Reviewerly/exHarmony huggingface dataset:
Enhancing recommendation systems performance using highly-effective similarity measures

In Recommendation Systems (RS) and Collaborative Filtering (CF), the similarity measures have been the operating component upon which CF performance is essentially reliant. A dozen of similarity measures have been proposed to reach the desired performance particularly under the circumstances of data sparsity (the cold-start problem). Nevertheless, these measures still suffer the cold-start problem, and have a complex design. Moreover, a comprehensive experimental work to study the impact of the cold-start problem on CF performance is still missing. To these ends, therefore, this paper introduces three simply-designed similarity measures, namely, difference-based similarity measure (SMD), hybrid difference-based similarity measure (HSMD), and, triangle-based cosine measure (TA). Along with proposing these measures, a comprehensive experimental guide for CF measures using the K-fold cross validation is also presented. In contrary to all previous CF studies, the evaluation process is split into two sub-processes: the estimation process and recommendation process to accurately obtain the desired appropriateness in the evaluation. In addition, a new formula to calculate the dynamic recommendation count is developed depending on both the dataset and rating vectors. To draw a comprehensive experimental analysis, a dozen state-of-the-art similarity measures (30 similarity measures) including the proposed and the most widely-used traditional measures are comparatively tested. The experimental study has critically been made on three datasets with five-fold cross-validation grounded on the K nearest neighbor algorithm (KNN). The obtained results on both estimation and recommendation processes prove unquestionably that SMD and TA are preeminent measures with the lowest computational complexity outperforming all state-of-the-art CF measures.
TSAN: Synthesized View Quality Enhancement via Two-Stream Attention Network for 3D-HEVC

In three-dimensional video system, the texture and depth videos are jointly encoded, and then the Depth Image Based Rendering (DIBR) is utilized to realize view synthesis. However, the compression distortion of texture and depth videos, as well as the disocclusion problem in DIBR degrade the visual quality of the synthesized view. To address this problem, a Two-stream Attention Network (TSAN)-based synthesized view quality enhancement method is proposed for 3D-High Efficiency Video Coding (3D-HEVC) in this article. First, the shortcomings of the view synthesis technique and traditional convolutional neural networks are analyzed. Then, based on these analyses, a TSAN with two information extraction streams is proposed for enhancing the quality of the synthesized view, in which the global information extraction stream learns the contextual information, and the local information extraction stream extracts the texture information from the rendered image. Third, a Multi-Scale Residual Attention Block (MSRAB) is proposed, which can efficiently detect features in different scales, and adaptively refine features by considering interdependencies among spatial dimensions. Extensive experimental results show that the proposed synthesized view quality enhancement method achieves significantly better performance than the state-of-the-art methods.
Social interactions and relationships with an intelligent virtual agent

The current study explores how humans interact with an intelligent virtual agent (IVA), Siri, on their mobile phones and how such experiences may impact their trust, social presence, and comfort level toward the IVA as a coworker, supervisor, and friend. A two (male- vs. female-voice of Siri) by two (functional vs. social task) by two (matched vs. ummatched with participants’ gender) between-subjects experiment was performed with 163 participants. Higher levels of trust in cognitive dimensions were associated with functional tasks assisted by Siri, and one interaction between participants’ gender and Siri's gendered voice was observed in an affective dimension of trust, faith. Copresence dimension of social presence was associated with technical competence, understandability, faith, and personal attachment dimension of trust whereas psychological involvement was related with reliability and technical competence. Copresence, alongside faith and personal attachment, both affective dimensions of trust, seemed to facilitate comfortable feelings toward Siri imagined as various relational partners.
Two-stream deep spatial-temporal auto-encoder for surveillance video abnormal event detection

With the improvement of public security awareness, video anomaly detection has become an indispensable demand in surveillance videos. To improve the accuracy of video anomaly detection, this paper proposes a novel two-stream spatial-temporal architecture called Two-Stream Deep Spatial-Temporal Auto-Encoder (Two-Stream DSTAE), which is composed of a spatial stream DSTAE and a temporal stream DSTAE. Firstly, the spatial stream extracts appearance characteristics whereas the temporal stream extracts the motion patterns, respectively. Then, based on the novel policy joint reconstruction error, this model fuses the spatial stream and the temporal stream to extract spatial-temporal characteristics to detect anomalies. Furthermore, since the optical flow is invariant to appearances such as color or light, we introduce optical flow to enhance the capability of extracting continuity between adjacent frames and inter-frame motion information. We demonstrate the accuracy of the proposed method on the publicly available standard datasets: UCSD, Avenue and UMN datasets. Our experiments demonstrate high accuracy, which is superior to the state-of-the-art methods.
A novel consensus learning approach to incomplete multi-view clustering

