Reviewerly/exHarmony huggingface dataset:
Reliability Prediction for Health-Related Content: A Replicability Study

Determining reliability of online data is a challenge that has recently received increasing attention. In particular, unreliable health-related content has become pervasive during the COVID-19 pandemic. Previous research [37] has approached this problem with standard classification technology using a set of features that have included linguistic and external variables, among others. In this work, we aim to replicate parts of the study conducted by Sondhi and his colleagues using our own code, and make it available for the research community ( https://github.com/MarcosFP97/Health-Rel ). The performance obtained in this study is as strong as the one reported by the original authors. Moreover, their conclusions are also confirmed by our replicability study. We report on the challenges involved in replication, including that it was impossible to replicate the computation of some features (since some tools or services originally used are now outdated or unavailable). Finally, we also report on a generalisation effort made to evaluate our predictive technology over new datasets [20, 35].
Soil Moisture Prediction in Peri-urban Beijing, China: Gene Expression Programming Algorithm

Soil moisture is an important indicator for agricultural planting and agricultural water management. People have been trying to guide crop cultivation, formulate irrigation systems, and develop intelligent agriculture by knowing exactly what the soil moisture is in real time. This paper considers the impact of meteorological parameters on soil-moisture change and proposes a soil-moisture prediction method based on the Gene Expression Programming (GEP) algorithm. The prediction model is tested on datasets from Shunyi, Yanqing and Daxing agricultural farms, Beijing. The results show that the GEP model can predict soil moisture with a maximum correlation coefficient of 0.98, and the root-mean-square errors in three different farms were below 2.32.
Robust countermeasure against detector control attack in a practical quantum key distribution system: comment

The physical imperfections of quantum key distribution systems compromise their information-theoretic security. By exploiting the imperfections on the detection unit, an eavesdropper can launch various detector-control attacks to steal the secret key. Recently, in Optica 6 , 1178 ( 2019 ) OPTIC8 2334-2536 10.1364/OPTICA.6.001178 entitled “Robust countermeasure against detector control attack in a practical quantum key distribution system,” Qian et al. proposed a countermeasure using variable attenuators in the detection unit that was claimed to be effective against detector-control attacks with or without blinding light. We comment on this paper, disputing this countermeasure by showing that their assumptions for proving this effectiveness are unrealistic.
Contextual and Semantic Fusion Network for Multiple-Choice Reading Comprehension

Multiple-choice reading comprehension (MCRC) aims to build an intelligent system that automatically selects an answer from a candidate set when given a passage and a question. Existing MCRC systems rarely consider incorporating external knowledge such as explicit semantic information. In this work, we propose a Contextual and Semantic Fusion Network (CSFN) which effectively integrates contextual and semantic representation. CSFN introduces explicit structured semantics from pre-trained semantic role labeling. Specially, we regard explicit semantic representation as an important feature to fuse with contextual representation, which enriches the representation of sentences. By combining with the transfer learning strategy, the CSFN model has better generalization over limited datasets. To evaluate the ability of our model, we conduct experiments on three MCRC benchmark datasets: RACE, DREAM, and MCTest. Experimental results demonstrate the effectiveness of our proposed model.
Learning flexible network representation via anonymous walks

Network representation learning aims to project nodes in a network into low-dimensional continuous vector space while preserving the network structure and inherent node properties. Most of the existing approaches focus merely on the local context of nodes but ignore the local structural patterns of nodes that are important in network analysis tasks. A flexible network representation learning algorithm that can be generalized across a variety of domains and tasks should conform to two principles: to learn representations where nodes sharing similar local contexts have similar vectors and to embed nodes sharing similar local structures closely together. In this paper, we propose a flexible framework that incorporates local structural information into a generic model capturing contextual information. Specifically, anonymous walks are exploited to capture local structural patterns. In addition, we design two strategies to incorporate local structural patterns into the basic continuous bag-of-words (CBOW) architecture through statistic-based structural similarity and embedding-based structural vectors. Extensive experiments on real-world network data sets indicate that the proposed models incorporating local structural patterns outperform seven state-of-the-art network representation learning models in various tasks.
Tuning sound for infrastructures: artificial intelligence, automation, and the cultural politics of audio mastering

