Reviewerly/exHarmony huggingface dataset:
A Novel Dual-Graph Convolutional Network based Web Service Classification Framework

Automated service classification is the foundation for service discovery and service composition. Currently, many existing methods extracting features from functional description documents suffer the problem of data sparsity. However, beside functional description documents, the Web API ecosystem has accumulated a wealth of information that can be used to improve the accuracy of Web service (API) classification. At the moment, there is an absence of a unified way to combine functional description documents with other sources of information (e.g., attributes, interactions and external knowledge) accumulated in the Web API ecosystem for API classification. To address this issue, we present a dual-GCN framework that can effectively suppress the noise propagation of textual contents by distinguishing functional description documents and other sources of information (specifically Mashup-API co-invocation patterns by default in this paper) for API classification. This framework is extensible with the ability to include different sources of information accumulated in the Web API ecosystem. Comprehensive experiments on a real-world public dataset demonstrate that our proposed method can outperform various representative methods for API classification.
Noninvasive human red blood cell counting based on dynamic spectrum

Noninvasive and real-time human red blood cell (RBC) counting has high application value in the process of surgery, basic medical research, disease diagnosis and daily management. In this paper, dynamic spectrum (DS) theory was applied to noninvasive human RBC counting under the guidance of “M + N” theory, meanwhile one-by-one elimination method was used to remove the redundant information and increase the signal-to-noise ratio (SNR) of DS for the first time. To illustrate the effectiveness of this method, DS of 317 volunteers were analyzed. According to the PLS modeling effect, the correlation coefficient of prediction set (Rp) is 0.893, the root mean square error of prediction set (RMSEP) is 0.196 × 1012/L, the correlation coefficient of calibration set (Rc) is 0.926 and the root mean square error of calibration set (RMSEC) is 0.180 × 1012/L. This paper suggests that the DS theory has the potential to realize clinical application of noninvasive human RBC counting.
Parallel lossless HSI compression based on RLS filter

The recent advancement in the field of electronics has led to development of sensors that capture the image of an area or object in spectral-domain along with spatial information. Due to continuity of spectral domain in hyperspectral images, it is difficult to store, process, analyze or transmit the critical information contained in it. Prediction based compression technique is used to reduce this size by a certain level. It predicts the value of a pixel with some error from previous pixels using a filter and finally, encode that error using variable length encoder. The execution time taken by this technique is very high which can be reduced by high performance computing. In this paper, we designed a mechanism to use high-performance computing techniques in the execution of prediction based image compression algorithms. The average execution time of the RLS-filter based compression algorithm is reduced significantly (by a factor of 29 using 2 nodes with 28 cores each, on PARAM SHIVAY supercomputer) with the proposed technique.
SnapCatch: Automatic Detection of Covert Timing Channels Using Image Processing and Machine Learning

With the rapid growth of data exfiltration carried out by cyber attacks, Covert Timing Channels (CTC) have become an imminent network security risk that continues to grow in both sophistication and utilization. These types of channels utilize inter-arrival times to steal sensitive data from the targeted networks. CTC detection relies increasingly on machine learning techniques, which utilize statistical-based metrics to separate malicious (covert) traffic flows from the legitimate (overt) ones. However, given the efforts of cyber attacks to evade detection and the growing column of CTC, covert channels detection needs to improve in both performance and precision to detect and prevent CTCs and mitigate the reduction of the quality of service caused by the detection process. In this article, we present an innovative image-based solution for fully automated CTC detection and localization. Our approach is based on the observation that the covert channels generate traffic that can be converted to colored images. Leveraging this observation, our solution is designed to automatically detect and locate the malicious part (i.e., set of packets) within a traffic flow. By locating the covert parts within traffic flows, our approach reduces the drop of the quality of service caused by blocking the entire traffic flows in which covert channels are detected. We first convert traffic flows into colored images, and then we extract image-based features for detection covert traffic. We train a classifier using these features on a large data set of covert and overt traffic. This approach demonstrates a remarkable performance achieving a detection accuracy of 95.83% for cautious CTCs and a covert traffic accuracy of 97.83% for 8 bit covert messages, which is way beyond what the popular statistical-based solutions can achieve.
A deep supervised learning approach for condition-based maintenance of naval propulsion systems

