Reviewerly/exHarmony huggingface dataset:
Geometric Structural Ensemble Learning for Imbalanced Problems

The classification on imbalanced data sets is a great challenge in machine learning. In this paper, a geometric structural ensemble (GSE) learning framework is proposed to address the issue. It is known that the traditional ensemble methods train and combine a series of basic classifiers according to various weights, which might lack the geometric meaning. Oppositely, the GSE partitions and eliminates redundant majority samples by generating hyper-sphere through the Euclidean metric and learns basic classifiers to enclose the minority samples, which achieves higher efficiency in the training process and seems easier to understand. In detail, the current weak classifier builds boundaries between the majority and the minority samples and removes the former. Then, the remaining samples are used to train the next. When the training process is done, all of the majority samples could be cleaned and the combination of all basic classifiers is obtained. To further improve the generalization, two relaxation techniques are proposed. Theoretically, the computational complexity of GSE could approach O(ndlog(nmin)log(n maj)) . The comprehensive experiments validate both the effectiveness and efficiency of GSE.
Hierarchical spatial-aware Siamese network for thermal infrared object tracking

Most thermal infrared (TIR) tracking methods are discriminative, treating the tracking problem as a classification task. However, the objective of the classifier (label prediction) is not coupled to the objective of the tracker (location estimation). The classification task focuses on the between-class difference of the arbitrary objects, while the tracking task mainly deals with the within-class difference of the same objects. In this paper, we cast the TIR tracking problem as a similarity verification task, which is coupled well to the objective of the tracking task. We propose a TIR tracker via a Hierarchical Spatial-aware Siamese Convolutional Neural Network (CNN), named HSSNet. To obtain both spatial and semantic features of the TIR object, we design a Siamese CNN that coalesces the multiple hierarchical convolutional layers. Then, we propose a spatial-aware network to enhance the discriminative ability of the coalesced hierarchical feature. Subsequently, we train this network end to end on a large visible video detection dataset to learn the similarity between paired objects before we transfer the network into the TIR domain. Next, this pre-trained Siamese network is used to evaluate the similarity between the target template and target candidates. Finally, we locate the candidate that is most similar to the tracked target. Extensive experimental results on the benchmarks VOT-TIR 2015 and VOT-TIR 2016 show that our proposed method achieves favorable performance compared to the state-of-the-art methods.
Social Attentional Memory Network

Social connections are known to be helpful for modeling users' potential preferences and improving the performance of recommender systems. However, in social-aware recommendations, there are two issues which influence the inference of users' preferences, and haven't been well-studied in most existing methods: First, the preferences of a user may only partially match that of his friends in certain aspects, especially when considering a user with diverse interests. Second, for an individual, the influence strength of his friends might be different, as not all friends are equally helpful for modeling his preferences in the system. To address the above issues, in this paper, we propose a novel Social Attentional Memory Network (SAMN) for social-aware recommendation. Specifically, we first design an attention-based memory module to learn user-friend relation vectors, which can capture the varying aspect attentions that a user share with his different friends. Then we build a friend-level attention component to adaptively select informative friends for user modeling. The two components are fused together to mutually enhance each other and lead to a finer extended model. Experimental results on three publicly available datasets show that the proposed SAMN model consistently and significantly outperforms the state-of-the-art recommendation methods. Furthermore, qualitative studies have been made to explore what the proposed attention-based memory module and friend-level attention have learnt, which provide insights into the model's learning process.
Improved heavy metal mapping and pollution source apportionment in Shanghai City soils using auxiliary information

Soil heavy metal pollution can be a serious threat to human health and the environment. The accurate mapping of the spatial distribution of soil heavy metal pollutant concentrations enables the detection of high pollution areas and facilitates pollution source apportionment and control. To make full use of auxiliary soil properties information and show that they can improve mapping, a synthesis of the Bayesian Maximum Entropy (BME) theory and the Geographically Weighted Regression (GWR) model is proposed and implemented in the study of the Shanghai City soils (China). The results showed that, compared to traditional techniques, the proposed BME-GWR synthesis has certain important advantages: (a) it integrates heavy metal measurements and auxiliary information on a sound theoretical basis, and (b) it performs better in terms of both prediction accuracy and implementation flexibility (including the assimilation of multiple data sources). Based on the heavy metal concentration maps generated by BME-GWR, we found that the As, Cr and Pb concentration levels are high in the eastern part of Shanghai, whereas high Cd concentration levels were observed in the northwestern part of the city. Organic carbon and pH were significantly correlated with most of the heavy metals in Shanghai soils. We concluded that Cd pollution is mainly the result of agricultural activities, and that the Cr pollution is attributed to natural sources, whereas Pb and As have compound pollution sources. Future studies should investigate the implementation of BME-GWR in the case of space-time heavy metal mapping and its ability to integrate human activity information and soil category variables.
Hardware-Efficient Quantum Random Access Memory with Hybrid Quantum Acoustic Systems