Multi-view data may lose some instances in real applications. Most existing methods for clustering such incomplete multi-view data still have at least one of the following limitations: 1) The common relations among data points across all views are ignored. 2) The complementary multi-view information of original data representation is not well exploited. 3) Arbitrary incomplete scenarios or data with negative entries cannot be handled. To address these limitations, in this paper, we propose a novel Consensus Learning approach to Incomplete Multi-view Clustering (CLIMC). Specifically, a low-dimensional consensus representation is introduced to exploit complementary multi-view information from the original feature representation of available instances by integrating index matrices into matrix factorization. In addition, by combining self-representation, index matrices, and consensus term, a consensus similarity graph is leveraged to explore the underlying cross-view relations among data points. Further, the key of the proposed CLIMC is that the consensus representation is correlated with the similarity graph by a graph Laplacian regularization. Consequently, the compactness of the low-dimensional representation and the accuracy of similarity degree of the graph are reciprocally promoted. Extensive experiments on several multi-view datasets demonstrate the effectiveness of CLIMC over state-of-the-arts.
SOSD-Net: Joint semantic object segmentation and depth estimation from monocular images

Depth estimation and semantic segmentation play essential roles in scene understanding. The state-of-the-art methods employ multi-task learning to simultaneously learn models for these two tasks at the pixel-wise level. They usually focus on sharing the common features or stitching feature maps from the corresponding branches. However, these methods lack in-depth consideration on the correlation of the geometric cues and the scene parsing. In this paper, we first introduce the concept of semantic objectness to exploit the geometric relationship of these two tasks through an analysis of the imaging process, then propose a Semantic Object Segmentation and Depth Estimation Network (SOSD-Net) based on the objectness assumption. To the best of our knowledge, SOSD-Net is the first network that exploits the geometry constraint for simultaneous monocular depth estimation and semantic segmentation. In addition, considering the mutual implicit relationship between these two tasks, we exploit the iterative idea from the expectation–maximization algorithm to train the proposed network more effectively. Extensive experimental results on the Cityscapes and NYU v2 dataset are presented to demonstrate the superior performance of the proposed approach.
TurboTransformers

The transformer is the most critical algorithm innovation of the Nature Language Processing (NLP) field in recent years. Unlike the Recurrent Neural Network (RNN) models, transformers are able to process on dimensions of sequence lengths in parallel, therefore leads to better accuracy on long sequences. However, efficient deployments of them for online services in data centers equipped with GPUs are not easy. First, more computation introduced by transformer structures makes it more challenging to meet the latency and throughput constraints of serving. Second, NLP tasks take in sentences of variable length. The variability of input dimensions brings a severe problem to efficient memory management and serving optimization.
Self-attention-based conditional random fields latent variables model for sequence labeling

To process data like text and speech, Natural Language Processing (NLP) is a valuable tool. As on of NLP's upstream tasks, sequence labeling is a vital part of NLP through techniques like text classification, machine translation, and sentiment analysis. In this paper, our focus is on sequence labeling where we assign semantic labels within input sequences. We present two novel frameworks, namely SA-CRFLV-I and SA-CRFLV-II, that use latent variables within random fields. These frameworks make use of an encoding schema in the form of a latent variable to be able to capture the latent structure in the observed data. SA-CRFLV-I shows the best performance at the sentence level whereas SA-CRFLV-II works best at the word level. In our in-depth experimental results, we compare our frameworks with 4 well-known sequence prediction methodologies which include NER, reference parsing, chunking as well as POS tagging. The proposed frameworks are shown to have better performance in terms of many well-known metrics.
Few-Shot Object Detection on Remote Sensing Images

In this paper, we deal with the problem of object detection on remote sensing images. Previous methods have developed numerous deep CNN-based methods for object detection on remote sensing images and the report remarkable achievements in detection performance and efficiency. However, current CNN-based methods mostly require a large number of annotated samples to train deep neural networks and tend to have limited generalization abilities for unseen object categories. In this paper, we introduce a few-shot learning-based method for object detection on remote sensing images where only a few annotated samples are provided for the unseen object categories. More specifically, our model contains three main components: a meta feature extractor that learns to extract feature representations from input images, a reweighting module that learn to adaptively assign different weights for each feature representation from the support images, and a bounding box prediction module that carries out object detection on the reweighted feature maps. We build our few-shot object detection model upon YOLOv3 architecture and develop a multi-scale object detection framework. Experiments on two benchmark datasets demonstrate that with only a few annotated samples our model can still achieve a satisfying detection performance on remote sensing images and the performance of our model is significantly better than the well-established baseline models.
The social dimension of domesticating technology: Interactions between older adults, caregivers, and robots in the home

