Reviewerly/exHarmony huggingface dataset:
Enabling Smart Anonymity Scheme for Security Collaborative Enhancement in Location-Based Services

Security enhancement is and always will be a prime concern for the deployment of point-of-interest (POI) recommendation services in mobile sensing environment. Recent tamper-proof technical protection such as strong encryption has undoubtedly become a major safeguard against threats to privacy in location-based services. Although the disclosure of location information could increase recommendation accuracy, the publication of trajectory data to untrusted entities could reveal sensitive details, e.g., daily routes, destinations, and favorite restaurants. In this paper, we propose a smart scheme named BUSA to approach the above problem by reconciling the tension between privacy protection and recommendation accuracy in location-based recommendation services. This scheme uses anonymizer agents positioned between the service-requesting users and location service providers; these agents operate by dividing the query information and using k -anonymity to enhance privacy protection. The scheme also utilizes clustering techniques to group users into clusters by learning their trajectory data and selects the spatial center cells as a cluster core and a benchmark for calculating recommendations via trust computing. An anonymizer coordination strategy is proposed to replace a low-performing anonymizer with one that provides stronger privacy protection for a recommendation service. The BUSA scheme adopts k-anonymity and clustering to protect privacy, and the calculated recommendation will be suitable for the cluster core that represents the entirety of users' location preferences. The security analysis reveals that the BUSA scheme can effectively protect privacy against fraudulent query requestors and the simulation results also indicate that it provides stronger privacy protection than its counterparts from the perspective of recommendation hit rate and the extent of disclosure.
Attention-Based Neural Tag Recommendation

Personalized tag recommender systems suggest tags to users when annotating specific items. Usually, recommender systems need to take both users' preference and items' features into account. Existing methods like latent factor models based on tensor factorization use low-dimensional dense vectors to represent latent features of users, items and tags. The problem with these models is using the static representation for the user, which neglects that users' preference keeps evolving over time. Other methods based on base-level learning (BLL) only use a simple time-decay function to weight users' preference. In this paper, we propose a personalized tag recommender system based on neural networks and attention mechanism. This approach utilizes the multi-layer perceptron to model the non-linearities of interactions among users, items and tags. Also, an attention network is introduced to capture the complex pattern of the user's tagging sequence. Extensive experiments on two real-world datasets show that the proposed model outperforms the state-of-the-art tag recommendation method.
Encumbered Interaction

Guitars are physical instruments that require skillful two-handed use. Their use is also supported by diverse digital and physical resources, such as videos and chord charts. To understand the challenges of interacting with supporting resources at the same time as playing we conducted an ethnographic study of the preparation activities of working musicians. We observe successive stages of individual and collaborative preparation, in which working musicians engage with a diverse range of digital and physical resources to support their preparation. Interaction with this complex ecology of digital and physical resources is finely interwoven into their embodied musical practices, which are usually encumbered by having their instrument in hand, and often by playing. We identify challenges for augmenting guitars within the rehearsal process by supporting interaction that is encumbered, contextual and connected, and suggest a range of possible responses.
Unsupervised Image Manipulation Localization With Non-Binary Label Attribution

Existing forensic techniques for image manipulation localization crucially assume that probe pixels belong to one of exactly two classes, genuine or manipulated. This letter argues that this convention fuels mis-labeling particularly in unsupervised settings, where singular but genuine content or the presence of multiple distinct manipulations may easily induce non-optimal partitions of the feature space. We propose to relax constraints via a greedy n-ary clustering approach, which we instantiate exemplarily in the popular pixel descriptor space of residual co-occurrences. Experimental results on widely used public benchmark datasets highlight the benefits of our approach.
A Lie Reveals the Truth

Designers are often discouraged from creating data visualizations that omit or distort information, because they can easily be misleading. However, the same representations that could be used to deceive can provide benefits when chosen to appropriately align with user tasks. We present an interaction technique, Perceptual Glimpses, which allows for the transparent presentation of so-called 'deceptive' views of information that are made temporary using quasimodes. When presented using Perceptual Glimpses, message-level exaggeration caused by a truncated axis on a bar chart was reduced under some conditions, but users require guidance to avoid errors, and view presentation order may affect trust. When Perceptual Glimpses was extended to display a range of views that might otherwise be deceptive or difficult to understand if shown out of context, users were able to understand and leverage these transformations to perform a range of low-level tasks. Design recommendations and examples suggest extensions of the technique.
Gabber

