Reviewerly/exHarmony huggingface dataset:
Re-Ranking High-Dimensional Deep Local Representation for NIR-VIS Face Recognition

Heterogeneous face recognition refers to matching facial images captured from different sensors or sources, which has wide applications in public security and law enforcement. Because of the great differences in sensing and creating procedure, there are huge feature gap between heterogeneous facial images. Existing methods merely focus on comparing the probe image with the gallery in feature space, while the true target may not appear at the first rank due to the appearance variations caused by different sensing patterns. In order to exploit valuable information from initial ranking result, this paper proposes to re-rank high-dimensional deep local representation for matching near-infrared (NIR) and visual (VIS) facial images, i.e. NIR-VIS face recognition. A high-dimensional deep local representation is firstly constructed by extracting and concatenating deep features on local facial patches via a convolutional neural network (CNN). The initial NIR-VIS recognition ranking results can be obtained by comparing the compressed deep features. We then propose a novel and efficient locally linear re-ranking (LLRe-Rank) technique to refine the initial ranking results, which can explore valuable information from initial ranking result. The proposed re-ranking method does not require any human interaction or data annotation, and can be served as an unsupervised post processing technique. Experimental results on the most challenging Oulu-CASIA NIR-VIS database and CASIA NIR-VIS 2.0 database demonstrate the effectiveness of our method.
A Novel Stream Clustering Framework for Spam Detection in Twitter

Stream clustering methods have been repeatedly used for spam filtering in order to categorize input messages/tweets into spam and nonspam clusters. These methods assume each cluster contains a number of neighbor small (micro) clusters, where each microcluster has a symmetric distribution. Nonetheless, this assumption is not necessarily correct and big microclusters might have asymmetric distribution. To enhance the assigning accuracy of former methods in their online phase, we suggest replacing the Euclidean distance by a set of classifiers in order to assign incoming samples to the most relative microcluster with arbitrary distribution. Here, a set of incremental Naïve Bayes (INB) classifier is trained for microclusters whose population exceeds a threshold. These INBs can capture the mean and boundary of microclusters, while the Euclidean distance just considers the mean of clusters and acts inaccurate for asymmetric big microclusters. In this paper, DenStream was promoted by the proposed framework, called here as INB-DenStream. To show the effectiveness of INB-DenStream, state-of-the-art methods such as DenStream, StreamKM++, and CluStream were applied to the Twitter datasets and their performance was determined in terms of purity, general precision, general recall, F1 measure, parameter sensitivity, and computational complexity. The compared results implied the superiority of our method to the rivals in almost the datasets.
Spatial Assessment of Solar Radiation by Machine Learning and Deep Neural Network Models Using Data Provided by the COMS MI Geostationary Satellite: A Case Study in South Korea

Although data-driven methods including deep neural network (DNN) were introduced, there was not enough assessment about spatial characteristics when using limited ground observation as reference. This work aimed to interpret the feasibility of several machine learning approaches to assess the spatial distribution of solar radiation on Earth based on the Communication, Ocean, and Meteorological Satellite (COMS) Meteorological Imager (MI) geostationary satellite. Four data-driven models were selected (artificial neural network (ANN), random forest (RF), support vector regression (SVR), and DNN), to compare their accuracy and spatial estimating performance. Moreover, we used a physical model to probe the ability of data-driven methods, implementing hold-out and k-fold cross-validation approaches based on pyranometers located in South Korea. The results of analysis showed the RF had the highest accuracy in predicting performance, although the difference between RF and the second-best technique (DNN) was insignificant. Temporal variations in root mean square error (RMSE) were dependent on the number of data samples, while the physical model showed relatively less sensitivity. Nevertheless, DNN and RF showed less variability in RMSE than the others. To examine spatial estimation performance, we mapped solar radiation over South Korea for each model. The data-driven models accurately simulated the observed cloud pattern spatially, whereas the physical model failed to do because of cloud mask errors. These exhibited different spatial retrieval performances according to their own training approaches. Overall analysis showed that deeper layers of networks approaches (RF and DNN), could best simulate the challenging spatial pattern of thin clouds when using satellite multispectral data.
A comprehensive survey of hardware-assisted security: From the edge to the cloud

