Reviewerly/exHarmony huggingface dataset:
The Internet of Bodies Needs a Human Data Model

Today, creating innovative Internet of Bodies solutions requires manually gathering the needed information from an increasing number of services and personal devices. In this article, we tackle this challenge by presenting Human Data Model-a programming framework for combining information from several sources, performing computations over that information to high-level abstractions, and then providing these abstractions to proactively schedule computer-human interactions.
Commonsense Knowledge Enhanced Memory Network for Stance Classification

Stance classification aims at identifying, in the text, the attitude toward the given targets as favorable, negative, or unrelated. In existing models for stance classification, only textual representation is leveraged, while commonsense knowledge is ignored. In order to better incorporate commonsense knowledge into stance classification, we propose a novel model named commonsense knowledge enhanced memory network, which jointly represents textual and commonsense knowledge representation of given target and text. The textual memory module in our model treats the textual representation as memory vectors, and uses attention mechanism to embody the important parts. For commonsense knowledge memory module, we jointly leverage the entity and relation embeddings learned by TransE model to take full advantage of constraints of the knowledge graph. Experimental results on the SemEval dataset show that the combination of the commonsense knowledge memory and textual memory can improve stance classification.
Reconstruction of Convolutional Neural Network for Sign Language Recognition

This paper presents a Sign Language translation model using Convolutional Neural Networks (CNN). A sign language is a language which allows mute and hearing-impaired people to communicate. It is a visually oriented, nonverbal communication which facilitates communication through body/facial postures, expressions and a collection of gestures. To contribute to the wellbeing of the affected population, we are motivated to implement a vision-based system to avert their day to day challenges. Our propose model constitutes object detection and classification phases. The first module is made up of single shot multi-box detector (SSD) used for hand detection. The second module constitutes convolutional neural network plus a fully connected network utilized to constructively translate the detected signs into text. The propose model is implemented using American sign language fingerspelling dataset. The propose system outperformed other published results in the comparative analysis, hence recommended for further exploitation in sign language recognition problems.
Unsupervised Multi-View Clustering by Squeezing Hybrid Knowledge From Cross View and Each View

Multi-view clustering methods have been a focus in recent years because of their superiority in clustering performance. However, typical traditional multi-view clustering algorithms still have shortcomings in some aspects, such as removal of redundant information, utilization of various views and fusion of multi-view features. In view of these problems, this paper proposes a new multi-view clustering method, low-rank subspace multi-view clustering based on adaptive graph regularization. We construct two new data matrix decomposition models into a unified optimization model. In this framework, we address the significance of the common knowledge shared by the cross view and the unique knowledge of each view by presenting new low-rank and sparse constraints on the sparse subspace matrix. To ensure that we achieve effective sparse representation and clustering performance on the original data matrix, adaptive graph regularization and unsupervised clustering constraints are also incorporated in the proposed model to preserve the internal structural features of the data. Finally, the proposed method is compared with several state-of-the-art algorithms. Experimental results for five widely used multi-view benchmarks show that our proposed algorithm surpasses other state-of-the-art methods by a clear margin.
Influence of the Internet of Things’ application on construction project performance

Internet of Things (IoT) is the network of interconnected devices that are embedded with network connectivity, sensors, software that enable them to amass and exchange data making them responsive. The study is aimed at examining the effects of the IoT on construction project performance with the view of increasing productivity and efficiency. Quantitative research method was adopted and questionnaires were structured on a five-point Likert scale to elicit the opinion of respondents. Descriptive and Inferential analysis were used in analyzing the retrieved data. The most important effects of IoT are security and privacy, collecting previous data, safety comfort and efficiency. Factor analysis was used to group the various impacts of IoT into six. The results showed that there were nine variables with a significant difference while the others ten are not. It was concluded that the IoT adoption has a high influence on the security, privacy, safety, productivity and performance of construction workers. It is recommended that variables with least influenced areas can be developed by improving the skills of professionals. Government is also encouraged to adopt the principles of IoT not only in construction but other sectors to improve the economy of the nation.
Multi-Operator Differential Evolution Algorithm for Solving Real-World Constrained Optimization Problems

Recently, many deferential evolution-based algorithms have been developed to solve constrained optimization problems. The performance of these methods outperforms the performance of single operator and/or algorithm-based ones. However, they do not perform consistently for all the problems tested in the literature. Also, the process of using the appropriate selection of algorithms and operators may be time-consuming since their designs are undertaken mainly through trial and error. In this paper, we propose an improved optimization algorithm that uses the benefits of multiple deferential evolution operators, with the best one is emphasized based on the quality and diversity of the population. The performance of the proposed algorithm is tested by solving 57 real-world constrained problems with different dimensions, number of equality and equality constraints, with its results showing a high success rate and that it outperformed different state-of-the-art algorithms.
An efficient model for predicting setting time of cement based on broad learning system

