Reviewerly/exHarmony huggingface dataset:
From soccer video to ball possession statistics

Ball possession statistics in a soccer match is evaluated by counting the number of valid passes by both teams. The valid passes are determined by monitoring the start and end of a ball passing event initiated by a player. In this work, we map pass detection as detection of split and merge of nodes of a flow network. The players and ball represent nodes in the network. A group is formed by the objects (ball and players) which are spatially close to each other. Objects belonging to the same group are allowed to split or merge. We use this group relation to check if the objects split or merge in the sequence of frames. A constraint is added to the network to make sure that two objects can split only if the objects were previously merged. Flow through the split or merge node of the network denotes a ball pass event. Additional nodes like appear and disappear are added to the network to map the possibility that new objects could appear or old objects may disappear to and from the frame. The minimum cost path in the flow network provides the solution for valid pass events. Experimental evaluation shows that our proposal is at least 4% better in estimating ball possession statistics and 8% better in pass detection of a soccer match seen in a broadcast video than that of competitive methods.
A two-stage clustering-based cold-start method for active learning

The problem of initialization of active learning is considered in this paper. Especially, this paper studies the problem in an imbalanced data scenario, which is called as class-imbalance active learning cold-start. The novel method is two-stage clustering-based active learning cold-start (ALCS). In the first stage, to separate the instances of minority class from that of majority class, a multi-center clustering is constructed based on a new inter-cluster tightness measure, thus the data is grouped into multiple clusters. Then, in the second stage, the initial training instances are selected from each cluster based on an adaptive candidate representative instances determination mechanism and a clusters-cyclic instance query mechanism. The comprehensive experiments demonstrate the effectiveness of the proposed method from the aspects of class coverage, classification performance, and impact on active learning.
Context and Knowledge Enriched Transformer Framework for Emotion Recognition in Conversations

Emotion Recognition in Conversation (ERC) is becoming increasingly popular due to the accessibility of an enormous measure of openly accessible conversational information. Moreover, it has potential applications in opinion mining, social media and the health care domain. In this paper, we propose a novel Context and Knowledge Enriched Transformer Framework (CKETF) in which we interpret the contextual information from the utterances using a pre-trained Bidirectional Encoder Representations from Transformers (BERT) model and leverage additive attention based hierarchical transformer for encoding the knowledge sentences. Experiments on the knowledge-grounded Topical Chat dataset shows that both context and external knowledge are important for conversational emotion recognition. We demonstrate through extensive experiments and analysis that our proposed model significantly outperforms the current state-of-the-art methods.
Characterizing solar PV grid overvoltages by data blending advanced metering infrastructure with meteorology

If rooftop-installed solar energy systems generate more energy than locally consumed, the excess is fed into the electricity grid, increasing the voltage. Rising penetration levels of solar photovoltaic (PV) systems increase voltage levels, thereby threatening power quality. The extent to which solar PV cause grid issues in actual, nation-wide distribution grids, and how these issues correlate with cloud conditions and irradiance variability has yet to be quantified. This work provides a spatial and temporal characterization of overvoltage events linked to solar PV, using novel data sources. The analysis is based on over 200,000 events from advanced metering infrastructure (AMI) spanning 1/3rd of the Netherlands, combined with satellite observations and 1-minute irradiance measurements. As a result, we find that the typical duration of overvoltage events is in the order of 5 min, and frequently-reporting meters are geographically dispersed. While overvoltages are driven by high PV generation, we do not find evidence that local, short-term irradiance peaks result in additional events as compared to clear sky conditions. However, we do find that median overvoltage event occurrence on Sundays is more than 2.1 times that of weekdays, which can be related to low energy consumption. Our findings indicate PV hosting capacity to be reached throughout the service area simultaneously, and surprisingly show no reduction in event duration by inverter- or grid control. Notably, while a sharp increase in occurrence is observed, overvoltage events are still scarce in absolute terms, with only 0.1% of the AMI reporting more than 10 events in spring-summer 2020.
RSO: A Novel Reinforced Swarm Optimization Algorithm for Feature Selection

Swarm optimization algorithms are widely used for feature selection before data mining and machine learning applications. The metaheuristic nature-inspired feature selection approaches are used for single-objective optimization task, though the major problem is their frequent premature convergence, leading to weak contribution to data mining. In this paper, we propose a novel feature selection algorithm named Reinforced Swarm Optimization (RSO) leveraging some of the existing problems in feature selection. This algorithm embeds the widely used Bee Swarm Optimization (BSO) algorithm along with Reinforcement Learning (RL) to maximize the reward of a superior search agent and punish the inferior ones. This hybrid optimization algorithm is more adaptive and robust with a good balance between exploitation and exploration of the search space. The proposed method is evaluated on 25 widely known UCI dataset containing a perfect blend of balanced and imbalanced data. The obtained results are compared with several other popular and recent feature selection algorithms with similar classifier configuration. The experimental outcome shows that our proposed model outperforms BSO in 22 out of 25 instances (88%). Moreover, experimental results also show that RSO performs the best among all the methods compared in this paper in 19 out of 25 cases (76%), establishing the superiority of our proposed method.
Graphical Modeling VS. Textual Modeling: An Experimental Comparison Based on iStar Models