Sensitive data processing occurs more and more on machines or devices out of users control. In the Internet of Things world, for example, the security of data could be posed at risk regardless the adopted deployment is oriented on Cloud or Edge Computing. In these systems different categories of attacks—such as physical bus sniffing, cold boot, cache side-channel, buffer overflow, code-reuse, or Iago—can be realized. Software-based countermeasures have been proposed. However, the severity and complexity of these attacks require a level of security that only the hardware support can ensure. In the last years, major companies released a number of architectural extensions aiming at provide hardware-assisted security to software. In this paper, we realize a comprehensive survey of HW-assisted technological solutions produced by vendors like Intel, AMD, and ARM for both embedded edge-devices and hosting machines such as cloud servers. The different approaches are classified based on the type of attacks prevented and the enforced techniques. An analysis on their mechanisms, issues, and market adoption is provided to support investigations of researchers approaching to this field of systems security.
Robust Target Detection Within Sea Clutter Based on Graphs

In this paper, a novel robust graph-based adequate and concise information representation paradigm is explored. This new signal representation framework can provide a promising alternative for manifesting the essential structure of random signals. A typical application, namely, target detection within sea clutter, can thus be carried out using our proposed new graph-based signal characterization. According to Monte Carlo simulation results, the proposed graph-based signal (target) detection method leads to the outstanding performance, compared to other existing techniques, especially when the signal-to-noise ratio is rather small (0-6 dB). This new graph-based target detector can be expected to be the future backbone technique for identifying and tracking marine vessels using high-resolution radars.
A Comparative Study of Virtual Reality Methods of Interaction and Locomotion Based on Presence, Cybersickness, and Usability

In recent years, virtual reality has experienced notorious technological advances in a quite short time. In an attempt to quickly response to this technical developments, some designs and developments of inmersive environments have caused different symptoms such as dizziness or disorientation. This work aims to analyze different methods of interaction and locomotion used in inmersive environments (Point of Interest, Gamepad, Teleport, and Room-Scale) in three different aspects: presence, cybersickness, and usability. We have designed and developed an experimental environment to carry out an empirical analysis with 48 subjects comparing the results obtained in different perceptual experiments. As a result, we provide a guideline for the use of these methods of interaction and locomotion in virtual reality.
Robust Interacting Multiple Model With Modeling Uncertainties for Maneuvering Target Tracking

This paper proposes an improved robust interacting multiple model (RIMM) algorithms with modeling uncertainties for maneuvering target tracking with changing dynamics.To mitigate the effects of the modeling uncertainty, a compensation step is introduced to adjust the degree of dependence of the filtering on the system or the measurement model based on the orthogonality principle between the state estimation error and innovation sequence of the subfilter model in the RIMM algorithm.By relying on the compensation parameter, the proposed algorithm fully utilizes the useful information in the innovation sequence and reduces the impact of system model error.The numerical simulation and car-mounted experiments using time difference of arrival (TDOA) measurements of the maneuvering target tracking with changing dynamics are conducted to verify the effectiveness of the proposed RIMM algorithm.Compared with the conventional approaches, the proposed RIMM algorithm results in a remarkable improvement in the state estimation accuracy and small bias while improving the consistency of the filter.
A Path-Planning Strategy for Unmanned Surface Vehicles Based on an Adaptive Hybrid Dynamic Stepsize and Target Attractive Force-RRT Algorithm

It is well known that path planning has always been an important study area for intelligent ships, especially for unmanned surface vehicles (USVs). Therefore, it is necessary to study the path-planning algorithm for USVs. As one of the basic algorithms for USV path planning, the rapidly-exploring random tree (RRT) is popular due to its simple structure, high speed and ease of modification. However, it also has some obvious drawbacks and problems. Designed to perfect defects of the basic RRT and improve the performance of USVs, an enhanced algorithm of path planning is proposed in this study, called the adaptive hybrid dynamic stepsize and target attractive force-RRT(AHDSTAF-RRT). The ability to pass through a narrow area and the forward speed in open areas of USVs are improved by adopting the AHDSTAF-RRT in comparison to the basic RRT algorithm. The improved algorithm is also applied to an actual gulf map for simulation experiments, and the experimental data is collected and organized. Simulation experiments show that the proposed AHDSTAF-RRT in this paper outperforms several existing RRT algorithms, both in terms of path length and calculating speed.
Understanding and Measuring User Experience in Conversational Interfaces