In this article we look at the home as an arena for care by exploring how care robots and technological care-systems can become part of older adults’ lives. We investigate the domestication of robot technology in the context of what in Scandinavia is called “welfare technology” (relating to the terms “gerontechnology” and “Active Assisted Living,”) that especially aims to mitigate older adults´ challenges with living in their own homes. Through our case study, we investigate a system called eWare, where a flowerpot robot called “Tessa” works in symbiosis with a sensor technology “SensaraCare.” Together, they create a socio-technical ecosystem involving older adult end-users living at home, formal caregivers (e.g. healthcare workers), and informal caregivers (normally family members). We analyze our ethnographic fieldwork through the theoretical concept of “domestication of technology,” focusing on an established three-dimensional model that includes practical, symbolic, and cognitive levels of analysis. We found that social bonds and different ways of using the same technology ecosystem were crucial, and so we supplement this model by suggesting a fourth dimension, which we term the social dimension of the domestication of technology.
Blockchain-Based Decentralized Virtual Power Plants of Small Prosumers

The deployment of small-scale renewable energy sources will transform the management of energy grids towards more decentralized solutions in which the prosumers will have a more active role. Regulatory and market barriers are driving the implementation of virtual aggregation models in which the small-scale prosumers work together on a larger scale to gain benefits that could not be obtained on an individual basis. In this paper, we propose to use public blockchain and self-enforcing smart contracts to construct Virtual Power Plants (VPPs) of prosumers to provide energy services. A model has been defined for capturing the prosumer level constraints in terms of available energy profiles and energy service requirements enabling their optimal aggregation in hierarchical structures. A lightweight decentralized solution for VPPs construction is implemented using smart contracts enabling its efficient running on the public blockchain. Smart contracts are encoding the model constraints and are defining functionalities for prosumers to initiate or join a VPP implementing the complete chain of Offer-Operate-Measure-Remunerate actions. The VPP will be managed on top of a distributed ledger technology offering decentralized functionality for tracking and validating the delivery of energy based on the blockchain transactions and for energy and financial settlement, the remuneration being done according to the amount of energy provided by individual prosumers. Experimental results show that the proposed solution runs successfully on the public blockchain with good execution time and can address Balancing Responsible Party requests for additional generation. The overhead in terms of gas consumption and transactional throughput stays within reasonable boundaries.
Boosting Privately: Federated Extreme Gradient Boosting for Mobile Crowdsensing

Recently, Google and other 24 institutions proposed a series of open challenges towards federated learning (FL), which include application expansion and homomorphic encryption (HE). The former aims to expand the applicable machine learning models of FL. The latter focuses on who holds the secret key when applying HE to FL. For the naive HE scheme, the server is set to master the secret key. Such a setting causes a serious problem that if the server does not conduct aggregation before decryption, a chance is left for the server to access the user’s update. Inspired by the two challenges, we propose FEDXGB, a federated extreme gradient boosting (XGBoost) scheme supporting forced aggregation. FEDXGB mainly achieves the following two breakthroughs. First, FEDXGB involves a new HE based secure aggregation scheme for FL. By combining the advantages of secret sharing and homomorphic encryption, the algorithm can solve the second challenge mentioned above, and is robust to the user dropout. Then, FEDXGB extends FL to a new machine learning model by applying the secure aggregation scheme to the classification and regression tree building of XGBoost. Moreover, we conduct a comprehensive theoretical analysis and extensive experiments to evaluate the security, effectiveness, and efficiency of FEDXGB. The results indicate that FEDXGB achieves less than 1% accuracy loss compared with the original XGBoost, and can provide about 23.9% runtime and 33.3% communication reduction for HE based model update aggregation of FL.
Transfer learning for mobile real-time face mask detection and localization

