Reviewerly/exHarmony huggingface dataset:
Remotely Shaping the View in Surgical Telementoring

Distributed collaboration on physical tasks is a social process that involves all actors iteratively proposing, assessing and modifying the view of a shared workspace. In this paper, we describe the ways in which a view of a shared workspace is shaped by a remote expert to weave their expertise into the accomplishment of a complex physical task during surgical telementoring. We focus on the communicative functions of talk and actions used by the remote experts and local workers and identify strategies the experts employ to remotely shape the view. This analysis reveals the possibility for collaborative shaping of a view in surgical telementoring as well as other mechanism for a remote expert to craft and present a view of the shared workspace.
Synthetic Oversampling of Multi-label Data Based on Local Label Distribution

Class-imbalance is an inherent characteristic of multi-label data which affects the prediction accuracy of most multi-label learning methods. One efficient strategy to deal with this problem is to employ resampling techniques before training the classifier. Existing multi-label sampling methods alleviate the (global) imbalance of multi-label datasets. However, performance degradation is mainly due to rare sub-concepts and overlapping of classes that could be analysed by looking at the local characteristics of the minority examples, rather than the imbalance of the whole dataset. We propose a new method for synthetic oversampling of multi-label data that focuses on local label distribution to generate more diverse and better labeled instances. Experimental results on 13 multi-label datasets demonstrate the effectiveness of the proposed approach in a variety of evaluation measures, particularly in the case of an ensemble of classifiers trained on repeated samples of the original data.
Efficiency Improvement of Function Point-Based Software Size Estimation With Deep Learning Model

Software cost estimation is crucial to software management, which has received considerable attention from both industry and academia. Software size is an important metric that forms the cornerstone of software cost estimation. The function point has been proven to be a useful software size unit for size estimation and has been successfully implemented in many countries. However, in current practice, the rule of function point size method is complicated and performed manually. Consequently, it is costly in both time and resources spent to apply these methods, especially in the scenario of large-scale software development in the industry. In this paper, a deep learning-based named entity recognition (NER) model was designed in place of manual function point recognition. In particular, a BiLSTM-CRF model was trained on previously labeled requirements in the industry to classify the function point type of new requirements in the same domain. The proposed method was verified on 29 real projects provided by our industry partner. A comparative experiment was designed for the quantitative evaluation of efficiency improvement of the proposed NER model aided function point estimation. The result suggests that, for the NER model, the precision and F1 of the BiLSTM-CRF-based function point analysis on test samples achieved 94.5% and 80.3%, respectively. Moreover, the improvement in the efficiency of the software size estimation process achieved an average of 38.6%, which is a significant enhancement for the function point-based software size estimation.
Polynomial Loops: Beyond Termination

In the last years, several works were concerned with identifying classes of programs where termination is decidable. We consider triangular weakly non-linear loops (twn-loops) over a ring Z ≤ S ≤ R_A , where R_A is the set of all real algebraic numbers. Essentially, the body of such a loop is a single assignment (x_1, ..., x_d) ← (c_1 · x_1 + pol_1, ..., c_d · x_d + pol_d) where each x_i is a variable, c_i ∈ S, and each pol_i is a (possibly non-linear) polynomial over S and the variables x_{i+1}, ..., x_d. Recently, we showed that termination of such loops is decidable for S = R_A and non-termination is semi-decidable for S = Z and S = Q. In this paper, we show that the halting problem is decidable for twn-loops over any ring Z ≤ S ≤ R_A. In contrast to the termination problem, where termination on all inputs is considered, the halting problem is concerned with termination on a given input. This allows us to compute witnesses for non-termination. Moreover, we present the first computability results on the runtime complexity of such loops. More precisely, we show that for twn-loops over Z one can always compute a polynomial f such that the length of all terminating runs is bounded by f( || (x_1, ..., x_d) || ), where || · || denotes the 1-norm. As a corollary, we obtain that the runtime of a terminating triangular linear loop over Z is at most linear.
Visualization of bibliometric networks of scientific publications on the study of the human factor in the operation of nuclear power plants based on the bibliographic database Dimensions

