Reviewerly/exHarmony huggingface dataset:
A Novel Approach for Ontology Focused Inter- Domain Personalized Search based on Semantic Set Expansion

The Web is overloaded with information and its exploration and retrieval is quite tedious is a cumbersome task. In the era of Semantic Web, there is a requisite of Semantic strategies for recommending webpages. In this paper, a strategic semantic paradigm for Ontology Driven Semantic search has been proposed. The proposed scheme incorporates a set expansion mechanism for interdomain exploratory semantic search. The proposed scheme for searching semantically computes concept similarity between concepts from most similar domains, and the set expansion is based on these concepts. The ontologies are visualized using a triple store and personalization has been imbibed into the proposed scheme. The initial step requires determination of the domains and scope of the ontologies, followed by identification of classes, defining instances of each class and the relationship among them. Multiple user profiles will be created to personalize the search results according to the search history of each user. Through this model, the problem of irrelevant search results is reduced, and in the process, reduces the probability of going through numerous results as in case of a normal search engine. Also, the search engine is made scalable to any dataset irrespective of its content. An overall F-measure of 96.64% has been achieved.
Selection Features and Support Vector Machine for Credit Card Risk Identification

For identifying credit card risk in massive and high dimensionality data, feature selection is considered very important to improve classification performance and fraud identification process. One of the commonly used feature selection methods is Random Forest Classifier (RFC), which is very suitable for large dataset. RFC has a good performance; it tends to identify the most predictive features, which may provide a significant improvement in classification performance of credit card risk identification model. In this paper, we propose an enhanced Credit Card Risk Identification (CCRI) method based on the features selection algorithm as Random Forest Classifier and Support Vector Machine to detecting fraud risk. Our experimental results show that the proposed algorithm outperforms the Local Outlier Factor, Isolation Forest and Decision Tree in term of classification performance on a larger dataset.
S-box Construction Based on Linear Fractional Transformation and Permutation Function

Substitution boxes (S-box) with strong and secure cryptographic properties are widely used for providing the key property of nonlinearity in block ciphers. This is critical to be resistant to a standard attack including linear and differential cryptanalysis. The ability to create a cryptographically strong S-box depends on its construction technique. This work aims to design and develop a cryptographically strong 8 × 8 S-box for block ciphers. In this work, the construction of the S-box is based on the linear fractional transformation and permutation function. Three steps involved in producing the S-box. In step one, an irreducible polynomial of degree eight is chosen, and all roots of the primitive irreducible polynomial are calculated. In step two, algebraic properties of linear fractional transformation are applied in Galois Field GF (28). Finally, the produced matrix is permuted to add randomness to the S-box. The strength of the S-box is measured by calculating its potency to create confusion. To analyze the security properties of the S-box, some well-known and commonly used algebraic attacks are used. The proposed S-box is analyzed by nonlinearity test, algebraic degree, differential uniformity, and strict avalanche criterion which are the avalanche effect test, completeness test, and strong S-box test. S-box analysis is done before and after the application of the permutation function and the analysis result shows that the S-box with permutation function has reached the optimal properties as a secure S-box.
Loss Functions of Generative Adversarial Networks (GANs): Opportunities and Challenges

Recently, the Generative Adversarial Networks (GANs) are fast becoming a key promising research direction in computational intelligence. To improve the modeling ability of GANs, loss functions are used to measure the differences between samples generated by the model and real samples, and make the model learn towards the goal. In this paper, we perform a survey for the loss functions used in GANs, and analyze the pros and cons of these loss functions. Firstly, the basic theory of GANs, and its training mechanism are introduced. Then, the loss functions used in GANs are summarized, including not only the objective functions of GANs, but also the application-oriented GANs' loss functions. Thirdly, the experiments and analyses of representative loss functions are discussed. Finally, several suggestions on how to choose appropriate loss functions in a specific task are given.
Encrypted traffic classification based on Gaussian mixture models and Hidden Markov Models

To protect user privacy (e.g., IP address and sensitive data in a packet), many traffic protection methods, like traffic obfuscation and encryption technologies, are introduced. However, these methods have been used by attackers to transmit malicious traffic, posing a serious threat to network security. To enhance network traffic supervision, this paper proposes a new traffic classification model based on Gaussian mixture models and hidden Markov models, named MGHMM. To evaluate the effectiveness of the proposed model, we first classify protocols and identify the obfuscated traffic by experiments. Then, we compare the classification performance of MGHMM with that of the latest Vector Quantiser-based traffic classification algorithm. On the basis of the experiment, the relation between the classification and the number of hidden Markov states, and the number of mixture of Gaussian distributions required to describe the hidden states, are analyzed.
An 8.93 TOPS/W LSTM Recurrent Neural Network Accelerator Featuring Hierarchical Coarse-Grain Sparsity for On-Device Speech Recognition