We describe the iterative design, development and learning process we undertook to produce Gabber, a digital platform that aims to support distributed capture of spoken interviews and discussions, and their qualitative analysis. Our aim is to reduce both expertise and cost barriers associated with existing technologies, making the process more inclusive. Gabber structures distributed audio data capture, facilitates participatory sensemaking, and supports collaborative reuse of audio. We describe our design and development journey across three distinct field trials over a two-year period. Reflecting on the iterative design process, we offer insights into the challenges faced by non-experts throughout their qualitative practices, and provide guidance for researchers designing systems to support engagement in these practices.
Criteria to detect genuine multipartite entanglement using spin measurements

We derive conditions in the form of inequalities to detect the genuine $N$-partite entanglement of $N$ systems. The inequalities are expressed in terms of variances of spin operators, and can be tested by local spin measurements performed on the individual systems. Violation of the inequalities is sufficient (but not necessary) to certify the multipartite entanglement, and occurs when a type of spin squeezing is created. The inequalities are similar to those derived for continuous-variable systems, but instead are based on the Heisenberg spin-uncertainty relation $\Delta J_{x}\Delta J_{y}\geq|\langle J_{z}\rangle|/2$. We also extend previous work to derive spin-variance inequalities that certify the full tripartite inseparability or genuine multi-partite entanglement among systems with fixed spin $J$, as in Greenberger-Horne-Zeilinger (GHZ) states and W states where $J=1/2$. These inequalities are derived from the planar spin-uncertainty relation $(\Delta J_{x})^{2}+(\Delta J_{y})^{2}\geq C_{J}$ where $C_{J}$ is a constant for each $J$. Finally, it is shown how the inequalities detect multipartite entanglement based on Stokes operators. We illustrate with experiments that create entanglement shared among separated atomic ensembles, polarization-entangled optical modes, and the clouds of atoms of an expanding spin-squeezed Bose-Einstein condensate. For each example, we give a criterion to certify the mutual entanglement.
Exploiting hierarchical visual features for visual question answering

Visual question answering (VQA) aims reasoning answers given a pair of textual question and image. Previous approaches for VQA use only the highest layer of a Convolutional Neural Network (CNN) for visual representation, which biases on object classification task. These object-categorization oriented features lose low-level semantics (attribute related questions), e.g., color, texture, and the number of instances. Consequently, conventional VQA methods are vulnerable to low-level semantic questions. On the other hand, low-level layer features retain the low-level semantics. Thus, we suggest that the low-level layer features are superior in low-level semantic questions, and justify it through our experiments. Furthermore, we propose a novel VQA model named Hierarchical Feature Network (HFnet), which exploits intermediate CNN layers to derive various semantics for VQA. In the answer reasoning stage, each hierarchical feature is combined with the attention map and multimodal pooled to consider both high and low level semantic questions. Our proposed model outperforms the existing methods. The qualitative experiments also demonstrate that our proposed HFnet is superior in reasoning attention regions.
Medical Question Retrieval Based on Siamese Neural Network and Transfer Learning Method

The online medical community websites have attracted an increase number of users in China. Patients post their questions on these sites and wait for professional answers from registered doctors. Most of these websites provide medical QA information related to the newly posted question by retrieval system. Previous researches regard such problem as question matching task: given a pair of questions, the supervised models learn question representation and predict it similar or not. In addition, there does not exist a finely annotated question pairs dataset in Chinese medical domain. In this paper, we declare two generation approaches to build large similar question datasets in Chinese health care domain. We propose a novel deep learning based architecture Siamese Text Matching Transformer model (STMT) to predict the similarity of two medical questions. It utilizes modified Transformer as encoder to learn question representation and interaction without extra manual lexical and syntactic resource. We design a data-driven transfer strategy to pre-train encoders and fine-tune models on different datasets. The experimental results show that the proposed model is capable of question matching task on both classification and ranking metrics.
Turkish vowel classification based on acoustical and decompositional features optimized by Genetic Algorithm

In digital speech processing (DSP), vowel classification is one of the important and challenging issue of automatic speech/speaker recognition (ASR) systems where vowels need to be processed in order to identify spoken speech or speaker identity. Because the vowels' acoustic features such as Formant Frequencies, Energy, Zero Crossing Rate (ZCR) etc. contain patterns revealing speech/speaker characteristics, vowels were used in many studies like speech/speaker recognition, speaker verification, accent/dialect estimation, illness detection via speech etc. In this work, digital speech signals of 8 vowels (/a/, /e/, /ı/, /i/, /o/, /ö/, /u/ and /ü/) in Turkish language were classified using a feature set optimized by Genetic Algorithm. The entire feature vector, which has length of 24, comprises acoustical features (Formant Frequency obtained by Linear Predictive Coding-LPC, Energy, ZCR, Mel-Frequency Cepstral Coefficients-MFCCs) and decompositional features (Wavelet Decomposition Shannon Entropy). All of the 8 isolated vowels were uttered by 10 male speakers (only male to avoid gender effect), and totally 2762 observations were obtained from the vowels signals for classification step. The results inferred that the feature vector optimized by Genetic Algorithm method reached 100% classification success accuracy via the simple 1NN classifier with Manhattan distance. These findings proved success of the proposed model and the Genetic Algorithm's efficiency on feature selection for the Turkish vowel classification.
Saliency Deficit and Motion Outlier Detection in Animated Scatterplots

