Reviewerly/exHarmony huggingface dataset:
Visually Consistent Color Correction for Stereoscopic Images and Videos

In stereoscopic 3D (S3D) color correction, visual inconsistency is a common problem that leads to perceptual quality degradations. In this paper, we propose an S3D image/video color correction strategy that resolves global, local, and temporal color discrepancies simultaneously. We achieve the image-based S3D color correction by three steps: a coarse-grain color correction for global color matching, a fine-grain color correction to further improve both global and local color consistencies, and a guided filtering process to guarantee the structural consistency before and after color correction. In addition, we extend the above strategy to S3D and multiview video color correction. To achieve temporal consistency between successive video frames, we develop an improved histogram matching within a sliding window on time axis. In our method, the mapping functions for each color channel change gradually following the video stream to avoid abrupt temporal changes in colors. The experimental results demonstrate that the proposed strategy outperforms the state-of-the-art color correction algorithms for images and videos.
Spontaneous talking gestures using Generative Adversarial Networks

This paper presents a talking gesture generation system based on Generative Adversarial Networks, along with an evaluation of its adequateness. The talking gesture generation system produces a sequence of joint positions of the robot’s upper body which keeps in step with an uttered sentence. The suitability of the approach is demonstrated with a real robot. Besides, the motion generation method is compared with other (non-deep) generative approaches. A two-step comparison is made. On the one hand, a statistical analysis is performed over movements generated by each approach by means of Principal Coordinate Analysis. On the other hand, the robot motion adequateness is measured by calculating the end effectors’ jerk, path lengths and 3D space coverage.
Revisiting Mobile Advertising Threats with MAdLife

Online advertising is one of the primary funding sources for various of content, services, and applications on both web and mobile platforms. Mobile in-app advertising reuses many existing web technologies under the same ad-serving model (i.e., users - publishers - ad networks - advertisers). Nevertheless, mobile in-app advertising is different from the traditional web advertising in many aspects. For example, malicious app developers can generate fraudulent ad clicks in an automated fashion, but malicious web publishers have to launch click fraud with bots. In spite of using the same underlying web infrastructure, advertising threats behave differently on the two platforms.
Decoupled Smoothing on Graphs

Graph smoothing methods are an extremely popular family of approaches for semi-supervised learning. The choice of graph used to represent relationships in these learning problems is often a more important decision than the particular algorithm or loss function used, yet this choice is less well-studied in the literature. In this work, we demonstrate that for social networks, the basic friendship graph itself may often not be the appropriate graph for predicting node attributes using graph smoothing. More specifically, standard graph smoothing is designed to harness the social phenomenon of homophily whereby individuals are similar to “the company they keep.” We present a decoupled approach to graph smoothing that decouples notions of “identity” and “preference,” resulting in an alternative social phenomenon of monophily whereby individuals are similar to “the company they're kept in,” as observed in recent empirical work. Our model results in a rigorous extension of the Gaussian Markov Random Field (GMRF) models that underlie graph smoothing, interpretable as smoothing on an appropriate auxiliary graph of weighted or unweighted two-hop relationships.
An Efficient LMS Platform and Its Test Bed

In order to develop an e-learning system as a method of education that frees both the teacher and learner from the constraints of time and space, it is necessary to develop software and to build the network equipment required to operate the software. The most basic system consists of a web server, a database server, and a video server. However, these elements are vulnerable to both internal and external threats. As for the web, database, and video servers, it is possible to respond to such threats by operating more than two devices, but this inevitably increases the cost of building the equipment. Therefore, this study proposed the use of a cloud service, such as AWS (Amazon Web Service), in order to save on the costs of purchasing, installing, and operating the servers, as well as a service designed to strengthen security by employees or trainees who understand the internal situation of the training institute. In other words, this study proposed the development of an efficient Learning Management System (LMS) platform and proved its efficiency using a test bed over a period of three years. The major contribution of this study is that the design of the proposed LMS has been improved to provide a more efficient performance than the existing LMSs by surmounting the traffic overload problem often found in video services. This is achieved by utilizing a lesser number of servers and maintaining the balance of the loads. Also, the interface used for the system can be adaptable to most of the web servers as they support Java, Android, and HTML-based systems. As a cloud-based LMS, this system has been tested for its efficiency and effectiveness for a period of three years during which the results have been satisfactory.
Point and imaging spectroscopy investigations on the Pedra Branca orogenic gold deposit, Troia Massif, Northeast Brazil: Implications for mineral exploration in amphibolite metamorphic-grade terrains