This paper traces the infrastructural politics of automated music mastering to reveal how contemporary iterations of artificial intelligence (AI) shape cultural production. The paper examines the emergence of LANDR, an online platform that offers automated music mastering, built on top of supervised machine learning branded as artificial intelligence. Increasingly, machine learning will become an integral part of signal processing for sounds and images, shaping the way media cultures sound, look, and feel. While LANDR is a product of the so-called ‘big bang’ in machine learning, it could not exist without specific conditions: specific kinds of commensurable data, as well as specific aesthetic and industrial conditions. Mastering, in turn, has become an indispensable but understudied part of music circulation as an infrastructural practice. Here we analyze the intersecting histories of machine learning and mastering, as well as LANDR’s failure at automating other domains of audio engineering. By doing so, we critique the discourse of AI’s inevitability and show the ways in which machine learning must frame or reframe cultural and aesthetic practices in order to automate them, in service of digital distribution, recognition, and recommendation infrastructures.
A Blockchain-Based System for Student E-Portfolio Assessment Using Smart Contract

Nowadays, as student e-portfolio systems increase in popularity, the large storage space and convenient retrieval of e-portfolio system provide a more efficient way for heavy file management, while the security concerns about personal information and privacy of students arise. The traditional student e-portfolio system adopts a centralized system which caused some problems such as an organization that has full control over the information that could tamper with the database, the insecurity of student e-portfolio. However, blockchain application has mushroomed, providing opportunities for solving the shortcomings proposed above. In this paper, we combine with the fabric blockchain technology to implement student e-portfolio system for permanently recording student growth information, which uses smart contract to realize the registration and authentication of student identity, the portfolio recording and management, the publication of learning evaluation. Finally, we analyze the system compared with traditional system, and the comparative result indicates that the proposed system can not only effectively manage student information, but also protect privacy.
Research on the strategy of adjustable load resources participating in distributed trading market

The participation of demand side resources in the distributed trading market has become an important means for countries in the world to alleviate the pressure of supply and demand during peak hours. How to make full use of the time-varying price signals in the spot market and effectively tap the demand side response potential of customers has become the key issue of the key marketing strategy research for power sales companies. Firstly, this paper constructs distributed transaction models to clarify the transaction characteristics of both parties; Then, the cost model of adjustable load participating in distributed trading market is constructed with the objective of minimizing the cost of power consumption and the cost of satisfaction reduction caused by participating in demand response regulation, and the corresponding constraints are set; Finally, based on blockchain technology and smart contract system, the optimal strategy of adjustable load participating in distributed trading market is solved. Case simulation results show that the proposed strategy can effectively achieve the goal of load reduction on the grid side, and stimulate the willingness of users to participate in demand response, which has a certain practical significance.
Syntactic- and morphology-based text augmentation framework for Arabic sentiment analysis

Arabic language is a challenging language for automatic processing. This is due to several intrinsic reasons such as Arabic multi-dialects, ambiguous syntax, syntactical flexibility and diacritics. Machine learning and deep learning frameworks require big datasets for training to ensure accurate predictions. This leads to another challenge faced by researches using Arabic text; as Arabic textual datasets of high quality are still scarce. In this paper, an intelligent framework for expanding or augmenting Arabic sentences is presented. The sentences were initially labelled by human annotators for sentiment analysis. The novel approach presented in this work relies on the rich morphology of Arabic, synonymy lists, syntactical or grammatical rules, and negation rules to generate new sentences from the seed sentences with their proper labels. Most augmentation techniques target image or video data. This study is the first work to target text augmentation for Arabic language. Using this framework, we were able to increase the size of the initial seed datasets by 10 folds. Experiments that assess the impact of this augmentation on sentiment analysis showed a 42% average increase in accuracy, due to the reliability and the high quality of the rules used to build this framework.
Digital Communication

The South African basic education sector has been looking at adopting technology as part of their digital pedagogy; however, progress was slow since this was an overly ambitious transformation. Schools in rural communities lack infrastructure and basic needs like electricity, piped water, and ablutions. The COVID-19 phenomenon brought about the locking down, and physical distancing has further ensured that normal operations had to cease. The educational sector had to also cease physical engagement and turn to online teaching. A qualitative research method was used for this research. This chapter focuses on how teaching practices have been adapted to digital pedagogy and barriers play in successful teaching on digital platforms. The constructivism, two-way communication model, and scaffolding to provide conceptualised this research. Therefore, as part of a future study, it would be interesting to see how basic education maintains a digital teaching presence post-COVID-19 lockdown.
HBRSS: Providing high-secure data communication and manipulation in insecure cloud environments

