Reviewerly/exHarmony huggingface dataset:
Multiobjective Evolutionary Feature Selection for Fuzzy Classification

The interpretability of classification systems refers to the ability of these to express their behaviour in a way that is easily understandable by a user.Interpretable classification models allow for external validation by an expert and, in certain disciplines such as medicine or business, providing information about decision making is essential for ethical and human reasons.Fuzzy rule-based classification systems are consolidated powerful classification tools based on fuzzy logic and designed to produce interpretable models; however, in presence of a large number of attributes, even rule-based models tend to be too complex to be easily interpreted.In this work, we propose a novel multivariate feature selection method in which both search strategy and classifier are based on multi-objective evolutionary computation.We designed a set of experiments to establish an acceptable setting with respect to the number of evaluations required by the search strategy and by the classifier, and we tested our strategy on a real-life dataset.Then, we compared our results against a wide range of feature selection methods that includes filter, wrapper, multivariate and univariate methods, with deterministic and probabilistic search strategies, and with evaluators of diverse nature.Finally, the fuzzy rule-based classification model obtained with the proposed method has been evaluated with standard performance metrics and compared with other wellknown fuzzy rule-based classifiers.We have used two real-life datasets extracted from a contact center; in one case, with the proposed method we obtained an accuracy of 0.7857 with 8 rules, while the best fuzzy classifier compared obtained 0.7679 with 8 rules, and in the second case, we obtained an accuracy of 0.7403 with 5 rules, while the best fuzzy classifier compared obtained 0.6364 with 4 rules.
A Blockchain and AutoML Approach for Open and Automated Customer Service

Customer service is transforming from traditional manual service toward automated service, which utilizes different computational informatics to achieve a higher efficient and quality services. Automated customer service requires big data and expertise in data analysis as prerequisites. However, many companies, especially small and medium enterprises, do not have sufficient data and experience due to their limited scale and resources. They need to rely on third parties, and this reliance results in the lack of development of core customer service competency. In order to overcome these challenges, an open and automated customer service platform based on Internet of things (IoT), blockchain, and automated machine learning (AutoML) is proposed. The data are gathered with the use of IoT devices during the customer service. An open but secured environment to achieve data trading is ensured by using blockchain. AutoML is adopted to automate the data analysis processes for reducing the reliance of costly experts. The proposed platform is analyzed through use case evaluation. A prototype system has also been developed and evaluated. The simulation results show that our platform is scalable and efficient.
A Survey of Human Action Analysis in HRI Applications

The human action is an important information source for human social interaction, and it simultaneously plays a crucial role in human-robot interaction (HRI). For a natural and fluent interaction, robots are required to understand human actions and have the capacity to predict action intentions and to imitate human actions for an appropriate response. Currently, existing survey papers for the action recognition mainly summarize algorithms that perform action recognition in experimental scenarios, and survey papers of the HRI mainly introduced various interaction interfaces in the HRI. Different from these surveys, we focus on the human action analysis on robot platforms for the HRI application, including the body motion and gestures. We review the existing HRI related references involving the action recognition, prediction, and the robot imitation of the human action. Moreover, we give a summary of robot platforms and action datasets that are frequently used in the study of HRI. Finally, we give an analysis on the development trend and future research directions of action analysis for the HRI applications.
Cloud Computing Adoption in Higher Education Institutions: A Systematic Review

Cloud computing (CC) is a recently developed computing paradigm that can be utilized to deliver everything-as-a-service to various businesses. In higher education institutions (HEIs), CC is rapidly being deployed and becoming an integral part of institution experience. CC adoption in HEIs is accompanied by numerous scientific contributions that address the topic from different perspectives. A systematic review of these heterogeneous contributions, which provide a coherent taxonomy, can be considered interesting for HEIs to identify opportunities to use CC in its own context. Therefore, this systematic literature review aims to analyze existing research on adopting and using CC in HEIs, review background research to develop a coherent taxonomy and provide a landscape for future research on CC in HEIs. The outcomes of this paper include a coherent taxonomy and an overview of the basic characteristics of this emerging field in terms of motivation and barriers of adopting CC in HEIs, existing individual and organizational theoretical models to understand the future requirements for extensively adopting and using CC in HEIs, and factors that influence the adoption of CC in HEIs at individual and organizational levels. Considerable information is available in relation to adopting and using CC in HEIs. This review will enhance this information by offering an in-depth analysis of the existing data to bridge any gap and expand on existing literature.
Efficient privacy preservation of big data for accurate data mining

