Reviewerly/exHarmony huggingface dataset:
Adaptive Summaries: A Personalized Concept-Based Summarization Approach by Learning from Users’ Feedback

Exploring the tremendous amount of data efficiently to make a decision, similar to answering a complicated question, is challenging with many real-world application scenarios. In this context, automatic summarization has substantial importance as it will provide the foundation for big data analytic. Traditional summarization approaches optimize the system to produce a short static summary that fits all users that do not consider the subjectivity aspect of summarization, i.e., what is deemed valuable for different users, making these approaches impractical in real-world use cases. This paper proposes an interactive concept-based summarization model, called Adaptive Summaries, that helps users make their desired summary instead of producing a single inflexible summary. The system learns from users' provided information gradually while interacting with the system by giving feedback in an iterative loop. Users can choose either reject or accept action for selecting a concept being included in the summary with the importance of that concept from users' perspectives and confidence level of their feedback. The proposed approach can guarantee interactive speed to keep the user engaged in the process. Furthermore, it eliminates the need for reference summaries, which is a challenging issue for summarization tasks. Evaluations show that Adaptive Summaries helps users make high-quality summaries based on their preferences by maximizing the user-desired content in the generated summaries.
Evaluation of Object Detection Systems and Video Tracking in Skiing Videos

Nowadays, modern ski resorts provide additional services to customers, such as recording videos of specific moments from their skiing experience. This and similar tasks can be achieved by using computer vision methods. In this work, we evaluate the detection performance of current object detection methods and the tracking performance of a detection-based tracking algorithm. The evaluation is based on videos of skiers and snowboarders from ski resorts. We collect videos of race tracks from different resorts and compile a public dataset of images and videos, where skiers and snowboarders are annotated with bounding boxes. Based on this data, we evaluate the performance of four state-of-the-art object detection methods. This evaluation is performed with general models trained on the MS COCO dataset as well as with custom models trained on our dataset. In addition, we review the performance of the detection-based, multi-object tracking algorithm Deep SORT, which we adapt for skier tracking.The results show promising performance and reveal that the MS COCO models already achieve high Precision, while training a custom model additionally improves the performance. Bigger models profit from custom training in terms of more accurate bounding box placement and higher Precision, while smaller models have an overall high training payoff. The modified Deep SORT tracker manages to follow a skier's trajectory over an extended period and operates with high accuracy, which indicates that the tracker is overall well suited for tracking of skiers and snowboarders on race tracks. Even when exposed to strong camera and skier movement changes, the tracker stays latched onto the target.
AGV path planning combining A* and ant colony algorithm

Abstract Aiming at the problems of lack of pheromone, slow convergence, and falling into local optimum when the ant colony algorithm is applied to the AGV path planning of Xinjiang cotton factory, a fusion A* ant colony algorithm path planning is proposed. The algorithm first initializes the ant colony pheromone with the two-way search A* heuristic function to solve the problem of low ant colony efficiency in the early stage. Secondly, according to the different times of selection of different paths, different pheromone weights are set, and the pheromone reaches a certain level. The optimal solution converges. The experimental results show that the improved algorithm effectively solves the problems of slow convergence, long search paths and falling into local optima.
Datastorming: Crafting Data into Design Materials for Design Students’ Creative Data Literacy

With the wave of data-driven design, data literacy is increasingly important for designers, so current design education aims to incorporate data literacy. However, designing with data can be challenging to design students as it might appear more abstract than tangible. In this research, we explore how design students could build creative data literacy by materializing data into tangible card formats, to inform their ideation process. We developed a method called Datastorming for design students to learn to create new design concepts using various data sets. Datastorming was tested in a university's service design course with 23 undergraduate students. Results show that Datastorming helped the students physically interact with various data sets to create meaningful connections and contextualize data for possible use scenarios. We discuss how Datastorming supports the designerly way of using data and helps students build creative data literacy for their design work.
Graph Analytics on Hybrid System (GAHS) Case Study: PageRank

