Reviewerly/exHarmony huggingface dataset:
Extreme clustering – A clustering method via density extreme points

Peak clustering, a density based clustering method, has shown remarkable performance in clustering analysis of data. In reality, peak clustering suffers from two major drawbacks: (i) when the difference in cluster sample density is significant, it becomes difficult for peak clustering to find cluster centres in low density clusters. (ii) in some cases, it will incorrectly detect many normal points as noises. In this paper, we propose a new extreme clustering method to overcome the drawbacks of peak clustering. The theme of extreme clustering is to identify density extreme points to find cluster centres. In addition, a noise detection module is also introduced to identify noisy data points from the clustering results. As a result, the extreme clustering is robust to datasets with different density distributions. Experiments and validations, on over 40 datasets, show that extreme clustering can not only inherit the cluster validity of peak clustering, but also overcome its shortages with significant performance gain. Case studies on real-world haze analysis also demonstrate the performance of extreme clustering method in finding some main haze origins in a Chinese city.
Mining user interest based on personality-aware hybrid filtering in social networks

With the emergence of online social networks and microblogging websites, user interest mining has been an active research topic for the past few years. However, most of the existing works suffer from two significant drawbacks, firstly, they focus on the user’s explicit content and social network structure to predicate the user’s interests, neglecting the fact that the user’s personality might be a rich source to infer the topical interests. Secondly, they represent the user’s content using the bag-of-words model that ignores the chronological order of the posted content, hence the predicted interests might contain outdated topics that the user does not interest anymore. In this paper, we propose a novel user interest mining system based on Big Five personality traits and dynamic interests. To prove the effectiveness of incorporating the user’s personality traits in the interest mining process, we have implemented a social network for news sharing and conducted different experiments on the collected data. The experiment results show that considering personality traits can increase the precision and recall of interest mining systems, as well as can help to tackle the cold start problem.
Data-driven fault detection and diagnosis for packaged rooftop units using statistical machine learning classification methods

This paper proposes and demonstrates a data-driven fault detection and diagnosis strategy for packaged rooftop units using statistical machine learning classification methods. The fault detection and diagnosis task is formulated as a multi-class classification problem. Seven typical rooftop unit faults are discriminated against one another as well as the normal condition. Since experimental data for rooftop units is rare and difficult to obtain, a simulated data library of model faults at steady state operation is used for training and validating the classifications models. Synthetic minority over-sampling technique is used to generate new artificial samples of minority class in order to balance the dataset. Nine well-known classification methods are applied to our dataset, and their performance is compared. The results show that support vector machine, with an overall accuracy rate of 96.2%, is the best classifier, and linear discriminant analysis, with an overall accuracy rate of 76.2%, is the worst classifier. The performance of the classification methods for individual faults is also characterized using true positive rate and false positive rate statistical measures. The relative importance of input variables is also discussed. The high accuracy of the classification methods shows the potential of a data-driven strategy in detecting and diagnosing the rooftop unit faults.
The state of the knowledge-based economy in the Arab world: cases of Qatar and Lebanon

Purpose The purpose of this paper is to examine the current state of the knowledge-based economy in two distinctive case studies in the Arab World: Qatar and Lebanon. Based on five aspects of the knowledge-based economy namely: ICT, human capital and education; innovation, entrepreneurship, and economic and institutional regime, we provide a careful view of the obstacles and challenges that Qatar and Lebanon are facing and how this is hindering their transformation to a knowledge-based economy. Design/methodology/approach The methodology of this research is based on a literature review and information collected through semi-structured interviews with the different stakeholders of the knowledge-based economy in Qatar and Lebanon. Findings The research reveals that numerous factors shape the knowledge-based economy in Qatar and Lebanon. In Qatar, the main strength of the knowledge-based economy is the determination of the Qatari government to diversify the economy and the main weaknesses are the shortage of qualified human resources, the fear of failure and the low performance of the innovation system. In Lebanon, the knowledge-based economy is driven by the education system and the entrepreneurship culture, nevertheless the political instability of the country and the weak ICT infrastructure impede its development. Originality/value These findings contribute to the clarification and critical analysis of the current state of the knowledge-based economy in Qatar and Lebanon, which would have several policy implications.
Relation-Guided Representation Learning

Deep auto-encoders (DAEs) have achieved great success in learning data representations via the powerful representability of neural networks. But most DAEs only focus on the most dominant structures which are able to reconstruct the data from a latent space and neglect rich latent structural information. In this work, we propose a new representation learning method that explicitly models and leverages sample relations, which in turn is used as supervision to guide the representation learning. Different from previous work, our framework well preserves the relations between samples. Since the prediction of pairwise relations themselves is a fundamental problem, our model adaptively learns them from data. This provides much flexibility to encode real data manifold. The important role of relation and representation learning is evaluated on the clustering task. Extensive experiments on benchmark data sets demonstrate the superiority of our approach. By seeking to embed samples into subspace, we further show that our method can address the large-scale and out-of-sample problem. Our source code is publicly available at: https://github.com/nbShawnLu/RGRL.
How To Build a Better Robot . . . for Quick-Service Restaurants

