Reviewerly/exHarmony huggingface dataset:
A non-singleton type-2 fuzzy neural network with adaptive secondary membership for high dimensional applications

This paper develops a non-singleton type-2 fuzzy neural network (NT2FNN) with type-2 3-dimensional membership functions (MFs) and adaptive secondary membership. A new approach based on the square-root cubature quadrature Kalman filter (SR-CQKF) is proposed for the training the level of the secondary membership and the centers of membership functions. The consequent parameters are learned by using rule-ordered extended Kalman filter (EKF). To show the applicability and effectiveness of proposed NT2FNN in high dimensional problems, four real-world datasets with 4, 7, 13 and 32 input variables are considered. Additionally, the performance of NT2FNN with the proposed learning algorithm is compared with other well-known neural networks and learning algorithms. The simulations demonstrate that the developed method results in high performance in contrast to the other methods.
Boosting the performance of over-sampling algorithms through under-sampling the minority class

Over-sampling algorithms are the most adopted approach to balance class distribution in imbalanced data problems, through random replication or synthesis of new examples in the minority class. Current over-sampling algorithms, however, usually use all available examples in the minority class to synthesise new instances, which may include noisy or outlier data. This work proposes k-INOS, a new algorithm to prevent over-sampling algorithms from being contaminated by noisy examples in the minority class. k-INOS is based on the concept of neighbourhood of influence and works as a wrapper around any over-sampling algorithm. A comprehensive experimentation was conducted to test k-INOS in 50 benchmark data sets, 8 over-sampling methods and 5 classifiers, with performance measured according to 7 metrics and Wilcoxon signed-ranks test. Results showed, particularly for weak classifiers (but not only), k-INOS significantly improved the performance of over-sampling algorithms in most performance metrics. Further investigations also allowed to identify conditions where k-INOS is likely to increase performance, according to features and rates measured from the data sets. The extensive experimentation framework evidenced k-INOS as an efficient algorithm to be applied prior to over-sampling methods.
SSR2: Sparse signal recovery for single-image super-resolution on faces with extreme low resolutions

Automatic face recognition in the wild still suffers from low-quality, low resolution, noisy, and occluded input images that can severely impact identification accuracy. In this paper, we present a novel technique to enhance the quality of such extreme low-resolution face images beyond the current state of the art. We model the correlation between high and low resolution faces in a multi-resolution pyramid and show that we can recover the original structure of an un-seen extreme low-resolution face image. By exploiting domain knowledge of the structure of the input signal and using sparse recovery optimization algorithms, we can recover a consistent sparse representation of the extreme low-resolution signal. The proposed super-resolution method is robust to noise and face alignment, and can handle extreme low-resolution faces up to 16x magnification factor with just 7 pixels between the eyes. Moreover, the formulation of the proposed algorithm allows for simultaneous occlusion removal capability, a desirable property that other super-resolution algorithms do not possess, to the best of our knowledge. Most importantly, we show that our method generalizes on real-world low-quality surveillance images, showing the potentially big impact this can have in a real-world scenario.
Using an Augmented Reality–Based Teaching Strategy to Teach Mathematics to Secondary Students With Disabilities

A basic understanding of math, numeracy, and related concepts are critical skills for functioning independently in society. Individuals with disabilities often struggle with basic math, which impedes their ability for independent living. Augmented reality (AR), which builds on the evidence-based principles of video modeling, is a promising teaching strategy of providing math instruction to individuals with disabilities. This article provides a practical framework (with step-by-step instructions and examples) for practitioners wishing to implement AR as a teaching strategy for secondary students with disabilities.
Generating Video Descriptions With Latent Topic Guidance

Automatic video description generation (a.k.a video captioning) is one of the ultimate goals for video understanding. Despite the wide range of applications such as video indexing and retrieval etc., the video captioning task remains quite challenging due to the complexity and diversity of video content. First, open-domain videos cover a broad range of topics, which results in highly variable vocabularies and expression styles to describe the video contents. Second, videos naturally contain multiple modalities including image, motion, and acoustic media. The information provided by different modalities differs in different conditions. In this paper, we propose a novel topic-guided video captioning model to address the above-mentioned challenges in video captioning. Our model consists of two joint tasks, namely, latent topic generation and topic-guided caption generation. The topic generation task aims to automatically predict the latent topic of the video. Since there is no groundtruth topic information, we mine multimodal topics in an unsupervised fashion based on video contents and annotated captions, and then distill the topic distribution to a topic prediction model. In the topic-guided generation task, we employ the topic guidance for two purposes. The first is to narrow down the language complexity across topics, where we propose the topic-aware decoder to leverage the latent topics to induce topic-related language models. The decoder is also generic and can be integrated with a temporal attention mechanism. The second is to dynamically attend to important modalities by topics, where we propose a flexible topic-guided multimodal ensemble framework and use the topic gating network to determine the attention weights. The two tasks are correlated with each other, and they collaborate to generate more detailed and accurate video captions. Our extensive experiments on two public benchmark datasets MSR-VTT and Youtube2Text demonstrate the effectiveness of the proposed topic-guided video captioning system, which achieves state-of-the-art performance on both datasets.
Semantic reasoning in service robots using expert systems