Hybrid quantum systems in which acoustic resonators couple to superconducting qubits are promising quantum information platforms. High quality factors and small mode volumes make acoustic modes ideal quantum memories, while the qubit-phonon coupling enables the initialization and manipulation of quantum states. We present a scheme for quantum computing with multimode quantum acoustic systems, and based on this scheme, propose a hardware-efficient implementation of a quantum random access memory (QRAM). Quantum information is stored in high-Q phonon modes, and couplings between modes are engineered by applying off-resonant drives to a transmon qubit. In comparison to existing proposals that involve directly exciting the qubit, this scheme can offer a substantial improvement in gate fidelity for long-lived acoustic modes. We show how these engineered phonon-phonon couplings can be used to access data in superposition according to the state of designated address modes-implementing a QRAM on a single chip.
Automated virtual reality exposure therapy for spider phobia vs. in-vivo one-session treatment: A randomized non-inferiority trial

This study compared the efficacy of a technician-assisted single-session virtual reality exposure therapy (VRET) for the treatment of spider phobia featuring low-cost consumer-available hardware and novel automated software to gold-standard in-vivo one-session treatment (OST), using a parallel group randomized non-inferiority design. Method Participants (N = 100) were randomized to VRET and OST arms. Assessors blinded to treatment allocation evaluated participants at pre- and post-treatment as well follow-up (3 and 12 months) using a behavioral approach test (BAT) and self-rated fear of spider, anxiety, depression and quality-of-life scales. A maximum post-treatment difference of 2-points on the BAT qualified as non-inferiority margin. Results Linear mixed models noted large, significant reductions in behavioral avoidance and self-reported fear in both groups at post-treatment, with VRET approaching the strong treatment benefits of OST over time. Non-inferiority was identified at 3- and 12- months follow-up but was significantly worse until 12-months. There was no significant difference on a questionnaire measuring negative effects. Conclusions Automated VRET efficaciously reduced spider phobia symptoms in the short-term and was non-inferior to in-vivo exposure therapy in the long-term. VRET effectiveness trials are warranted to evaluate real-world benefits and non-specific therapeutic factors accruing from the presence of a technician during treatment. ClinicalTrials.gov (NCT02533310).
Classification and Clustering of Arguments with Contextualized Word Embeddings

We experiment with two recent contextualized word embedding methods (ELMo and BERT) in the context of open-domain argument search.For the first time, we show how to leverage the power of contextualized word embeddings to classify and cluster topic-dependent arguments, achieving impressive results on both tasks and across multiple datasets.For argument classification, we improve the state-of-the-art for the UKP Sentential Argument Mining Corpus by 20.8 percentage points and for the IBM Debater -Evidence Sentences dataset by 7.4 percentage points.For the understudied task of argument clustering, we propose a pre-training step which improves by 7.8 percentage points over strong baselines on a novel dataset, and by 12.3 percentage points for the Argument Facet Similarity (AFS) Corpus. 1
Multi-Level Context Ultra-Aggregation for Stereo Matching

Exploiting multi-level context information to cost volume can improve the performance of learning-based stereo matching methods. In recent years, 3-D Convolution Neural Networks (3-D CNNs) show the advantages in regularizing cost volume but are limited by unary features learning in matching cost computation. However, existing methods only use features from plain convolution layers or a simple aggregation of multi-level features to calculate cost volume, which is insufficient because stereo matching requires discriminative features to identify corresponding pixels in rectified stereo image pairs. In this paper, we propose a unary features descriptor using multi-level context ultra-aggregation (MCUA), which encapsulates all convolutional features into a more discriminative representation by intra- and inter-level features combination. Specifically, a child module that takes low-resolution images as input captures larger context information; the larger context information from each layer is densely connected to the main branch of the network. MCUA makes good usage of multi-level features with richer context and performs the image-to-image prediction holistically. We introduce our MCUA scheme for cost volume calculation and test it on PSM-Net. We also evaluate our method on Scene Flow and KITTI 2012/2015 stereo datasets. Experimental results show that our method outperforms state-of-the-art methods by a notable margin and effectively improves the accuracy of stereo matching.
Neural News Recommendation with Attentive Multi-View Learning

