Reviewerly/exHarmony huggingface dataset:
A Comprehensive “Real-World Constraints”-Aware Requirements Engineering Related Assessment and a Critical State-of-the-Art Review of the Monitoring of Humans in Bed

Currently, abnormality detection and/or prediction is a very hot topic. In this paper, we addressed it in the frame of activity monitoring of a human in bed. This paper presents a comprehensive formulation of a requirements engineering dossier for a monitoring system of a "human in bed" for abnormal behavior detection and forecasting. Hereby, practical and real-world constraints and concerns were identified and taken into consideration in the requirements dossier. A comprehensive and holistic discussion of the anomaly concept was extensively conducted and contributed to laying the ground for a realistic specifications book of the anomaly detection system. Some systems engineering relevant issues were also briefly addressed, e.g., verification and validation. A structured critical review of the relevant literature led to identifying four major approaches of interest. These four approaches were evaluated from the perspective of the requirements dossier. It was thereby clearly demonstrated that the approach integrating graph networks and advanced deep-learning schemes (Graph-DL) is the one capable of fully fulfilling the challenging issues expressed in the real-world conditions aware specification book. Nevertheless, to meet immediate market needs, systems based on advanced statistical methods, after a series of adaptations, already ensure and satisfy the important requirements related to, e.g., low cost, solid data security and a fully embedded and self-sufficient implementation. To conclude, some recommendations regarding system architecture and overall systems engineering were formulated.
A Framework for Probabilistic Reasoning on Knowledge Graphs

In this paper we introduce a framework for probabilistic reasoning on knowledge graphs. The framework leverages the notion of probabilistic knowledge graphs (PKGs), a dedicated probabilistic graphical model, as well as Soft Vadalog, a specific language for knowledge representation and reasoning on such model. We illustrate PKGs, the language and the general problem of probabilistic reasoning, providing approximate algorithmic tools to make it feasible and efficient. This work—a short version of our recent contribution to the International Joint Conference on Rules and Reasoning 2020—aims at making our results available to the broader statistical community.
Using Fuzzy Cluster Analysis to Find Interesting Clusters

Fuzzy clustering algorithms are not only popular within the field of fuzzy sets and systems, but are also often used in other areas. It is clear that membership degrees in fuzzy cluster analysis provide more information than crisp assignments of data objects to clusters. But fuzzy clustering has additional technical advantages compared to classical clustering approaches. Fuzzy clustering algorithms are less prone to converge to undesired results that correspond to local optima of the underlying objective function that is used for clustering. Furthermore, fuzzy clustering enables the definition of cluster validity measures that cannot be used in the context of crisp clustering. These purely technical advantages can be exploited to define fuzzy clustering algorithms that search for single clusters step-by-step. Finally, it is demonstrated how the approach of finding single clusters can be modified to estimate reference intervals or “normal ranges” in laboratory medicine.
Partial Calibrated Multi-label Ranking

An approach to solve Multi-Label Classification (MLC) is to transform this task into a label ranking problem. An example of this is the Calibrated Label Ranking algorithm (CLR). In this research, we propose a new method, called Partial Calibrated Label Ranking (PCLR), that considers a binary classifier for each pair of labels. Such a classifier predicts, for a given instance, the relative relevance of a label versus the other one. Unlike CLR, PCLR predicts relevance through probability intervals. The predicted intervals are combined to obtain a partial order between the labels. Experimental results reveal that our proposed PCLR leads to incomplete but more robust predictions than CLR.
DEVELOPMENT OF ECLIPSE CROSSWORD ASSISTED CROSSWORD LEARNING MEDIA IN DIGESTIVE SYSTEM MATERIALS IN JUNIOR HIGH SCHOOL

This study aims to produce a product of learning media, namely crossword puzzles assisted by eclipse crossword on the Digestive System material conducted at MTs Walisongo Wonokerto Lumajang. Operationally, the objectives are broken down into two, namely describing the validity of the eclipse crossword-assisted learning media on the digestive system material and describing the students' responses to the eclipse crossword-assisted learning media on the digestive system material. This type of research is a research and development (Research and Development) Borg and Gall model using 7 steps, namely: Research and Information Collection, Planning, Develop Preliminary form of Product, Preliminary Field Testing, Main Product Revision, Main Field Testing, and Operational Product Revision . Based on the results of the validation analysis from material experts, media experts and practitioner experts, it shows that the percentage of the validity of each assessment is 96.5%, 94.5% and 95% with an average value from the experts which is 95% which these results include in the "Very Valid" category, while the results of the analysis of the limited-scale trial and large-scale trials resulted in a score of 87% and 88% which resulted in an average of 88%, this value was included in the "Very Interesting" criteria.
<i>DETOXER:</i> A Visual Debugging Tool With Multiscope Explanations for Temporal Multilabel Classification