Computing technologies pervade physical spaces and human lives, and produce a vast amount of data that is available for analysis. However, there is a growing concern that potentially sensitive data may become public if the collected data are not appropriately sanitized before being released for investigation. Although there are more than a few privacy-preserving methods available, they are not efficient, scalable, or have problems with data utility, or privacy. This paper addresses these issues by proposing an efficient and scalable nonreversible perturbation algorithm, PABIDOT, for privacy preservation of big data via optimal geometric transformations. PABIDOT was tested for efficiency, scalability, attack resistance, and accuracy using nine datasets and five classification algorithms. Experiments show that PABIDOT excels in execution speed, scalability, attack resistance, and accuracy in large-scale privacy-preserving data classification when compared with two other, related privacy-preserving algorithms.
Self-Paced Balance Learning for Clinical Skin Disease Recognition

Class imbalance is a challenging problem in many classification tasks. It induces biased classification results for minority classes that contain less training samples than others. Most existing approaches aim to remedy the imbalanced number of instances among categories by resampling the majority and minority classes accordingly. However, the imbalanced level of difficulty of recognizing different categories is also crucial, especially for distinguishing samples with many classes. For example, in the task of clinical skin disease recognition, several rare diseases have a small number of training samples, but they are easy to diagnose because of their distinct visual properties. On the other hand, some common skin diseases, e.g., eczema, are hard to recognize due to the lack of special symptoms. To address this problem, we propose a self-paced balance learning (SPBL) algorithm in this paper. Specifically, we introduce a comprehensive metric termed the complexity of image category that is a combination of both sample number and recognition difficulty. First, the complexity is initialized using the model of the first pace, where the pace indicates one iteration in the self-paced learning paradigm. We then assign each class a penalty weight that is larger for more complex categories and smaller for easier ones, after which the curriculum is reconstructed by rearranging the training samples. Consequently, the model can iteratively learn discriminative representations via balancing the complexity in each pace. Experimental results on the SD-198 and SD-260 benchmark data sets demonstrate that the proposed SPBL algorithm performs favorably against the state-of-the-art methods. We also demonstrate the effectiveness of the SPBL algorithm's generalization capacity on various tasks, such as indoor scene image recognition and object classification.
XQA: A Cross-lingual Open-domain Question Answering Dataset

Open-domain question answering (OpenQA) aims to answer questions through text retrieval and reading comprehension.Recently, lots of neural network-based models have been proposed and achieved promising results in OpenQA.However, the success of these models relies on a massive volume of training data (usually in English), which is not available in many other languages, especially for those low-resource languages.Therefore, it is essential to investigate cross-lingual OpenQA.In this paper, we construct a novel dataset XQA for cross-lingual OpenQA research.It consists of a training set in English as well as development and test sets in eight other languages.Besides, we provide several baseline systems for cross-lingual OpenQA, including two machine translation-based methods and one zero-shot cross-lingual method (multilingual BERT).Experimental results show that the multilingual BERT model achieves the best results in almost all target languages, while the performance of cross-lingual OpenQA is still much lower than that of English.Our analysis indicates that the performance of cross-lingual OpenQA is related to not only how similar the target language and English are, but also how difficult the question set of the target language is.The XQA dataset is publicly available at
Social Relation Recognition From Videos via Multi-Scale Spatial-Temporal Reasoning

Discovering social relations, e.g., kinship, friendship, etc., from visual contents can make machines better interpret the behaviors and emotions of human beings. Existing studies mainly focus on recognizing social relations from still images while neglecting another important media--video. On one hand, the actions and storylines in videos provide more important cues for social relation recognition. On the other hand, the key persons may appear at arbitrary spatial-temporal locations, even not in one same image from beginning to the end. To overcome these challenges, we propose a Multi-scale Spatial-Temporal Reasoning (MSTR) framework to recognize social relations from videos. For the spatial representation, we not only adopt a temporal segment network to learn global action and scene information, but also design a Triple Graphs model to capture visual relations between persons and objects. For the temporal domain, we propose a Pyramid Graph Convolutional Network to perform temporal reasoning with multi-scale receptive fields, which can obtain both long-term and short-term storylines in videos. By this means, MSTR can comprehensively explore the multi-scale actions and storylines in spatial-temporal dimensions for social relation reasoning in videos. Extensive experiments on a new large-scale Video Social Relation dataset demonstrate the effectiveness of the proposed framework.
Research on logistics supply chain of iron and steel enterprises based on block chain technology

