Reviewerly/exHarmony huggingface dataset:
Optimized Binary Bat algorithm for classification of white blood cells

The quantitative and differential analysis of leukocytes present in human body provide conducive hematological information to physicians for diagnosis of various infections and ailments. This paper proposes an Optimized Binary Bat algorithm, an enhanced version of the original Binary Bat Algorithm, for classification of different types of leukocytes. It is used for the first time in this field of application to the best of our knowledge. A set of features are extracted from images of WBCs and then the optimized algorithm is used to obtain a subset of those features which are essential and more relevant from the high-dimensional dataset. Similar to the original BBA, the optimized BBA is an evolutionary algorithm inspired by the echolocation technique used by bats for locating a prey or an object. OBBA aims to reduce the dimensionality of the dataset by determining the features which are most discriminative. The proposed algorithm is implemented using four different classifiers, K-nearest neighbors (KNN), Logistic Regression, Random Forest and Decision Tree, and their performance is compared. The proposed OBBA can be used in classification of WBCs with an average accuracy of 97.3% and help in analysis of numerous hematological conditions. The optimized BBA is also compared with other nature inspired algorithms including Optimized Crow Search algorithm and Optimized Cuttlefish algorithm, and the results indicate that the suggested algorithm is sufficiently fast and accurate to be used in hematological analysis.
Enhancing unsupervised neural networks based text summarization with word embedding and ensemble learning

The vast amounts of data being collected and analyzed have led to invaluable source of information, which needs to be easily handled by humans. Automatic Text Summarization (ATS) systems enable users to get the gist of information and knowledge in a short time in order to make critical decisions quickly. Deep neural networks have proven their ability to achieve excellent performance in many real-world Natural Language Processing and computer vision applications. However, it still lacks attention in ATS. The key problem of traditional applications is that they involve high dimensional and sparse data, which makes it difficult to capture relevant information. One technique for overcoming these problems is learning features via dimensionality reduction. On the other hand, word embedding is another neural network technique that generates a much more compact word representation than a traditional Bag-of-Words (BOW) approach. In this paper, we are seeking to enhance the quality of ATS by integrating unsupervised deep neural network techniques with word embedding approach. First, we develop a word embedding based text summarization, and we show that Word2Vec representation gives better results than traditional BOW representation. Second, we propose other models by combining word2vec and unsupervised feature learning methods in order to merge information from different sources. We show that unsupervised neural networks models trained on Word2Vec representation give better results than those trained on BOW representation. Third, we also propose three ensemble techniques. The first ensemble combines BOW and word2vec using a majority voting technique. The second ensemble aggregates the information provided by the BOW approach and unsupervised neural networks. The third ensemble aggregates the information provided by Word2Vec and unsupervised neural networks. We show that the ensemble methods improve the quality of ATS, in particular the ensemble based on word2vec approach gives better results. Finally, we perform different experiments to evaluate the performance of the investigated models. We use two kind of datasets that are publically available for evaluating ATS task. Results of statistical studies affirm that word embedding-based models outperform the summarization task compared to those based on BOW approach. In particular, ensemble learning technique with Word2Vec representation surpass all the investigated models.
Temporal Prediction of Multiapplication Consolidated Workloads in Distributed Clouds

With their fast development and deployment, a large number of cloud services provided by distributed cloud data centers have become the most important part of Internet services. In spite of numerous benefits, their providers face some challenging issues, e.g., dynamic resource scaling and power consumption. Workload prediction plays a crucial role in addressing them. Accuracy and fast learning are the key performances. Its consistent efforts have been made for their improvement. This paper proposes an integrated prediction method that combines the Savitzky-Golay filter and wavelet decomposition with stochastic configuration networks to predict workload at the next time slot. In this approach, a task time series is first smoothed by the SG filter, and the smoothed one is then decomposed into multiple components via wavelet decomposition. Based on them, an integrated model is, for the first time, established and can well characterize the statistical features of both trend and detailed components. Experimental results demonstrate that it achieves better prediction results and faster learning speed than some representative prediction methods.
Hierarchical Lifelong Learning by Sharing Representations and Integrating Hypothesis