In many applications, developed deep-learning models need to be iteratively debugged and refined to improve the model efficiency over time. Debugging some models, such as temporal multilabel classification (TMLC) where each data point can simultaneously belong to multiple classes, can be especially more challenging due to the complexity of the analysis and instances that need to be reviewed. In this article, focusing on video activity recognition as an application of TMLC, we propose DETOXER, an interactive visual debugging system to support finding different error types and scopes through providing multiscope explanations.
An Implicit Identity-Extended Data Augmentation for Low-Resolution Face Representation Learning

Low-resolution (LR) face recognition (LRFR) tackles <i>tiny face images</i> detected from real-world surveillance camera footage, which are unconstrained and generally poor in quality. Owing to the absence of a million-scale labeled LR face dataset, <i>identity-invariant</i> data augmentation (DA) transformations such as flipping, rotation, rescaling, etc., are applied to inflate the effective training examples with respect to the source identities for representation learning. Unfortunately, the identity-invariant property incurs additional intra-class disparity that impairs generalization performance. In this paper, we put forward a new means of DA strategy, termed <i>identity-extended</i> DA, that satisfies both <i>affinity</i> and <i>diversity</i> requirements essential to DA. We instantiate an implicit identity-extended augmentation network, or simply IDEA-Net, to realize the proposed identity-extended DA for LRFR. More specifically, training an IDEA-Net instance augments the small-scale LR (query) face dataset with identity-extended (auxiliary) face examples implicitly in the representation space. We also introduce a calibrator to regulate the disordered representation space by refining the intra-class compactness and the inter-class separation. This diminishes the distribution shift between the original and the augmented examples (affinity) and increases the learning complexity (diversity). We substantiate that IDEA-Net renders a high affinity and diversity representation space. On the other hand, our experimental results on three real-world LR face datasets demonstrate that IDEA-Nets outperform the baselines and other counterparts trained without leveraging the identity-extended examples for LRFR.
Fusing User Preferences and Spatiotemporal Information for Sequential Recommendation

At present, the research on sequence recommendation mainly focuses on using the historical interaction data between users and items to mine their relationship, so as to predict the next interaction between users and items, then generate the personalized recommendation.Spatiotemporal information is very important to further improve the accuracy and quality of recommendation, but the existing sequence recommendation models are mainly based on recurrent neural network (RNN), and pay less attention to spatiotemporal information.Most of the recommendation models are still in the early stage of merging spatiotemporal context information, and the processing effect of long sequence data is not ideal.a sequential recommendation model integrating user preferences and spatiotemporal information is proposed.The model captures user item long-term preferences through spatiotemporal GRU algorithm and user item short-term preferences through attention mechanism.Finally, the learned long-term and short-term preference features and user portrait features are combined to predict the next recommendation location.The experimental results on two real data sets Foursquare and Brightkite show that the proposed model performs better than state-of-the-arts in three evaluation indicators HR@K, NDCG@K and MAP@K.
A Process Evaluation Method for Crossover Service Recommendation

With the rapid development of the digital economy and the new generation of information technology, digital services often need to cross the boundaries of different industries, organizations, and regions. Choosing an appropriate recommendation method for crossover service providers has become challenging. Generally, the performance of the recommendation revealed by the evaluation indicators is used to reflect the pros and cons of the recommendation method. Compared with the traditional evaluation based on results, this paper proposes a procedural evaluation model. It comprehensively considers the laws of economic activities. From three different stages of crossover cooperation: input, execution and output, three process evaluation indicators of entropy, cost and profit are proposed respectively, and dynamic analysis of crossover service recommendation is carried out. Take the commonly used collaborative filtering recommendation method as an example; the experimental results show that the process evaluation model proposed in this paper can select the recommendation method. The method conforms to the law of market changes according to the different states of crossover cooperation of service providers.
Ring-Overlap: A Storage Scaling Mechanism For Consortium Blockchain

With the rapid development of blockchain applications, the data that need to be stored increases dramatically, and the blockchain is about to face the problem of storage limitations. To deal with this problem, this paper proposes a storage scaling mechanism for Hyperledger Fabric, which relieves the storage pressure by dividing the peer nodes into clusters, and each node only stores partial data instead of the whole ledger. First, all accounting nodes are divided into clusters that include several nodes respectively, and part of the whole block data is stored in a single cluster; then, the block data are stored overlappingly on some nodes in the cluster, and each block is guaranteed to have some copies in a cluster. By arranging the copies on selected nodes according to our proposed mechanism, all the blocks are overlapped in a cluster. Theoretical analysis and simulation show that the storage volume occupied by nodes is decreased significantly in blockchain applications with frequent transactions, and in the case that the number of node failures in a single cluster does not exceed the threshold, the mechanism can still guarantee data integrity. Moreover, for applications with frequent transactions, storage space consumption can be significantly reduced without increasing excessive query time overhead.
HERMS: A Hierarchical Electronic Records Management System Based on Blockchain with Distributed Key Generation