Long short-term memory (LSTM) is a type of recurrent neural networks (RNNs), which is widely used for time-series data and speech applications, due to its high accuracy on such tasks. However, LSTMs pose difficulties for efficient hardware implementation because they require a large amount of weight storage and exhibit computation complexity. Prior works have proposed compression techniques to alleviate the storage/computation requirements of LSTMs but elementwise sparsity schemes incur sizable index memory overhead and structured compression techniques report limited compression ratios. In this article, we present an energy-efficient LSTM RNN accelerator, featuring an algorithm-hardware co-optimized memory compression technique called hierarchical coarse-grain sparsity (HCGS). Aided by the HCGS-based blockwise recursive weight compression, we demonstrate LSTM networks with up to 16× fewer weights while achieving minimal error rate degradation. The prototype chip fabricated in 65-nm LP CMOS achieves up to 8.93 TOPS/W for real-time speech recognition using compressed LSTMs based on HCGS. HCGS-based LSTMs have demonstrated energy-efficient speech recognition with low error rates for TIMIT, TED-LIUM, and LibriSpeech data sets.
Cross-modality Synthesis from MRI to PET Using Adversarial U-Net with Different Normalization

Multi-modality biomedical images (especially magnetic resonance imaging (MRI) and positron emission tomography (PET)) are critical for auxiliary diagnosis in brain diseases. However, there are some common concerns in PET scans including the high cost, the usage of radioactive tracer and so on. These factors jointly result in a lack of PET datas. To overcome this limitation, in this study we proposed an effective U-Net architecture with the adversarial training strategy to synthesizing PET datas from their corresponding MRI. In addition, we notice that many prior works commonly adopted Batch Normalization (BN) as the normalization method by default. But in this specific task which always needs a small mini-batch, BN's performance decreases rapidly. To alleviate this issue, we compared the performance of using other popular normalization methods rather than using BN as default. Experimental results on a subset of ADNI database demonstrated that the synthetic PET images from our proposed method were reasonable, and it was valuable to replace the Batch Normalization with the Instance Normalization in the tasks of cross-modality synthesis. Our study could help future research in this field.
Preparing for smart voice assistants: Cultural histories and media innovations

Smart voice assistants have become popular thanks largely to their default naturalistic female voices and helpful personae. In this article, we trace changes in robot voices in popular culture and explain how this history influenced the voice design of smart voice assistants. Our research draws on cultural analysis of Hollywood and international films, television and literature, and observations from our personal experiences with voice assistants. We argue that designers of devices like the Google Home and Amazon Echo inherited a cultural imaginary of alien and dangerous robots with artificial voices and personalities. Manufacturers leveraged techniques of modality, personae and invocation and pre-existing social connotations of the voice to create positive associations of these devices in the home. We conclude by arguing that smart voice assistants are new media innovations prepared for consumers through pre-domestication and represent an emerging regime of power and influence based on technologised voice interaction.
A Slide Window Variational Adaptive Kalman Filter

A slide window variational adaptive Kalman filter is presented in this brief based on adaptive learning of inaccurate state and measurement noise covariance matrices, which is composed of the forward Kalman filtering, the backward Kalman smoothing, and the online estimates of noise covariance matrices. By imposing an approximation on the smoothing posterior distribution of slide window state vectors, the posterior distributions of noise covariance matrices can be analytically updated as inverse Wishart distributions by exploiting the variational Bayesian method, which avoids the fixed-point iterations and achieves good computational efficiency. Simulation comparisons demonstrate that the proposed method has better filtering accuracy and consistency than the existing cutting-edge method.
Disrupting Image-Translation-Based DeepFake Algorithms with Adversarial Attacks

DeepNude, a deep generative software based on image-to-image translation algorithm, excelling in undressing photos of humans and producing realistic nude images. Although the software was later purged from the Internet, image translation algorithms such as CycleGAN, pix2pix, or pix2pixHD can easily be applied by anyone to recreate a new version of DeepNude. This work addresses the issue by introducing a novel aspect of image translating algorithms, namely the possibility of adversarially attacking these algorithms. We modify the input images by the adversarial loss, and thereby the edited images would not be counterfeited easily by these algorithms. The proposed technique can provide a guideline to future research on defending personal images from malicious use of image translation algorithms.
Alexa as Coach: Leveraging Smart Speakers to Build Social Agents that Reduce Public Speaking Anxiety

