Reviewerly/exHarmony huggingface dataset:
Multi-view coupled dictionary learning for person re-identification

In recent years, person re-identification is becoming an important technique, which can be applied in computer vision, pedestrian tracking and intelligent monitoring. Due to the large variations of visual appearance caused by view angle, pose changing, light changing, background clutter and occlusion, person re-identification is very challenging. In practice, there exist large differences among different types of features and among different cameras. To improve the favorable representation of different features, we propose a multi-view based coupled dictionary pair learning framework, which can learn dictionary pairs for multiple categories of features, e.g., the color features, texture features and hybrid features etc. Specifically, with the learned color feature dictionary pair, we can obtain the color feature representation coefficients of each person from different cameras. The texture feature dictionary pair seeks to learn the texture feature representation coefficients of each person from both cameras. The hybrid feature dictionary pair aims to learn the hybrid feature coefficients for each person. The learned coupled dictionary pairs can demonstrate the intrinsic relationship of different cameras and different types of features. When the resolution of image is too low, the texture information will be lost to some extent. There is few high-resolution person dataset so far, we contribute a newly collected dataset, named High-Resolution Pedestrian re-Identification Dataset (HRPID) on the campus of Wuhan University. The size of person images is normalized to 230 × 560 pixels, which is bigger than existing person re-identification datasets. Experimental results on a new dataset and two public pedestrian datasets demonstrate that our proposed approach can perform better than the other competing methods.
Information systems model for targeting policies: A graph-theoretic analysis of expert knowledge

This paper develops a graph-theoretic approach to analyzing information system priorities using expert knowledge. The constructed information system model lends itself to the identification and quantification of priority pathways of information flow in the system where agents have different learning and information distribution capacities. The approach is developed in four steps. In step 1, a policy issue is formulated using systems approach. In step 2, through a workshop, expert knowledge is gathered on the interactions of system components. Distribution and learning capacities of individual components are approximated through a questionnaire. In step 3, with the aid of graph-theoretic concepts, system priorities are quantitatively refined. In step 4, using the findings from graph-theoretic analysis, specific policy interventions are developed for effective targeting of the policy issue. The approach developed is then illustrated with an application to food and nutrition security information system in Azerbaijan. Specific policy intervention strategies are proposed.
Variable-domain fuzzy sets—Part I: Representation

Despite the fact that fuzzy sets can generally have different domains, standard definitions of fuzzy set operations assume a common reference set for all their operands. The usual way to satisfy this precondition is to assign zero membership to elements outside the domain of each operand. In Part I of this two-part paper we point out several issues with this method and argue that keeping track of the original domains is necessary to ensure the intended behavior of numerous fuzzy set operations. We discuss several ways of representing fuzzy sets with variable domains, preparing the grounds for the apparatus of variable-domain fuzzy set theory described in Part II of the paper.
Stability-based Dynamic Bayesian Network method for dynamic data mining

In this article we introduce a new stability-based dynamic Bayesian network method for dynamic systems represented by their time series. Based on the Grow Shrink algorithm and the stability of the network through time, new variables and arcs could be added to the network in order to generate missing data or predict future values. The concept of stability in the network is maintained through a stability matrix which contains learned values that indicate the strength of dependencies between variables along the time. Moreover, we present the application of the proposed method to deal with the problem of prediction in a real-life air quality case study, in which we try to predict the level of Carbon monoxide in the air, comparing between the results obtained using the proposed method and those obtained using the Vector Autoregression model.
Efficient tumor detection in medical images using pixel intensity estimation based on nonparametric approach

Identification of tumor in medical images is an important task which leads to increased survival chances. In many cases, tumor diagnosis in medical images is very complicated since the pixel intensities between benign and malignant tissues may be very close. The identification problem is defined as the intensification of the mutual data between the image pixel intensities and the desired areas (tumors in this paper), depending on a limitation of the overall size of the desired area borders. This paper introduces a new approach for image segmentation in differentiating between benign and malignant tissues through a nonparametric approach. The proposed approach assumes that the probability densities correlated with pixel intensities in the image for each region are not already known. The image intensity is used instead of using the probability. A nonparametric density estimation is proposed and formulates the theoretical data optimization problem by applying curve evolution methods and deriving the correlated gradient flows. It uses level-set techniques to achieve the resulting evolution. The algorithm is applied to 100 sets of different real data in patient images with benign or/and malignant tissues. The experimental results show that the proposed algorithm proved effective in tumor identification with an acceptable accuracy of 93.1%, especially in-patient images diagnosed with carcinoma at an early stage.
Quantum information capsule and information delocalization by entanglement in multiple-qubit systems