This paper presents the semantic-reasoning module of VIRBOT, our proposed architecture for service robots. We show that by combining symbolic AI with digital-signal processing techniques this module achieves competitive performance. Our system translates a voice command into an unambiguous representation that helps an inference engine, built around an expert system, to perform action and motion planning. First, in the natural-language interpretation process, the system generates two outputs: (1) conceptual dependence, expressing the linguistic meaning of the statement, and (2) verbal confirmation, a paraphrase in natural language that is repeated to the user to confirm that the command has been correctly understood. Then, a conceptual-dependency interpreter extracts semantic role structures from the input sentence and looks for such structures in a set of known interpretation patterns. We evaluate this approach in a series of skill-specific semantic-reasoning experiments. Finally, we demonstrate our system in the general-purpose service robot test of the RoboCup-at-Home international competition, where incomplete information is given to a robot and the robot must recognize and request the missing information, and we compare our results with a series of baselines from the competition where our proposal performed best.
A Low-Cost Indoor Activity Monitoring System for Detecting Frailty in Older Adults

Indoor localization systems have already wide applications mainly for providing localized information and directions. The majority of them focus on commercial applications providing information such us advertisements, guidance and asset tracking. Medical oriented localization systems are uncommon. Given the fact that an individual's indoor movements can be indicative of his/her clinical status, in this paper we present a low-cost indoor localization system with room-level accuracy used to assess the frailty of older people. We focused on designing a system with easy installation and low cost to be used by non technical staff. The system was installed in older people houses in order to collect data about their indoor localization habits. The collected data were examined in combination with their frailty status, showing a correlation between them. The indoor localization system is based on the processing of Received Signal Strength Indicator (RSSI) measurements by a tracking device, from Bluetooth Beacons, using a fingerprint-based procedure. The system has been tested in realistic settings achieving accuracy above 93% in room estimation. The proposed system was used in 271 houses collecting data for 1⁻7-day sessions. The evaluation of the collected data using ten-fold cross-validation showed an accuracy of 83% in the classification of a monitored person regarding his/her frailty status (Frail, Pre-frail, Non-frail).
Control-Enhanced Sequential Scheme for General Quantum Parameter Estimation at the Heisenberg Limit

The advantage of quantum metrology has been experimentally demonstrated for phase estimations where the dynamics are commuting. General noncommuting dynamics, however, can have distinct features. For example, the direct sequential scheme, which can achieve the Heisenberg scaling for the phase estimation under commuting dynamics, can have even worse performances than the classical scheme when the dynamics are noncommuting. Here we realize a scalable optimally controlled sequential scheme, which can achieve the Heisenberg precision under general noncommuting dynamics. We also present an intuitive geometrical framework for the controlled scheme and identify sweet spots in time at which the optimal controls used in the scheme can be prefixed without adaptation, which simplifies the experimental protocols significantly. We successfully implement the scheme up to eight controls in an optical platform and demonstrate a precision near the Heisenberg limit. Our work opens the avenue for harvesting the power of quantum control in quantum metrology, and provides a control-enhanced recipe to achieve the Heisenberg precision under general noncommuting dynamics.
Effective image clustering based on human mental search

