Reviewerly/exHarmony huggingface dataset:
Key pose recognition toward sports scene using deeply-learned model

Key pose recognition (KPR) is widely used in sport analysis, which provides effective tools for coaches, athletes and other professionals to conduct game analysis and auxiliary training. KPR from video stream can be divided into individual-oriented and group-oriented. The former method is based on the segmentation and tracking of each target, and the characteristics of the individual are used to study the events in the group. The latter is to process and sample the global image, obtain the overall information, and then process the collected data to classify the abnormal situation and normal situation. In this paper, we propose key pose recognition method based on deep learning. Specifically, we first train an FCN network for foreground extraction with weightlifting video frame images to remove a large amount of background interference in the weightlifting video image. Further, by fine-tuning the CNN network, a network model suitable for the weight-of-weight video classification of the region of interest is obtained. Finally, according to the classification result, the classification result selection strategy is designed to extract the key pose. In addition, our algorithm can select high image quality key pose frame, which is important for sports training. The experimental results show that the proposed method is very competitive.
A Payment Channel Based Hybrid Decentralized Ethereum Token Exchange

Traditional centralized token exchange (CEX) is criticized for its security and privacy issues, since crypto currency users are required to surrender their private keys to the exchange. In contrast, decentralized token exchange (DEX) solves this issue by introducing additional trading gas fee and latency to the system. Hybrid decentralized token exchange (HEX) has been proposed to combine the advantages of CEX and DEX. However, existing HEX is still suffering from two issues. The first issue is that it is unfriendly for a trader who needs to exchange tokens frequently within a certain period of time, due to the fact that it is time-consuming and expensive. The second issue is the potential network congestion in Ethereum caused by excessive simultaneous transactions from the exchange. In this paper, we propose a payment channel based HEX, which extends existing solutions by adding a new payment channel layer to benefit frequent traders and alleviate network congestion.
Failure Recovery in Resilient X10

Cloud computing has made the resources needed to execute large-scale in-memory distributed computations widely available. Specialized programming models, e.g., MapReduce, have emerged to offer transparent fault tolerance and fault recovery for specific computational patterns, but they sacrifice generality. In contrast, the Resilient X10 programming language adds failure containment and failure awareness to a general purpose, distributed programming language. A Resilient X10 application spans over a number of places. Its formal semantics precisely specify how it continues executing after a place failure. Thanks to failure awareness, the X10 programmer can in principle build redundancy into an application to recover from failures. In practice, however, correctness is elusive, as redundancy and recovery are often complex programming tasks. This article further develops Resilient X10 to shift the focus from failure awareness to failure recovery, from both a theoretical and a practical standpoint. We rigorously define the distinction between recoverable and catastrophic failures. We revisit the happens-before invariance principle and its implementation. We shift most of the burden of redundancy and recovery from the programmer to the runtime system and standard library. We make it easy to protect critical data from failure using resilient stores and harness elasticity—dynamic place creation—to persist not just the data but also its spatial distribution. We demonstrate the flexibility and practical usefulness of Resilient X10 by building several representative high-performance in-memory parallel application kernels and frameworks. These codes are 10× to 25× larger than previous Resilient X10 benchmarks. For each application kernel, the average runtime overhead of resiliency is less than 7%. By comparing application kernels written in the Resilient X10 and Spark programming models, we demonstrate that Resilient X10’s more general programming model can enable significantly better application performance for resilient in-memory distributed computations.
Impact of Quality Factors on Users’ Viewing Behaviors in Adaptive Bitrate Streaming Services

Adaptive bitrate streaming services for mobile terminals have drastically spread in recent years, and it is becoming more important for service providers to increase users' satisfaction by understanding users' viewing behaviors (e.g., how long users watch videos, and why users quit viewing videos) and taking measures such as appropriately designing the quality levels of the videos to be placed on their distribution servers. To investigate the impacts of quality factors on users' viewing behaviors in adaptive bitrate streaming, we conducted an experiment in which participants could freely search and watch videos on smartphones under various network conditions. Through 800 10-minute tests, 1,449 valid views were collected, and the collected dataset was analyzed to characterize the impacts of the initial loading delay, average bitrate, and stalling events on the cumulative quit rate (CQR) of users. Furthermore, the impacts of the average bitrate and stalling events were evaluated quantitatively, using the 2-sample Anderson-Darling test, as well as the combined impact of these two quality factors. The characteristics of the impacts of the above quality factors indicate the possibility of applying survival analysis for our dataset, and suggest that the average bitrate, the number of stalling events, and the average stalling duration should be considered as the external covariates when building a model to estimate the users' viewing time.
Introduction to Deep Learning