Personalized news recommendation is very important for online news platforms to help users find interested news and improve user experience. News and user representation learning is critical for news recommendation. Existing news recommendation methods usually learn these representations based on single news information, e.g., title, which may be insufficient. In this paper we propose a neural news recommendation approach which can learn informative representations of users and news by exploiting different kinds of news information. The core of our approach is a news encoder and a user encoder. In the news encoder we propose an attentive multi-view learning model to learn unified news representations from titles, bodies and topic categories by regarding them as different views of news. In addition, we apply both word-level and view-level attention mechanism to news encoder to select important words and views for learning informative news representations. In the user encoder we learn the representations of users based on their browsed news and apply attention mechanism to select informative news for user representation learning. Extensive experiments on a real-world dataset show our approach can effectively improve the performance of news recommendation.
Mazajak: An Online Arabic Sentiment Analyser

Sentiment analysis (SA) is one of the most useful natural language processing applications.Literature is flooding with many papers and systems addressing this task, but most of the work is focused on English.In this paper, we present "Mazajak", an online system for Arabic SA.The system is based on a deep learning model, which achieves state-of-theart results on many Arabic dialect datasets including SemEval 2017 and ASTD.The availability of such system should assist various applications and research that rely on sentiment analysis as a tool.
Advanced orthogonal learning-driven multi-swarm sine cosine optimization: Framework and case studies

Sine cosine algorithm (SCA) is a widely used nature-inspired algorithm that is simple in structure and involves only a few parameters. For some complex tasks, especially high-dimensional problems and multimodal problems, the basic method may have problems in harmonic convergence or trapped into local optima. To efficiently alleviate this deficiency, an improved variant of basic SCA is proposed in this paper. The orthogonal learning, multi-swarm, and greedy selection mechanisms are utilized to improve the global exploration and local exploitation powers of SCA. In preference, the orthogonal learning procedure is introduced into the conventional method to expand its neighborhood searching capabilities. Next, the multi-swarm scheme with three sub-strategies is adopted to enhance the global exploration capabilities of the algorithm. Also, a greedy selection strategy is applied to the conventional approach to improve the qualities of the search agents. Based on these three strategies, we called the improved SCA as OMGSCA. The proposed OMGSCA is compared with a comprehensive set of meta-heuristic algorithms including six other improved SCA variants, basic version, and ten advanced meta-heuristic algorithms. We employed thirty IEEE CEC2014 benchmark functions, and eight advanced meta-heuristic algorithms on seventeen real-world benchmark problems from IEEE CEC2011. Also, non-parametric statistical Wilcoxon sign rank and the Friedman tests are performed to monitor the performance of the proposed method. The obtained experimental results demonstrate that the introduced strategies can significantly improve the exploratory and exploitative inclinations of the basic algorithm. The convergence speed of the original method has also been improved, substantially. The results suggest the proposed OMGSCA can be used as an effective and efficient auxiliary tool for solving complex optimization problems.
Rethinking the Bottom-Up Framework for Query-Based Video Localization

In this paper, we focus on the task query-based video localization, i.e., localizing a query in a long and untrimmed video. The prevailing solutions for this problem can be grouped into two categories: i) Top-down approach: It pre-cuts the video into a set of moment candidates, then it does classification and regression for each candidate; ii) Bottom-up approach: It injects the whole query content into each video frame, then it predicts the probabilities of each frame as a ground truth segment boundary (i.e., start or end). Both two frameworks have respective shortcomings: the top-down models suffer from heavy computations and they are sensitive to the heuristic rules, while the performance of bottom-up models is behind the performance of top-down counterpart thus far. However, we argue that the performance of bottom-up framework is severely underestimated by current unreasonable designs, including both the backbone and head network. To this end, we design a novel bottom-up model: Graph-FPN with Dense Predictions (GDP). For the backbone, GDP firstly generates a frame feature pyramid to capture multi-level semantics, then it utilizes graph convolution to encode the plentiful scene relationships, which incidentally mitigates the semantic gaps in the multi-scale feature pyramid. For the head network, GDP regards all frames falling in the ground truth segment as the foreground, and each foreground frame regresses the unique distances from its location to bi-directional boundaries. Extensive experiments on two challenging query-based video localization tasks (natural language video localization and video relocalization), involving four challenging benchmarks (TACoS, Charades-STA, ActivityNet Captions, and Activity-VRL), have shown that GDP surpasses the state-of-the-art top-down models.
Blockchain-Enabled Distributed Security Framework for Next-Generation IoT: An Edge Cloud and Software-Defined Network-Integrated Approach