Cement is the main building material in the construction industry. Its setting time directly affects the setting time and strength of concrete, which further affects construction schedule and building quality. However, traditional measurement technology not only has high labor intensity and high time consumption, but has a high technical requirement. Various human factors, such as insufficient operation, will result in great errors in the measurements. The accurate prediction of setting time enables manpower savings, avoids large errors caused by insufficient operation, and guides the production of high-performance cement. In this paper, an efficient model based on the broad learning system is proposed to predict the initial and final setting time. It is committed to directly predicting setting time from clinker composition and physical properties, which is of great significance to the optimization of clinker formula. The experimental results show that it can accurately predict the setting time and behave good generalization ability, which addresses the problem of labor intensity in measurement and saves many resources. In addition, the broad learning system can rapidly build a setting time prediction model with few errors, satisfying industrial demands for the rapid modeling of various specialty cements.
Analyzing Process Concept Drifts Based on Sensor Event Streams During Runtime

Business processes have to adapt to constantly changing requirements at a large scale due to, e.g., new regulations, and at a smaller scale due to, e.g., deviations in sensor event streams such as warehouse temperature in manufacturing or blood pressure in health care. Deviations in the process behavior during runtime can be detected from process event streams as so called concept drifts. Existing work has focused on concept drift detection so far, but has neglected why the drift occurred. To close this gap, this paper provides online algorithms to analyze the root cause for a concept drift using sensor event streams. These streams are typically gathered externally, i.e., separated from the process execution, and can be understood as time sequences. Supporting domain experts in assessing concept drifts through their root cause facilitates process optimization and evolution. The feasibility of the algorithms is shown based on a prototypical implementation. Moreover, the algorithms are evaluated based on a real-world data set from manufacturing.
Unsupervised Video Summarization via Relation-Aware Assignment Learning

We address the problem of unsupervised video summarization that automatically selects key video clips. Most state-of-the-art approaches suffer from two issues: (1) they model video clips without explicitly exploiting their relations, and (2) they learn soft importance scores over all the video clips to generate the summary representation. However, a meaningful video summary should be inferred by taking the relation-aware context of the original video into consideration, and directly selecting a subset of clips with a hard assignment. In this paper, we propose to exploit clip-clip relations to learn relation-aware hard assignments for selecting key clips in an unsupervised manner. First, we consider the clips as graph nodes to construct an assignment-learning graph. Then, we utilize the magnitude of the node features to generate hard assignments as the summary selection. Finally, we optimize the whole framework via a proposed multi-task loss including a reconstruction constraint, and a contrastive constraint. Extensive experimental results on three popular benchmarks demonstrate the favourable performance of our approach.
How agile teams make self-assignment work: a grounded theory study

Abstract Self-assignment, a self-directed method of task allocation in which teams and individuals assign and choose work for themselves, is considered one of the hallmark practices of empowered, self-organizing agile teams. Despite all the benefits it promises, agile software teams do not practice it as regularly as other agile practices such as iteration planning and daily stand-ups, indicating that it is likely not an easy and straighforward practice. There has been very little empirical research on self-assignment. This Grounded Theory study explores how self-assignment works in agile projects. We collected data through interviews with 42 participants representing 28 agile teams from 23 software companies and supplemented these interviews with observations. Based on rigorous application of Grounded Theory analysis procedures such as open, axial, and selective coding, we present a comprehensive grounded theory of making self-assignment work that explains the (a) context and (b) causal conditions that give rise to the need for self-assignment, (c) a set of facilitating conditions that mediate how self-assignment may be enabled, (d) a set of constraining conditions that mediate how self-assignment may be constrained and which are overcome by a set of (e) strategies applied by agile teams, which in turn result in (f) a set of consequences, all in an attempt to make the central phenomenon, self-assignment, work. The findings of this study will help agile practitioners and companies understand different aspects of self-assignment and practice it with confidence regularly as a valuable practice. Additionally, it will help teams already practicing self-assignment to apply strategies to overcome the challenges they face on an everyday basis.
Multiple Model Poisson Multi-Bernoulli Mixture Filter for Maneuvering Targets