In the last years, predictive maintenance has gained a central position in condition-based maintenance tasks planning. Machine learning approaches have been very successful in simplifying the construction of prognostic models for health assessment based on available historical labeled data issued from similar systems or specific physical models. However, if the collected samples suffer from lack of labels (small labeled dataset or not enough samples), the process of generalization of the learning model on the dataset as well as on the newly arrived samples (application) can be very difficult. In an attempt to overcome such drawbacks, a new deep supervised learning approach is introduced in this paper. The proposed approach aims at extracting and learning important patterns even from a small amount of data in order to produce more general health estimator. The algorithm is trained online based on local receptive field theories of extreme learning machines using data issued from a propulsion system simulator. Compared to extreme learning machine variants, the new algorithm shows a higher level of accuracy in terms of approximation and generalization under several training paradigms.
A Lightweight Provably Secure Digital Short-Signature Technique Using Extended Chaotic Maps for Human-Centered IoT Systems

Internet of Things (IoT) consists of numerous smart devices for sharing sensed data through the availability of online services. Direct communication by smart devices with people to identify parameters of healthcare and send them to a central repository is crucial. There is a need to secure messages among the sender and recipient during data exchange in order to tackle the malicious attacks by human. To provide secure communication, various signature-based schemes are presented in the literature. However, smart devices require lightweight tasks by guaranteeing essential security strengths. The main difficulty in signature-based methods is more computational cost incurred for signature and verification stages involving large numbers. This article introduces a lightweight provably secure short digital signature technique for safe communication amongst smart devices in human-centered IoT (HCIoT), the security of which is closely related to an extended chaotic maps assumption in a random oracle model (ROM). Moreover, we used less comprehensive operations to accomplish processes of verification and signing, similar to human signing on legitimate documents and then check as per witness. The proposed technique provides a stronger guarantee of protection than existing signature techniques. The key advantage of the presented technique over the DSA techniques is that it takes less computation in the verification stage and signing length; it retains the degree of protection. The presented short signature takes less bandwidth for communication, storage, and computing resources.
Multi independent latent component extension of naive Bayes classifier

Naive Bayes (NB) classifier ease of use along with its remarkable performance has led many researchers to extend the scope of its applications to real-world domains by relaxing the conditional independence assumption of features given the information about the class variable. However, fulfilling this objective, most of the generalizations, cut their own way through compromising the model’s simplicity, make more complex classifiers with a substantial deviation from the original one. Multi Independent Latent Component Naive Bayes Classifier (MILC-NB) leverages a set of latent variables to preserve the overall structure of naive Bayes classifier while rectifying its major restriction. Each latent variable is responsible for keeping a subset of conditionally dependent features d-connected within a component, and the set of features is divided into non-overlapping partitions across different components accordingly. We prove that components are conditionally independent given the information about the class variable which allows us to devise novel mathematical methods with a substantial reduction in the complexities of classification and learning. Experiments on 34 datasets obtained from the OpenML repository indicate that MILC-NB outperforms state-of-the-art classifiers in terms of area under the ROC curve (AUC) and classification accuracy (ACC).
MutualRec: Joint friend and item recommendations with mutualistic attentional graph neural networks

Many social studies and practical cases suggest that people's consumption behaviors and social behaviors are not isolated but interrelated. However, most existing research either predicts users' consumption preference or recommends friends to users without dealing with them simultaneously. In this paper, we propose a novel framework called MutualRec that jointly accomplishes the two tasks based on graph neural networks, attention mechanisms, and mutualistic model. MutualRec first uses a spatial attention layer and a spectral attention layer to learn latent embeddings from observed data, and then merges them via a mutualistic attention layer. The first two layers can relieve data sparsity without violating users' preference sequence, while the last captures the relationship between user’ consumption and social behaviors. We demonstrate the effectiveness of MutualRec in both social recommendation and link prediction via extensive experiments.
An Empirical Study of Developer Discussions in the Gitter Platform

Developer chatrooms (e.g., the Gitter platform) are gaining popularity as a communication channel among developers. In developer chatrooms, a developer ( asker ) posts questions and other developers ( respondents ) respond to the posted questions. The interaction between askers and respondents results in a discussion thread . Recent studies show that developers use chatrooms to inquire about issues, discuss development ideas, and help each other. However, prior work focuses mainly on analyzing individual messages of a chatroom without analyzing the discussion thread in a chatroom. Developer chatroom discussions are context-sensitive, entangled, and include multiple participants that make it hard to accurately identify threads. Therefore, prior work has limited capability to show the interactions among developers within a chatroom by analyzing only individual messages. In this article, we perform an in-depth analysis of the Gitter platform (i.e., developer chatrooms) by analyzing 6,605,248 messages of 709 chatrooms. To analyze the characteristics of the posted questions and the impact on the response behavior (e.g., whether the posted questions get responses), we propose an approach that identifies discussion threads in chatrooms with high precision (i.e., 0.81 F-score). Our results show that inactive members responded more often and unique questions take longer discussion time than simple questions. We also find that clear and concise questions are more likely to be responded to than poorly written questions. We further manually analyze a randomly selected sample of 384 threads to examine how respondents resolve the raised questions. We observe that more than 80% of the studied threads are resolved. Advanced-level/beginner-level questions along with the edited questions are the mostly resolved questions. Our results can help the project maintainers understand the nature of the discussion threads (e.g., the topic trends). Project maintainers can also benefit from our thread identification approach to spot the common repeated threads and use these threads as frequently asked questions (FAQs) to improve the documentation of their projects.
A Persona-Based Approach for Identifying Accessibility Issues in Elderly and Disabled Users’ Interaction with Home Appliances