Abstract Objective Due to the COVID-19 pandemic, our daily habits have suddenly changed. Gatherings are forbidden and, even when it is possible to leave the home for health or work reasons, it is necessary to wear a face mask to reduce the possibility of contagion. In this context, it is crucial to detect violations by people who do not wear a face mask. Materials and Methods For these reasons, in this article, we introduce a method aimed to automatically detect whether people are wearing a face mask. We design a transfer learning approach by exploiting the MobileNetV2 model to identify face mask violations in images/video streams. Moreover, the proposed approach is able to localize the area related to the face mask detection with relative probability. Results To asses the effectiveness of the proposed approach, we evaluate a dataset composed of 4095 images related to people wearing and not wearing face masks, obtaining an accuracy of 0.98 in face mask detection. Discussion and Conclusion The experimental analysis shows that the proposed method can be successfully exploited for face mask violation detection. Moreover, we highlight that it is working also on device with limited computational capability and it is able to process in real time images and video streams, making our proposal applicable in the real world.
Software Defect Prediction Based on Gated Hierarchical LSTMs

Software defect prediction, aimed at assisting software practitioners in allocating test resources more efficiently, predicts the potential defective modules in software products. With the development of defect prediction technology, the inability of traditional software features to capture semantic information is exposed, hence related researchers have turned to semantic features to build defect prediction models. However, sometimes traditional features such as lines of code (LOC) also play an important role in defect prediction. Most of the existing researches only focus on using a single type of feature as the input of the model. In this article, a defect prediction method based on gated hierarchical long short-term memory networks (GH-LSTMs) is proposed, which uses hierarchical LSTM networks to extract both semantic features from word embeddings of abstract syntax trees (ASTs) of source code files, and traditional features provided by the PROMISE repository. More importantly, we adopt a gated fusion strategy to combine the outputs of the hierarchical networks properly. Experimental results show that GH-LSTMs outperforms existing methods under both noneffort-aware and effort-aware scenarios.
Spoken Instruction Understanding in Air Traffic Control: Challenge, Technique, and Application

In air traffic control (ATC), speech communication with radio transmission is the primary way to exchange information between the controller and aircrew. A wealth of contextual situational dynamics is embedded implicitly; thus, understanding the spoken instruction is particularly significant to the ATC research. In this paper, a comprehensive review related to spoken instruction understanding (SIU) in the ATC domain is provided from the perspective of the challenges, techniques, and applications. Firstly, a full pipeline is represented to achieve the SIU task, including automatic speech recognition, language understanding, and voiceprint recognition. A total of 10 technique challenges are analyzed based on the ATC task specificities. In succession, the common techniques for SIU tasks are categorized from common applications, and extensive works in the ATC domain are also reviewed. Finally, a series of future research topics are also prospected based on the corresponding challenges. The author sincerely hopes that this work is able to provide a clear technical roadmap for the SIU tasks in the ATC domain and further make contributions to the research community.
The Use and Misuse of Counterfactuals in Ethical Machine Learning

The use of counterfactuals for considerations of algorithmic fairness and explainability is gaining prominence within the machine learning community and industry. This paper argues for more caution with the use of counterfactuals when the facts to be considered are social categories such as race or gender. We review a broad body of papers from philosophy and social sciences on social ontology and the semantics of counterfactuals, and we conclude that the counterfactual approach in machine learning fairness and social explainability can require an incoherent theory of what social categories are. Our findings suggest that most often the social categories may not admit counterfactual manipulation, and hence may not appropriately satisfy the demands for evaluating the truth or falsity of counterfactuals. This is important because the widespread use of counterfactuals in machine learning can lead to misleading results when applied in high-stakes domains. Accordingly, we argue that even though counterfactuals play an essential part in some causal inferences, their use for questions of algorithmic fairness and social explanations can create more problems than they resolve. Our positive result is a set of tenets about using counterfactuals for fairness and explanations in machine learning.
Forecasting crude oil price with a new hybrid approach and multi-source data

Faced with the growing research toward crude oil price fluctuations influential factors following the accelerated development of Internet technology, accessible data such as Google search volume index (GSVI) are increasingly quantified and incorporated into forecasting approaches. In this study, we apply multi-scale data that including both traditional economic data and GSVI data reflecting macro and micro mechanisms affecting crude oil price respectively, so as to reduce the forecasting deviation and improve the forecasting accuracy at source. In addition, a new hybrid approach: K-means＋KPCA＋KELM based on “divide and conquer” strategy is proposed for deeply exploring the information of above multi-data so that improve monthly crude oil price forecasting accuracy. Empirical results can be analyzed from data and method levels. At the data level, GSVI data perform better than economic data in level forecasting accuracy but with opposite performance in directional forecasting accuracy because of “Herd Behavior”, while hybrid data combined their advantages and obtain best forecasting performance in both level and directional accuracy. At the method level, the approaches with “divide and conquer” strategy gain a better forecasting performance, which demonstrates that “divide and conquer” strategy can effectively improve the forecasting performance.