Reviewerly/exHarmony huggingface dataset:
Stretchable e-Skin Patch for Gesture Recognition on the Back of the Hand

Gesture recognition is important for human-computer interaction and a variety of emerging research and commercial areas including virtual and augmented reality. Current approaches typically require sensors to be placed on the forearm, wrist, or directly across finger joints; however, they can be cumbersome or hinder human movement and sensation. In this paper, we introduce a novel approach to recognize hand gestures by estimating skin strain with multiple soft sensors optimally placed across the back of the hand. A pilot study was first conducted by covering the back of the hand with 40 small 2.5 mm reflective markers and using a high-precision camera system to measure skin strain patterns for individual finger movements. Optimal strain locations are then determined and used for sensor placement in a stretchable e-skin patch prototype. Experimental testing is performed to evaluate the stretchable e-skin patch performance in classifying individual finger gestures and American Sign Language 0-9 number gestures. Results showed classification accuracies of 95.3% and 94.4% for finger gestures and American Sign Language 0-9 gestures, respectively. These results demonstrate the feasibility of a stretchable e-skin patch on the back of the hand for hand gesture recognition and their potential to significantly enhance human-computer interaction.
Combining colour and grey‐level co‐occurrence matrix features: a comparative study

Grey-level co-occurrence matrix (GLCM) is a widely used texture feature descriptor that is extracted from grey-level images. A considerable amount of work in the literature has been done trying to combine colour with GLCM features. The aim of this study is to examine the effect of integrating colour with GLCM. To this end, 14 descriptors that have been proposed in literature based on different colour combining approaches, four Convolutional Neural Networks-based, two aggregation-based, and five other handcrafted descriptors have been engaged in this comparison. Five widely known data sets have been used to experimentally evaluate each descriptor. The evaluation has been conducted for image classification and image retrieval. Results indicated that there is no co-occurrence-matrix-based descriptor that overcomes all the others in all data sets. Nevertheless, some descriptors have shown a remarkably higher performance than the others.
Utilizing CNNs and transfer learning of pre-trained models for age range classification from unconstrained face images

Automatic age classification from real-world and wild face images is a challenging task and has an increasing importance due to its wide range of applications in current and future lifestyles. As a result of increasing age specific human-computer interactions, it is expected that computerized systems should be capable of estimating the age from face images and respond accordingly. Over the past decade, many research studies have been conducted on automatic age classification from face images. However, the performance of the developed age classification systems suffered due to the absence of large, comprehensive benchmarks. In this work, we propose and show that pre-trained CNNs which were trained on large benchmarks for different purposes can be retrained and fine-tuned for age range classification from unconstrained face images. Also, we propose to reduce the dimension of the output of the last convolutional layer in pre-trained CNNs to improve the performance of the designed CNNs architectures. The experimental results show significant improvements in exact and 1-off accuracies on the Adience benchmark.
Two-Layer QR Codes

A quick-response code (QR code) is a two-dimensional code akin to a barcode that encodes a message of limited length. In this paper, we present a variant of QR code, a two-layer QR code. Its two-layer structure can display two alternative messages when scanned from two different directions. We propose a method to generate such two-layer QR codes encoding two given messages in a few seconds. We also demonstrate the robustness of our method on both synthetic and fabricated examples. All source code will be made publicly available (https://github.com/yuantailing/two-layer-qrcode).
A novel hybrid model based on particle swarm optimisation and extreme learning machine for short-term temperature prediction using ambient sensors

In modern buildings which are becoming smart day by day, indoor temperature can be forecasted with the data obtained from outfitted sensors. Predictive models based on data can accurately forecast temperature which further saves energy by optimisation of resources and technologies such as heating, ventilation and air conditioners for making the atmosphere conducive and comfortable in the ambient environment. This paper discusses an experiment from such house fitted with sensors for different parameter affecting indoor temperature. We propose a hybrid model which is based on particle swarm optimisation and emerging extreme learning machine to forecast the temperature to optimise the use of energy further. The proposed hybrid model also includes the variant online sequential extreme learning machine(OSELM) which accepts data online and is adaptive to the changing environmental conditions. We perform experiment based on many sensors combinations affecting temperature using particle swarm optimization and statistical tools to determine their relevance and correlation with temperature and compare results with conventional methods. Proposed methods improved the accuracy of the forecasting and also generalisation performance over other methods on the same dataset.
A Comparative Analysis on Hindi and English Extractive Text Summarization

