-the-art methods.
Meta-analysis for families of experiments in software engineering: a systematic review and reproducibility and validity assessment

Previous studies have raised concerns about the analysis and meta-analysis of crossover experiments and we were aware of several families of experiments that used crossover designs and meta-analysis. To identify families of experiments that used meta-analysis, to investigate their methods for effect size construction and aggregation, and to assess the reproducibility and validity of their results. We performed a systematic review (SR) of papers reporting families of experiments in high quality software engineering journals, that attempted to apply meta-analysis. We attempted to reproduce the reported meta-analysis results using the descriptive statistics and also investigated the validity of the meta-analysis process. Out of 13 identified primary studies, we reproduced only five. Seven studies could not be reproduced. One study which was correctly analyzed could not be reproduced due to rounding errors. When we were unable to reproduce results, we provide revised meta-analysis results. To support reproducibility of analyses presented in our paper, it is complemented by the reproducer R package. Meta-analysis is not well understood by software engineering researchers. To support novice researchers, we present recommendations for reporting and meta-analyzing families of experiments and a detailed example of how to analyze a family of 4-group crossover experiments.
Blockchain: A Panacea for Electronic Health Records?

Healthcare systems have been supported by technology to help improve the user experience with the entire health system. However, many operational challenges still remain, in particular those related to a unified management of electronic health records (EHR) that could enable multiple doctors to have access to the complete health history of their patient. Blockchain could support unified records, data security and privacy improvement and insurance decisions/transactions making it an effective solution for the above mentioned healthcare technology challenges. The main contribution of this paper is providing preliminary results of a literature review on the adoption of blockchain to support the management of EHR in health systems - along with the benefits and challenges.
Modified Convolutional Neural Network Architecture Analysis for Facial Emotion Recognition

Facial expressions are one of the key features of a human being and it can be used to speculate the emotional state at a particular moment. This paper employs the Convolutional Neural Network and Deep Neural Network to develop a facial emotion recognition model that categorizes a facial expression into seven different emotions categorized as Afraid, Angry, Disgusted, Happy, Neutral, Sad and Surprised. This paper compares the performance of two existing deep neural network architectures with our proposed architecture, namely the Venturi Architecture in terms of training accuracy, training loss, testing accuracy and testing loss. This paper uses the Karolinska Directed Emotional Faces dataset which is a set of 4900 pictures of human facial expressions. Two layers of feature maps were used to convolute the features from the images, and then it was passed on to the deep neural network with up to 6 hidden layers. The proposed Venturi architecture shows significant accuracy improvement compared to the modified triangular architecture and the rectangular architecture.
Cluster-Volume-Based Merging Approach for Incrementally Evolving Fuzzy Gaussian Clustering—eGAUSS+

In this article, a new dynamic merging approach for incrementally evolving clustering is presented. This means that the cluster partitions are incrementally learned on-line from streams of data. The criterion of merging is based on the comparison between the sum of volumes of two clusters that fulfill the criteria of a minimal number of samples in the cluster and the expected volume of the newly generated merged cluster. The newly generated merged cluster is conducted by using the weighted averaging of cluster centers and the calculation of the joint covariance matrix from the covariance matrices of the clusters. It has been shown that the proposed new evolving algorithm eGAUSS+ together with the new merging concept is very easy to implement, can work on higher-dimensional data sets, can perform all necessary computation on-line, and can produce reliable clusters.
Key Phrase Extraction for Generating Educational Question-Answer Pairs

Automatic question generation is a promising tool for developing the learning systems of the future. Research in this area has mostly relied on having answers (key phrases) identified beforehand and given as a feature, which is not practical for real-world, scalable applications of question generation. We describe and implement an end-to-end neural question generation system that generates question and answer pairs given a context paragraph only. We accomplish this by first generating answer candidates (key phrases) from the paragraph context, and then generating questions using the key phrases. We evaluate our method of key phrase extraction by comparing our output over the same paragraphs with question-answer pairs generated by crowdworkers and by educational experts. Results demonstrate that our system is able to generate educationally meaningful question and answer pairs with only context paragraphs as input, significantly increasing the potential scalability of automatic question generation.
Feature Preserving and Uniformity-Controllable Point Cloud Simplification on Graph

With the development of 3D sensing technologies, point clouds have attracted increasing attention in a variety of applications for 3D object representation, such as autonomous driving, 3D immersive tele-presence and heritage reconstruction. However, it is challenging to process large-scale point clouds in terms of both computation time and storage due to the tremendous amounts of data. Hence, we propose a point cloud simplification algorithm, aiming to strike a balance between preserving sharp features and keeping uniform density during resampling. In particular, leveraging on graph spectral processing, we represent irregular point clouds naturally on graphs, and propose concise formulations of feature preservation and density uniformity based on graph filters. The problem of point cloud simplification is finally formulated as a trade-off between the two factors and efficiently solved by our proposed algorithm. Experimental results demonstrate the superiority of our method, as well as its efficient application in point cloud registration.