In lifelong machine learning (LML) systems, consecutive new tasks from changing circumstances are learned and added to the system. However, sufficiently labeled data are indispensable for extracting intertask relationships before transferring knowledge in classical supervised LML systems. Inadequate labels may deteriorate the performance due to the poor initial approximation. In order to extend the typical LML system, we propose a novel hierarchical lifelong learning algorithm (HLLA) consisting of two following layers: 1) the knowledge layer consisted of shared representations and integrated knowledge basis at the bottom and 2) parameterized hypothesis functions with features at the top. Unlabeled data is leveraged in HLLA for pretraining of the shared representations. We also have considered a selective inherited updating method to deal with intertask distribution shifting. Experiments show that our HLLA method outperforms many other recent LML algorithms, especially when dealing with higher dimensional, lower correlation, and fewer labeled data problems.
Robust real-time hand detection and localization for space human–robot interaction based on deep learning

Hand gestures are quite suitable for space human–robot interaction (SHRI) because of their natural and convenient features. While the detection and localization of hands are the premise and foundation for SHRI based on hand gestures. But hand gestures are very complicated and hand sizes are very small in some images. These problems make the robust real-time hand detection and localization very difficult. In this paper, a feature-map-fused single shot multibox detector (FF-SSD) which is a deep learning network is designed to deal with the problems of hand detection and localization in SHRI. First, the background of the method is introduced in this paper, including an astronaut assistant robot platform, the difficulties of hand detection and localization, and introduction of the state-of-the-art deep learning networks for object detection and localization. Then, the FF-SSD is proposed for detecting and localizing hands especially pony-size hands. This network takes into consideration both accuracy and speed with balanced performance. And in the experiment part, the FF-SSD is trained and tested on hand databases which include a homemade database and two public databases. At last, the superiority of the proposed method is demonstrated compared with the state-of-the-art methods.
OIM: Oscillator-Based Ising Machines for Solving Combinatorial Optimisation Problems

We present a new way to make Ising machines, i.e., using networks of coupled self-sustaining nonlinear oscillators. Our scheme is theoretically rooted in a novel result that establishes that the phase dynamics of coupled oscillator systems, under the influence of subharmonic injection locking, are governed by a Lyapunov function that is closely related to the Ising Hamiltonian of the coupling graph. As a result, the dynamics of such oscillator networks evolve naturally to local minima of the Lyapunov function. Two simple additional steps (i.e., adding noise, and turning subharmonic locking on and off smoothly) enable the network to find excellent solutions of Ising problems. We demonstrate our method on Ising versions of the MAX-CUT and graph colouring problems, showing that it improves on previously published results on several problems in the G benchmark set. Our scheme, which is amenable to realisation using many kinds of oscillators from different physical domains, is particularly well suited for CMOS IC implementation, offering significant practical advantages over previous techniques for making Ising machines. We present working hardware prototypes using CMOS electronic oscillators.
Reviewing Cyber Security Social Engineering Training and Awareness Programs—Pitfalls and Ongoing Issues

The idea and perception of good cyber security protection remains at the forefront of many organizations’ information and communication technology strategy and investment. However, delving deeper into the details of its implementation reveals that organizations’ human capital cyber security knowledge bases are very low. In particular, the lack of social engineering awareness is a concern in the context of human cyber security risks. This study highlights pitfalls and ongoing issues that organizations encounter in the process of developing the human knowledge to protect from social engineering attacks. A detailed literature review is provided to support these arguments with analysis of contemporary approaches. The findings show that despite state-of-the-art cyber security preparations and trained personnel, hackers are still successful in their malicious acts of stealing sensitive information that is crucial to organizations. The factors influencing users’ proficiency in threat detection and mitigation have been identified as business environmental, social, political, constitutional, organizational, economical, and personal. Challenges with respect to both traditional and modern tools have been analyzed to suggest the need for profiling at-risk employees (including new hires) and developing training programs at each level of the hierarchy to ensure that the hackers do not succeed.
Smart Home Security Cameras and Shifting Lines of Creepiness

Through a design-led inquiry focused on smart home security cameras, this research develops three key concepts for research and design pertaining to new and emerging digital consumer technologies. Digital leakage names the propensity for digital information to be shared, stolen, and misused in ways unbeknownst or even harmful to those to whom the data pertains or belongs. Hole-and-corner applications are those functions connected to users' data, devices, and interactions yet concealed from or downplayed to them, often because they are non-beneficial or harmful to them. Foot-in-the-door devices are product and services with functional offerings and affordances that work to normalize and integrate a technology, thus laying groundwork for future adoption of features that might have earlier been rejected as unacceptable or unnecessary. Developed and illustrated through a set of design studies and explorations, this paper shows how these concepts may be used analytically to investigate issues such as privacy and security, anticipatorily to speculate about the future of technology development and use, and generatively to synthesize design concepts and solutions.
Augmented Reality in the Integrative Internet of Things (AR-IoT): Application for Precision Farming