At present, the service trading system of steel logistics industry is highly centralized, with poor collaborative ability. Information flow, capital flow and logistics need to be certified by logistics center nodes. It cannot achieve information transparency, traceability and tampering, and the information security of customers cannot be effectively guaranteed. This has affected the information security, automation and intelligence of logistics enterprise service transactions. Firstly, this paper introduces the data layer, network layer and consensus layer in the block chain, and then establishes the steel logistics architecture under the block chain technology. Then, it analyzes the management of logistics information resources in the supply chain from the object, attribute and function dimensions. Finally, the improved PFBT algorithm is used to test their de-centralization security and algorithm performance in the system of iron and steel enterprise logistics transactions. The experimental comparison shows that the improved PBFT algorithm has been greatly improved in the aspects of algorithm security, algorithm delay, algorithm throughput and algorithm fault tolerance.
Achieving Super-Linear Speedup across Multi-FPGA for Real-Time DNN Inference

Real-time Deep Neural Network (DNN) inference with low-latency requirement has become increasingly important for numerous applications in both cloud computing (e.g., Apple’s Siri) and edge computing (e.g., Google/Waymo’s driverless car). FPGA-based DNN accelerators have demonstrated both superior flexibility and performance; in addition, for real-time inference with low batch size, FPGA is expected to achieve further performance improvement. However, the performance gain from the single-FPGA design is obstructed by the limited on-chip resource. In this paper, we employ multiple FPGAs to cooperatively run DNNs with the objective of achieving super-linear speed-up against single-FPGA design. In implementing such systems, we found two barriers that hinder us from achieving the design goal: (1) the lack of a clear partition scheme for each DNN layer to fully exploit parallelism, and (2) the insufficient bandwidth between the off-chip memory and the accelerator due to the growing size of DNNs. To tackle these issues, we propose a general framework, “Super-LIP”, which can support different kinds of DNNs. In this paper, we take Convolutional Neural Network (CNN) as a vehicle to illustrate Super-LIP. We first formulate an accurate system-level model to support the exploration of best partition schemes. Then, we develop a novel design methodology to effectively alleviate the heavy loads on memory bandwidth by moving traffic from memory bus to inter-FPGA links. We implement Super-LIP based on ZCU102 FPGA boards. Results demonstrate that Super-LIP with 2 FPGAs can achieve 3.48× speedup, compared to the state-of-the-art single-FPGA design. What is more, as the number of FPGAs scales up, the system latency can be further reduced while maintaining high energy efficiency.
Zero-Shot Learning via Latent Space Encoding

Zero-shot learning (ZSL) is typically achieved by resorting to a class semantic embedding space to transfer the knowledge from the seen classes to unseen ones. Capturing the common semantic characteristics between the visual modality and the class semantic modality (e.g., attributes or word vector) is a key to the success of ZSL. In this paper, we propose a novel encoder-decoder approach, namely latent space encoding (LSE), to connect the semantic relations of different modalities. Instead of requiring a projection function to transfer information across different modalities like most previous work, LSE performs the interactions of different modalities via a feature aware latent space, which is learned in an implicit way. Specifically, different modalities are modeled separately but optimized jointly. For each modality, an encoder-decoder framework is performed to learn a feature aware latent space via jointly maximizing the recoverability of the original space from the latent space and the predictability of the latent space from the original space. To relate different modalities together, their features referring to the same concept are enforced to share the same latent codings. In this way, the common semantic characteristics of different modalities are generalized with the latent representations. Another property of the proposed approach is that it is easily extended to more modalities. Extensive experimental results on four benchmark datasets [animal with attribute, Caltech UCSD birds, aPY, and ImageNet] clearly demonstrate the superiority of the proposed approach on several ZSL tasks, including traditional ZSL, generalized ZSL, and zero-shot retrieval.
Social-IQ: A Question Answering Benchmark for Artificial Social Intelligence