Graph analytics represent an important application domain widely used in many fields such as web graphs, social networks, and Bayesian networks. In many instances, the processing requirements of a given analytics computation are heavily data-dependent and rely heavily on the characteristics of the graph instance, which, if known could influence the computational solution. This paper examines two hypotheses. First, a variety of properties of a set of graph instances are extracted and examined to determine if they can be used to predict the analytical execution profile. Second, can this data be used to partition the workload and properly schedule it on a hybrid CPU-FPGA system. This work is intended to enhance the performance and scalability of graph processing. In this paper, we propose a framework (Graph Analytics on Hybrid Systems "GAHS") for workload partitioning and scheduling of graph analytics on hybrid systems. Additionally, we configure the decision-making process of the framework to be guided by data input properties. The goal of this work is to expand the design space exploration to not only depend on the characteristics of the application and the available hardware resources but also include data input properties. Results show that up to 6.5× speedup can be attained over a CPU-only or FPGA-only implementations through proper partitioning. Moreover, we achieve an average of18× speedup compared to state-of-the-art hybrid FPGA solvers.
Does Code Review Promote Conformance? A Study of OpenStack Patches

Code Review plays a crucial role in software quality, by allowing reviewers to discuss and critique any new patches before they can be successfully integrated into the project code. Yet, it is unsure the extent to which coding pattern changes (i.e., repetitive code) from when a patch is first submitted and when the decision is made (i.e., during the review process). In this study, we revisit coding patterns in code reviews, aiming to analyze whether or not the coding pattern changes during the review process. Comparing prior submitted patches, we measure differences in coding pattern between pre-review (i.e., patch before the review) and post-review (i.e., patch after a review) from 27,736 reviewed OpenStack patches. Results show that patches after review, tend to conform to similar coding patterns of accepted patches, compared to when they were first submitted. We also find that accepted patches do have similar coding patterns to prior accepted patches. Our study reveals insights into the review process, supporting the potential for automated tool support for newcomers and lays the groundwork for work into understanding conformance and how it makes for an efficient code review process.
An Improved Content Based Image Retrieval System using Unsupervised Deep Neural Network and Locality Sensitive Hashing

Nowadays, many types of digital devices generate data in the form of text, image and video. Hence, it is essential to have a data archival system that maintains and accesses the data. In this context, Content Based Image Retrieval (CBIR) is the problem of searching for images in a large database of images. The CBIR involves feature extraction and identification of similar images. Existing works have utilized supervised deep neural networks to learn the features and used exhaustive searching technique to identify the similar images. However, since real datasets of images are unlabelled and large in size, the usage of supervised deep neural networks and exhaustive searching technique will be a time-consuming process. Thus, this paper proposes an Improved CBIR system that first utilizes an unsupervised deep learning approach to extract the features. Further, it utilizes Locality Sensitive Hashing (LSH) to reduce search space and K Nearest Neighbor (KNN) algorithm on the reduced search space to find the required number of similar images.
SIMT: A Semantic Interest Modeling Toolkit

In this paper, we focus on semantic interest modeling and present SIMT as a toolkit that harnesses the semantic information to effectively generate user interest models and compute their similarities. SIMT follows a mixed-method approach that combines unsupervised keyword extraction algorithms, knowledge bases, and word embedding techniques to address the semantic issues in the interest modeling task.
Integration of Blockchain and Machine Learning for Microgrids

Recently, the emerging applications of ubiquitous renewable energy has driven the increasing research on microgrids where distributed green energy sources, e.g., solar and wind power, are deployed. The microgrid integrating distributed renewable energy generators, energy storage elements, and loads provides power nearby the users, has been proposed for massive distributed applications of renewable energy sources as a small-scale power system due to its easy and flexible deployment. In this paper, we study to integrate blockchain techniques and machine learning for solving the problems of data sharing, processing, and prediction in microgrids. First, we present the existing research and overviews on blockchain techniques and machine learning. Then, we point out the potential applications of blockchain combined with machine learning for microgrids. Finally, we further discuss the open issues, challenges, and future prospects of adopting blockchain techniques and machine learning in microgrids.
Mining Dependencies in Large-Scale Agile Software Development Projects: A Quantitative Industry Study

Context: Coordination in large-scale software development is critical yet difficult, as it faces the problem of dependency management and resolution. In this work, we focus on managing requirement dependencies that in Agile software development (ASD) come in the form of user stories. Objective: This work studies decisions of large-scale Agile teams regarding identification of dependencies between user stories. Our goal is to explain detection of dependencies through users’ behavior in large-scale, distributed projects. Method: We perform empirical evaluation on a large real-world dataset from an Agile software organization, provider of a leading software for Agile project management. We mine the usage data of the Agile Lifecycle Management (ALM) tool to extract large-scale development project data for more than 70 teams running over a five-year period. Results: Our results demonstrate that dependencies among user stories are not frequently observed (the problem affects around 10% of user stories), however, their implications on large-scale ASD are considerable. Dependencies have impact on software releases and increase work coordination complexity for members of different teams. Conclusion: Requirement dependencies undermine Agile teams’ autonomy and are difficult to manage at scale. We conclude that leveraging ALM monitoring data to automatically detect dependencies could help Agile teams address work coordination needs and manage risks related to dependencies in a timely manner.
When Should We Use Likelihood Ratio Target Detection with QTMS Radar and Noise Radar?