The Poisson multi-Bernoulli mixture (PMBM) filter is conjugate prior composed of the union of a Poisson point process (PPP) and a multi-Bernoulli mixture (MBM). Considering that the single model is not enough to guarantee stable tracking performance for maneuvering targets, in this article, a multiple model PMBM (MM-PMBM) filter is proposed to cope with this problem. The proposed MM-PMBM filter extends the single-model PMBM filter recursion to multiple motion models by exploiting the jump Markov system (JMS). The performance of the proposed algorithm is examined from two scenarios with different detection probabilities. Moreover, the robustness of Markovian model transition probability matrices (TPMs) for the proposed MM-PMBM filter is also explored. The simulation results demonstrate that the proposed MM-PMBM filter performs well in terms of the tracking accuracy, including the target states and cardinality estimates, and also has good tolerance with respect to different TPMs.
Intercomparison of XRF Core Scanning Results From Seven Labs and Approaches to Practical Calibration

Abstract X‐ray fluorescence (XRF) scanning of marine sediment has the potential to yield near‐continuous and high‐resolution records of elemental abundances, which are often interpreted as proxies for paleoceanographic processes over different time scales. However, many other variables also affect scanning XRF measurements and convolute the quantitative calibrations of element abundances and comparisons of data from different labs. Extensive interlab comparisons of XRF scanning results and calibrations are essential to resolve ambiguities and to understand the best way to interpret the data produced. For this study, we sent a set of seven marine sediment sections (1.5 m each) to be scanned by seven XRF facilities around the world to compare the outcomes amidst a myriad of factors influencing the results. Results of raw element counts per second (cps) were different between labs, but element ratios were more comparable. Four of the labs also scanned a set of homogenized sediment pellets with compositions determined by inductively coupled plasma‐optical emission spectrometry (ICP‐OES) and ICP‐mass spectrometry (MS) to convert the raw XRF element cps to concentrations in two ways: a linear calibration and a log‐ratio calibration. Although both calibration curves are well fit, the results show that the log‐ratio calibrated data are significantly more comparable between labs than the linearly calibrated data. Smaller‐scale (higher‐resolution) features are often not reproducible between the different scans and should be interpreted with caution. Along with guidance on practical calibrations, our study recommends best practices to increase the quality of information that can be derived from scanning XRF to benefit the field of paleoceanography.
Developing and Preliminary Validating an Automatic Cell Classification System for Bone Marrow Smears: a Pilot Study

Abstract Bone marrow smear examination is an indispensable diagnostic tool in the evaluation of hematological diseases, but the process of manual differential count is labor extensive. In this study, we developed an automatic system with integrated scanning hardware and machine learning-based software to perform differential cell count on bone marrow smears to assist diagnosis. The initial development of the artificial neural network was based on 3000 marrow smear samples retrospectively archived from Sir Run Run Shaw Hospital affiliated to Zhejiang University School of Medicine between June 2016 and December 2018. The preliminary field validating test of the system was based on 124 marrow smears newly collected from the Second Affiliated Hospital of Harbin Medical University between April 2019 and November 2019. The study was performed in parallel of machine automatic recognition with conventional manual differential count by pathologists using the microscope. We selected representative 600,000 marrow cell images as training set of the algorithm, followed by random captured 30,867 cell images for validation. In validation, the overall accuracy of automatic cell classification was 90.1% (95% CI, 89.8–90.5%). In a preliminary field validating test, the reliability coefficient (ICC) of cell series proportion between the two analysis methods were high (ICC ≥ 0.883, P &lt; 0.0001) and the results by the two analysis methods were consistent for granulocytes and erythrocytes. The system was effective in cell classification and differential cell count on marrow smears. It provides a useful digital tool in the screening and evaluation of various hematological disorders.
Enhanced Prediction Performance of a Neuromorphic Reservoir Computing System Using a Semiconductor Nanolaser With Double Phase Conjugate Feedbacks

A neuromorphic reservoir computing (RC) system using a semiconductor nanolaser (SNL) with double phase conjugate feedbacks (PCF) is proposed for the first time and demonstrated numerically. The prediction performance of such RC system is investigated via Santa Fe chaotic time series prediction task. The Purcell cavity-enhanced spontaneous emission factor F and the spontaneous emission coupling factor β are included in the rate equations, and the influences of F and β on the prediction performance of such RC system are analyzed extensively. For the purpose of comparison, the prediction performance of SNL-based RC system with single PCF is also considered. The simulation results indicate that, compared with the SNL-based RC system with single PCF, enhanced prediction performance can be obtained for the SNL-based RC system with double PCF. Moreover, the influences of bias current, the modulation depth of input signal, feedback strength, as well as feedback delay, are also taken into account. The proposed SNL-based RC system subject to double PCF in this paper has the potential to develop the RC-based neuromorphic photonic integrated circuit.
Knowledge Distillation-Based Representation Learning for Short-Utterance Spoken Language Identification