Image segmentation is one of the fundamental techniques in image analysis. One group of segmentation techniques is based on clustering principles, where association of image pixels is based on a similarity criterion. Conventional clustering algorithms, such as k-means, can be used for this purpose but have several drawbacks including dependence on initialisation conditions and a higher likelihood of converging to local rather than global optima. In this paper, we propose a clustering-based image segmentation method that is based on the human mental search (HMS) algorithm. HMS is a recent metaheuristic algorithm based on the manner of searching in the space of online auctions. In HMS, each candidate solution is called a bid, and the algorithm comprises three major stages: mental search, which explores the vicinity of a solution using Levy flight to find better solutions; grouping which places a set of candidate solutions into a group using a clustering algorithm; and moving bids toward promising solution areas. In our image clustering application, bids encode the cluster centres and we evaluate three different objective functions. In an extensive set of experiments, we compare the efficacy of our proposed approach with several state-of-the-art metaheuristic algorithms including a genetic algorithm, differential evolution, particle swarm optimisation, artificial bee colony algorithm, and harmony search. We assess the techniques based on a variety of metrics including the objective functions, a cluster validity index, as well as unsupervised and supervised image segmentation criteria. Moreover, we perform some tests in higher dimensions, and conduct a statistical analysis to compare our proposed method to its competitors. The obtained results clearly show that the proposed algorithm represents a highly effective approach to image clustering that outperforms other state-of-the-art techniques.
Twitter Text Mining for Sentiment Analysis on People’s Feedback about Oman Tourism

Sentiment analysis plays vital role in the internet era due to extensive range of business applications and social media. Inspiration behind sentiment analysis is that it provides people`s opinion about the product, which helps to improve the product quality. It also supports to take purchase/manufacturing decisions. In this paper we apply sentiment analysis to catch people feedback about Oman tourism using social media messages. For this we use tweeter data set to analyze tourist opinion about this country. In this paper, we recommend innovative sentiment analysis method based on common sense knowledge (Domain Specific Ontology). We created our own Oman tourism ontology based on ConceptNet. Entities are identified from the tweets using POS tagger and entities are compared with concepts in the domain specific ontology. Further the sentiment of the extracted entities are determined by the combined sentiment lexicon approach. Finally semantic orientations of domain specific features are combined with respect to the domain. We deliberate conceptual semantic as feature which can be combined with machine learning algorithm to enhance the performance of sentiment analysis of Oman tourism.
BBC Net: Bounding-Box Critic Network for Occlusion-Robust Object Detection

Object detection has received significant interest in the research field of computer vision and is widely used in human-centric applications. The occlusion problem is a frequent obstacle that degrades detection quality. In this paper, we propose a novel object detection framework targeting robust object detection in occlusion. The proposed deep learning-based network consists mainly of two parts: 1) object detection framework, which classifies the object categories and localizes the object location and 2) plug-in bounding-box (BB) estimator, which estimates the object and occlusion region from the feature map of the backbone network and the corresponding critic network for evaluating the predicted BB map. The BB estimator and the critic network are the plug-in modules added to the object detection framework and learned competitively with adversarial manner. As the plug-in BB estimator is learned to estimate the BB map containing the object and occlusion pattern information, the backbone network can embed this information to enable robust detection under occlusion in the test phase. The comprehensive experimental results on the PASCAL VOC, MS COCO, and KITTI dataset showed that the performance is improved with the plug-in BB-Critic network by predicting and criticizing object and occlusion in general generic object detection framework.
A Literature Review on Bitcoin: Transformation of Crypto Currency Into a Global Phenomenon

Bitcoin is a crypto-currency-based open-source technology that operates in the peer-to-peer grid as a private payment mechanism. Bitcoin works on sophisticated cryptography supported by a local community in a peer-to-peer network. This study reviews scholarly articles to understand how bitcoin is addressed in the literature. The study reports on the attributes of bitcoin through a systematic literature review. The paper is based on primary data from existing literature and secondary data from relevant case studies in the public domain. Unlike other currencies, Bitcoin seems to have faced many hurdles and with many applications in day-to-day life, created unique challenges for the end user community. When Bitcoin came into existence, it seemed to signal hope for a better future, but the growth of bitcoin is hard to predict. Bitcoin opens up an entirely new world for both practitioners and academicians. Further, this study presents an idea about the “potential” of bitcoin, highlights the prerequisites, needs, implications, and challenges faced by bitcoin in processing business transactions.
Quantum information science and technology in Japan

We review the national projects on quantum information science and technology in Japan over the past 30 years. The main funding agencies for this field have been the Japan Science and Technology Agency, the National Institute of Information and Communications Technology, the Japan Society for the Promotion of Science, and the Cabinet Office of the Government of Japan. The total investment in the quantum information science and technology field from these funding agencies for the past 15 years amounts to US$250 million. A similar amount of investment has been put in parallel by industry. As a result, new science frontiers have been created, and some service prototypes, such as quantum key distribution systems and coherent Ising machines, have been delivered to user premises. Recently, Japan has launched new initiatives to advance quantum information science and technology to the next phase, and to make full use of them for the resolution of societal problems.
Online Multi-Object Tracking Based on Feature Representation and Bayesian Filtering Within a Deep Learning Architecture