We analyze the potential application of a generalized likelihood ratio (GLR)-based detector function to quantum two-mode squeezing (QTMS) radars and standard noise radars. We give an expression for the likelihood ratio (LR) in terms of the maximum-likelihood estimate of the correlation coefficient between the received and reference signals of the radar. Interestingly, we found that a previously-studied detector function outperforms the GLR detector, though not in all parameter regimes. This runs counter to the intuition, based on the Neyman-Pearson lemma, that the LR test is optimal. We discuss why the lemma does not hold in this particular case and why the search for detector functions for QTMS radars and noise radars remains open. However, the GLR detector is a good choice when the correlation coefficient is high, the number of integrated samples is low, and appropriate computational resources are available.
A Multiple stage Discriminative Event Based Model for Alzheimer’s Disease Progression Timeline Estimation

As an irreversible neurodegenerative disease, the progression of Alzheimer's disease includes three stages: cognitively normal (CN), mild cognitive impairment (MCI) and Alzheimer's disease (AD). Early diagnosis of the disease i.e. the stage of MCI is helpful for its early intervention. In this paper we propose a multistage discriminative event-based model (MDEBM). By modelling the discriminative events in cross-sectional data of the patients into three categories through Gaussian Mixture, then staging the patient by Bayesian classifier, the model can detect not only the patients with AD but also those with MCI. besides, the diagnosis of Alzheimer's disease is often based on a combination of factors including the patient's medical history, the results of cognitive tests and neuroimaging. This model supports gradually improving the accuracy of diagnosis through the consecutive input of different categories of patient's examination results. Experiments on Alzheimer's Disease Neuroimaging Initiative (ADNI) data prove the effectiveness of our model.
A Novel Ensemble Learning Method for Online Learning Scenarios

Online Sequential Extreme Learning Machine (OS-ELM) is a modeling algorithm for data stream mining that has attracted much attention in recent years. OS-ELM has extremely fast training speed and good generalization ability, however, because it uses random input weights for the model initialization and these parameters remain unchanged throughout the training process, which causes instability in the performance of the model. To alleviate this problem, we propose an improved OS-ELM algorithm based on the ensemble learning mechanism, which we call ELOS-ELM. ELOS-ELM uses multiple initialization methods to construct diverse base online learners, and the prediction results of the model are determined by voting by these base models. In this way, the final model can avoid misjudgments caused by the knowledge blind zone of a single base model. Extensive experimental results on eight benchmarks show that ELOS-ELM can effectively improve the stability and generalization ability of the OS-ELM.
AR-TV and AR-Diànshì: Cultural Differences in Users’ Preferences for Augmented Reality Television

As Augmented Reality television gains momentum, it is important to understand whether cultural differences among viewers favor different expectations and preferences for immersion in such new television environments. A previous study documented the preferences of 172 participants from various European countries for twenty application scenarios for ARTV, such as virtual objects coming out of the TV screen into the room. In this work, we conduct an empirical generalization of this previous study to understand potential cultural differences in users’ preferences for and expectations of ARTV. To this end, we report insights from data collected from a sample of 147 participants from China, which we compare against the preferences expressed by the participants from Europe from the original study. Our findings reveal similarities, but also differences in terms of expectations of ARTV across the two cultural groups. We draw implications for future research on culturally-aware augmentations of the television watching experience.
Named Entity Extraction Model Based on the Random Walk Method

In connection with the rapid development of Internet technologies, modern society in recent decades has experienced an information explosion characterized by an exponential increase in the volume of information, including low quality information. This work is intended to provide all interested parties with intelligent tools to support decision-making by automatically extracting knowledge from heterogeneous data sources, including the Internet. In the work, we examined the primary processing and morphological analysis of texts, implemented a random walk method to extract semantically related words. As a result of the calculations, we got a matrix with the affinities of words, as well as a dictionary that connects the word with the vector component. In addition, the neural network, trained to retrieve linguistic constructions, which include the possible values of descriptors of named text entities, was described in the work.
Explainable Nonlinear Modelling of Multiple Time Series with Invertible Neural Networks