Abstract Although various methods have been developed to evaluate conversational interfaces, there has been a lack of methods specifically focusing on evaluating user experience. This paper reviews the understandings of user experience (UX) in conversational interfaces literature and examines the six questionnaires commonly used for evaluating conversational systems in order to assess the potential suitability of these questionnaires to measure different UX dimensions in that context. The method to examine the questionnaires involved developing an assessment framework for main UX dimensions with relevant attributes and coding the items in the questionnaires according to the framework. The results show that (i) the understandings of UX notably differed in literature; (ii) four questionnaires included assessment items, in varying extents, to measure hedonic, aesthetic and pragmatic dimensions of UX; (iii) while the dimension of affect was covered by two questionnaires, playfulness, motivation, and frustration dimensions were covered by one questionnaire only. The largest coverage of UX dimensions has been provided by the Subjective Assessment of Speech System Interfaces (SASSI). We recommend using multiple questionnaires to obtain a more complete measurement of user experience or improve the assessment of a particular UX dimension. RESEARCH HIGHLIGHTS Varying understandings of UX in conversational interfaces literature. A UX assessment framework with UX dimensions and their relevant attributes. Descriptions of the six main questionnaires for evaluating conversational interfaces. A comparison of the six questionnaires based on their coverage of UX dimensions.
Lightweight Privacy-Preserving Identity-Based Verifiable IoT-Based Health Storage System

The Internet of Things (IoT)-based health system provides patients and doctors a lot of conveniences, doctors can diagnose patients in time through monitored health data. However, doctors may make a wrong diagnosis of the patient's illness, if the health data stored on the cloud is tampered or lost due to an external attack or power failure. Therefore, it is of great importance to verify the integrity of the patient's health data stored on the cloud. Existing data integrity verification technologies usually need data owner to compute data authenticators for verification, but sensors on a patient in IoT-based health system have limited computation ability, thus existing data integrity verification technologies are not very suitable in IoT-based health system. This paper proposes a lightweight secure IoT-based health storage system to solve this problem. The proposed system employs edge server to compute data authenticators and verify data integrity, greatly reduces the system's computation cost and management burden of third party verifier. Security analysis and performance evaluation show the proposed system is feasible.
A knowledge-based semantic framework for query expansion

Searching for relevant material that satisfies the information need of a user, within a large document collection is a critical activity for web search engines. Query Expansion techniques are widely used by search engines for the disambiguation of user’s information need and for improving the information retrieval (IR) performance. Knowledge-based, corpus-based and relevance feedback, are the main QE techniques, that employ different approaches for expanding the user query with synonyms of the search terms (word synonymy) in order to bring more relevant documents and for filtering documents that contain search terms but with a different meaning (also known as word polysemy problem) than the user intended. This work, surveys existing query expansion techniques, highlights their strengths and limitations and introduces a new method that combines the power of knowledge-based or corpus-based techniques with that of relevance feedback. Experimental evaluation on three information retrieval benchmark datasets shows that the application of knowledge or corpus-based query expansion techniques on the results of the relevance feedback step improves the information retrieval performance, with knowledge-based techniques providing significantly better results than their simple relevance feedback alternatives in all sets.
Prediction and performance assessment of global solar radiation in Indian cities: A comparison of satellite and surface measured data

The design and performance assessment of solar photovoltaic/thermal system requires the knowledge of global solar radiation. Generalised models to assess the monthly average global solar radiation of twelve locations in India were established using sunshine hours available for 14 years (2000–2014) obtained from the World Radiation Data Centre (WRDC), Russia. Statistical analysis is carried out to assess the accurate model to estimate the global solar radiation which includes mean bias error, root mean square error, mean absolute percentage error, the coefficient of determination and uncertainty at 95%. Also, the assessment of the global radiation values obtained from satellite data by the National Renewable Energy Laboratory (NREL) is performed. Further, Global Performance Indicator (GPI) is introduced to find the ranking of models from the overall performance of the investigated models by scaling statistical indicators. The comparison of the NREL estimated data and WRDC data shows that the satellite data overestimates the surface measured data between 10 and 15% for seven locations, whereas, for the remaining four stations, it is between 7 and 9.5%.
Multi-Path Dilated Residual Network for Nuclei Segmentation and Detection

As a typical biomedical detection task, nuclei detection has been widely used in human health management, disease diagnosis and other fields. However, the task of cell detection in microscopic images is still challenging because the nuclei are commonly small and dense with many overlapping nuclei in the images. In order to detect nuclei, the most important key step is to segment the cell targets accurately. Based on Mask RCNN model, we designed a multi-path dilated residual network, and realized a network structure to segment and detect dense small objects, and effectively solved the problem of information loss of small objects in deep neural network. The experimental results on two typical nuclear segmentation data sets show that our model has better recognition and segmentation capability for dense small targets.
Perceived responsiveness in text messaging: The role of emoji use