[Context] Establishing requirements models is an effective way to analyze them, which is typically dealt with in a graphical manner (i.e., the drag-and-draw fashion). However, as the size of models increases, the scalability issue has become an unignorable challenge, hindering the practical adoption of requirements modeling approach. Some researchers have recently proposed and promoted textual modeling approaches, mitigating these issues of requirements modeling. [Objective] In this paper, we aim at evaluating the two modeling methods, i.e., a graphical modeling method VS. a textual modeling method. In particular, we apply these two methods to iStar modeling language, which has been widely recognized as an effective means to model and analyze requirements. [Methods] We have systematically designed and conducted a controlled experiment with 38 participants to compare two iStar modeling methods (graphical and textual) using two corresponding modeling tools (piStar and T-Star). The experimental results reveal that the numbers of iStar model nodes and relationships built by the participants had no significant difference, regardless of the modeling method adopted. [Conclusions] First, the results show that the textual modeling method is as usable as the graphical modeling method when creating iStar models. Second, we have identified a number of issues that contribute to improving the utility and practicality of the iStar modeling method.
Person-identification Methodusing Active Acoustic Sensing Applied to Nose

In smart glasses and head-mounted displays, the home screen is displayed without user authentication after the power is turned on. While using such eyeglass-type devices, one may be asked to enter passwords when logging in to applications or making payments, and it is common to use the attached touchpad or handheld controller to enter information from the software keyboard on the screen. We propose a method of automatically and periodically identifying individuals by applying active acoustic sensing technology with a speaker and microphone mounted on the nose pads of eye-glass-type devices. We conducted an experiment involving 11 participants to evaluate the effectiveness of the proposed method.
Hierarchical Latent Relation Modeling for Collaborative Metric Learning

Collaborative Metric Learning (CML) recently emerged as a powerful paradigm for recommendation based on implicit feedback collaborative filtering. However, standard CML methods learn fixed user and item representations, which fails to capture the complex interests of users. Existing extensions of CML also either ignore the heterogeneity of user-item relations, i.e. that a user can simultaneously like very different items, or the latent item-item relations, i.e. that a user's preference for an item depends, not only on its intrinsic characteristics, but also on items they previously interacted with. In this paper, we present a hierarchical CML model that jointly captures latent user-item and item-item relations from implicit data. Our approach is inspired by translation mechanisms from knowledge graph embedding and leverages memory-based attention networks. We empirically show the relevance of this joint relational modeling, by outperforming existing CML models on recommendation tasks on several real-world datasets. Our experiments also emphasize the limits of current CML relational models on very sparse datasets.
Implementation of a High-Speed and High-Throughput Advanced Encryption Standard

Data security is an essential aspect of data communication and data storage. To provide high-level security against all kinds of unauthorized accesses, cryptographic algorithms have been applied to various fields such as medical and military applications. Advanced Encryption Standard (AES), a symmetric cryptographic algorithm, is acknowledged as the most secure algorithm for the cryptographic process globally. Several modifications have been made to the original architecture after it was proposed by two Belgian researchers, Joan Daemen and Vincent Rijment, at the third AES candidate Conference in 2000. The existing modifications aim to increase security and speed. This paper proposes an efficient pipelined architecture for the key expansion process, effectively reducing the propagation delay to generate the required subkeys. Along with the pipeline structure, the fork and join architecture is also used in the key expansion part of the AES architecture, and it significantly reduces the time taken to generate the required subkeys. The proposed architecture is simulated and implemented on Xilinx Virtex4 XC4VLX200 FPGA. The result indicates that the proposed architecture achieves an improvement of about 37% in throughput compared with the original architecture. Also, the proposed architecture can convert the given plain text to cipher with a minimal propagation delay.
Statistical generalization performance guarantee for meta-learning with data dependent prior