Hospitality firms are exploring opportunities to incorporate innovative technologies, such as robotics, into their operations. This qualitative study used focus groups to investigate diner perspectives on issues related to using robot technology in quick-service restaurant (QSR) operations. QSR guests have major concerns regarding the societal impact of robotics entering the realm of QSR operations; the cleanliness and food safety of robot technology; and communication quality, especially voice recognition, from both native and nonnative English speakers. Participants also offered opinions about the functionality and physical appearance of robots, the value of the “human touch,” and devised creative solutions for deploying this technology. Surprisingly, few differences in attitudes and perceptions were found between age groups, and the participants were highly ambivalent about the technology. Future research may consider further exploration of robot applications in other restaurant segments, using quantitative methods with a larger sample.
Improving Quality of Experience by Adaptive Video Streaming with Super-Resolution

Given high-speed mobile Internet access today, audiences are expecting much higher video quality than before. Video service providers have deployed dynamic video bitrate adaptation services to fulfill such user demands. However, legacy video bitrate adaptation techniques are highly dependent on the estimation of dynamic bandwidth, and fail to integrate the video quality enhancement techniques, or consider the heterogeneous computing capabilities of client devices, leading to low quality of experience (QoE) for users. In this paper, we present a super-resolution based adaptive video streaming (SRAVS) framework, which applies a Reinforcement Learning (RL) model for integrating the video super-resolution (VSR) technique with the video streaming strategy. The VSR technique allows clients to download low bitrate video segments, reconstruct and enhance them to high-quality video segments while making the system less dependent on estimating dynamic bandwidth. The RL model investigates both the playback statistics and the distinguishing features related to the client-side computing capabilities. Trace-driven emulations over real-world videos and bandwidth traces verify that SRAVS can significantly improve the QoE for users compared to the state-of-the-art video streaming strategies with or without involving VSR techniques.
The Extended UTXO Model

Bitcoin and Ethereum, hosting the two currently most valuable and popular cryptocurrencies, use two rather different ledger models, known as the UTXO model and the account model, respectively. At the same time, these two public blockchains differ strongly in the expressiveness of the smart contracts that they support. This is no coincidence. Ethereum chose the account model explicitly to facilitate more expressive smart contracts. On the other hand, Bitcoin chose UTXO also for good reasons, including that its semantic model stays simple in a complex concurrent and distributed computing environment. This raises the question of whether it is possible to have expressive smart contracts, while keeping the semantic simplicity of the UTXO model. In this paper, we answer this question affirmatively. We present Extended UTXO (EUTXO), an extension to Bitcoin’s UTXO model that supports a substantially more expressive form of validation scripts, including scripts that implement general state machines and enforce invariants across entire transaction chains. To demonstrate the power of this model, we also introduce a form of state machines suitable for execution on a ledger, based on Mealy machines and called Constraint Emitting Machines (CEM). We formalise CEMs, show how to compile them to EUTXO, and show a weak bisimulation between the two systems. All of our work is formalised using the Agda proof assistant.
A Hierarchical Hand Gesture Recognition Framework for Sports Referee Training-Based EMG and Accelerometer Sensors

To cultivate professional sports referees, we develop a sports referee training system, which can recognize whether a trainee wearing the Myo armband makes correct judging signals while watching a prerecorded professional game. The system has to correctly recognize a set of gestures related to official referee's signals (ORSs) and another set of gestures used to intuitively interact with the system. These two gesture sets involve both large motion and subtle motion gestures, and the existing sensor-based methods using handcrafted features do not work well on recognizing all kinds of these gestures. In this work, deep belief networks (DBNs) are utilized to learn more representative features for hand gesture recognition, and selective handcrafted features are combined with the DBN features to achieve more robust recognition results. Moreover, a hierarchical recognition scheme is designed to first recognize the input gesture as a large or subtle motion gesture, and the corresponding classifiers for large motion gestures and subtle motion gestures are further used to obtain the final recognition result. Moreover, the Myo armband consists of eight-channel surface electromyography (sEMG) sensors and an inertial measurement unit (IMU), and these heterogeneous signals can be fused to achieve better recognition accuracy. We take basketball as an example to validate the proposed training system, and the experimental results show that the proposed hierarchical scheme considering DBN features of multimodality data outperforms other methods.
YOLO-compact: An Efficient YOLO Network for Single Category Real-time Object Detection