A method for nonlinear topology identification is proposed, based on the assumption that a collection of time series are generated in two steps: i) a vector autoregressive process in a latent space, and ii) a nonlinear, component-wise, monotonically increasing observation mapping. The latter mappings are assumed invertible, and are modeled as shallow neural networks, so that their inverse can be numerically evaluated, and their parameters can be learned using a technique inspired in deep learning. Due to the function inversion, the backpropagation step is not straightforward, and this paper explains the steps needed to calculate the gradients applying implicit differentiation. Whereas the model explainability is the same as that for linear VAR processes, preliminary numerical tests show that the prediction error becomes smaller.
Cryptocurrency Architecture and Interaction With Market Shocks

Blockchain technology appears to be ready to revolutionise a broad number of industries. However, the blockchain itself contains a number of inefficiencies and areas for improvement, namely: transaction fees and transaction speeds. Directed acyclic graphs (DAGs) address, and improve on these inefficiencies and a number of digital currencies utilising this technology have already begun to appear. This paper provides an explanation of the technology behind DAG-based assets, while identifying and highlighting strategic advantages that DAGs possess over traditional blockchains. We conduct a GARCH volatility analysis of a range of blockchain-based and DAG-based cryptocurrencies in the aftermath of a range of market shocks. We find that DAG-based assets become increasingly responsive to market shocks as they mature. Such behavior mirrors that of established cryptocurrencies such as Bitcoin, Ethereum and Litecoin, providing evidence that DAG-based cryptocurrencies now share similar characteristics to traditional blockchain-chain based products.
Vertex-Centric Visual Programming for Graph Neural Networks

Graph neural networks (GNNs) have achieved remarkable performance in many graph analytics tasks such as node classification, link prediction and graph clustering. Existing GNN systems (e.g., PyG and DGL) adopt a tensor-centric programming model and train GNNs with manually written operators. Such design results in poor usability due to the large semantic gap between the API and the GNN models, and suffers from inferior efficiency because of high memory consumption and massive data movement. We demonstrateSeastar, a novel GNN training framework that adopts avertex-centric programming paradigm and supportsautomatic kernel generation, to simplify model development and improve training efficiency. We will (i) show how to express GNN models succinctly using a visual "drag-and-drop'' interface or Seastar's vertex-centric python API; (ii) demonstrate the performance advantage of Seastar over existing GNN systems in convergence speed, training throughput and memory consumption; and (iii) illustrate how Seastar's optimizations (e.g., operator fusion and constant folding) improve training efficiency by profiling the run-time performance.
Real Time Sign Language Recognition Using Image Classification

Image classification using Convolutional Neural Networks is a key component of Computer Vision. This paper proposes a real-time sign language translation system based on American Sign Language. The system aims at effective communication between vocally and hearing-impaired people and common people in society by translating sign language gestures into text and speech in English. Built-in webcam using OpenCV is the mode of video capturing used to track the hand gestures by the system. Convolutional Neural Networks (CNNs), a powerful deep learning tool, are used to train the system for recognizing signs. The proposed methodology in the paper aims to give effective outputs with minimum delay in real-time.
Evaluating the Effectiveness of Problem Frames for Contextual Modeling of Cyber-Physical Systems: a Tool Suite with Adaptive User Interfaces

Bridging the gap between academic research and industrial application is an important issue to promote Jackson's Problem Frames approach (PF) to the software engineering community. Various attempts have been made to tackle this problem, such as defining formal semantics of PF for software development, and providing a semi-formal approach to model transformations of problem diagrams, with automated tool support. In this paper, we propose to exclusively focus on exploring and evaluating the effectiveness of Jackson's problem diagrams for modeling the context of cyber-physical systems, by developing a suite of support tools enhanced with adaptive user interfaces, and empirically and comprehensively assess its usability. This paper introduces the state of the art, corresponding research questions, research methodologies and current progress of our research.
Secret Sharing from Correlated Gaussian Random Variables and Public Communication

We study a secret sharing problem, where a dealer distributes shares of a secret among a set of participants under the constraints that (i) authorized sets of users can recover the secret by pooling their shares, (ii) non-authorized sets of colluding users cannot learn any information about the secret. We assume that the dealer and the participants observe the realizations of correlated Gaussian random variables and that the dealer can communicate with the participants through a one-way, authenticated, rate-limited, and public channel. Our main result is a closed-form characterization of the trade-off between secret rate and public communication rate. Unlike traditional secret sharing protocols, in our setting, no perfectly secure channel is needed between the dealer and the participants, and the size of the shares does not depend exponentially but rather linearly on the number of participants and the size of the secret for arbitrary access structures.