Public speaking anxiety is one of the most common social phobias. We explore the feasibility of using a conversational agent to reduce this anxiety. We developed a public-speaking tutor on the Amazon Alexa platform that enables users to engage in cognitive reconstruction exercises. We also investigated how the sociability of the agent might affect its performance as a tutor. A user study of 53 college students with fear of public speaking showed that the interaction with the agent served to assuage pre-speech state anxiety. Agent sociability improved the sense of interpersonal closeness, which was associated with lower pre-speech anxiety. Moreover, sociability of the agent increased participants' satisfaction and their willingness to continue engagement. Our findings, thus, have implications not only for addressing public speaking anxiety in a scalable way but also for the design of future conversational agents using smart speaker platforms.
Supercorrelated Radiance in Nonlinear Photonic Waveguides

We study the collective decay of two-level emitters coupled to a nonlinear waveguide, for example, a nanophotonic lattice or a superconducting resonator array with strong photon-photon interactions. Under these conditions a new decay channel into bound photon pairs emerges, through which spatial correlations between emitters are established by regular interference as well as interactions between the photons. We derive an effective Markovian theory to model the resulting decay dynamics of an arbitrary distribution of emitters and identify collective effects beyond the usual phenomena of super- and subradiance. Specifically, in the limit of many close-by emitters, we find that the system undergoes a super-correlated decay process where either all the emitters are in the excited state or in the ground state, but not in any of the intermediate states. The predicted effects can be probed in state-of-the-art waveguide QED experiments and provide a striking example of how the dynamics of open quantum systems can be modified by many-body effects in a non-harmonic environment.
An Interaction Design for Machine Teaching to Develop AI Tutors

Intelligent tutoring systems (ITSs) have consistently been shown to improve the educational outcomes of students when used alone or combined with traditional instruction. However, building an ITS is a time-consuming process which requires specialized knowledge of existing tools. Extant authoring methods, including the Cognitive Tutor Authoring Tools' (CTAT) example-tracing method and SimStudent's Authoring by Tutoring, use programming-by-demonstration to allow authors to build ITSs more quickly than they could by hand programming with model-tracing. Yet these methods still suffer from long authoring times or difficulty creating complete models. In this study, we demonstrate that Simulated Learners built with the Apprentice Learner (AL) Framework can be combined with a novel interaction design that emphasizes model transparency, input flexibility, and problem solving control to enable authors to achieve greater model completeness in less time than existing authoring methods.
Concept of Orbital Entanglement and Correlation in Quantum Chemistry

A recent development in quantum chemistry has established the quantum mutual information between orbitals as a major descriptor of electronic structure. This has already facilitated remarkable improvements of numerical methods and may lead to a more comprehensive foundation for chemical bonding theory. Building on this promising development, our work provides a refined discussion of quantum information theoretical concepts by introducing the physical correlation and its separation into classical and quantum parts as distinctive quantifiers of electronic structure. In particular, we succeed in quantifying the entanglement. Intriguingly, our results for different molecules reveal that the total correlation between orbitals is mainly classical, raising questions about the general significance of entanglement in chemical bonding. Our work also shows that implementing the fundamental particle number superselection rule, so far not accounted for in quantum chemistry, removes a major part of correlation and entanglement previously seen. In that respect, realizing quantum information processing tasks with molecular systems might be more challenging than anticipated.
Wearable Microphone Jamming

We engineered a wearable microphone jammer that is capable of disabling microphones in its user's surroundings, including hidden microphones. Our device is based on a recent exploit that leverages the fact that when exposed to ultrasonic noise, commodity microphones will leak the noise into the audible range.
Trackly

Self-tracking is an important part of self-care. However, predefined self-tracking approaches can impede people's agency in managing their health. We investigated a customisable and pictorial self-tracking approach in multiple sclerosis self-management by implementing and conducting a field study of Trackly: a prototype app that supports people in defining and colouring pictorial trackers, such as body shapes. We found that participants utilised the elements of Trackly designed to support agentive behaviour: they defined personally meaningful tracking parameters in their own words, and particularly valued being able to flexibly colour in and make sense of their pictorial trackers. Having been able to support their individual self-care intentions with Trackly, participants reported a spectrum of interrelated experiences of agency, including a sense of ownership, identity, self-awareness, mindfulness, and control. Our findings demonstrate the importance of supporting people's individual needs and creative capacities to foster mindful and personally meaningful engagement with health and wellbeing data.
Human-in-the-loop Outlier Detection