Disabled and elderly populations may not fully appreciate the benefits of advanced technologies and every-day products due to accessibility issues. However, the diverse characteristics of disabled and elderly users make conducting user research a challenging task for stakeholders who oversee the needs of developing accessible products. They need a tool providing rich information to empathize with the disabled and elderly users, instead of standards and documents without detailed explanation of real-world applications. Therefore, this study aims to identify accessibility issues based on the interview and observation data from 52 people with different disabilities and ages. We developed eight personas representing four different user groups under the context of home appliance usage: visually impaired, hearing impaired, spinal-cord impaired, and elderly. Each persona takes a role as a tool to understand the target users and has a persona card representing their task barriers, frustrations, needs, and quotations along with a cartoonized character. In this study, we address two common accessibility issues and two persona-specific issues within each user group. The issues are presented in stakeholder’s language personas to help them comprehend and empathize with their users.
FedMax: Enabling a Highly-Efficient Federated Learning Framework

IoT devices produce a wealth of data desired for learning models to empower more intelligent applications. However, such data is often privacy sensitive making data owners reluctant upload their data to a central server for learning purposes. Federated learning provides a promising privacy-preserving learning approach, which decouples the model training from the need of accessing to the sensitive data. However, realizing a deployed, dependable federated learning system faces critical challenges, such as frequent dropouts of learning workers, heterogeneity of workers computation, and limited communication. In this paper, we focus on the systems aspects to advance federated learning and contribute a highly efficient and reliable distributed federated learning framework, FedMax, aiming to tackle these challenges. In designing FedMax, we contribute new techniques in light of the properties of a real federated learning setting, including a relaxed synchronization communication scheme and a similarity-based worker selection approach. We have implemented a prototype of FedMax and evaluated FedMax upon multiple popular machine learning models and datasets, showing that FedMax significantly increases the robustness of a federated learning system, speeds up the convergence rate by 25%, and increases the system efficiency by 50%, in comparison with state-of-the-art approaches.
A comprehensive study of mobility functioning information in clinical notes: Entity hierarchy, corpus annotation, and sequence labeling

Secondary use of Electronic Health Records (EHRs) has mostly focused on health conditions (diseases and drugs). Function is an important health indicator in addition to morbidity and mortality. Nevertheless, function has been overlooked in accessing patients' health status. The World Health Organization (WHO)'s International Classification of Functioning, Disability and Health (ICF) is considered the international standard for describing and coding function and health states. We pioneer the first comprehensive analysis and identification of functioning concepts in the Mobility domain of the ICF.Using physical therapy notes at the National Institutes of Health's Clinical Center, we induced a hierarchical order of mobility-related entities including 5 entities types, 3 relations, 8 attributes, and 33 attribute values. Two domain experts manually curated a gold standard corpus of 14,281 nested entity mentions from 400 clinical notes. Inter-annotator agreement (IAA) of exact matching averaged 92.3 % F1-score on mention text spans, and 96.6 % Cohen's kappa on attributes assignments. A high-performance Ensemble machine learning model for named entity recognition (NER) was trained and evaluated using the gold standard corpus. Average F1-score on exact entity matching of our Ensemble method (84.90 %) outperformed popular NER methods: Conditional Random Field (80.4 %), Recurrent Neural Network (81.82 %), and Bidirectional Encoder Representations from Transformers (82.33 %).The results of this study show that mobility functioning information can be reliably captured from clinical notes once adequate resources are provided for sequence labeling methods. We expect that functioning concepts in other domains of the ICF can be identified in similar fashion.
Is there a nonlinear relationship between financial development and poverty in Africa?