Meta-learning aims to leverage experience from previous tasks to achieve an effective and fast adaptation ability when encountering new tasks. However, it is unclear how the generalization property applies to new tasks. Probably approximately correct (PAC) Bayes bound theory provides a theoretical framework to analyze the generalization performance for meta-learning with an explicit numerical generalization error upper bound. A tighter upper bound may achieve better generalization performance. However, for the PAC-Bayes meta-learning bound, the prior distribution is selected randomly which results in poor generalization performance. In this paper, we derive three novel generalization error upper bounds for meta-learning based on the PAC-Bayes relative entropy bound. Furthermore, in order to avoid randomly prior distribution, based on the empirical risk minimization (ERM) method, a data-dependent prior for the PAC-Bayes meta-learning bound algorithm is developed and the sample complexity and computational complexity are analyzed. The experiments illustrate that the proposed three PAC-Bayes bounds for meta-learning achieve a competitive generalization guarantee, and the extended PAC-Bayes bound with a data-dependent prior can achieve rapid convergence ability.
CASR: A Collaborative Attention Model for Session-based Recommendation

Today the technological society is developing quickly, recommendation system is becoming an increasingly important technology. Session-based recommendation systems have become a hot research topic due to their ability to provide recommendations for anonymous users. Traditional session-based recommendation systems have some limitations. They either lack the ability to learn complex dependencies or only focus on the current session without explicitly considering collaborative information. We propose a collaborative attention for session-based recommendation model called CASR, which can mine users’ long-term and short-term preferences to obtain users’ real intention by using the close interaction relationship in time structure. We first model a Gate Recurrent Unit (GRU) framework with attention mechanism to obtain short-term preferences of users in a period of the time. Then, we propose a collaborative session search strategy and design a neighbor session search algorithm. It can not only obtain users’ long-term preferences, but also alleviate the sparseness of the original session data to a certain extent. Finally, we use the capsule network with update strategy to get better prediction results. Extensive experiments on two real datasets show that our CASR model outperforms many mainstream methods.
Generation of Game Stages With Quality and Diversity by Reinforcement Learning in Turn-Based RPG

Many recent studies in procedural content generation (PCG) are based on machine learning. One of the promising approaches is generative models, which have shown impressive results in generating new pictures and videos from existing ones. However, it is usually costly to collect sufficient content for training on PCG. To address this issue, we consider reinforcement learning (RL), which does not need to collect training data in advance but learns from its interaction with an environment. In this work, RL agents are trained to generate stages, which we define as series of events in turn-based role playing games. It is a challenging task since several events in a stage are usually highly correlated to each other. We first formulate the stage generation problem into a Markov decision process. A hand-crafted evaluation function, which simulates players’ enjoyment, is defined to evaluate generated stages. Two RL algorithms are selected in the experiments, which are deep Q-network for discrete action space and deep deterministic policy gradient for continuous action space. The generated stages from both models receive evaluation values indicating good quality. To solve the delayed reward problem and further improve the quality of the stages, we employ virtual simulations (VS) to give rewards to intermediate actions and get stages with higher average scores. In addition, we introduce noise to avoid generating similar stages while trying to keep the quality as high as possible. The proposed methods succeed in generating good and diverse stages.
Words Matter: An Analysis of the Content and Readability of COVID-19 Information on Clinic Websites

Objective: To examine content and readability of COVID-19 information on outpatient clinic websites in South Carolina. Participants: Thirty-three outpatient clinic websites. Methods: Using a multi-step search strategy, we located three COVID-19 information content sections from each website. Descriptive statistics were calculated for content section characteristics (focus, information source, target population/race, presence of graphics, mobilizing information). Flesch Reading Ease (FRE), Flesch Kincaid Grade Level (FKGL), and Simple Measure of Gobbledygook (SMOG) were used to calculate reading levels. Mann Whitney U and Kruskal Wallis tests were performed to examine readability levels by clinic type (primary care vs. specialty) and content section characteristics. Results: Twenty-six clinics offered COVID-19 information; 70 content sections across all 26 clinics were analyzed. Sections focused on COVID-19 clinic policies (48.4%), prevention (22.6%), testing (19.4%), and symptoms (9.7%). 93.5% lacked target population, 41.9% provided no information source, 38.7% had no graphics, and none mentioned racial/ethnic groups. M FRE = 54.3, M FKGL = 9.9, M SMOG = 9.5. Conclusion: COVID-19 information focused mainly on clinic policy and was written at a ninth-grade skill level. Findings suggest there is opportunity for clinics to update their online content to convey more plain language and sourced information, especially for high-risk groups.
Image Caption Generation Using Neural Network Models and LSTM Hierarchical Structure

