Reviewerly/exHarmony huggingface dataset:
An Exploration of Early Childhood Teachers’ Technology, Pedagogy, and Content Knowledge (TPACK) in Mainland China

Research Findings: This study aims to validate a scale highlighting the applicability of selected TPACK (Technological Pedagogical Content Knowledge) domains of knowledge to early childhood education and to explore the internal relationships between TPACK’s components in the Chinese context. A survey instrument was designed and administered to 1,192 early childhood (EC) in-service teachers enrolled in their national training programs. The instrument consisted of 29 items classified into four categories that align with the conceptual TPACK framework and are applicable to the EC context, including Technology Knowledge (TK), Technological Pedagogical Knowledge (TPK), Pedagogical Knowledge (PK), and TPACK. CFA and path analysis generated a model with good fit. The confirmed model indicated that (1) TK, TPK, and PK were significantly correlated; (2) TK, TPK, and PK all had direct positive effects on TPACK; however, (3) PK had a smaller influence on TPACK. Practice or Policy: The instrument was a reliable and valid assessment for measuring the TPACK of Chinese EC teachers and may be used to inform future research. The findings suggest that ECE training should shift away from focusing on discrete technological skills (i.e., TK) and instead emphasize preparing teachers with mastery in pedagogic approaches for integrating digital technology into instruction.
MASS: Modality-collaborative semi-supervised segmentation by exploiting cross-modal consistency from unpaired CT and MRI images

Training deep segmentation models for medical images often requires a large amount of labeled data. To tackle this issue, semi-supervised segmentation has been employed to produce satisfactory delineation results with affordable labeling cost. However, traditional semi-supervised segmentation methods fail to exploit unpaired multi-modal data, which are widely adopted in today's clinical routine. In this paper, we address this point by proposing Modality-collAborative Semi-Supervised segmentation (i.e., MASS), which utilizes the modality-independent knowledge learned from unpaired CT and MRI scans. To exploit such knowledge, MASS uses cross-modal consistency to regularize deep segmentation models in aspects of both semantic and anatomical spaces, from which MASS learns intra- and inter-modal correspondences to warp atlases' labels for making predictions. For better capturing inter-modal correspondence, from a perspective of feature alignment, we propose a contrastive similarity loss to regularize the latent space of both modalities in order to learn generalized and robust modality-independent representations. Compared to semi-supervised and multi-modal segmentation counterparts, the proposed MASS brings nearly 6% improvements under extremely limited supervision.
Optimizing Data-intensive Systems in Disaggregated Data Centers with TELEPORT

Recent proposals for the disaggregation of compute, memory, storage, and accelerators in data centers promise substantial operational benefits. Unfortunately, for resources like memory, this comes at the cost of performance overhead due to the potential insertion of network latency into every load and store operation. This effect is particularly felt by data-intensive systems due to the size of their working sets, the frequency at which they need to access memory, and the relatively low computation per access. This performance impairment offsets the elasticity benefit of disaggregated memory. This paper presents TELEPORT, a compute pushdown framework for data-intensive systems that run on disaggregated architectures; compared to prior work on compute pushdown, TELEPORT is unique in its efficiency and flexibility. We have developed optimization prin- ciples for several popular systems including a columnar in-memory DBMS, a graph processing system, and a MapReduce system. The evaluation results show that using TELEPORT to push down simple operators improves the performance of these systems on state-of-the-art disaggregated OSes by an order of magnitude, thus fully exploiting the elasticity of disaggregated data centers.
USST: A two-phase privacy-preserving framework for personalized recommendation with semi-distributed training

Personalized recommendations are becoming indispensable for assisting online users in discovering items of interest. However, existing recommendation algorithms rely heavily on the collection of personal information, which poses significant privacy concerns to users. In this paper, we propose a two-phase privacy-preserving framework called user sampling and semi-distributed training (USST) for personalized recommendations, which can protect user privacy while ensuring high recommendation accuracy. In the USST framework, rather than directly training the model with all user records, a shared model is first trained with a small set of records contributed by sampled users (e.g., paid users and volunteers). This shared model is then distributed to each user, who further trains a personalized model using personal information. Thus, the USST guarantees that all unsampled users never disclose their private information. To validate the effectiveness and practicality of USST, we designed two USST-based privacy-preserving recommendation algorithms, USST-SVD and USST-NCF based on SVD and NCF algorithms, respectively. We conducted evaluations using MovieLens and Netflix Prize datasets, and the results show that, using only 20% of sampled users' records, the recommendation accuracy of USST-based algorithms is very close to that of all users' records. Thus, USST can significantly improve the level of privacy protection in recommender systems.
Knowledge-enhanced attentive learning for answer selection in community question answering systems