This research investigated how emojis can be used in text messaging to communicate perceived responsiveness, guide impression formation, and contribute to reflected appraisal. Participants (N = 179) disclosed a positive and negative event to a responder (a confederate) over iMessage. Participants were randomly assigned to receive either text only responses or a mixture of text and emoji responses from the responder. For positive self-disclosures, participants had higher ratings of perceived responsiveness when there was convergence in emoji use between the participant and responder than when there was divergence. In other words, participants rated the confederate higher in responsiveness when both or neither used emojis (converged) than when only one used emojis (diverged). There were no effects of emoji use on perceived responsiveness for negative self-disclosures. Additionally, following the set of interactions, participants had more positive impressions of the responder and more positive perceptions of how the responder felt towards the participant (reflected appraisal) when there was convergence rather than divergence in emoji use. Discussion centers around whether emojis can serve as a substitute for nonverbal cues typically found in face-to-face conversations.
New Clustering Algorithms for Twitter Sentiment Analysis

In this last decade, the use of social networks became ubiquitous in our daily life. Twitter, one of the famous social networks became a rich source of discussed topics. The users in Twitter express their sentiments or points of view by tweets concerning different topics in variety of fields, such as politics, commercial products, etc. These important information are exploited by sentiment analysis tools. Clustering algorithms are one of the used solutions to discover the sentiment provided by users in tweets. However, knowing that the users sentiments are generally divided into three categories: positive, negative, and neutral, it was mandatory to find a strong clustering algorithm, which leads to a good clustering performance and produce an appropriate number of clusters in an acceptable run time. To achieve this goal, we combine in this paper two well-known clustering methods: K-means and DENCLUE (DENsity-based CLUstEring) with its variants. This combination allows to exploit the precise number of cluster from K-means and the clustering performance from DENCLUE and its variants. Experimental results on four Twitter datasets demonstrate the competitiveness of the proposed algorithms against the state-of-the-art methods to provide a tradeoff between clustering performance, number of returned clusters, and runtime.
User engagement for mid-air haptic interactions with digital signage

Interactive digital signage is increasingly deployed in urban environments, from airports and train stations, to cinemas and shopping malls, whilst their integration into public spaces introduces new possibilities in multimedia presentation. In this paper, we explore the impact of mid-air haptic feedback on user engagement during gesture-based interactions with digital posters. To that end, a user study with seventeen participants was undertaken with two independent variables: interactivity (high/low), and mid-air haptic cues (on/off), whilst user engagement and emotional affect was measured with respect to various metrics. In this first attempt to understand the significance of mid-air haptic interactions for digital signage, we found increased user engagement levels, comparable to, if not greater than those achieved by content gamification. In particular, our analysis suggests that mid-air haptic feedback significantly improved usability and aesthetic appeal in comparison to digital signage without haptic feedback. Similarly, a higher level of gamification was also found to boost user engagement and helped to offer more compelling experiences with digital signage.
Experimental Optimal Verification of Entangled States Using Local Measurements

The initialization of a quantum system into a certain state is a crucial aspect of quantum information science. While a variety of measurement strategies have been developed to characterize how well the system is initialized, for a given one, there is in general a trade-off between its efficiency and the accessible information of the quantum state. Conventional quantum state tomography can characterize unknown states while requiring exponentially expensive time-consuming postprocessing. Alternatively, recent theoretical breakthroughs show that quantum state verification provides a technique to quantify the prepared state with significantly fewer samples, especially for multipartite entangled states. In this Letter, we modify the original proposal to be robust to practical imperfections, and experimentally implement a scalable quantum state verification on two-qubit and four-qubit entangled states with nonadaptive local measurements. For all the tested states, the estimated infidelity is inversely proportional to the number of samples, which illustrates the power to characterize a quantum state with a small number of samples. Compared to the globally optimal strategy which requires nonlocal measurements, the efficiency in our experiment is only worse by a small constant factor (<2.5). We compare the performance difference between quantum state verification and quantum state tomography in an experiment to characterize a four-photon Greenberger-Horne-Zeilinger state, and the results indicate the advantage of quantum state verification in both the achieved efficiency and precision.