The caption generation is nothing but the generation of textual information from images. For this, objects from images are extracted and classified among predefined classes. The logical objects from the image are extracted and transformed into natural sentences. The recognizing process requires an iterative task that incorporates image recognition as well as machine vision. The process must define relations among objects, persons, and animals and create the textual description of these relations. The paper is about the study of deep learning techniques to discover, identify and produce good captions for a source image. The process of making explanations in the form of sentences for a source image is image captioning, which involves machine vision and natural sentence forming techniques. For these processes, recent models have used deep learning techniques to acquire a great improvement in performance. Second, a more advanced trend is set in utilizing attention-dependent structure for captioning. Recent interpreters use a process of attention for each produced term containing seen term and unseen term. However, these unseen terms are effortlessly detected by considering a model for language in the absence of taking seen indicators, but unseen words could cause and a give bad performance for visual captioning. Taking these issues into consideration, the hierarchy of LSTM [Long–Short-Term Memory] with adaptive attention approach for the creation of captions for images and videos is presented.
Recommendation of Project Management Practices: A Contribution to Hybrid Models

Dealing with an uncertain and dynamic environment when facing multiple and fast-paced challenges forces professionals to adopt agile practices in different environments, resulting in the use of hybrid project management models. However, identifying the right practice to adopt can be challenging, given the variety of project types and environmental factors. In this article, a recommendation method that would allow for the identification of patterns of project management practices for different environments—using an agility indicator—is proposed. The proposed method is tested with a dataset of 856 projects. A cluster analysis is applied to divide the projects into three groups according to environmental characteristics, called scenarios: waterfall, agile, and hybrid. Then, we apply the association rule technique for each group separately, identifying specific patterns of practice for each group. Through a comparative analysis, we verify the consistency between the recommendations of the proposed method for each scenario and the literature on project management. The results indicate the feasibility of the proposed method, thus opening up new research opportunities for hybrid models that can be customized for different projects. This article can help project management professionals apply the agile method beyond its use in software development and improve the process of combining project management practices. We also suggest directions for new research to advance the knowledge of useful decision support tools for hybrid model customization.
CONSECUTIVE SQUARE-FREE NUMBERS IN PIATETSKI-SHAPIRO SEQUENCES

Abstract Using a method due to Rieger [‘Remark on a paper of Stux concerning squarefree numbers in non-linear sequences’, Pacific J. Math. 78 (1) (1978), 241–242], we prove that the Piatetski-Shapiro sequence defined by $\{\lfloor n^c \rfloor : n\in \mathbb {N}\}$ contains infinitely many consecutive square-free integers whenever $1&lt;c&lt;3/2$ .
Envisioning ‘anthropology through design’: A design interventionist approach to generate anthropological knowledge

The literature on Design Anthropology (DA) is skewed towards discussion exploring anthropology's potential for design. In contrast, discourse on how design can contribute to anthropology is somewhat limited. This article proposes an 'Anthropology through Design' (AtD) approach by reflecting on a study on the emergent phenomenon of 'energy exchange'. The AtD approach aims to generate anthropological knowledge of an emergent sociocultural phenomenon through the use of a design intervention. This article describes four intertwined tracks—Framing, Design Intervening, Ethnographic Particular Understanding, and Anthropological General Understanding—of our AtD process. The proposed AtD approach takes a strategic step in relocating 'design' from being an object of anthropology to becoming an instrument for doing anthropology.
Pedestrian Re-identification using a Surround-view Fisheye Camera System

In a multi-camera system, matching the same pedestrian across different camera views is a challenging problem. Pedestrian detection and ReIDentification (ReID) plays an important role in preventing traffic accidents involving pedestrians, for both conventional and autonomous vehicles. To the best of our knowledge, there is no existing work which addresses the problem of pedestrian ReID in a typical vehicle setting, where the vehicle is equipped with a 360° surround-view with fisheye cameras. In this paper, we propose a deep learning system, Seeing Pedestrians in Surround-View Moving Cameras (SPinSVMC), that consists of a Single Camera Detection and Tracking (Single-Cam-D&T) module and Two Cameras ReID (2Cam-ReID) module applied to multi-camera views. The Single-Cam module uses a YOLOv3 model to detect the pedestrians in single camera view videos, and a model that combines OSnet with DeepSORT to track pedestrians and assign an ID for each detected pedestrian. Both models were adapted to the fisheye images through transfer learning processes. The 2Cam-ReID module consists of a camera constraint model and a pedestrian ReID model developed for tracking pedestrians in images captured by a pair of adjacent cameras with overlapping views and assigning unique IDs to the pedestrians captured by both cameras. We evaluated both models on a real-world traffic dataset captured by surround-view fisheye cameras mounted on top of a vehicle. Our experiments demonstrate that the proposed two modules in SPinSVMC achieve high accuracy in both pedestrian detection, tracking and ReID in fisheye image domains.