Benefitted by the Internet of Things (IoT), visualization capabilities facilitate the improvement of precision farming, especially in dynamic indoor planting. However, conventional IoT data visualization is usually carried out in offsite and textual environments, i.e., text and number, which do not promote a user’s sensorial perception and interaction. This paper introduces the use of augmented reality (AR) as a support to IoT data visualization, called AR-IoT. The AR-IoT system superimposes IoT data directly onto real-world objects and enhances object interaction. As a case study, this system is applied to crop monitoring. Multi-camera, a non-destructive and low-cost imaging platform of the IoT, is connected to the internet and integrated into the system to measure the three-dimensional (3D) coordinates of objects. The relationships among accuracy, object coordinates, augmented information (e.g., virtual objects), and object interaction are investigated. The proposed system shows a great potential to integrate IoT data with AR resolution, which will effectively contribute to updating precision agricultural techniques in an environmentally sustainable manner.
Achieving Searchable and Privacy-Preserving Data Sharing for Cloud-Assisted E-Healthcare System

The integration of wearable wireless devices and cloud computing in e-health systems has significantly improved their effectiveness and availability. Patients can upload their personal health information (PHI) files to the cloud, from where the health service providers (HSPs) can obtain appropriate information to determine the health state. This system not only reduces the costs associated to healthcare but also provides timely diagnosis to save lives. However, a number of privacy concerns arise while sharing sensitive information. In this paper, we propose a novel privacy-preserving patient health information sharing scheme, which allows HSPs to access and search PHI files in a secure yet efficient manner. We make use of the searchable encryption technique with keyword range search and multikeyword search. The proposed privacy-preserving equality test protocol allows different types of numeric comparison searches on encrypted data. We also use a variant of bloom filter and message authentication code to classify PHI files, filter false data, and check integrity of search results. The simulations on real-world and synthetic data show the feasibility and efficiency of the system, and security analysis proves the privacy-preservation properties.
Global perspectives of research data sharing: A systematic literature review

Studies investigating data sharing from a world perspective are seemingly rare. By employing a quantitative design,this systematic review investigates and presents a comprehensive account of factors hampering data sharing at three levels of the global research hierarchy (individual, institutional and international). The study analyses secondary data extracted from 105 publications (n=105). Journal publishers and research grant organisations are key players in promoting data sharing activities by formulating, adopting and implementing policies on data sharing. Despite concerted efforts to promote data sharing, various factors frustrate these initiatives; they include lack of time and data misappropriation (individual level); data sharing training, absence of compensation and unfavourable internal policies (institutional level); and weak policies, ethical and legal norms, lack of data infrastructure and interoperability issues (international level). To counter these challenges, there is a need for research stakeholders to recognise researchers who share data through data citations, acknowledgement and incentives; invest in infrastructure, conduct training and advocacy programs; formulate stringent and fair policies. Data sharing will only become a success if research stakeholders apply equal efforts in managing data to that of research publications in general. The study offers a unique and comprehensive account of factors hampering data sharing from a global perspective. Solutions suggested could be adopted by research stakeholders in their efforts to enhance data sharing activities at various research levels.
Adversary Guided Asymmetric Hashing for Cross-Modal Retrieval

Cross-modal hashing has attracted considerable attention for large-scale multimodal retrieval task. A majority of hashing methods have been proposed for cross-modal retrieval. However, these methods inadequately focus on feature learning process and cannot fully preserve higher-ranking correlation of various item pairs as well as the multi-label semantics of each item, so that the quality of binary codes may be downgraded. To tackle these problems, in this paper, we propose a novel deep cross-modal hashing method, called Adversary Guided Asymmetric Hashing (AGAH). Specifically, it employs an adversarial learning guided multi-label attention module to enhance the feature learning part which can learn discriminative feature representations and keep the cross-modal invariability. Furthermore, in order to generate hash codes which can fully preserve the multi-label semantics of all items, we propose an asymmetric hashing method which utilizes a multi-label binary code map that can equip the hash codes with multi-label semantic information. In addition, to ensure higher-ranking correlation of all similar item pairs than those of dissimilar ones, we adopt a new triplet-margin constraint and a cosine quantization technique for Hamming space similarity preservation. Extensive empirical studies show that AGAH outperforms several state-of-the-art methods for cross-modal retrieval.
On the Compatibility of Privacy and Fairness