Outlier detection is critical to a large number of applications from finance fraud detection to health care. Although numerous approaches have been proposed to automatically detect outliers, such outliers detected based on statistical rarity do not necessarily correspond to the true outliers to the interest of applications. In this work, we propose a human-in-the-loop outlier detection approach HOD that effectively leverages human intelligence to discover the true outliers. There are two main challenges in HOD. The first is to design human-friendly questions such that humans can easily understand the questions even if humans know nothing about the outlier detection techniques. The second is to minimize the number of questions. To address the first challenge, we design a clustering-based method to effectively discover a small number of objects that are unlikely to be outliers (aka, inliers) and yet effectively represent the typical characteristics of the given dataset. HOD then leverages this set of inliers (called context inliers) to help humans understand the context in which the outliers occur. This ensures humans are able to easily identify the true outliers from the outlier candidates produced by the machine-based outlier detection techniques. To address the second challenge, we propose a bipartite graph-based question selection strategy that is theoretically proven to be able to minimize the number of questions needed to cover all outlier candidates. Our experimental results on real data sets show that HOD significantly outperforms the state-of-the-art methods on both human efforts and the quality of the discovered outliers.
Distributed optimal linear fusion estimators

This paper is concerned with the distributed information fusion estimation problem in multi-sensor environments. A universal distributed optimal linear fusion estimation (DOLFE) algorithm, which has a Kalman-type structure with matrix gains, is presented under the linear unbiased minimum variance criterion. To reduce the computational burden, two suboptimal linear fusion estimation algorithms with diagonal-matrix gains and scalar gains are also presented. Based on the proposed DOLFE algorithm, a distributed optimal linear fusion filter (DOLFF) is presented for multi-sensor linear discrete-time stochastic systems. It has better accuracy than that based on the matrix-weighted fusion of local estimators but worse accuracy than the centralized fusion filter (CFF). The stability and steady-state property of the proposed DOLFF are analyzed. Then, the corresponding multi-step predictor and smoother are also developed based on DOLFF. To obtain the distributed fusion estimators, some estimation error cross-covariance matrices that are used to compute the gains are derived. At last, distributed optimal linear fusion estimators with feedback are also presented. Furthermore, it is strictly proved that the proposed distributed optimal linear fusion filter with feedback (DOLFFWF) has the same accuracy as the CFF. Two simulation examples show the effectiveness of the proposed algorithms.
Conversational Technologies for In-home Learning: Using Co-Design to Understand Children's and Parents' Perspectives

Today, Conversational Agents (CA) are deeply integrated into the daily lives of millions of families, which has led children to extensively interact with such devices. Studies have suggested that the social nature of CA makes them a good learning companion for children. Therefore, to understand children's preferences for the use of CAs for the purpose of in-home learning, we conducted three participatory design sessions. In order to identify parents' requirements in this regard, we also included them in the third session. We found that children expect such devices to possess a personality and an advanced level of intelligence, and support multiple content domains and learning modes and human-like conversations. Parents desire such devices to include them in their children's learning activities, foster social engagement, and to allow them to monitor their children's use. This understanding will inform the design of future CAs for the purpose of in-home learning.
Semi-Supervised Multi-view clustering based on orthonormality-constrained nonnegative matrix factorization

Multi-view clustering aims at integrating the complementary information between different views so as to obtain an accurate clustering result. In addition, the traditional clustering is a kind of unsupervised learning method, which does not take the label information into learning. In this paper, we propose a novel model, called semi-supervised multi-view clustering based on orthonormality-constrained nonnegative matrix factorization (MVOCNMF), to cluster the multi-view data into a number of categories. In the proposed model, based on the label information, we first learn the low-dimensional representations of data by the constrained NMF technique, and simultaneously cluster the samples with the same label into the clustering prototypes for each view. After that, we put forward a novel orthonormality constraint term to obtain the desirable representations for each view, and use the co-regularization to integrate the complementary information from different views. We further develop an alternating minimization algorithm to solve the proposed model, and present the convergence analysis and computational complexity of the proposed method. Extensive experimental results on several multi-view datasets have shown that the proposed MVOCNMF method outperforms the existing multi-view clustering methods.
"How do I make this thing smile?"

Despite the proliferation of platforms for social Virtual Reality (VR) communicating emotional expression via an avatar remains a significant design challenge. In order to better understand the design space for expressive Nonverbal Communication (NVC) in social VR we undertook an inventory of the ten most prominent social VR platforms. Our inventory identifies the dominant design strategies for movement, facial control, and gesture in commercial VR applications, and identifies opportunities and challenges for future design and research into social expression in VR. Specifically, we highlight the paucity of interaction paradigms for facial expression and the near nonexistence of meaningful control over ambient aspects of nonverbal communication such as posture, pose, and social status.