Purpose The paper examines whether there is a threshold between financial development and poverty in African economies. Design/methodology/approach The study adopts the innovative dynamic panel threshold model of Seo and Shin (2016) made practicable by Seo et al. (2019)–the model estimates threshold relationship even in the presence of endogeneity. Also, following the recommendations of Cihak et al. (2013) and Sahay et al. (2015), we also adopt a robust measure of financial development based on the four pillars of financial deepening, stability, efficiency and access derived from the principal component analysis (PCA). Findings The empirical results show that there exists a threshold level of financial development necessary for poverty reduction in Africa. Research limitations/implications Our result is important for policy formulations. First, individual African country must discover the level of financial development necessary for spurring poverty reduction. Second, policymakers, especially in lower-income countries, must keep improving their financial sector development to achieve the threshold level necessary for achieving poverty reduction even though financial development might seem less relevant at its present level. Practical implications The policymakers in Africa should note that there exists a threshold level of financial development that reduces poverty. Hence, the present level of financial development might have not yielded a considerate effect on poverty. Still, the policymakers must keep pushing on until the threshold is achieved. Social implications Financial development reduces poverty level but it must reach a certain threshold level before it does so. So, we advise African policymakers to continue to develop their financial sector to achieve this threshold. Originality/value This seems to be the first work to document the threshold relationship using the dynamic panel threshold. Besides, the study specifically concentrates on Africa dividing the continent into different income levels. Moreover, we adopt a robust measure of financial development unlike extant studies on Africa.
Retrieval Evaluation Measures that Agree with Users’ SERP Preferences

We examine the “goodness” of ranked retrieval evaluation measures in terms of how well they align with users’ Search Engine Result Page (SERP) preferences for web search. The SERP preferences cover 1,127 topic-SERP-SERP triplets extracted from the NTCIR-9 INTENT task, reflecting the views of 15 different assessors. Each assessor made two SERP preference judgements for each triplet: one in terms of relevance and the other in terms of diversity. For each evaluation measure, we compute the Agreement Rate (AR) of each triplet: the proportion of assessors that agree with the measure’s SERP preference. We then compare the mean ARs of the measures as well as those of best/median/worst assessors using Tukey HSD tests. Our first experiment compares traditional ranked retrieval measures based on the SERP relevance preferences: we find that normalised Discounted Cumulative Gain (nDCG) and intentwise Rank-biased Utility (iRBU) perform best in that they are the only measures that are statistically indistinguishable from our best assessor; nDCG also statistically significantly outperforms our median assessor. Our second experiment utilises 119,646 document preferences that we collected for a subset of the above topic-SERP-SERP triplets (containing 894 triplets) to compare preference-based evaluation measures as well as traditional ones. Again, we evaluate them based on the SERP relevance preferences. The results suggest that measures such as wpref5 are the most promising among the preference-based measures considered, although they underperform the best traditional measures such as nDCG on average. Our third experiment compares diversified search measures based on the SERP diversity preferences as well as the SERP relevance preferences, and it shows that D♯-measures are clearly the most reliable: in particular, D♯-nDCG and D♯-RBP statistically significantly outperform the median assessor and all intent-aware measures; they also outperform the recently proposed RBU on average. Also, in terms of agreement with SERP diversity preferences, D♯-nDCG statistically significantly outperforms RBU. Hence, if IR researchers want to use evaluation measures that align well with users’ SERP preferences, then we recommend nDCG and iRBU for traditional search, and D♯-measures such as D♯-nDCG for diversified search. As for document preference-based measures that we have examined, we do not have a strong reason to recommended them over traditional measures like nDCG, since they align slightly less well with users’ SERP preferences despite their quadratic assessment cost.
STNet: Scale Tree Network With Multi-Level Auxiliator for Crowd Counting

State-of-the-art approaches for crowd counting resort to deepneural networks to predict density maps. However, counting people in congested scenes remains a challenging task because the presence of drastic scale variation, density inconsistency, and complex background can seriously degrade their counting accuracy. To battle the ingrained issue of accuracy degradation, in this paper, we propose a novel and powerful network called Scale Tree Network (STNet) for accurate crowd counting. STNet consists of two key components: a Scale-Tree Diversity Enhancer and a Multi-level Auxiliator. Specifically, the Diversity Enhancer is designed to enrich scale diversity, which alleviates limitations of existing methods caused by insufficient level of scales. A novel tree structure is adopted to hierarchically parse coarse-to-fine crowd regions. Furthermore, a simple yet effective Multi-level Auxiliator is presented to aid in exploiting generalisable shared characteristics at multiple levels, allowing more accurate pixel-wise background cognition. The overall STNet is trained in an end-to-end manner, without the needs for manually tuning loss weights between the main and the auxiliary tasks. Extensive experiments on five challenging crowd datasets demonstrate the superiority of the proposed method.