In this work, we investigate whether privacy and fairness can be simultaneously achieved by a single classifier in several different models. Some of the earliest work on fairness in algorithm design defined fairness as a guarantee of similar outputs for "similar'' input data, a notion with tight technical connections to differential privacy. We study whether tensions exist between differential privacy and statistical notions of fairness, namely Equality of False Positives and Equality of False Negatives (EFP/EFN). We show that even under full distributional access, there are cases where the constraint of differential privacy precludes exact EFP/EFN. We then turn to ask whether one can learn a differentially private classifier which approximately satisfies EFP/EFN, and show the existence of a PAC learner which is private and approximately fair with high probability. We conclude by giving an efficient algorithm for classification that maintains utility and satisfies both privacy and approximate fairness with high probability.
Emotional Text Mining: Customer profiling in brand management

The widespread use of the Internet and the constant increase in users of social media platforms has made a large amount of textual data available. This represents a valuable source of information about the changes in people’s opinions and feelings. This paper presents the application of Emotional Text Mining (ETM) in the field of brand management. ETM is an unsupervised procedure aiming to profile social media users. It is based on a bottom-up approach to classify unstructured data for the identification of social media users’ representations and sentiments about a topic. It is a fast and simple procedure to extract meaningful information from a large collection of texts. As customer profiling is relevant for brand management, we illustrate a business application of ETM on Twitter messages concerning a well-known sportswear brand in order to show the potential of this procedure, highlighting the characteristics of Twitter user communities in terms of product preferences, representations, and sentiments.
Interpretable Fashion Matching with Rich Attributes

Understanding the mix-and-match relationships of fashion items receives increasing attention in fashion industry. Existing methods have primarily utilized the visual content to learn the visual compatibility and performed matching in a latent space. Despite their effectiveness, these methods work like a black box and cannot reveal the reasons that two items match well. The rich attributes associated with fashion items, e.g.,off-shoulder dress and black skinny jean, which describe the semantics of items in a human-interpretable way, have largely been ignored.
Digital Transformation in Health Care: Augmented Reality for Hands-Free Service Innovation

Abstract Health care professionals regularly require access to information systems throughout their daily work. However, existing smart devices like smartphones and tablets are difficult to use at the point of care, because health care professionals require both hands during their work. Following a design science research approach including ethnographic fieldwork and prototype tests with focus groups, we find that Augmented Reality smart glass applications offer potential for service innovation in the health care sector. Our smart glass prototype supports health care professionals during wound treatment by allowing them to document procedures hands-free while they perform them. Furthermore, we investigate the use of audio based and physical interaction with the smart glasses in a within-subjects design experiment.
DrivMan: Driving Trust Management and Data Sharing in VANETs with Blockchain and Smart Contracts

The development of Internet of Things (IoT) has paved way for the Internet of Vehicles (IoV) and intelligent transportation systems (ITS). Vehicular ad-hoc networks (VANETs) are indispensable for ITS with intelligent vehicles (IV) as their key players. To ensure proper and reliable VANET operation, IVs need secure inter- and intra-network communication with trust and reliability of data (provenance). This paper aims to provide trust management in VANETs by proposing a trustless system model using blockchain and a certificate authority (CA) for registering IVs as well as revoking their registration if need be. Furthermore, to preserve data reliability, this paper uses physical unclonable functions (PUFs). Implementation of DrivMan shows that it is able to establish distributed trust management and enables secure data sharing while preserving the privacy of IVs.
Exploring Context and Visual Pattern of Relationship for Scene Graph Generation

Relationship is the core of scene graph, but its prediction is far from satisfying because of its complex visual diversity. To alleviate this problem, we treat relationship as an abstract object, exploring not only significative visual pattern but contextual information for it, which are two key aspects when considering object recognition. Our observation on current datasets reveals that there exists intimate association among relationships. Therefore, inspired by the successful application of context to object-oriented tasks, we especially construct context for relationships where all of them are gathered so that the recognition could benefit from their association. Moreover, accurate recognition needs discriminative visual pattern for object, and so does relationship. In order to discover effective pattern for relationship, traditional relationship feature extraction methods such as using union region or combination of subject-object feature pairs are replaced with our proposed intersection region which focuses on more essential parts. Therefore, we present our so-called Relationship Context - InterSeCtion Region (CISC) method. Experiments for scene graph generation on Visual Genome dataset and visual relationship prediction on VRD dataset indicate that both the relationship context and intersection region improve performances and realize anticipated functions.