Machine learning systems, with shallow or deep architectures, have ability to learn and improve with experience. The process of machine learning begins with the raw data which is used for extracting useful information that helps in decision-making. The primary aim is to allow a machine to learn useful information just like humans do. At abstract level, machine learning can be carried out using following approaches.
Affective Impression: Sentiment-Awareness POI Suggestion via Embedding in Heterogeneous LBSNs

Location-based social networks (LBSNs) add geographical information into traditional social networks and link people&#x2019;s virtual and physical lives. As an important application of LBSNs, point-of-interest (POI) suggestion has become an important method to help users explore interesting and attractive locations in LBSNs. The main problems of POI suggestion include data sparsity and cold start, which have been paid much attention by existing techniques. There are two major challenges which can greatly influence the performance of suggestion accuracy. One is the fuzzy boundary between sentiments, i.e., the fine distinction between sentiments makes it difficult to classify words and texts after word-sentiment mapping operation. The other challenge is the unreliability of data quality represented by similarity metrics, which relies on data integrity and path reachability of a heterogeneous network. To cope with the above two challenges, we present a novel framework called <b>C</b>ommunity-based Sentiment <b>E</b>xtraction and <b>N</b>e<b>t</b>work <b>E</b>mbedding for POI <b>R</b>ecommendation (CENTER) for suggesting impressive POIs to a specific user in an effective fashion. The CENTER framework contains two essential techniques: (1) a latent probabilistic generative model called <b>C</b>ommunity-based <b>S</b>entiment <b>E</b>xtraction (CSE), which can accurately capture the sentiments from review content in LBSNs by taking into consideration the characteristics of social communities. The parameters of the CSE model can be inferred effectively by the Gibbs sampling method. The primary sentiments are obtained based on the distribution of sentiments; (2) a network embedding model called <b>S</b>entiment-aware <b>N</b>ework <b>E</b>mbedding for POI <b>R</b>ecommendation (SNER) is employed to learn the representation of the factors including POIs, users and textual sentiments in a low-dimensional embedding space. The joint training is utilized to alternatively sample all sets of edges in a heterogeneous information network. Extensive experiments were conducted on two large-scale real datasets, in order to evaluate the performance of the proposed CENTER framework. The results demonstrate that CENTER is superior to the state-of-the-art baseline methods in the effectiveness and efficiency of POI suggestion.
Latent-Space-Level Image Anonymization With Adversarial Protector Networks

Along with recent achievements in deep learning empowered by enormous amounts of training data, preserving the privacy of an individual related to the gathered data has been becoming an essential part of the public data collection and publication. Advancements in deep learning threaten traditional image anonymization techniques with model inversion attacks that try to reconstruct the original image from the anonymized image. In this paper, we propose a privacy-preserving adversarial protector network (PPAPNet) as an image anonymization tool to convert an image into another synthetic image that is both realistic and immune to model inversion attacks. Our experiments on various datasets show that PPAPNet can effectively convert a sensitive image into a high-quality and attack-immune synthetic image.
Cascaded Detection Framework Based on a Novel Backbone Network and Feature Fusion

Due to the ability of powerful feature representation, deep-learning-based object detection has attracted considerable research attention, and many methods have been proposed for remote sensing images. However, there are still some problems that need to be addressed. In this paper, a novel and effective detection framework based on faster region-based convolutional neural network is designed. Specifically, first, in order to locate the boundaries of large objects and find the missing small objects, DetNet is incorporated into the detection framework as the backbone network. DetNet fixes the spatial resolution in deep layers and adopts dilated bottleneck with convolution projection to increase the divergence between input and output feature maps. Then, the proposed framework uses the backbone network to extract the scene features and region features simultaneously, which are both mapped to feature vectors and then fused together. The feature fusion operation can improve the feature representation of the generated region. Last, to improve the performance of localization, the cascade structure is adopted in the framework. The cascade structure has multiple phases and every phase has independent classifier and regressor. The results obtained from the previous phase are used as the regions of interest in the next phase. Therefore, the multiphase detector can increase the detection accuracy phase by phase. Comprehensive evaluations on a public ten-class object detection dataset demonstrate the effectiveness of the proposed framework. Moreover, ablation experiments are also implemented to show the respective influence of different parts of the framework on the performance improvement.
The Impact of Network Latency on Gaming QoE for an FPS VR Game