With successful applications of deep feature learning algorithms, spoken language identification (LID) on long utterances obtains satisfactory performance. However, the performance on short utterances is drastically degraded even when the LID system is trained using short utterances. The main reason is due to the large variation of the representation on short utterances which results in high model confusion. To narrow the performance gap between long, and short utterances, we proposed a teacher-student representation learning framework based on a knowledge distillation method to improve LID performance on short utterances. In the proposed framework, in addition to training the student model on short utterances with their true labels, the internal representation from the output of a hidden layer of the student model is supervised with the representation corresponding to their longer utterances. By reducing the distance of internal representations between short, and long utterances, the student model can explore robust discriminative representations for short utterances, which is expected to reduce model confusion. We conducted experiments on our in-house LID dataset, and NIST LRE07 dataset, and showed the effectiveness of the proposed methods for short utterance LID tasks.
Dynamic Proof of Data Possession and Replication With Tree Sharing and Batch Verification in the Cloud

Cloud storage attracts a lot of clients to join the paradise. For a high data availability, some clients require their files to be replicated and stored on multiple servers. Because clients are generally charged based on the redundancy level required by them, it is critical for clients to obtain convincing evidence that all replicas are stored correctly and are updated to the up-to-date version. In this article, we propose a dynamic proof of data possession and replication (DPDPR) scheme, which is proved to be secure in the defined security model. Our scheme shares a single authenticated tree across multiple replicas, which reduces the tree's storage cost significantly. Our scheme allows for batch verification for multiple challenged leaves and can verify multiple replicas in a single batch way, which considerably save bandwidth and computation resources during audit process. We also evaluate the DPDPR's performance and compare it with the most related scheme. The evaluation results show that our scheme saves almost 66 percent tree's storage cost for three replicas, and obtains almost 60 and 80 percent efficiency improvements in terms of the overall bandwidth and computation costs, respectively, when three replicas are checked and each challenged with 460 blocks.
An Empirical Study of C++ Vulnerabilities in Crowd-Sourced Code Examples

Software developers share programming solutions in Q&A sites like Stack Overflow, Stack Exchange, Android forum, and so on. The reuse of crowd-sourced code snippets can facilitate rapid prototyping. However, recent research shows that the shared code snippets may be of low quality and can even contain vulnerabilities. This paper aims to understand the nature and the prevalence of security vulnerabilities in crowd-sourced code examples. To achieve this goal, we investigate security vulnerabilities in the C++ code snippets shared on Stack Overflow over a period of 10 years. In collaborative sessions involving multiple human coders, we manually assessed each code snippet for security vulnerabilities following CWE (Common Weakness Enumeration) guidelines. From the 72,483 reviewed code snippets used in at least one project hosted on GitHub, we found a total of 99 vulnerable code snippets categorized into 31 types. Many of the investigated code snippets are still not corrected on Stack Overflow. The 99 vulnerable code snippets found in Stack Overflow were reused in a total of 2859 GitHub projects. To help improve the quality of code snippets shared on Stack Overflow, we developed a browser extension that allows Stack Overflow users to be notified for vulnerabilities in code snippets when they see them on the platform.
Scikit-ELM: An Extreme Learning Machine Toolbox for Dynamic and Scalable Learning

This paper presents a novel library for Extreme Learning Machines (ELM) called Scikit-ELM ( https://github.com/akusok/scikit-elm , https://scikit-elm.readthedocs.io ). Usability and flexibility of the approach are the main focus points in this work, achieved primarily through a tight integration with Scikit-Learn, a de facto industry standard library in Machine Learning outside Deep Learning. Methodological advances enable great flexibility in dynamic addition of new classes to a trained model, or by allowing a model to forget previously learned data.
Simulation of student classroom behavior recognition based on cluster analysis and random forest algorithm

Classroom student behavior recognition has important guiding significance for the development of distance education strategies. At present, the accuracy of students’ classroom behavior recognition algorithms has problems. In order to improve the effect of distance education student status analysis, this study combines the traditional clustering analysis algorithm and the random forest algorithm to improve the traditional algorithm and combines the human skeleton model to identify students’ classroom behavior in real time. Moreover, this research combines with the needs of students’ classroom behavior recognition to build a network topology model. The error rate of feature reconstruction using spatio-temporal features is lower than that of a single feature. Through experiments, this study verifies the effectiveness of the extracted spatial angle features based on the human skeleton model. The results of algorithm performance test show that the proposed algorithm network structure is superior to the network structure of single feature extraction algorithm.