Text summarization is the process of transfiguring a large documental information into a clear and concise form. In this article, we present a detailed comparative study of various extractive methods for automatic text summarization on Hindi and English text datasets of news articles. We consider 13 different summarization techniques, namely, TextRank, LexRank, Luhn, LSA, Edmundson, ChunkRank, TGraph, UniRank, NN-ED, NN-SE, FE-SE, SummaRuNNer, and MMR-SE, and we evaluate their performance using various performance metrics, such as precision, recall, F 1 , cohesion, non-redundancy, readability, and significance. A thorough analysis is done in eight different parts that exhibits the strengths and limitations of these methods, effect of performance over the summary length, impact of language of a document, and other factors as well. A standard summary evaluation tool (ROUGE) and extensive programmatic evaluation using Python 3.5 in Anaconda environment are used to evaluate their outcome.
Efficient Attribute-Based Encryption with Privacy-Preserving Key Generation and Its Application in Industrial Cloud

Due to the rapid development of new technologies such as cloud computing, Internet of Things (IoT), and mobile Internet, the data volumes are exploding. Particularly, in the industrial field, a large amount of data is generated every day. How to manage and use industrial Big Data primely is a thorny challenge for every industrial enterprise manager. As an emerging form of service, cloud computing technology provides a good solution. It receives more and more attention and support due to its flexible configuration, on-demand purchase, and easy maintenance. Using cloud technology, enterprises get rid of the heavy data management work and concentrate on their main business. Although cloud technology has many advantages, there are still many problems in terms of security and privacy. To protect the confidentiality of the data, the mainstream solution is encrypting data before uploading. In order to achieve flexible access control to encrypted data, attribute-based encryption (ABE) is an outstanding candidate. At present, more and more applications are using ABE to ensure data security. However, the privacy protection issues during the key generation phase are not considered in the current ABE systems. That is to say, the key generation center (KGC) knows both of attributes and corresponding keys of each user. This problem is especially serious in the industrial big data scenario, because it will cause great damage to the business secrets of industrial enterprises. In this paper, we design a new ABE scheme that protects user’s privacy during key issuing. In our new scheme, we separate the functionality of attribute auditing and key generating to ensure that the KGC cannot know user’s attributes and that the attribute auditing center (AAC) cannot obtain the user’s secret key. This is ideal for many privacy-sensitive scenarios, such as industrial big data scenario.
Joint Principal Component and Discriminant Analysis for Dimensionality Reduction

Linear discriminant analysis (LDA) is the most widely used supervised dimensionality reduction approach. After removing the null space of the total scatter matrix St via principal component analysis (PCA), the LDA algorithm can avoid the small sample size problem. Most existing supervised dimensionality reduction methods extract the principal component of data first, and then conduct LDA on it. However, "most variance" is very often the most important, but not always in PCA. Thus, this two-step strategy may not be able to obtain the most discriminant information for classification tasks. Different from traditional approaches which conduct PCA and LDA in sequence, we propose a novel method referred to as joint principal component and discriminant analysis (JPCDA) for dimensionality reduction. Using this method, we are able to not only avoid the small sample size problem but also extract discriminant information for classification tasks. An iterative optimization algorithm is proposed to solve the method. To validate the efficacy of the proposed method, we perform extensive experiments on several benchmark data sets in comparison with some state-of-the-art dimensionality reduction methods. A large number of experimental results illustrate that the proposed method has quite promising classification performance.
Sentiment Analysis of Customer Reviews Using Ensemble Method

With increasing interest in e-commerce and online shopping recently, customer reviews are one of the most important elements that determine the satisfaction of customers about the products. Also, it gives a comprehensive picture of products to business owners. Hence, the aim of this paper is to conduct a sentimental analysis approach on a set of customer reviews collected from Amazon. As will, classify each review into one of these class: positive review or negative review by using ensemble machine learning method. The ensemble machine learning method used in this research is Voting which combined five classifiers: Naive Bayes, Support Vector Machines (SVMs), Random forest, Bagging and Boosting. All the Experiment in this paper done using Weka. We test six different scenarios to evaluate our proposed model against the five classifiers. The scenarios are using unigram (with/without) stop of word removal, using bigram (with/without) stop of word removal and using trigram (with/without) stop of word removal. The result shows the random forest technique give the highest accuracy which equals to 89.87% in the case of using unigram and with a stop of word removal but voting algorithm shows the best performance on the other scenarios.
DUSKG: A fine-grained knowledge graph for effective personalized service recommendation