In a traditional electronic records management system (ERMS), the legitimacy of the participants’ identities is verified by Certificate Authority (CA) certifications. The authentication process is complicated and takes up lots of memory. To overcome this problem, we construct a hierarchical electronic records management system by using a Hierarchical Identity-Based Cryptosystem (HIBC) to replace CA. However, there exist the threats of malicious behavior from a private key generator (PKG) or an entity in the upper layer because the private keys are generated by a PKG or upper entity in HIBC. Thus, we adopt distributed key generation protocols in HIBC to avoid the threats. Finally, we use blockchain technology in our system to achieve decentralized management.
Investigating the Linguistic Design Quality of Public, Partner, and Private REST APIs

Application Programming Interfaces (APIs) define how Web services, middle-wares, frameworks, and libraries communicate with their clients. An API that conforms to REpresentational State Transfer (REST) design principles is known as REST API. At present, it is an industry-standard for interaction among Web services. There exist mainly three categories of APIs: public, partner, and private. Public APIs are designed for external consumers, whereas partner APIs are designed aiming at organizational partners. In contrast, private APIs are designed solely for internal use. The API quality matters regardless of their category and intended consumers. To assess the (linguistic) design of APIs, researchers defined linguistic patterns (i.e., best API design practices) and linguistic antipatterns (i.e., poor API design practices.) APIs that follow linguistic patterns are easy to understand, use, and maintain. In this study, we analyze and compare the design quality of public, partner, and private APIs. More specifically, we made a large survey by analyzing and performing the detection of nine linguistic patterns and their corresponding antipatterns on more than 2,500 end-points from 37 APIs. Our results suggest that (1) public, partner, and private APIs lack quality linguistic design, (2) among the three API categories, private APIs lack linguistic design the most, and (3) end-points are amorphous, contextless, and non-descriptive in partner APIs. End-points have contextless design and poor documentation regardless of the API categories.
A Study of Contributing Factors to Power Aware Vertical Scaling of Deadline Constrained Applications

The adoption of virtualization technologies in datacenters has increased dramatically in the past decade. Clouds have pivoted from being just an infrastructure rental to offering platforms and solutions, made possible by having several layers of abstraction, providing internal and external users the ability to focus on core business logic. Efficient resource management has in turn become salient in ensuring operational efficiency. In this work, we study key factors that can influence vertical scaling decisions, propose a policy to vertically scale deadline constrained applications and surface our findings from experimentation. We observe that (a) the duration for which an application is profiled has an almost cyclic influence on the accuracy of behavior predictions and is inversely proportional to the time spent consuming backlog, (b) the duration for which an application is scaled can help achieve up to a 9.6% and 4.2% reduction in the 75 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> and 95 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> percentile of power usage respectively, (c) reducing the tolerance towards accrual of backlog influences the application execution time and can reduce the number of SLA violations by 50% or 100% at times and (d) increasing the time to deadline offers power saving opportunities and can help achieve a 9.3% improvement in the 75 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> percentile of power usage.
HSG-CDM: A Heterogeneous Service Graph Contextual Deep Model for Web Service Classification

Service classification plays an important role in the field of Service-Oriented Computing (SOC). Significant research efforts have been made over the past years focusing on service classification, either adopting advanced NLP models to build service feature space from their textual attributes (such as service descriptions and titles), or leveraging graph models to learn service representation from structural information built from the relationships among services and other related entities such as users and mashups. This paper proposes a hybrid model, a Heterogeneous Service Graph Contextual Deep Model (HSG-CDM), that combines NLP models and graph deep models to capture both structural and textual features learned from service data. More specifically, we model service data as a heterogeneous information graph to capture the rich information of services, textual and structural, and adopt advanced deep learning techniques, such as Graph Convolution Network (GCN) and Bidirectional Encoder Representations from Transformers (BERT), to learn contextual service embeddings for classification. We conducted a comprehensive experimental study on a real world dataset to assess the performance of HSG-CDM. The results showed that HSG-CDM outperforms existing deep learning (DL) based models for service classification, such as ServeNet and Dual-Graph, as well as other non DL based approaches.
Research on Identification of Natural and Unnatural Earthquake Events Based on AlexNet Convolutional Neural Network