In this paper we present the results of two user studies aimed at exploring the effects of network latency on user experience in a First Person Shooter Virtual Reality multiplayer game. In both studies we observed the following metrics: Quality of Experience (QoE), willingness to continue playing in given network conditions, and the final outcome of the game (survival/death). After conducting the first study, we concluded that test methodology and contextual factors (e.g., social context, level of difficulty, weapon choice) may have had a strong influence on perceived QoE, subsequently causing inconclusive results. Therefore, we aimed to mitigate those factors through different methodology choices in the second study, which led to more conclusive, statistically significant results, indicating that user experience for the chosen game begins to suffer in cases of latency greater than approx. 100 ms (round trip time between client and server). In addition to the analysis of acquired results, we discuss the significance of methodology and context when conducting studies exploring gaming QoE in multiplayer environments.
Mining Fuzzy-Temporal Gradual Patterns

Gradual patterns allow for retrieval of the correlations between attributes through rules such as "the more exercise, the less stress". However, it is possible that a temporal lag may exist between changes in some attributes and their impact on others, current methods do not take this into account. In this paper, we extend GRAANK approach using fuzzy temporal constraints to handle these situations and retrieve patterns such as: "the more exercise, the less stress almost 1 month later". For this kind of patterns, we designed three algorithms that were implemented and tested on real data.
GPU-Job Migration: The rCUDA Case

Virtualization techniques have been shown to report benefits to data centers and other computing facilities.In this regard, not only virtual machines allow to reduce the size of the computing infrastructure while increasing overall resource utilization, but also virtualizing individual components of computers may provide significant benefits.This is the case, for instance, for the remote GPU virtualization technique, implemented in several frameworks during the recent years.The large degree of flexibility provided by the remote GPU virtualization technique can be further increased by applying the migration mechanism to it, so that the GPU part of applications can be live-migrated to another GPU elsewhere in the cluster during execution time in a transparent way.In this paper we present the implementation of the migration mechanism within the rCUDA remote GPU virtualization middleware.Furthermore, we present a thorough performance analysis of the implementation of the migration mechanism within rCUDA.To that end, we leverage both synthetic and real production applications as well as three different generations of NVIDIA GPUs.Additionally, two different versions of the InfiniBand interconnect are used in this study.Several use cases are provided in order to show the extraordinary benefits that the GPU-job migration mechanism can report to data centers.
Hybrid Clustering of Shared Images on Social Networks for Digital Forensics

Clustering the images shared through social network (SN) platforms according to the acquisition cameras embedded in smartphones is regarded as a significant task in forensic investigations of cybercrimes.The sensor pattern noise (SPN) caused by the camera sensor imperfections during the manufacturing process can be extracted from the images and used to fingerprint the smartphones.The process of content compression performed by the SNs causes loss of image details and weakens the SPN, making the clustering task even more challenging.In this paper, we present a hybrid algorithm capable of clustering the images captured and shared through SNs without prior knowledge about the types and number of the acquisition smartphones.The hybrid method exploits batch partitioning, image resizing, hierarchical and graph-based clustering approaches to cluster the images.Using Markov clustering, the hierarchical clustering is conducted in such a way that the representative clusters with a higher probability of belonging to the same camera are selected for merging, which accelerates the clustering.For merging the clusters, the adaptive threshold updated iteratively through the hybrid clustering is used, which results in more precise clusters even for images from the same model of smartphones.The results on the VISION dataset, including both native and shared images, prove the effectiveness and efficiency of the hybrid method in comparison with the state-of-the-art SPN-based image clustering algorithms.
A Broad Evaluation of the Tor English Content Ecosystem

Tor is among most well-known dark net in the world. It has noble uses, including as a platform for free speech and information dissemination under the guise of true anonymity, but may be culturally better known as a conduit for criminal activity and as a platform to market illicit goods and data. Past studies on the content of Tor support this notion, but were carried out by targeting popular domains likely to contain illicit content. A survey of past studies may thus not yield a complete evaluation of the content and use of Tor. This work addresses this gap by presenting a broad evaluation of the content of the English Tor ecosystem. We perform a comprehensive crawl of the Tor dark web and, through topic and network analysis, characterize the 'types' of information and services hosted across a broad swath of Tor domains and their hyperlink relational structure. We recover nine domain types defined by the information or service they host and, among other findings, unveil how some types of domains intentionally silo themselves from the rest of Tor. We also present measurements that (regrettably) suggest how marketplaces of illegal drugs and services do emerge as the dominant type of Tor domain. Our study is the product of crawling over 1 million pages from 20,000 Tor seed addresses, yielding a collection of over 150,000 Tor pages. The domain structure is publicly available as a dataset at \urlhttps://github.com/wsu-wacs/TorEnglishContent.
High‐precision Determination of Gold Mass Fractions in Geological Reference Materials by Internal Standardisation