Cloud storage and cloud services provide a stronger computing power and distributed computing capability for IoT users with a minimal cost. However, the security issues of cloud always limit the development of cloud computing and storage. In the meanwhile, the channel instability and exposure of the public network make the security of data in transmission challenged (HTTPS protocol cannot guarantee the security of data after receiving by servers). Even if homomorphic encryption can protect IoTs’ sensitive data, attackers still can infer sensitive behaviors about users by listening to the frequency of cloud services usage. To solve the above problems, in this paper, we propose a novel data transmission structure named HBRSS for high-security data transmission and data processing in insecure cloud environments and channels. HBRSS harnesses proposed data splitting principle to divide the data into blocks, packages the block data and forms a block ring based on the concept of blockchain to ensure the non-tamperability and non-destructibility of data. In addition, we propose an improved partial homomorphic encryption algorithm, which adds fuzzy processing for the data service functions to improve function-privacy. We also build a virtual mistrusted cloud service scene by using Docker and Kubernetes to evaluate our method’s performance, which can also be utilized as a standard attack drill platform for all researchers to test their own security algorithms. Based on our best knowledge, this platform is the first open-source automatic cloud attack exploitation system that contains attacks against browsers, channels, and servers. The experimental results indicate that our new encryption algorithm brings larger key-space and lower power consumption compared with some encryption algorithms.
Automated Scoring of Spontaneous Speech from Young Learners of English Using Transformers

This study explores the use of Transformer-based models for the automated assessment of children's non-native spontaneous speech. Traditional approaches for this task have relied heavily on delivery features (e.g., fluency), whereas the goal of the current study is to build automated scoring models based solely on transcriptions in order to see how well they capture additional aspects of speaking proficiency (e.g., content appropriateness, vocabulary, and grammar) despite the high word error rate (WER) of automatic speech recognition (ASR) on children's non-native spontaneous speech. Transformer-based models are built using both manual transcriptions and ASR hypotheses, and versions of the models that incorporated the prompt text were investigated in order to more directly measure content appropriateness. Two baseline systems were used for comparison, including an attention-based Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) and a Support Vector Regressor (SVR) with manually engineered content-related features. Experimental results demonstrate the effectiveness of the Transformer-based models: the automated prompt-aware model using ASR hypotheses achieves a Pearson correlation coefficient (r) with holistic proficiency scores provided by human experts of 0.835, outperforming both the attention-based RNN-LSTM baseline (r = 0.791) and the SVR baseline (r = 0.767).
A Novel Optimal Layout Method of Airborne Distributed POS Based on MAC and GMC Hybrid Optimization Criterion

The layout of the airborne distributed position and orientation system (POS) directly affects the overall accuracy of the motion parameters of all load points obtained by the distributed POS. However, there are restrictions of space, weight and cost of the distributed POS, it is unrealistic to install an inertial measurement unit (IMU) at the location of each load. To solve this problem, based on modal analysis of wing structure, considering the two factors of the modal spatial intersection angle and the vibration amplitude, a novel fitness function model based on modal assurance criterion (MAC) and Gramian matrix criterion (GMC) is proposed to evaluate the population in the optimization algorithm. The hybrid adaptive particle swarm optimization and genetic algorithm (HAPSOGA) based on this proposed model is applied to the multiple system layout of airborne distributed POS to obtain the optimal layout scheme. The results of semi-physical simulation and ground verification experiment show that compared with the optimal method based on MAC, the optimal layout scheme determined by the proposed method can use less IMU and obtain higher overall accuracy of motion parameters for the remote sensing loads.
Dynamic Binary Translation for SGX Enclaves

Enclaves, such as those enabled by Intel SGX, offer a hardware primitive for shielding user-level applications from the OS. While enclaves are a useful starting point, code running in the enclave requires additional checks whenever control or data is transferred to/from the untrusted OS. The enclave-OS interface on SGX, however, can be extremely large if we wish to run existing unmodified binaries inside enclaves. This article presents Ratel , a dynamic binary translation engine running inside SGX enclaves on Linux. Ratel offers complete interposition , the ability to interpose on all executed instructions in the enclave and monitor all interactions with the OS. Instruction-level interposition offers a general foundation for implementing a large variety of inline security monitors in thefuture. We take a principled approach in explaining why complete interposition on SGX is challenging. We draw attention to five design decisions in SGX that create fundamental trade-offs between performance and ensuring complete interposition, and we explain how to resolve them in the favor of complete interposition. To illustrate the utility of the Ratel framework, we present the first attempt to offer binary compatibility with existing software on SGX. We report that Ratel offers binary compatibility with over 200 programs we tested, including micro-benchmarks and real applications, such as Linux shell utilities. Runtimes for two programming languages, namely, Python and R, tested with standard benchmarks work out-of-the-box on Ratel without any specialized handling.
Method of using non-specific target to complete structured light plane calibration