Where do entangled multiple-qubit systems store information? For information injected into a qubit, this question is nontrivial and interesting since the entanglement delocalizes the information. So far, a common picture is that of a qubit and its purification partner sharing the information quantum mechanically. Here, we introduce a new picture of a single qubit in the correlation space, referred to as quantum information capsule (QIC), confining the information perfectly. This picture is applicable for the entangled multiple-qubit system in an arbitrary state. Unlike the partner picture, in the QIC picture, by swapping the single-body state, leaving other subsystems untouched, the whole information can be retrieved out of the system. After the swapping process, no information remains in the system.
Side-Channel Attacks

Side-channel attacks (SCAs) aim at extracting secrets from a chip or a system, through measurement and analysis of physical parameters. Examples of such parameters include supply current, execution time, and electromagnetic emission. These attacks pose a serious threat to modules that integrate cryptographic systems, as many side-channel analysis techniques have proven successful in breaking an algorithmically robust cryptographic operation (for example, encryption) and extracting the secret key. In this chapter, we focus on different side-channel attack modalities in hardware implementations of cryptographic algorithms. Chapter 16, on the other hand, covers side-channels in a processor micro-architecture, which can be controlled by a software, and are visible through it. We discuss the analysis techniques that can be applied on various side-channel parameters to retrieve cryptographic keys. Next, we describe possible countermeasures, based on design modifications, to mitigate side-channel attacks. Finally, we point to various metrics that can be used to quantify the amount of information leakage in a side-channel parameter, and discuss ways to provide resistance to SCAs.
Optimal Uniform Secret Sharing

An important problem in secret sharing schemes is minimizing the share size. For (k, n)-threshold schemes and (k, L, n)-ramp schemes, constructions that minimize the share size are known. This paper presents optimal constructions for a more general class of access structures in which subsets with the same cardinality have the same amount of information about the secret. We refer to schemes with such uniform access structures as uniform secret sharing. We first derive a tight lower bound for share entropy and then present an optimal construction. Our lower bound exceeds that previously reported. The optimal construction encodes the secret value using one or more ramp schemes.
Background on dynamic neural networks

This chapter presents an introduction to the problem field of Artificial Neural Networks (ANNs) which are very effective at their intended tasks such as classification or segmentation. Two types of ANNs exist: Feedforward NNs (or static NNs) and recurrent (in discrete time) or differential (in continuous time) neural networks. The latter ones frequently are treated as dynamic NNs (DNNs). They turn out to be extremely useful when we deal with the identification and/or control of dynamic processes with non-exactly given dynamic models: they occur when noise perturbations or some unknown parameters are present in the expressions of a mathematical model. The environment problems considered in this book exactly deal with such uncertain situation, and therefore DNNs will be used throughout this book. Mainly, the concept of DNNs will be applied for the construction of the so-called Universal Dynamic Neuro-Observer (DNO), which is used for designing both artificial software sensors (dynamic state observers) and robust controllers, providing a good workability in the absence of complete and exact information on a corresponding mathematical model of the environment processes considered. The concept of the Attractive Ellipsoid Method (AEM) is discussed in detail: it is used to analyze numerically the quality of the corresponding state-estimation and control processes. The description of the main ideas on how to design DNO's and controllers using standard MATLAB Toolboxes are given in the end of this chapter.
Four-point trapezoidal calibration algorithm for human-computer interaction system based on 3D sensor

In the depth image interaction system, there is a deviation between the detection range of the Kinect sensor and the projection interaction surface. Aiming at this problem, this paper proposes a trapezoidal correction algorithm based on the three-dimensional sensor Kinect, and performs the calibration on the interaction space to achieve precise interactive control. The performance of the algorithm is verified by the human-computer interaction control platform, which is developed independently. The algorithm proposed in this paper was applied to the 3D interactive software system, which can achieve accurate single-touch and multi-touch performance. Compared with the traditional optical trapezoidal calibration algorithm, the algorithm tracking accuracy rate and the tracking time have been greatly improved, and a good interactive effect can be well obtained. The presented method can be widely applied to various interactive systems such as image interaction systems, human-computer interaction systems, and multi-touch systems, etc.
New Hole-Filling Method Using Extrapolated Spatio-Temporal Background Information for a Synthesized Free-View