The reliability of the human operator is an essential indicator of the safe operation of nuclear power plants.Mistakes can be made during performance checks, maintenance, at the stage of accident management, etc.The need to take into account the human factor in the analysis of safety and reliability of nuclear power plants is justified by the fact that erroneous actions of personnel and operators of nuclear power plants directly or indirectly can lead to accidents.Therefore, the analysis of personnel reliability (ANP) when performing a probabilistic safety analysis (PSA) and risk assessments afford to identify the most likely erroneous actions of NPP personnel and to develop a set of measures to reduce them.Depending on the likely consequences of the accident and the time available to the operator for the intervention, the personnel will be exposed to various levels of stress, which will directly affect the personnel's actions to troubleshoot in the event of an emergency.If an accident has occurred, then in a short period of time the operator needs to make a number of decisions, the correctness of which can both save and aggravate the situation.This paper considers the issues of visualization of bibliometric networks of scientific publications on the study of the human factor in the operation of nuclear power plants.
Using Blockchain for Online Multimedia Management: Characteristics of Existing Platforms

In this descriptive study we investigate the use of blockchain in the online multimedia industry. We analyze the content of 30 peer-reviewed academic publications, white papers and industry websites published between 2016 and 2018 which report the application of blockchain for multimedia management. This includes diverse use cases in the music and advertising industries, healthcare, social media, and content delivery networks. Ethereum was found to be the most popular blockchain and proof of work the favorite consensus mechanism. More than half of the platforms reward their users for content curation and community development. The majority of the platforms have implemented tokens and smart contracts to automate the distribution of earnings or to enable data access. Our study further shows that the majority of multimedia blockchain platforms have already implemented monetization capabilities.
A Survey of Artificial Intelligence in Smart Phones and its Applications among the Students of Higher Education in and Around Chennai City

In the current scenario, Smart Phones are one of the Artificial Intelligence devices regularly used by the students. The study aimed to know the utility of the SmartPhones in their day to day life, most useful applications and the valid security features of the Smart Phones and its impact among the Students at Higher Education level. Primary data collected from the College Students in and around Chennai City during September 2019 and December 2019 was used for the present study. The collected data were analyzed by using Statistical tools of Percentage, Mean Score, and Mean Rank.
The relative contribution of shape and colour to object memory

Abstract The current studies examined the relative contribution of shape and colour in object representations in memory. A great deal of evidence points to the significance of shape in object recognition, with the role of colour being instrumental under certain circumstances. A key but yet unanswered question concerns the contribution of colour relative to shape in mediating retrieval of object representations from memory. Two experiments (N=80) used a new method to probe episodic memory for objects and revealed the relative contribution of colour and shape in recognition memory. Participants viewed pictures of objects from different categories, presented one at a time. During a practice phase, participants performed yes/no recognition with some of the studied objects and their distractors. Unpractised objects shared shape only (Rp–Shape), colour only (Rp–Colour), shape and colour (Rp–Both), or neither shape nor colour (Rp–Neither), with the practised objects. Interference effects in memory between practised and unpractised items were revealed in the forgetting of related unpractised items – retrieval-induced forgetting . Retrieval-induced forgetting was consistently significant for Rp–Shape and Rp–Colour objects. These findings provide converging evidence that colour is an automatically encoded object property, and present new evidence that both shape and colour act simultaneously and effectively to drive retrieval of objects from long-term memory.
Contributing factors to increased susceptibility to social media phishing attacks

Background: The migration of phishing scams to social media platforms poses a serious information security threat to social media users. Users often remain unaware of the various phishing threats on social media and consequently they thoughtlessly engage on these platforms. Objectives: The objective of this article was to identify the factors that contribute to an increased susceptibility to social media phishing attacks and propose a model to reduce this susceptibility. Method: A systematic literature review was conducted in Emerald Insight, ScienceDirect and Google Scholar by using a search string. The identified articles underwent two rounds of screening and the articles thus included moved on to a quality assessment round. Finally, these articles were imported into MAXQDA where a content analysis was conducted, which involved extracting, coding and analysing the relevant data. Results: The final 25 articles included in the study indicated that women with low technical and security knowledge between the age of 18 and 25, who habitually use social media and process content heuristically, are more susceptible to phishing attacks. The insights gained from conducting this review resulted in developing a model that highlights the individuals who are most susceptible to phishing attacks on social media. Conclusion: This article concludes that certain people are more susceptible to phishing attacks on social media as a result of their online habits, information processing, demographics, Information and Communications Technology (ICT) knowledge and personality traits. As such, these identified people should be more aware that they fall into this susceptibility group and thus should behave more cautiously when engaging on social media platforms.
Relabel the Noise: Joint Extraction of Entities and Relations via Cooperative Multiagents