In a community question-answering (CQA) system, the answer selection task is used to identify the best answer for a specific question. This plays a key role in improving service quality by recommending appropriate answers to new questions. Recent advances in CQA answer selection have focused on enhancing performance by incorporating community information, and particularly the expertise (previous answers) and authority (position in the social network) of a respondent. However, existing approaches to incorporating this information are limited, as they (a) consider either the expertise or the authority, but not both; (b) ignore domain knowledge that could differentiate between the topics of previous answers; or (c) simply use authority information to adjust the similarity score, rather than fully integrating it into the process of measuring the similarity between the question and answer segments. We propose a new approach called the knowledge-enhanced attentive answer selection (KAAS) model, which enhances performance by (a) considering both the expertise and the authority of the answerer; (b) utilizing human-labeled tags, a taxonomy of tags, and votes as domain knowledge to infer the expertise of the respondent; (c) using a matrix decomposition of the social network (based on ‘following’ relationships) to infer the authority of the respondent and incorporating this information into the process of evaluating the similarity between segments. In addition, we incorporate an external knowledge graph to capture more professional information for CQA systems for vertical communities. We also adopt an attention mechanism to integrate our analysis of both questions and answers texts and the aforementioned community information. Experiments with both vertical and general CQA sites demonstrate the superior performance of the proposed KAAS model.
A Survey on AWS Cloud Computing Security Challenges &amp; Solutions

Amazon offers a comprehensive range of IT solutions to let businesses construct their private virtual clouds and maintain total control over their infrastructure. It is possible to use Amazon Web Services for both businesses and IT projects. Security professionals are drawn to the cloud because of its cost savings and efficiency, but it also poses numerous security and compliance issues. EC2 instances, which claim to make cloud computing safe for highly regulated companies, have been introduced as part of Amazon Web Services' (AWS) effort to relieve business security and compliance issues with cloud computing. Cloud computing has its drawbacks; however, these drawbacks also provide an opportunity to study a variety of cloud computing-related topics. The security and privacy of data stored and processed on cloud service providers' servers is a major concern. Several studies on cloud computing security and privacy are reviewed in this study. A better knowledge of cloud computing's security problems has been shown and the techniques and solutions which have been used by the cloud service sector have been highlighted in this article. The objective of this report is to shed light on immerging cloud services market and the different upcoming challenges like network issues.
An Introduction to Weave Structure for HCI: A How-to and Reflection on Modes of Exchange

As HCI continues to integrate craft techniques into its repertoire, tensions emerge between what is new and known, knowledge that resides in communities and histories rather than individuals, and how to transfer between the written word and material know-how. We explore these tensions through the process of writing, instructing, and create an introduction to weaving force sensors. The goal of this project is two fold: first, to help HCI understand the potential that a deep understanding of weave structures can hold for advancing our field, and second, to explore how a pictorial might support formats that have long been used for communicating craft knowledge.
Potent Blockchain-Enabled Socket RPC Internet of Healthcare Things (IoHT) Framework for Medical Enterprises

Present-day intelligent healthcare applications offer digital healthcare services to users in a distributed manner. The Internet of Healthcare Things (IoHT) is the mechanism of the Internet of Things (IoT) found in different healthcare applications, with devices that are attached to external fog cloud networks. Using different mobile applications connecting to cloud computing, the applications of the IoHT are remote healthcare monitoring systems, high blood pressure monitoring, online medical counseling, and others. These applications are designed based on a client-server architecture based on various standards such as the common object request broker (CORBA), a service-oriented architecture (SOA), remote method invocation (RMI), and others. However, these applications do not directly support the many healthcare nodes and blockchain technology in the current standard. Thus, this study devises a potent blockchain-enabled socket RPC IoHT framework for medical enterprises (e.g., healthcare applications). The goal is to minimize service costs, blockchain security costs, and data storage costs in distributed mobile cloud networks. Simulation results show that the proposed blockchain-enabled socket RPC minimized the service cost by 40%, the blockchain cost by 49%, and the storage cost by 23% for healthcare applications.
A Combinatorial Approach to Fairness Testing of Machine Learning Models