We report the results of a crowdsourced experiment that measured the accuracy of motion outlier detection in multivariate, animated scatterplots. The targets were outliers either in speed or direction of motion, and were presented with varying levels of saliency in dimensions that are irrelevant to the task of motion outlier detection (e.g., color, size, position). We found that participants had trouble finding the outlier when it lacked irrelevant salient features and that visual channels contribute unevenly to the odds of an outlier being correctly detected. Direction of motion contributes the most to accurate detection of speed outliers, and position contributes the most to accurate detection of direction outliers. We introduce the concept of saliency deficit in which item importance in the data space is not reflected in the visualization due to a lack of saliency. We conclude that motion outlier detection is not well supported in multivariate animated scatterplots.
Deep Transfer Learning for Image Emotion Analysis: Reducing Marginal and Joint Distribution Discrepancies Together

A lot of research attentions have been paid to image emotion analysis in recent years. Meanwhile, as convolutional neural networks (CNNs) have made great successful in computer vision, many researchers start to employ CNN to discriminate image emotions. However, the training procedure of CNNs depends on sufficient labeled data. Therefore, a CNN is hard to perform well in an image domain with scant labeled information. In this paper, we propose a deep transfer learning method for image emotion analysis. The method can leverage rich emotion knowledge from a source domain to the target domain. Our method reduces both marginal and joint domain distribution discrepancies at fully-connected layers. Through this way, we can effectively extract more transferable features and advance the performance of CNNs on poor-label emotion-image domains.
The Effect of Color Channel Representations on the Transferability of Convolutional Neural Networks

Image classification is one of the most important tasks in computer vision, since it can be used to retrieve, store, organize, and analyze digital images. In recent years, deep learning convolutional neural networks have been successfully used to classify images surpassing previous state of the art performances. Moreover, using transfer learning techniques, very complex models have been successfully utilized for other tasks different from the original task for which they were trained for. Here, the influence of the color representation of the input images was tested when using a transfer learning technique in three different well-known convolutional models. The experimental results showed that color representation in the CIE-L*a*b* color space gave reasonably good results compared to the RGB color format originally used during training. These results support the idea that the features learned can be transferred to new models with images using different color channels such as the CIE-L*a*b* space, and opens up new research questions as to the transferability of image representation in convolutional neural networks.
Speech Recognition in Kannada using HTK and Julius: A Comparative Study

This paper describes a comparative study of continuous live speech recognition for selected commonly used words and phrase in the Kannada language using the HTK Toolkit, Julia Script, and the Julius live speech recognition engine. The feature vectors used in this study are the Mel Frequency Cepstral Coefficients (MFCC). A speaker independent continuous speech recognition system has been built for a vocabulary size of 115 phrases using 16 speakers for training using the Hidden Markov Modeling(HMM) technique, Baum Welch re-estimation and the Viterbi algorithm for decoding. On comparing the HTK and Julius Live recognition engine accuracies ,it is found that the Julius engine performs better under moderate noise conditions, which is explained in this work.
Image Segmentation based on Markov Random Field Probabilistic Approach

Image segmentation is the process of partitioning the digital image into many quarters or pixels. It is very important to build a mathematical model to segment out the area of interest than using complex techniques. Markov Random Field (MRF) is a tool which is reducing the tough procedure as well as simplifies the procedure of the handling techniques. The aim of this paper is to segment cancer affected region of the input mammogram image using Markov Random Fields probabilistic measure. The goal of this technique is to simplify as well as to give a meaningful representation of the image. The pectoral muscle always mimics as a tumor is identified from the actual breast region. As an initiative, the input image is processed to highlight the upper region muscle using enhancement and adaptive thresholding. The pixel variation in muscle density is differentiated and is marked. The boundary of the chest muscle is highlighted. The remaining breast density is subjected to Clique potential with Markov to label cancer affected regions and is segmented.
Dynamic Demand Prediction and Allocation in Cloud Service Brokerage