As intelligent systems increasingly blend into our everyday life, artificial social intelligence becomes a prominent area of research. Intelligent systems must be socially intelligent in order to comprehend human intents and maintain a rich level of interaction with humans. Human language offers a unique unconstrained approach to probe through questions and reason through answers about social situations. This unconstrained approach extends previous attempts to model social intelligence through numeric supervision (e.g. sentiment and emotions labels). In this paper, we introduce Social-IQ, a unconstrained benchmark specifically designed to train and evaluate socially intelligent technologies. By providing a rich source of open-ended questions and answers, Social-IQ opens the door to explainable social intelligence. The dataset contains rigorously annotated and validated videos, questions and answers, as well as annotations for the complexity level of each question and answer. Social-IQ contains 1,250 natural in-the-wild social situations, 7,500 questions and 52,500 correct and incorrect answers. Although humans can reason about social situations with very high accuracy (95.08%), existing state-of-the-art computational models struggle on this task. As a result, Social-IQ brings novel challenges that will spark future research in social intelligence modeling, visual reasoning, and multimodal question answering (QA).
A Self-Training Approach for Short Text Clustering

Short text clustering is a challenging problem when adopting traditional bag-of-words or TF-IDF representations, since these lead to sparse vector representations for short texts.Lowdimensional continuous representations or embeddings can counter that sparseness problem: their high representational power is exploited in deep clustering algorithms.While deep clustering has been studied extensively in computer vision, relatively little work has focused on NLP.The method we propose, learns discriminative features from both an autoencoder and a sentence embedding, then uses assignments from a clustering algorithm as supervision to update weights of the encoder network.Experiments on three short text datasets empirically validate the effectiveness of our method.
Personalized real-time movie recommendation system: Practical prototype and evaluation

With the eruption of big data, practical recommendation schemes are now very important in various fields, including e-commerce, social networks, and a number of web-based services.Nowadays, there exist many personalized movie recommendation schemes utilizing publicly available movie datasets (e.g., MovieLens and Netflix), and returning improved performance metrics (e.g., Root-Mean-Square Error (RMSE)).However, two fundamental issues faced by movie recommendation systems are still neglected: first, scalability, and second, practical usage feedback and verification based on real implementation.In particular, Collaborative Filtering (CF) is one of the major prevailing techniques for implementing recommendation systems.However, traditional CF schemes suffer from a time complexity problem, which makes them bad candidates for real-world recommendation systems.In this paper, we address these two issues.Firstly, a simple but high-efficient recommendation algorithm is proposed, which exploits users' profile attributes to partition them into several clusters.For each cluster, a virtual opinion leader is conceived to represent the whole cluster, such that the dimension of the original useritem matrix can be significantly reduced, then a Weighted Slope One-VU method is designed and applied to the virtual opinion leader-item matrix to obtain the recommendation results.Compared to traditional clusteringbased CF recommendation schemes, our method can significantly reduce the time complexity, while achieving comparable recommendation performance.Furthermore, we have constructed a real personalized web-based movie recommendation system, MovieWatch, opened it to the public, collected user feedback on recommendations, and evaluated the feasibility and accuracy of our system based on this real-world data.
Efficient Supervised Discrete Multi-View Hashing for Large-Scale Multimedia Search

Hashing has recently received substantial attention in large-scale multimedia search for its extremely low-cost storage cost and high retrieval efficiency. However, most existing hashing techniques focus on learning hash codes for single-view or cross-view retrieval. It is still an unsolved problem that how to efficiently learn discriminative binary codes for multi-view data that is common in real world multimedia search. In this paper, we propose an efficient Supervised Discrete Multi-view Hashing (SDMH) to solve the problem. SDMH first properly detects the shared binary hash codes, with an integrated multi-view feature mapping and latent hash coding, by exploiting the complementarity of different view-specific features and removing the involved inter-view redundancy. To further enhance the discriminative capability of hash codes, SDMH directly represses the explicit semantic labels of data samples with their corresponding binary codes. Different from most existing multi-view hashing methods that adopt “relaxing+rounding” hash optimization strategy or the discrete optimization method based on discrete cyclic coordinate descent, an efficient augmented Lagrangian multiplier (ALM) based discrete hash optimization method is developed in this paper to optimize the hash codes within a single step. Experimental results on four benchmark datasets demonstrate the superior performance of the proposed approach over state-of-the-art hashing techniques, in terms of both learning efficiency and retrieval accuracy.
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Microwave trapped-ion quantum logic gates avoid spontaneous emission as a fundamental source of decoherence. However, microwave two-qubit gates are still slower than laser-induced gates and hence more sensitive to fluctuations and noise of the motional mode frequency. We propose and implement amplitude-shaped gate drives to obtain resilience to such frequency changes without increasing the pulse energy per gate operation. We demonstrate the resilience by noise injection during a two-qubit entangling gate with ^{9}Be^{+} ion qubits. In the absence of injected noise, amplitude modulation gives an operation infidelity in the 10^{-3} range.