This paper introduces a new hole-filling method using extrapolated spatio-temporal background information to obtain a synthesized free-view. New temporal background modeling is proposed, which incorporates stationary temporal information in the hole-filling process and preserves the temporal consistency of synthesized views. A background codebook is distinguished from a non-overlapped patch-based codebook, which contributes to extracting reliable temporal background information. Furthermore, a depth-map driven spatial local background estimation is also addressed to discriminate the background holes in each disocclusion and to define two spatial BG constraints that represent the lower and upper bounds of a background candidate. Holes are filled by comparing the similarities between the temporal background information and the spatial background constraints. In addition, a depth map-based ghost removal filter is described to solve the problem of the non-fit between a color image and the corresponding depth map of a virtual view. Finally, an exemplar-based inpainting is applied to fill in the remaining holes with a priority function that includes a new depth term. The experimental results demonstrated that the proposed method led to results that promised subjective and objective improvement over state-of-the-art methods.
Exponential Separation between Quantum Communication and Logarithm of Approximate Rank

Chattopadhyay, Mande and Sherif (CMS19) recently exhibited a total Boolean function, the sink function, that has polynomial approximate rank and polynomial randomized communication complexity. This gives an exponential separation between randomized communication complexity and logarithm of the approximate rank, refuting the log-approximate-rank conjecture. We show that even the quantum communication complexity of the sink function is polynomial, thus also refuting the quantum log-approximate-rank conjecture. Our lower bound is based on the fooling distribution method introduced by Rao and Sinha (Theory Comput., 2018) for the classical case and extended by Anshu, Touchette, Yao and Yu (STOC, 2017) for the quantum case. We also give a new proof of the classical lower bound using the fooling distribution method.
A hierarchical memory network-based approach to uncertain streaming data

Various interferences can cause uncertainties such as missing data, outliers, noise, and redundancies that are persistent either in stationary or streaming data. In such case, preprocessing methods are widely accepted, of which PCA (Principal Component Analysis), filter, and Bayesian are most popular. As batch learning schemes, they work well particularly in the stationary environment through making correction and revision for bad samples. However, for the streaming data an in-time response for the unusual is needed, where an online learning scheme is even more suitable. As we have known, human brain is a highly complex and intelligent information processing organ, in which memory serves as a crucial role. The more repeated information will be remembered more deeply, and the less repeated will be forgot soon. Considering the mechanisms of memory such as remembering, forgetting, and recalling, a human memory-inspired approach is proposed in this paper. Differ from any mathematic methods, it is an instance-replaced approach by retrieving superior historical instances in memory library to replace the abnormal ones. Through modeling these mechanisms and adding different priorities for each instance on storing and retrieving, a hierarchical memory network (HMN) is constructed, which contains three levels, namely, perpetual, long-term, and short-term. In HMN, data instances migrate dynamically to change their hierarchies to adapt changing circumstances. The perpetual level stores data with the deepest memory and never forgets them again. The long-term level interacts with other two levels through migrating instances with deeper memory into perpetual memory and forgetting shallower ones back to the short-term level. Likewise, data in short-term level will be migrated into long-term memory once up to a predefined threshold, meanwhile some instances under the lowest index will be forever discarded. Benchmark and real-world industrial datasets are utilized to build and test HMN, and simulation results verify its effectiveness.
A Novel Dehazing Method for Color Fidelity and Contrast Enhancement on Mobile Devices

Mobile devices that can capture images outdoors often suffer from poor weather conditions. Observed scenes lose contrast and visibility, and are also subject to color shifts due to the presence of fog and haze. In this paper, a single image dehazing method for removing color distortion and enhancing contrast on mobile devices is presented. The method can effectively remove haze from images with an uneven haze density. First, based on the local haze density, a superpixel segmentation algorithm is employed to adaptively divide a hazy image into several regions. Second, the local atmospheric light in each image region is estimated, and an initial transmission map is calculated. Finally, based on the haze imaging model, an iterative algorithm is developed to estimate the local atmospheric light and transmission for restoring the image. The proposed method can effectively recover detailed image information, resulting in an image with natural color, with an acceptable time performance. Experimental results show that the proposed method can be applied effectively to mobile devices, including digital cameras and mobile phones for enhancing the contrast and visibility without introducing color distortion.
An investigation of the text features of discrepantly-scored ESL essays: A mixed methods study