Accurately and quickly identifying the types of natural and unnatural earthquake events is the basic premise of monitoring, prediction, early warning, and other study in the field of seismology, which is of great significance to the prevention, evaluation, emergency rescue, and other work of earthquake disasters. Convolutional neural network model is a representative artificial intelligence deep learning algorithm, which has been widely used in computer vision, natural language processing, object type identification, and other fields in recent years. In this study, AlexNet convolutional neural network model is selected to study the type identification of 1539 earthquake event waveform records in and around Ningxia Hui Autonomous Region, China. Earthquake event waveform records contain three types: natural earthquake, explosion, and collapse, in which both explosion and collapse are unnatural earthquakes. MATLAB software is used to build the training module and test module for AlexNet convolutional neural network model, and the earthquake event waveform record is transformed into an image format file of 224 times 224 pixels as input parameters. Finally, AlexNet convolutional neural network model has the ability of automatic identification of earthquake event types. The results of this study show that the identification accuracy of earthquake event type in training module is 99.97%, the average value of loss function is 0.001, the identification accuracy of earthquake event type in test set is 98.51%, and the average value of loss function is 0.059. After training and testing, 60 different types of earthquake event waveform records were randomly selected, and AlexNet convolutional neural network model was used to identify them automatically. The automatic identification accuracy of natural earthquakes, explosions, and collapses was 90%, 80%, and 85%, respectively. After training AlexNet convolutional neural network model with earthquake event waveform records, it can have accurate and fast automatic identification ability. The accuracy of automatic identification is comparable to that of professional seismic workers, and the time of automatic identification is greatly reduced compared with that of professional seismic workers. This study can provide an implementation idea of deep learning based on artificial intelligence for the identification of earthquake event types and make contributions to the cause of earthquake prevention and disaster reduction.
Network Aware Container Orchestration for Telco Workloads

In recent years, with the maturation of container orchestration platforms like Kubernetes, containers are now becoming the default way to deploy cloud-native applications, designed as microservices, on public and private clouds. These trends have also spread to the field of Telecommunications, boosted by the onset of 5G. Network functions processing millions of packets per second, earlier run as proprietary physical boxes, are now being realized as disaggregated container based microservices (CNFs) running on commodity clusters managed by orchestrators, like Kubernetes, on Telco clouds. While container orchestrators have evolved to meet the needs of enterprise applications, Telco workloads still remain a second class citizen, as the orchestrator is presently unaware of the networking needs of CNFs and cannot guarantee QoS of network intensive functions. In this work, we examine orchestration of network sensitive functions and identify the key networking requirements of containerized Telco workloads from the orchestration platform. We design and propose NACO - Network Aware Container Orchestration, a minimal, cloud-native and scalable extension to the Kubernetes platform to address these requirements and provide first class lifecycle management of CNFs used in Telco workloads. We implement a prototype of the system and demonstrate that we can achieve network aware container orchestration with minimal operation times.
Subject-Specific Human Modeling for Human Pose Estimation

3-D human pose estimation or human tracking has always been the focus of research in the human–computer interaction community. As the calibration step of human pose estimation, subject-specific modeling is crucially important to the subsequent pose estimation process. It not only provides a priori knowledge but also clearly defines the tracking target. This article presents a fully automatic subject modeling framework to reconstruct human pose, shape, as well as the body texture in a challenging optimization scenario. By integrating powerful differentiable rendering into the subject-specific modeling pipeline, the proposed method transforms the texture reconstruction problem into analysis by synthesis minimization and solves it efficiently by a gradient-based method. Furthermore, a novel covariance matrix adaptation annealing algorithm is proposed to attack the high-dimensional multimodal optimization problem in an adaptive manner. The domain knowledge of hierarchical human anatomy is seamlessly injected to the annealing optimization process by using a soft covariance matrix mask. All together contributes to the novel algorithm robust to the temptation of local minima. Experiments on the Human3.6 M dataset and the People-Snapshot dataset demonstrate the competitive results to the state of the art both qualitatively and quantitatively.
An Approximation Algorithm for Minimizing the Cloud Carbon Footprint through Workload Scheduling

In this paper, we address the problem of workload scheduling in data centers, while considering the greenness of the power sources. We prove that finding a feasible solution for the problem is NP-hard. Therefore, we develop an LP-based approximation algorithm to solve the problem in polynomial time. The proposed algorithm provides strong approximation bounds on the constraints and the objective of the problem. We conduct an extensive experimental analysis to evaluate the performance of the proposed algorithm using real world data.