Machine Learning (ML) models could exhibit biased behavior, or algorithmic discrimination, resulting in unfair or discriminatory outcomes. The bias in the ML model could emanate from various factors such as the training dataset, the choice of the ML algorithm, or the hyperparameters used to train the ML model. In addition to evaluating the model’s correctness, it is essential to test ML models for fair and unbiased behavior. In this paper, we present a combinatorial testing-based approach to perform fairness testing of ML models. Our approach is model agnostic and evaluates fairness violations of a pre-trained ML model in a two-step process. In the first step, we create an input parameter model from the training data set and then use the model to generate a t-way test set. In the second step, for each test, we modify the value of one or more protected attributes to see if we could find fairness violations. We performed an experimental evaluation of the proposed approach using ML models trained with tabular datasets. The results suggest that the proposed approach can successfully identify fairness violations in pre-trained ML models.
Multi-Criteria Assessment for City-Wide Rooftop Solar PV Deployment: A Case Study of Bandung, Indonesia

The world faces the threat of an energy crisis that is exacerbated by the dominance of fossil energy sources that negatively impact the sustainability of the earth’s ecosystem. Currently, efforts to increase the supply of renewable energy have become a global agenda, including using solar energy which is one of the rapidly developing clean energies. However, studies in solar photovoltaic (PV) modelling that integrates geospatial information of urban morphological building characters, solar radiation, and multiple meteorological parameters in low-cost scope have not been explored fully. Therefore, this research aims to model the urban rooftop solar PV development in the Global South using Bandung, Indonesia, as a case study. This research also has several specific purposes: developing a building height model as well as determining the energy potential of rooftop solar PV, the energy needs of each building, and the residential property index. This study is among the first to develop the national digital surface model (DSM) of buildings. In addition, the analysis of meteorological effects integrated with the hillshade parameter was used to obtain the solar PV potential value of the roof in more detail. The process of integrating building parameters in the form of rooftop solar PV development potential, energy requirements, and residential property index of a building was expected to increase the accuracy of determining priority buildings for rooftop solar PV deployment in Bandung. This study shows that the estimated results of effective solar PV in Bandung ranges from 351.833 to 493.813 W/m2, with a total of 1316 and 36,372 buildings in scenarios 1 and 2 being at a high level of priority for solar PV development. This study is expected to be a reference for the Indonesian government in planning the construction of large-scale rooftop solar PV in urban areas to encourage the rapid use of clean energy. Furthermore, this study has general potential for other jurisdictions for the governments focusing on clean energy using geospatial information in relation with buildings and their energy consumption.
HET-GMP: A Graph-based System Approach to Scaling Large Embedding Model Training

Embedding models have been recognized as an effective learning paradigm for high-dimensional data. However, a major embedding model training obstacle is that updating and retrieving the shared large-scale embedding parameters usually dominates the distributed training cycle, leading to significant scalability issues. This paper presents HET-GMP, a distributed system on training embedding models. Uniquely, HET-GMP takes advantage of a graph-based approach to efficiently increase scalability. The key insight guiding our design is the "graph way of thinking". HET-GMP creates a bigraph abstraction to represent the access relationships between data samples and embedding vectors. This enables HET-GMP to embrace graph locality and skewness as new performance opportunities and to exploit graph-based replication/partitioning and bounded-asynchronous synchronization to reduce communication overhead. We evaluate the system on the embedding models for click-through rate (CTR) prediction, which presents the most significant challenge and communication bottleneck due to heavy access concurrency to a huge embedding table. The result shows that HET-GMP supports embedding model training with 1011 parameters, achieving a reduction in communication up to 87.5% and an up-to 27.5x speedup over the state-of-the-art baseline systems.
Optimal Tuning of the Speed Control for Brushless DC Motor Based on Chaotic Online Differential Evolution

The efficiency in the controller performance of a BLDC motor in an uncertain environment highly depends on the adaptability of the controller gains. In this paper, the chaotic adaptive tuning strategy for controller gains (CATSCG) is proposed for the speed regulation of BLDC motors. The CATSCG includes two sequential dynamic optimization stages based on identification and predictive processes, and also the use of a novel chaotic online differential evolution (CODE) for providing controller gains at each predefined time interval. Statistical comparative results with other tuning approaches evidence that the use of the chaotic initialization based on the Lozi map included in CODE for the CATSCG can efficiently handle the disturbances in the closed-loop system of the dynamic environment.
Blockchain Adoption to Secure the Food Industry: Opportunities and Challenges