Distant supervision based methods for entity and relation extraction have received increasing popularity due to the fact that these methods require light human annotation efforts.In this paper, we consider the problem of shifted label distribution, which is caused by the inconsistency between the noisy-labeled training set subject to external knowledge graph and the human-annotated test set, and exacerbated by the pipelined entity-then-relation extraction manner with noise propagation.We propose a joint extraction approach to address this problem by re-labeling noisy instances with a group of cooperative multiagents.To handle noisy instances in a fine-grained manner, each agent in the cooperative group evaluates the instance by calculating a continuous confidence score from its own perspective; To leverage the correlations between these two extraction tasks, a confidence consensus module is designed to gather the wisdom of all agents and re-distribute the noisy training set with confidence-scored labels.Further, the confidences are used to adjust the training losses of extractors.Experimental results on two realworld datasets verify the benefits of re-labeling noisy instance, and show that the proposed model significantly outperforms the state-ofthe-art entity and relation extraction methods.
Landscape Connectivity and Dropout Stability of SGD Solutions for Over-parameterized Neural Networks

The optimization of multilayer neural networks typically leads to a solutionwith zero training error, yet the landscape can exhibit spurious local minimaand the minima can be disconnected. In this paper, we shed light on thisphenomenon: we show that the combination of stochastic gradient descent (SGD)and over-parameterization makes the landscape of multilayer neural networksapproximately connected and thus more favorable to optimization. Morespecifically, we prove that SGD solutions are connected via a piecewise linearpath, and the increase in loss along this path vanishes as the number ofneurons grows large. This result is a consequence of the fact that theparameters found by SGD are increasingly dropout stable as the network becomeswider. We show that, if we remove part of the neurons (and suitably rescale theremaining ones), the change in loss is independent of the total number ofneurons, and it depends only on how many neurons are left. Our results exhibita mild dependence on the input dimension: they are dimension-free for two-layernetworks and depend linearly on the dimension for multilayer networks. Wevalidate our theoretical findings with numerical experiments for differentarchitectures and classification tasks.
Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules

Robust perception relies on both bottom-up and top-down signals. Bottom-up signals consist of what's directly observed through sensation. Top-down signals consist of beliefs and expectations based on past experience and short-term memory, such as how the phrase `peanut butter and~...' will be completed. The optimal combination of bottom-up and top-down information remains an open question, but the manner of combination must be dynamic and both context and task dependent. To effectively utilize the wealth of potential top-down information available, and to prevent the cacophony of intermixed signals in a bidirectional architecture, mechanisms are needed to restrict information flow. We explore deep recurrent neural net architectures in which bottom-up and top-down signals are dynamically combined using attention. Modularity of the architecture further restricts the sharing and communication of information. Together, attention and modularity direct information flow, which leads to reliable performance improvements in perceptual and language tasks, and in particular improves robustness to distractions and noisy data. We demonstrate on a variety of benchmarks in language modeling, sequential image classification, video prediction and reinforcement learning that the \emph{bidirectional} information flow can improve results over strong baselines.
Assessing the Impact of Head-Related Transfer Function Individualization on Task Performance: Case of a Virtual Reality Shooter Game

This paper presents the results of an extended experiment to assess the impact of individualized binaural rendering on player performance in an ecologically valid use context, specifically that of a VR
Cyber Threat Actors for the Factory of the Future

The increasing degree of connectivity in factory of the future (FoF) environments, with systems that were never designed for a networked environment in terms of their technical security nature, is accompanied by a number of security risks that must be considered. This leads to the necessity of relying on risk assessment-based approaches to reach a sufficiently mature cyber security management level. However, the lack of common definitions of cyber threat actors (CTA) poses challenges in untested environments such as the FoF. This paper analyses policy papers and reports from expert organizations to identify common definitions of CTAs. A significant consensus exists only on two common CTAs, while other CTAs are often either ignored or overestimated in their importance. The identified motivations of CTAs are contrasted with the specific characteristics of FoF environments to determine the most likely CTAs targeting FoF environments. Special emphasis is given to corporate competitors, as FoF environments probably provide better opportunities than ever for industrial espionage if they are not sufficiently secured. In this context, the study aims to draw attention to the research gaps in this area.
Self-supervised deep reconstruction of mixed strip-shredded text documents