To maximize its own profit, cloud service brokerage (CSB) aims to distribute tenant demands to reserved servers such that the total reservation cost is minimized with the tenants’ service level agreement (SLA) being satisfied. The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">demand allocation</i> problem for CSB is non-trivial to solve due to uncertainty of tenants’ behavior. To avoid possible violations among demands, existing schemes allocate additional padding resources on the predicted demands, which leads to under-utilization of reserved resources. Accordingly, we propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Probabilistic Demand Allocation</i> (PDA) system to address the demand allocation problem for CSB. In PDA, we not only predict tenants’ demands based on their historical records, but also estimate the probability distribution of prediction errors. As over- and under-estimation are equally likely to happen with our prediction method, when allocating demands to a single server, their errors are possibly offset. Hence, it is unnecessary to allocate additional resource to each demand for violation prevention. Given the predication results, we formulate the demand allocation problem by probabilistic optimization, of which the objective is to minimize the overall cost from reserved servers while satisfying tenants’ SLA with high probability. Both simulation and real-world experimental results demonstrate the superiority of PDA in reducing servers’ reservation cost.
Distributed Kernel Extreme Learning Machines for Aircraft Engine Failure Diagnostics

Kernel extreme learning machine (KELM) has been widely studied in the field of aircraft engine fault diagnostics due to its easy implementation. However, because its computational complexity is proportional to the training sample size, its application in time-sensitive scenarios is limited. Therefore, in the case of largescale samples, the original KELM is difficult to meet the real-time requirements of aircraft engine onboard condition. To address this shortcoming, a novel distributed kernel extreme learning machines (DKELMs) algorithm is proposed in this paper. The distributed subnetwork is adopted to reduce the computational complexity, and then the likelihood probability and Dempster-Shafer (DS) evidence theory is used to design the fusion scheme to ensure the accuracy after fusion is not reduced. Afterwards, the verification on the benchmark datasets shows that the algorithm can greatly reduce the computational complexity and improve the real-time performance of the original KELM algorithm without sacrificing the accuracy of the model. Finally, the performance estimation and fault pattern recognition experiments of an aircraft engine show that, compared with the original KELM algorithm and support vector machine (SVM) algorithm, the proposed algorithm has the best performance considering both real-time capability and model accuracy.
Evaluating the Usability of a Moroccan University Research Management Web Platform

The success of any system or interface depends on the satisfaction of the user and users are only satisfied when they are able to do their job in a simple and effective way [1]. The purpose of the SIMARECH information system [2], [3] is the piloting of research in Moroccan universities, their institutions and structures, to support researchers and enhance their scientific output. Ensuring the proper use of the SIMARECH information system has always been a real concern of the Abdelmalek Essaadi University (UAE) in Moroccan universities and national research institutions. The purpose of this study is to investigate usability issues and provide recommendations for achieving the system goal within the UAE university institutions. The objective of this research paper is to evaluate the usability of the SIMARECH platform based on a complete test methodology. The study was conducted from January to March 2018 based on Jakob Nielsen methodology [4] and the methods of data collection Nielsen’s Attributes of Usability Questionnaire (NAU) [4]. The results showed a high level of usability for the platform, but it should be further improved. It is found that the followed approach can effectively assess the usability of scientific research information systems.
A flexible and easy-to-implement single-camera microscopic 3D digital image correlation technique

We propose a novel, flexible and easy-to-implement single-camera microscopic three-dimensional digital image correlation (3D-DIC) technique for accurate full-field shape, motion and deformation measurements at the microscale. The established microscopic 3D-DIC system comprises a single 3CCD color camera, a high-magnification zoom lens, and a specially designed color separation device using a beam splitter, two optical bandpass filters and three plane mirrors. To perform accurate stereo measurement, color images of a test sample surface are recorded by the 3CCD camera, which observes the test sample via the microscopic imaging system from two different optical paths with the aid of the color separation device. The recorded color images can be subsequently separated to red and green channel sub-images which constitute stereo pairs corresponding to different perspectives. The separated stereo pairs can be subsequently analyzed using regular 3D-DIC to extract full-field 3D shape and deformation of the test object surface. The method was first validated by a series of benchmark tests, involving the 3D shape reconstruction of steel balls with different diameters, in-plane and out-of-plane translation tests. Surface 3D deformation measurement of a planar acrylic plate with micro-features was also performed to show the practicality of the proposed method. This method provides new simple and practical avenues for performing shape and deformation measurement at microscale, showing great potential in both material testing and bio-tissue characterization.