In this explanatory sequential mixed methods study I investigated which textual features are common in L2 essays scored inconsistently by two raters. In the first quantitative phase, I compiled a set of 109 test essays on one topic that received two matching scores or two discrepant scores. I used automatized tools (i.e., Coh-Metrix and Authorial Voice Analyzer) to measure the essays' complexity, fluency, and interactional metadiscourse. For accuracy and impressionistic measures, two raters coded the essays' linguistic errors and gave them scores on apparent length and handwriting quality. With this comprehensive set of textual measures representing different levels of language use, I performed a discriminant function analysis and found that seven textual features (i.e., spelling, syntactic diversity, noun phrase density, negation density, voice strength, conceptual cohesion, and apparent length) predicted score discrepancy with a 94.4% accuracy rate. In the second qualitative phase, I used intro- and retrospective data to understand what textual features raters found distinct in the discrepantly scored essays and whether they referred to the seven statistically identified textual features. They attended to spelling errors, apparent length, authorial voice, and syntactic diversity, but did not consider the three other features that were uncovered in the quantitative phase of the study (i.e., negation density, conceptual cohesion, and noun phrase density).
How teachers in middle schools design technology integration activities

The purpose of the study was to examine the procedures to design technology-integrating activities and the factors that influence discussion among middle schools teachers. A multiple-case study design was employed to achieve the purpose of the study. The participants of the study were 27 teachers from different middle schools in Palestine, which is a conflict zone. Twenty-two teachers were organized in four teams. Five participants preferred to design mobile technology-integrating activities individually. The 27 teachers have different experiences with mobile technology integration into the classroom and have different backgrounds. Teachers participated voluntarily in the study. Different instruments were used to collect data including design teams observation, semi-structured interviews, and focus group discussions. The findings of the study revealed that violence has a negative impact on design teams. Furthermore, the cultural and religious factors were important factors in the discussion and the content of the activity. In addition, beliefs, experience, and the policy of the Ministry of Education influenced the discussion. The main challenges of the design teams were to find a time to schedule the meetings, social responsibility for team members, and the lack of training.
Digital Forensic Investigation of Social Media, Acquisition and Analysis of Digital Evidence

Various social networking sites (SNSs), widely referred to as social media, provide services such as email, blogging, instant messaging and photo sharing for social and commercial interactions. SNSs are facilitating new forms of social interaction, dialogue, exchange and collaboration. They allow millions of users and organisations worldwide to exchange ideas, post updates and comments or participate in activities and events, while sharing their wider interests. At the same time, such a phenomenon has led to an upsurge in significant criminal activities by perpetrators who are becoming increasingly sophisticated in their attempts to deploy technology to circumvent detection. Digital forensic Examiners (DFEs) often face serious challenges in relation to data acquisition. Therefore, this article aims to analyse the significance of SNSs in DFIs and challenges that DFEs often encounter when acquiring evidence from SNSs. Furthermore, this article describes the steps of the digital forensic investigation process that must be taken to acquire digital evidence that is both authentic and forensically sound.
Mapping copper mineralization using EO-1 Hyperion data fusion with Landsat 8 OLI and Sentinel-2A in Moroccan Anti-Atlas

In Morocco, the oriental Anti-Atlas encompasses many valuable deposits, including that of copper (Cu) which is located in the Sidi Flah-Bouskour inlier. This deposit is known for its high importance on the national scale. The present work aims to compare the Hyperion raw and fused data for mapping Cu mineralization. After applying the pre-processing steps on the images used, the fusion (hyperspectral/multispectral) process was done using the colour normalized spectral sharpening method. Subsequently, the mineralogical mapping task was performed using the mixture tuned matched filtering and the independent component analysis methods. In the last step, a geophysical data, a field survey and spectroradiometric measurements were done for checking and validating the results obtained. The analysis showed a high performance of the fused images for mineralogical mapping. Consequently, the methodology proposed can be exploited in mineralogical mapping, and also in other remote sensing works.
A library for automatic natural language generation of spanish texts

In this article we present a novel system for natural language generation (nlg) of Spanish sentences from a minimum set of meaningful words (such as nouns, verbs and adjectives) which, unlike other state-of-the-art solutions, performs the nlg task in a fully automatic way, exploiting both knowledge-based and statistical approaches. Relying on its linguistic knowledge of vocabulary and grammar, the system is able to generate complete, coherent and correctly spelled sentences from the main word sets presented by the user. The system, which was designed to be integrable, portable and efficient, can be easily adapted to other languages by design and can feasibly be integrated in a wide range of digital devices. During its development we also created a supplementary lexicon for Spanish, aLexiS, with wide coverage and high precision, as well as syntactic trees from a freely available definite-clause grammar. The resulting nlg library has been evaluated both automatically and manually (annotation). The system can potentially be used in different application domains such as augmentative communication and automatic generation of administrative reports or news.