In practical applications, the number of category in object detection is always single. In this paper, an efficient YOLO-compact network designed for single category real-time object detection is proposed. This paper first explored a series of methods for converting a large and deep network to a compact and efficient network, through a series of ablation experiments. Then these methods were assembled to YOLOv3 network, which obtains the YOLO-compact's infrastructure. A network structure design approach that separates the down sampling layer from all network modules was proposed, which facilitates the modular design of the network. The shortcut structure in the RFB module is changed to the direct connection structure, and the 1×3+3×1+3×3 convolution structure is used instead of 5×5 convolution, which obtains efficient RFB-c module. The residual bottleneck block has been improved, by removing the last 1×1 convolution layer and using 3×3 depth wise separable convolution. Since pedestrian is the most representative object in practical applications, this paper uses the result on person category in VOC2007 test set to represent the network performance. The model size of YOLO-compact is only 9MB, which is 3.7 times smaller than tiny-yolov3, 6.7 times smaller than tiny-yolov2, and 26 times smaller than YOLOv3. The AP result of YOLO-compact is 86.85%, which is 37% higher than tiny-yolov3, 32% higher than tiny-yolov2, and even 2.7% higher than the YOLOv3.
A Hybrid BERT Model That Incorporates Label Semantics via Adjustive Attention for Multi-Label Text Classification

The multi-label text classification task aims to tag a document with a series of labels. Previous studies usually treated labels as symbols without semantics and ignored the relation among labels, which caused information loss. In this paper, we show that explicitly modeling label semantics can improve multi-label text classification. We propose a hybrid neural network model to simultaneously take advantage of both label semantics and fine-grained text information. Specifically, we utilize the pre-trained BERT model to compute context-aware representation of documents. Furthermore, we incorporate the label semantics in two stages. First, a novel label graph construction approach is proposed to capture the label structures and correlations. Second, we propose a neoteric attention mechanism-adjustive attention to establish the semantic connections between labels and words and to obtain the label-specific word representation. The hybrid representation that combines context-aware feature and label-special word feature is fed into a document encoder to classify. Experimental results on two publicly available datasets show that our model is superior to other state-of-the-art classification methods.
A Survey on NLP based Text Summarization for Summarizing Product Reviews

No one can imagine life without a smartphone and internet nowadays. It has become essential for people of all age groups. With an increase in the usage of internet and smartphones, there has been a steady increase in online shopping too. Everyone wishes to get their products delivered at their home without any hassle. How to detect which products are genuine and pick the best among the unlimited options at the same price? Every user looks at the reviews before ordering anything online. Nevertheless reading those long reviews is not easy for everyone. Therefore, there must be something that can reduce the long reviews to short sentences of limited words depicting the same meaning. Text Summarization can come in hand in this aspect. Many NLP researchers are interested in Text Summarization. This paper is a survey on the various types of text summarization techniques starting from the basic to the advanced techniques. According to this survey, seq2seq model along with the LSTM and attention mechanism is used for increased accuracy.
Improved TrAdaBoost and its Application to Transaction Fraud Detection

AdaBoost is a boosting-based machine learning method under the assumption that the data in training and testing sets have the same distribution and input feature space. It increases the weights of those instances that are wrongly classified in a training process. However, the assumption does not hold in many real-world data sets. Therefore, AdaBoost is extended to transfer AdaBoost (TrAdaBoost) that can effectively transfer knowledge from one domain to another. TrAdaBoost decreases the weights of those instances that belong to the source domain but are wrongly classified in a training process. It is more suitable for the case that data are of different distribution. Can it be improved for some special transfer scenarios, e.g., the data distribution changes slightly over time? We find that the distribution of credit card transaction data can change with the changes in the transaction behaviors of users, but the changes are slow most of the time. These changes are yet important for detecting transaction fraud since they result in a so-called concept drift problem. In order to make TrAdaBoost more suitable for the abovementioned case, we, thus, propose an improved TrAdaBoost (ITrAdaBoost) in this article. It updates (i.e., increases or decreases) the weight of a wrongly classified instance in a source domain according to the distribution distance from the instance to a target domain, and the calculation of distance is based on the theory of reproducing kernel Hilbert space. We do a series of experiments over five data sets, and the results illustrate the advantage of ITrAdaBoost.
Impact of rescanning and normalization on convolutional neural network performance in multi-center, whole-slide classification of prostate cancer