The last decade has witnessed the rapid development of service-oriented computing, resulting in a tremendous growth of services which further calls for novel approaches for efficient and effective service recommendations. Various types of data can be used for service recommendations, such as user feedback on services, service profile, user profile, etc. In traditional recommendation algorithms, such data is usually handled separately and in the form of matrix. Since there are underlying logical relations between the data, we conjecture that deep convergence of all the data and full considerations of such logical relations would help boost accuracy of service recommendations. By a comprehensive survey on various existing data convergence methods, we found that knowledge graph had been a typical approach to describe the different relations between data. In this paper, we propose an elegant, natural and compact data representation model incorporating such kinds of logical relations, namely Domain-oriented User and Service Interaction Knowledge Graph (DUSKG). Three types of entities (User, Service and ServiceValueFeature(VF)), five types of fine-grained relations (FOCUSON, BELONGTO, USIMILAR, SSIMILAR and FSIMILAR), and weight vectors that quantitatively delineate these relations are elaborately defined. An approach for constructing DUSKG from service data is presented. Extracted from DUSKG, each user's value preference is then represented in terms of VFs. Five personalized service recommendation methods are presented in terms of such value preferences and various relations in DUSKG. To verify the proposed methods, a set of experiments are conducted on Yelp dataset. Experimental results show that our approaches achieve better recommendation performance compared to the state-of-the-art methods, and among the five ones, HR-DUSKG (Hybrid Recommendation Depending on DUSKG) exhibits the best performance with quite good achievements but least calculation time.
Trusted Transactions in Micro-Grid Based on Blockchain

In order to build a local electricity market (LEM), community members can trade electricity peer-to-peer (P2P) with their neighbors. This paper proposes a Hierarchical Bidding and Transaction Structure based on blockchain (HBTS). First, combined with the multi-agents, each microgrid corrects the estimated cost probability distribution of other microgrids by Bayesian theorem, making its probability closer to the accurate probability. Second, for maximize the benefits of the microgrid, this paper uses the Nash equilibrium in the Cournot model to find the optimal quotation and output of different bidding strategies for the microgrid under different power demand conditions. Then the exchange of electricity translates into an exchange of digital proof of electricity purchases and sales of electricity on the Hyperledger Fabric, ensuring the security of the transaction process and the irreparable modification of ledgers. Finally, we verify the effectiveness of the bidding strategy through experiments, and analyze the transaction process.
The intelligent water drops algorithm: why it cannot be considered a novel algorithm

In this article, we rigorously analyze the intelligent water drops (IWD) algorithm, a metaphor-based approach for the approximate solution of discrete optimization problems proposed by Shah-Hosseini (in: Proceedings of the 2007 congress on evolutionary computation (CEC 2007), IEEE Press, Piscataway, NJ, pp 3226–3231, 2007). We demonstrate that all main algorithmic components of IWD are simplifications or special cases of ant colony optimization (ACO), and therefore, IWD is simply a particular instantiation of ACO. We show that the natural metaphor of “water drops flowing in rivers removing the soil from the riverbed”, the source of inspiration of IWD, is unnecessary, misleading and based on unconvincing assumptions of river dynamics and soil erosion that lack a real scientific rationale. We carry out a detailed review of modifications and extensions proposed to IWD since its first publication in 2007. We find that research on IWD is for the most part misguided and that the vast majority of the ideas explored in the literature on IWD have been studied many years before in the context of ACO. Finally, we discuss the use of natural metaphors as a source of inspiration for optimization algorithms, which has become an extremely popular trend in the last 15 years, and propose some criteria to limit their usage to the cases in which the metaphor is indeed useful.
Diagonal Acceleration for Covariance Matrix Adaptation Evolution Strategies