The reconstruction of shredded documents consists of coherently arranging fragments of paper (shreds) to recover the original document(s). A great challenge in computational reconstruction is to properly evaluate the compatibility between the shreds. While traditional pixel-based approaches are not robust to real shredding, more sophisticated solutions compromise significantly time performance. The solution presented in this work extends our previous deep learning method for single-page reconstruction to a more realistic/complex scenario: the reconstruction of several mixed shredded documents at once. In our approach, the compatibility evaluation is modeled as a two-class (valid or invalid) pattern recognition problem. The model is trained in a self-supervised manner on samples extracted from simulated-shredded documents, which obviates manual annotation. Experimental results on three datasets – including a new collection of 100 strip-shredded documents produced for this work – have shown that the proposed method outperforms the competing ones on complex scenarios, achieving accuracy superior to 90%.
An In-Depth Security Assessment of Maritime Container Terminal Software Systems

Attacks on software systems occur world-wide on a daily basis targeting individuals, corporations, and governments alike. The systems that facilitate maritime shipping are at risk of serious disruptions, and these disruptions can stem from vulnerabilities in the software and processes used in these systems. These vulnerabilities leave such systems open to cyber-attack. Assessments of the security of maritime shipping systems have focused on identifying risks but have not taken the critical (and expensive) next step of actually identifying vulnerabilities present in these systems. While such risk assessments are important, they have not provided the detailed identification of security issues in the systems that control these ports and their terminals. In response, we formed a key collaboration between an experienced academic cybersecurity team and a well-known commercial software provider that manages maritime shipping. We performed an analysis of the information flow involved in the maritime shipping process, and then executed an in-depth vulnerability assessment of the software that manages freight systems. In this paper, we show the flow of information involved in the freight shipping process and explain how we performed the in-depth assessment, summarizing our findings. Like every large software system, maritime shipping systems have vulnerabilities.
KdMutual: A novel clustering algorithm combining mutual neighboring and hierarchical approaches using a new selection criterion

New clustering algorithms are expected to manage complex data, meaning various shapes and densities while being user friendly. This work addresses this challenge. A new clustering algorithm KdMutual 1 driven by the number of clusters is proposed. The idea behind the algorithm is based on the assumption that working with cluster cores rather than considering frontiers makes the clustering process easier. KdMutual is based on three steps: The first one aims at identifying the potential core clusters. It relies on mutual neighborhood and includes specific mechanisms to identify and preserve potential core clusters. The second step is based on a constrained hierarchical process that deals with noise. In the last step the potential clusters are selected using a specific ranking criterion and the final partition is built. KdMutual combines the best characteristics of density peaks and connectivity-based approaches. It is capable of detecting the non-presence of natural clusters. Tests were carried out to compare the proposal with 14 other clustering algorithms. Using 2-dimensional benchmark datasets of various shapes and densities they showed that KdMutual was highly effective in matching a ground truth target. It also proved efficient in high dimensions when clusters are well separated. Moreover, it is able to identify clusters of various densities, partially overlapping and including a large amount of noise within spaces of moderate dimension.
Agile Software Development

Agile software development represents a major departure from traditional, plan-based approaches to software engineering.The selections of appropriate software development methodologies for a given project and tailoring the methodologies to a specific requirement have been a challenge since the establishment of software development as a discipline.The research speaks about what is currently known about the benefits and limitations of agile methods and methodologies.Agile software development is a particularly important topic in software engineering and information systems.We approach the idea of the four core of agile software development that are essential to ease the process of software development.
A Study of Graph Analytics for Massive Datasets on Distributed Multi-GPUs

There are relatively few studies of distributed GPU graph analytics systems in the literature and they are limited in scope since they deal with small data-sets, consider only a few applications, and do not consider the interplay between partitioning policies and optimizations for computation and communication.In this paper, we present the first detailed analysis of graph analytics applications for massive real-world datasets on a distributed multi-GPU platform and the first analysis of strong scaling of smaller real-world datasets. We use D-IrGL, the state-of-the-art distributed GPU graph analytical framework, in our study. Our evaluation shows that (1) the Cartesian vertex-cut partitioning policy is critical to scale computation out on GPUs even at a small scale, (2) static load imbalance is a key factor in performance since memory is limited on GPUs, (3) device-host communication is a significant portion of execution time and should be optimized to gain performance, and (4) asynchronous execution is not always better than bulk-synchronous execution.