The Internet of Things (IoT) plays a vital role in the real world by providing autonomous support for communications and operations, thus enabling and promoting novel services that are commonly used in day-to-day life. It is important to do research on security frameworks for next-generation IoT and develop state-of-the-art confidentiality protection schemes to deal with various attacks on IoT networks. In order to offer prominent features like continuous confidentiality, authentication, and robustness, the blockchain technology comes out as a sustainable solution. A blockchain-enabled distributed security framework using edge cloud and software-defined networking (SDN) is presented in this article. The security attack detection is achieved at the cloud layer, and security attacks are consequently reduced at the edge layer of the IoT network. The SDN-enabled gateway offers dynamic network traffic flow management, which contributes to the security attack recognition through determining doubtful network traffic flows and diminishes security attacks through hindering doubtful flows. The results obtained show that the proposed security framework can efficiently and effectively meet the data confidentiality challenges introduced by the integration of blockchain, edge cloud, and SDN paradigm.
A visually secure image encryption scheme based on semi-tensor product compressed sensing

In traditional compressed sensing (CS), the measurement matrix always faces problems such as large data storage, high memory usage and a large amount of data calculation. To reduce the storage space of the measurement matrix, improve memory usage and reduce the amount of data, this paper proposes a CS strategy based on a semitensor product (STP). The STP is employed to sample a sparse matrix from the measurement matrix, and Arnold scrambling is used to increase the security of the secret image. Furthermore, a visual security strategy for the secret image is employed to transform it into a visually meaningful image. The simulations show that the proposed visual security strategy based on STP-CS can not only greatly reduce the storage space occupied by the measurement matrix but also maintain high reconstruction quality compared with the traditional CS model while ensuring visual security quality.
Improving Description-Based Person Re-Identification by Multi-Granularity Image-Text Alignments

Description-based person re-identification (Re-id) is an important task in video surveillance that requires discriminative cross-modal representations to distinguish different people. It is difficult to directly measure the similarity between images and descriptions due to the modality heterogeneity (the crossmodal problem). And all samples belonging to a single category (the fine-grained problem) makes this task even harder than the conventional image-description matching task. In this paper, we propose a Multi-granularity Image-text Alignments (MIA) model to alleviate the cross-modal fine-grained problem for better similarity evaluation in description-based person Re-id. Specifically, three different granularities, i.e., global-global, global-local and local-local alignments are carried out hierarchically. Firstly, the global-global alignment in the Global Contrast (GC) module is for matching the global contexts of images and descriptions. Secondly, the global-local alignment employs the potential relations between local components and global contexts to highlight the distinguishable components while eliminating the uninvolved ones adaptively in the Relation-guided Global-local Alignment (RGA) module. Thirdly, as for the local-local alignment, we match visual human parts with noun phrases in the Bi-directional Fine-grained Matching (BFM) module. The whole network combining multiple granularities can be end-to-end trained without complex preprocessing. To address the difficulties in training the combination of multiple granularities, an effective step training strategy is proposed to train these granularities step-by-step. Extensive experiments and analysis have shown that our method obtains the state-of-the-art performance on the CUHK-PEDES dataset and outperforms the previous methods by a significant margin.
Global Techno-Economic Performance of Bifacial and Tracking Photovoltaic Systems

This work presents a worldwide analysis on the yield potential and cost effectiveness of photovoltaic farms composed of monofacial fixed-tilt and single/dual (1T/2T) tracker installations, as well as their bifacial counterparts. Our approach starts by estimating the irradiance reaching both the front and rear surfaces of the modules for the different system designs (validated based on data from real photovoltaic systems and results from the literature) to estimate their energy production. Subsequently, the overall system cost during their 25-year lifetime is factored in, and the levelized cost of electricity (LCOE) is obtained. The results reveal that bifacial-1T installations increase energy yield by 35% and reach the lowest LCOE for the majority of the world (93.1% of the land area). Although dual-axis trackers achieve the highest energy generation, their costs are still too high and are therefore not as cost effective. Sensitivity analyses are also provided to show the general robustness of our findings.
Employee retention and organizational performance: Evidence from banking industry

It may be queried why highly-qualified and skilled employees are allowed to leave their organizations.This study aims to determine the main factors affecting employee retention and how employee retention affects organizational performance in the commercial banking sector in Jordan.The employee retention drivers that are discussed in this study include economic security, psychological security, affiliation, and self-actualization factors.The researchers use a questionnaire as a research instrument to collect the primary data from employees using the simple random sampling technique.SEM-SPLS is used to analyze the collected data, to test the study model and the proposed hypotheses.The findings reveal that economic, psychological, affiliation, and self-actualization factors affected employee retention.The paper also discusses the study's theoretical and practical implications.