We introduce an acceleration for covariance matrix adaptation evolution strategies (CMA-ES) by means of adaptive diagonal decoding (dd-CMA). This diagonal acceleration endows the default CMA-ES with the advantages of separable CMA-ES without inheriting its drawbacks. Technically, we introduce a diagonal matrix [Formula: see text] that expresses coordinate-wise variances of the sampling distribution in DCD form. The diagonal matrix can learn a rescaling of the problem in the coordinates within a linear number of function evaluations. Diagonal decoding can also exploit separability of the problem, but, crucially, does not compromise the performance on nonseparable problems. The latter is accomplished by modulating the learning rate for the diagonal matrix based on the condition number of the underlying correlation matrix. dd-CMA-ES not only combines the advantages of default and separable CMA-ES, but may achieve overadditive speedup: it improves the performance, and even the scaling, of the better of default and separable CMA-ES on classes of nonseparable test functions that reflect, arguably, a landscape feature commonly observed in practice. The article makes two further secondary contributions: we introduce two different approaches to guarantee positive definiteness of the covariance matrix with active CMA, which is valuable in particular with large population size; we revise the default parameter setting in CMA-ES, proposing accelerated settings in particular for large dimension. All our contributions can be viewed as independent improvements of CMA-ES, yet they are also complementary and can be seamlessly combined. In numerical experiments with dd-CMA-ES up to dimension 5120, we observe remarkable improvements over the original covariance matrix adaptation on functions with coordinate-wise ill-conditioning. The improvement is observed also for large population sizes up to about dimension squared.
BrownoutCon: A software system based on brownout and containers for energy-efficient cloud computing

VM consolidation and Dynamic Voltage Frequency Scaling approaches have been proved to be efficient to reduce energy consumption in cloud data centers. However, the existing approaches cannot function efficiently when the whole data center is overloaded. An approach called brownout has been proposed to solve the limitation, which dynamically deactivates or activates optional microservices or containers. In this paper, we propose a brownout-based software system for container-based clouds to handle overloads and reduce power consumption. We present its design and implementation based on Docker Swarm containers. The proposed system is integrated with existing Docker Swarm without the modification of their configurations. To demonstrate the potential of BrownoutCon software in offering energy-efficient services in brownout situation, we implemented several policies to manage containers and conducted experiments on French Grid’5000 cloud infrastructure. The results show the currently implemented policies in our software system can save about 10%–40% energy than the existing baselines while ensuring quality of services.
AgentGraph: Toward Universal Dialogue Management With Structured Deep Reinforcement Learning

Dialogue policy plays an important role in task-oriented spoken dialogue systems. It determines how to respond to users. The recently proposed deep reinforcement learning (DRL) approaches have been used for policy optimization. However, these deep models are still challenging for two reasons: first, many DRL-based policies are not sample efficient; and second, most models do not have the capability of policy transfer between different domains. In this paper, we propose a universal framework, AgentGraph, to tackle these two problems. The proposed AgentGraph is the combination of graph neural network (GNN) based architecture and DRL-based algorithm. It can be regarded as one of the multi-agent reinforcement learning approaches. Each agent corresponds to a node in a graph, which is defined according to the dialogue domain ontology. When making a decision, each agent can communicate with its neighbors on the graph. Under AgentGraph framework, we further propose dual GNN-based dialogue policy, which implicitly decomposes the decision in each turn into a high-level global decision and a low-level local decision. Experiments show that AgentGraph models significantly outperform traditional reinforcement learning approaches on most of the 18 tasks of the PyDial benchmark. Moreover, when transferred from the source task to a target task, these models not only have acceptable initial performance but also converge much faster on the target task.
Region-based active learning for efficient labeling in semantic segmentation

As vision-based autonomous systems, such as self-driving vehicles, become a reality, there is an increasing need for large annotated datasets for developing solutions to vision tasks. One important task that has seen significant interest in recent years is semantic segmentation. However, the cost of annotating every pixel for semantic segmentation is immense, and can be prohibitive in scaling to various settings and locations. In this paper, we propose a region-based active learning method for efficient labeling in semantic segmentation. Using the proposed active learning strategy, we show that we are able to judiciously select the regions for annotation such that we obtain 93.8% of the baseline performance (when all pixels are labeled) with labeling of 10% of the total number of pixels. Further, we show that this approach can be used to transfer annotations from a model trained on a given dataset (Cityscapes) to a different dataset (Mapillary), thus highlighting its promise and potential.
Enact

Professional designers and developers often struggle when transitioning between the design and implementation of an interactive system. We conducted three studies that focused on the design of custom interactions to understand the mismatches between their processes, tools, and representations. We found that current practices induce unnecessary rework and cause discrepancies between design and implementation. We identified three recurring types of breakdowns: omitting critical details, ignoring edge cases, and disregarding technical limitations. We propose four design principles to create tools that mitigate these problems: Provide multiple viewpoints , maintain a single source of truth , reveal the invisible, and support design by enaction . We applied these principles to create E NACT , a live environment for prototyping touch-based interactions. We conducted two studies to assess E NACT and to compare designer–developer collaboration with E NACT versus current tools. Results suggest that E NACT helps participants detect more edge cases, increases designers’ participation and provides new opportunities for co-creation.