Abstract Algorithms can improve the objectivity and efficiency of histopathologic slide analysis. In this paper, we investigated the impact of scanning systems (scanners) and cycle-GAN-based normalization on algorithm performance, by comparing different deep learning models to automatically detect prostate cancer in whole-slide images. Specifically, we compare U-Net, DenseNet and EfficientNet. Models were developed on a multi-center cohort with 582 WSIs and subsequently evaluated on two independent test sets including 85 and 50 WSIs, respectively, to show the robustness of the proposed method to differing staining protocols and scanner types. We also investigated the application of normalization as a pre-processing step by two techniques, the whole-slide image color standardizer (WSICS) algorithm, and a cycle-GAN based method. For the two independent datasets we obtained an AUC of 0.92 and 0.83 respectively. After rescanning the AUC improves to 0.91/0.88 and after style normalization to 0.98/0.97. In the future our algorithm could be used to automatically pre-screen prostate biopsies to alleviate the workload of pathologists.
Predicting Defective Lines Using a Model-Agnostic Technique

Defect prediction models are proposed to help a team prioritize source code areas files that need Software QualityAssurance (SQA) based on the likelihood of having defects. However, developers may waste their unnecessary effort on the whole filewhile only a small fraction of its source code lines are defective. Indeed, we find that as little as 1%-3% of lines of a file are defective. Hence, in this work, we propose a novel framework (called LINE-DP) to identify defective lines using a model-agnostic technique, i.e., an Explainable AI technique that provides information why the model makes such a prediction. Broadly speaking, our LINE-DP first builds a file-level defect model using code token features. Then, our LINE-DP uses a state-of-the-art model-agnostic technique (i.e.,LIME) to identify risky tokens, i.e., code tokens that lead the file-level defect model to predict that the file will be defective. Then, the lines that contain risky tokens are predicted as defective lines. Through a case study of 32 releases of nine Java open source systems, our evaluation results show that our LINE-DP achieves an average recall of 0.61, a false alarm rate of 0.47, a top 20%LOC recall of0.27, and an initial false alarm of 16, which are statistically better than six baseline approaches. Our evaluation shows that our LINE-DP requires an average computation time of 10 seconds including model construction and defective line identification time. In addition, we find that 63% of defective lines that can be identified by our LINE-DP are related to common defects (e.g., argument change, condition change). These results suggest that our LINE-DP can effectively identify defective lines that contain common defectswhile requiring a smaller amount of inspection effort and a manageable computation cost.
Spoofing and Anti-Spoofing Technologies of Global Navigation Satellite System: A Survey

A large number of facts prove that Global Navigation Satellite System (GNSS) has certain security risks and is threatened by attacks. In particular, GNSS civilian signal receivers have some vulnerability in responding to spoofing attack and jamming attack, because the format and modulation of GNSS civilian signals are public. Based on the concerns of existing GNSS spoofing scenarios, this article reviews the research on GNSS anti-spoofing technologies from two aspects. In this article, the strategies of spoofing attacks discovered at this stage are classified in detail, the protection effect and scheme implementation complexity of various anti-spoofing techniques against spoofing attacks are analyzed from the perspective of signal-level and data-level. More specifically, we comprehensively analyze the ability for the combined anti-spoofing methods at the signal-level and the data-level to protect from combinations of different spoofing attack. Finally, the future development trend and potential research direction of GNSS anti-spoofing technology are summarized and predicted.
A quick-scan method to assess photovoltaic rooftop potential based on aerial imagery and LiDAR

A quick-scan yield prediction method has been developed to assess rooftop photovoltaic (PV) potential. The method has three main parts. For each roof, first (i) virtual 3D roof segments were reconstructed using aerial imagery, then, (ii) PV modules were automatically fitted onto roof segments using a fitting algorithm and finally, (iii) expected annual yield was calculated. For each roof, the annual yield was calculated by three different quick yield calculation approaches. Two approaches are commercial software packages of Solar Monkey (SM) and Photovoltaic Geographical Information System (PVGIS) whereas the other one is the simplified skyline-based approach developed in photovoltaic material and devices (PVMD) group of Delft University of Technology. To validate the quick-scan method, a set of 145 roofs and 215 roof segments were chosen in urban areas in the Netherlands. For the chosen roofs, the number of fitted modules and calculated yield were compared with the actual modular layout and the measured yield of existing PV systems. Results showed a satisfactory agreement between the quick-scan yield prediction and measured annual yield per roof, with relative standard deviations of 7.2%, 9.1%, and 7.5% respectively for SM, PVGIS, and PVMD approaches. It was concluded that the obstacle-including approaches (e.g. SM and PVMD) outperformed the approaches which neglect the shading by surrounding obstacles (e.g. PVGIS). Results also showed that 3D roof segments had added value as input for the quick-scan PV yield prediction methods since the precision of yield prediction was significantly lower using only 2D land register data of buildings.