In detection-based multi-object tracking (MOT), one challenging problem is to design a robust affinity model for data association. Moreover, since these approaches entirely rely on detection responses to locate targets, a strategy should be taken to deal with a detector's defect. In this paper, we propose a robust online MOT tracking method that can handle these two issues effectively. We first present a novel affinity model by jointly learning more powerful feature representation and distance metric within a deep architecture. Specifically, we design a convolutional neural network to extract appearance cue tailored toward person Re-ID and a long short-term memory network to extract motion cue to encode dynamics of targets. Both the cues are then combined with a triplet loss function, which performs end-to-end deep metric learning to encode dependences across both cues automatically and thus generates fused features in embedding space to distinguish targets. To overcome the detector's limitation, a trajectory estimation strategy is presented. We design a recurrent neural network-based Bayesian filtering module, which takes a hidden state of the above-mentioned LSTM network as an input and performs recursive prediction and update for explicitly estimating targets state. In this way, we can reconstruct trajectories by filling the gaps where no detections are present or adjusting the exact locations of trajectory where detections are imprecise. The experiments on the challenging MOT 2015 and 2016 datasets show very competitive results when comparing our method with the recent state-of-the-art batch and online tracking approaches. We achieve top one in terms of multiple objects tracking accuracy and multiple objects tracking precision among online methods on the MOT2016 dataset.
Improving Attention Model Based on Cognition Grounded Data for Sentiment Analysis

Attention models are proposed in sentiment analysis and other classification tasks because some words are more important than others to train the attention models. However, most existing methods either use local context based information, affective lexicons, or user preference information. In this work, we propose a novel attention model trained by cognition grounded eye-tracking data. First,a reading prediction model is built using eye-tracking data as dependent data and other features in the context as independent data. The predicted reading time is then used to build a cognition grounded attention layer for neural sentiment analysis. Our model can capture attentions in context both in terms of words at sentence level as well as sentences at document level. Other attention mechanisms can also be incorporated together to capture other aspects of attentions, such as local attention, and affective lexicons. Results of our work include two parts. The first part compares our proposed cognition ground attention model with other state-of-the-art sentiment analysis models. The second part compares our model with an attention model based on other lexicon based sentiment resources. Evaluations show that sentiment analysis using cognition grounded attention model outperforms the state-of-the-art sentiment analysis methods significantly. Comparisons to affective lexicons also indicate that using cognition grounded eye-tracking data has advantages over other sentiment resources by considering both word information and context information. This work brings insight to how cognition grounded data can be integrated into natural language processing (NLP) tasks.
Big Data Goes Small: Real-Time Spectrum-Driven Embedded Wireless Networking Through Deep Learning in the RF Loop

The explosion of 5G networks and the Internet of Things will result in an exceptionally crowded RF environment, where techniques such as spectrum sharing and dynamic spectrum access will become essential components of the wireless communication process. In this vision, wireless devices must be able to (i) learn to autonomously extract knowledge from the spectrum on-the-fly; and (ii) react in real time to the inferred spectrum knowledge by appropriately changing communication parameters, including frequency band, symbol modulation, coding rate, among others. Traditional CPU-based machine learning suffers from high latency, and requires application-specific and computationally-intensive feature extraction/selection algorithms. Conversely, deep learning allows the analysis of massive amount of unprocessed spectrum data without ad-hoc feature extraction. So far, deep learning has been used for offline wireless spectrum analysis only. Therefore, additional research is needed to design systems that bring deep learning algorithms directly on the device's hardware and tightly intertwined with the RF components to enable real-time spectrum-driven decision-making at the physical layer. In this paper, we present RFLearn, the first system enabling spectrum knowledge extraction from unprocessed I/Q samples by deep learning directly in the RF loop. RFLearn provides (i) a complete hardware/software architecture where the CPU, radio transceiver and learning/actuation circuits are tightly connected for maximum performance; and (ii) a learning circuit design framework where the latency vs. hardware resource consumption trade-off can explored. We implement and evaluate the performance of RFLearn on custom software-defined radio built on a system-on-chip (SoC) ZYNQ-7000 device mounting AD9361 radio transceivers and VERT2450 antennas. We showcase the capabilities of RFLearn by applying it to solving the fundamental problems of modulation and OFDM parameter recognition. Experimental results reveal that RFLearn decreases latency and power by about 17x and 15x with respect to a software-based solution, with a comparatively low hardware resource consumption.