Determination of gold abundances in natural rock is critical for applications, but very challenging. Here, we report a method for determining gold with a very low mass fraction (&gt; 0.01 ng g −1 ) in rocks. The method involves Carius tube digestion with reverse aqua regia , chromatographic separation to remove most of the sample matrix and measurement by high‐sensitivity ICP ‐ MS . The mono‐isotopic element gold was quantified by external calibration using an internal standardisation of gold to platinum that was precisely determined by isotope dilution. The method is robust and the obtained results are indistinguishable (&lt; 5–10%, 2 s ) from those independently obtained by a standard addition technique on the same solution. The results from reference materials TDB ‐1 and GP t‐2 are consistent with the certified values and those determined by HF ‐ aqua regia digestion, confirming the validity of the method. TDB ‐1 ( n = 20), GP t‐2 ( n = 6), BHVO ‐2 ( n = 9) and other mafic RM s are homogenous for gold (10–20%, 2 s ) at the 2 g test portion level; however, sample heterogeneity affects some RM s. Gold and platinum‐group elements also display different extents of sample heterogeneity for different RM s. Given the homogeneity observed for TDB ‐1, GP t‐2 and BHOV ‐2, they are recommended as well‐suited RM s for inter‐laboratory comparison studies of gold.
Knights and Knaves Run Elections: Internet Voting and Undetectable Electoral Fraud

The cryptographic weaknesses recently discovered in SwissPost's e-voting system are described in this article. We explain how they relate to security problems in other Internet voting systems and discuss the necessary principles for building trustworthy elections.
On the Accuracy of Eye Gaze-driven Classifiers for Predicting Image Content Familiarity in Graphical Passwords

Graphical passwords leverage the picture superiority effect to enhance memorability, and reflect today's haptic users' interaction realms. Images related to users' past sociocultural experiences (e.g., retrospective) enable the creation of memorable and secure passwords, while randomly system-assigned images (e.g., generic) lead to easy-to-predict hotspot regions within graphical password schemes. What remains rather unexplored is whether the image type could be inferred during the password creation. In this work, we present a between-subjects user study in which 37 participants completed a recall-based graphical password creation task with retrospective and generic images, while we were capturing their visual behavior. We found that the image type can be inferred within a few seconds in real-time. User adaptive mechanisms might benefit from our work's findings, by providing users early feedback whether they are moving towards the creation of a weak graphical password.
Challenges and facilitators practices for knowledge management in large-scale scrum teams

Sharing and managing knowledge is an essential resource for the success of a Scrum team. However, widely known knowledge management practices typically address only team level collaboration, which cannot be easily scaled to multiple Scrum teams. In this sense, this paper conducts an empirical study at one of the largest information technology companies operating in Portugal, in which multiple Scrum teams working locally and in remote offices develops e-commerce solutions for the African market. Several facilitators' knowledge practices are identified and discussed, and also the main challenges and difficulties of scaling Scrum practices for large-scale teams are addressed.
How Much Authenticity can be Achieved in Software Engineering Project Based Courses?

Software engineering (SE) students not only need sufficient technical knowledge and problem solving ability but also social and interpersonal skills in order to be industry ready. To prepare the students for the 'real world' the SE educators frequently use 'Authentic Assessment' and 'Project Based Learning (PBL)' approaches in their curricula. However, the level of 'authenticity' should vary within PBL courses offered in different years of a degree program. In this paper, we present and discuss the results of the data collected and analyzed from the first SE course offered to the students. The aim of our research is to explore how much authenticity can be achieved in the first SE course. Our study was conducted at the University of Calgary with 64 software development project teams, totaling 229 undergraduate students. The data is collected from three semesters (2016-2018) in order to assess and monitor students performance. The course design used seven authentic assessments that focused on students skills while covering a complete software development lifecycle. The results from data analysis show that students made progress in some areas of problem solving skills, however, they struggled in their social skills (e.g. people handling skills, negotiations skills and organizational skills), understanding software quality and adaptability.
Redesigning subject guides with usability testing: a case study

This study considers best practices in subject guide design on the LibGuides platform based on usability tests and semi-structured interviews conducted at a mid-sized university library. The study investigated student preferences for guide navigation and was then expanded to address the overall usefulness of subject guides to students. The results indicate that the choice between side and tabbed navigation is highly individual, with students sometimes using the same reasoning to explain opposite preferences. The findings also suggest that thoughtfully maintained subject guides remain relevant to students. The authors recommend that librarians should enlist help from faculty members to promote subject guides and reconsider the inclusion and presentation of subject librarian photos in their guides. They also recommend that librarians conduct their own usability testing to ensure that their subject guides are best serving their particular institutional context.