The accuracy of the line structured light measurement system is determined by the calibration results of the structured light plane Existing structured light calibration methods usually need to fabricate a specific target with high manufacturing precision to complete the calibration. In the real environment, how to calibrate the line structured light plain without a specific target becomes a key issue. In this paper, a stereo vision calibration (SVC) method is proposed and used to complete the structured light calibration with non-specific targets. It adopts the principle of stereo vision to calculate all point clouds projected on the target by structured light, and then the parameters of the structured light plane are optimized based on the probability distribution during point clouds processing. Taking 304 steel plate, aluminum round can, and book as non-specific targets, the accuracy of the plane calibrated by SVC method is measured. The minimum average measurement error is about 0.068 mm by comparing the nominal values with the measurement values of the checkerboard and different workpieces. It indicates that the SVC method can acquire the high-precision calibration with non-specific targets.
Combining Classification and User-Based Collaborative Filtering for Matching Footwear Size

Size mismatch is a serious problem in online footwear purchase because size mismatch implies an almost sure return. Not only foot measurements are important in selecting a size, but also user preference. This is the reason we propose several methodologies that combine the information provided by a classifier with anthropometric measurements and user preference information through user-based collaborative filtering. As novelties: (1) the information sources are 3D foot measurements from a low-cost 3D foot digitizer, past purchases and self-reported size; (2) we propose to use an ordinal classifier after imputing missing data with different options based on the use of collaborative filtering; (3) we also propose an ensemble of ordinal classification and collaborative filtering results; and (4) several methodologies based on clustering and archetype analysis are introduced as user-based collaborative filtering for the first time. The hybrid methodologies were tested in a simulation study, and they were also applied to a dataset of Spanish footwear users. The results show that combining the information from both sources predicts the foot size better and the new proposals provide better accuracy than the classic alternatives considered.
Deep Trajectory Classification Model for Congestion Detection in Human Crowds

In high-density gatherings, crowd disasters frequently occur despite all the safety measures. Timely detection of congestion in human crowds using automated analysis of video footage can prevent crowd disasters. Recent work o... | Find, read and cite all the research you need on Tech Science Press
WITHDRAWN: Effective utilization of YOLOv3 model for aircraft detection in Remotely Sensed Images

Aircraft detection in remote sensing images (RSI) is receiving significant attention from researchers in recent years as it is a challenging problem in satellite image analysis field and has wide range of applications. From literature survey, we found that complex backgrounds, vertical views and variation in size are the factors that affect the speed and accuracy of an algorithm. In this paper, we have used You Only Look Only (YOLOv3) model which is a state-of-the art object detection algorithm and is fast and accurate than Region-based Convolutional Neural Network (R-CNN), Fast R-CNN, Faster R-CNN and Single Shot Multi-box Detector (SSD). With the help of multi-scale detection and Non-max suppression techniques (NMS) available in YOLOv3, we have successfully eliminated overlapping bounding boxes and detected aircrafts of varying size which is a challenging task in object detection algorithms. We have evaluated our approach on two public datasets namely NWPU 45-class and PatternNet dataset and demonstrated that it achieves relatively good detection performance.
Ranking-Based Implicit Regularization for One-Class Collaborative Filtering

One-class collaborative filtering (OCCF) problems are ubiquitous in real-world recommendation systems, such as news recommendation, but suffer from data sparsity and lack of negative items. To address the challenge, the state-of-the-art algorithm assigns uninteracted items with smaller weights of being negative and performs low-rank approximation over the user-item interaction matrix. However, the prior ratings are usually suggested to be zero but may not be well-defined. To avert the direct utilization of prior ratings for uninteracted items, we propose a novel ranking-based implicit regularizer by hypothesizing that users’ preference scores for uninteracted items should not deviate a lot from each other. The regularizer is then used in a ranking-based OCCF framework to penalize large differences of preference scores between uninteracted items. To efficiently optimize model parameters in this framework, we develop the scalable alternating least square algorithm and coordinate descent algorithm, whose time complexity is linearly proportional to the data size. Finally, we extensively evaluate the proposed algorithms on six public real-world datasets. The results show that the proposed regularizer significantly improves the recommendation quality of ranking-based OCCF algorithms, such as BPRMF and RankALS. Moreover, the ranking-based framework with the proposed regularizer outperforms the state-of-the-art recommendation algorithms for implicit feedback.