With the growth in food products’ usage, ensuring their quality and safety has become progressively difficult. Specifically, food traceability turns out to be a very critical task for retailers, sellers, consumers, surveillance authorities, and other stakeholders in the food supply chain system. There are requirements for food authenticity verification (correct declaration of cultivation, origin, and variety), quality checks (e.g., justification for higher prices), and preventing food products from fraudsters in the food industry. The ubiquitous and promising technology of blockchain ensures the traceability of food trade networks with high potential and handles the aforementioned issues. Blockchain makes the food industry more transparent at all levels by storing data immutably and enabling quick tracking across the stages of the food supply chain. Hence, commodities, stakeholders, and semi-finished food items can be recognized significantly faster. Motivated by these facts, in this paper, we present an in-depth survey of state-of-the-art approaches to the food industry’s security, food traceability, and food supply chain management. Further, we propose a blockchain-based secure and decentralized food industry architecture to alleviate security and privacy aspects and present a comprehensive solution taxonomy for a blockchain-based food industry. Then, a comparative analysis of existing approaches with respect to various parameters, i.e., scalability, latency, and food quality, is presented, which facilitates the end-user in selecting approaches based on the merits over other approaches. Finally, we provide insights into the open issues and research challenges with concluding remarks.
Class‐index corpus‐index measure: A novel feature selection method for imbalanced text data

Summary In the field of text classification, some of the datasets are unbalanced datasets. In these datasets, feature selection stage is important to increase performance. There are many studies in this area. However, existing methods have been developed based on the document frequency of only intra‐class. In this study, a new method is proposed considering the situation of the feature in class and corpus. A new feature selection method, namely class‐index corpus‐index measure (CiCi) was presented for unbalanced text classification. The CiCi is a probabilistic method which is calculated using feature distribution in both class and corpus. It has shown a higher performance compared to successful methods in the literature. Multinomial Naïve Bayes and support vector machines were used as classifiers in the experiments. Three different unbalanced datasets are used in the experiments. These benchmark datasets are reuters‐21578, ohsumed, and enron1. Experimental results show that the proposed method has more performance in terms of three different success measures.
Multi-Image Encryption Algorithm for 2D and 3D Images Based on Chaotic System

In order to accommodate multiple types of image encryption, a multi-image encryption algorithm for 2D and 3D images is designed in this paper. After recording the type and number of images, the pixels/coordinates of multiple images are stored in a cube block and are subjected to confusion and diffusion operations. The confusion step uses the random length sequence position swapping method to swap a row (column) vector of variable length with another row (column) vector of the same length at a random position. The diffusion is done by Exclusive OR to combine pixels/coordinates at different locations with different chaotic matrices. Finally, the cipher images are output one by one. Experimental simulations and security analysis verify the effectiveness and security of the algorithm.
Multi-label active learning through serial–parallel neural networks

Multi-label active learning is an extension of supervised learning with high-dimensional label spaces and interactive scenarios. Its key issues include the exploitation of label correlations, handling of missing labels, and selection of query labels. Various techniques have been proposed for this purpose; however, there is still room for performance improvement. In this study, we propose a multi-label active learning through serial–parallel neural networks (MASP) algorithm with simple and effective mechanisms. For label correlations, the serial part of the network extracts features that are common to all the labels. This mechanism is more effective than the explicit feature extraction or compressed sensing methods. Regarding the missing labels, the network sets the corresponding losses to zero for backpropagation. Thus, it does not require label completions that may introduce additional errors. For label queries, the parallel part of the network provides independent pairwise predictions for each label. Such pairwise predictions present appropriate information for computing label uncertainty. Three sets of experiments were conducted on 22 benchmark datasets using 14 popular algorithms for comparison. The results show that our algorithm achieves state-of-the-art active learning performance.
Novel Approach of Diagnosing Significant Metrics of Load Balancing using CloudSim

Cloud computing is an emerging field in this era. Everybody wants to follow less is more approach means in less money wants to avail more services. These services are application based services or hardware based services like CPU, or I/O based services. An effective scheduling approach is required which can guarantee minimum average waiting time and maximum utilization of resources. Generally, algorithms like Shortest Job First (SJF), Round Robin (RR), First Come First Serve (FCFS), and Priority scheduling are used for allocating resources to Virtual Machines (VMs) in a cloud culture. The Simulation for these algorithms has been done in CloudSim simulator for checking effectiveness. Specifically, this paper discloses a novel approach of diagnosing significant metrics of load balancing such as turnar o und time and waiting time. Based on the analysis, it has been noted that the SJF is better than the rest of the algorithms based on turnaround time and minimum average waiting time.
CoDL

Concurrent inference execution on heterogeneous processors is critical to improve the performance of increasingly heavy deep learning (DL) models. However, available inference frameworks can only use one processor at a time, or hardly achieve speedup by concurrent execution compared to using one processor. This is due to the challenges to 1) reduce data sharing overhead, and 2) properly partition each operator between processors.