The Pedra Branca gold deposit is a recent discovery conducted by private exploration companies in the Troia Massif of the northern Borborema Province, Northeast Brazil. It comprises an orogenic gold deposit hosted by the amphibolite-facies Serra das Pipocas greenstone belt. Airborne hyperspectral images (ProSpecTIR-VNIR-SWIR system) covering the Pedra Branca deposit as well as spectroscopic data (ASD-Fieldspec and SisuCHEMA instruments) from representative rock samples in the area were used in this study to investigate the spectro-mineralogical patterns of alteration minerals at both district and deposit scales, respectively. The spectroscopic data were integrated with geological information from the deposit, and also with X-ray powder diffraction and petrographic analyses, in order to reveal alteration footprints that could guide gold exploration targeting in the region. The results indicate the occurrence of a main hydrothermal calc-silicate alteration in ferromagnesian host rocks, consistent with proximal alteration assemblages found in amphibolite-facies gold deposits. White mica compositional trends suggest a later hydrothermal alteration that overprinted the pre-existing mineral assemblages and crystallized Al-rich white mica in distal zones and Al-poor white mica in the ore zone. Reflectance spectroscopy analyses of outcrop samples indicate that well-ordered kaolinite occurs as a weathering product of Al-poor white mica-bearing samples, and therefore could be used as a proxy to mineralization at the deposit scale. Mineral maps produced by processing airborne hyperspectral images, combined with soil geochemical anomalies, further support the notion that well-ordered kaolinite can be used as a proxy to mineralization in weathered ferromagnesian host rocks. Target areas for gold exploration can be defined by airborne hyperspectral data based on the identification of spectral mixtures of nontronite and well-ordered kaolinite. The mineral and physicochemical trends identified in this study set an important baseline for mineral exploration of amphibolite-facies orogenic gold deposits in greenstone belt terranes. The mineral guides defined here can be sensed by VNIR-SWIR hyperspectral sensors at multiple scales using portable, core scanning, wall-imaging, airborne or satellite-borne instruments, respectively.
An Indoor Room Classification System for Social Robots via Integration of CNN and ECOC

The ability to classify rooms in a home is one of many attributes that are desired for social robots. In this paper, we address the problem of indoor room classification via several convolutional neural network (CNN) architectures, i.e., VGG16, VGG19, &amp; Inception V3. The main objective is to recognize five indoor classes (bathroom, bedroom, dining room, kitchen, and living room) from a Places dataset. We considered 11600 images per class and subsequently fine-tuned the networks. The simulation studies suggest that cleaning the disparate data produced much better results in all the examined CNN architectures. We report that VGG16 &amp; VGG19 fine-tuned models with training on all layers produced the best validation accuracy, with 93.29% and 93.61% on clean data, respectively. We also propose and examine a combination model of CNN and a multi-binary classifier referred to as error correcting output code (ECOC) with the clean data. The highest validation accuracy of 15 binary classifiers reached up to 98.5%, where the average of all classifiers was 95.37%. CNN and CNN-ECOC, and an alternative form called CNN-ECOC Regression, were evaluated in real-time implementation on a NAO humanoid robot. The results show the superiority of the combination model of CNN and ECOC over the conventional CNN. The implications and the challenges of real-time experiments are also discussed in the paper.
An Approach to Identify Use Case Scenarios from Textual Requirements Specification

Use case modeling refers to the process of identifying scenarios written in some natural language text, particularly to capture interactions between the system and associated actors. Several approaches have been proposed to maintain the synergy of use cases with other software models, but no systematic transformation approach is available to extract use case scenarios from the textual requirements specification. In this paper, we propose a systematic transformation approach that automatically extracts various use case elements from textual problem specifications. The approach uses Natural Language (NL) parser to identify Parts-Of-Speech (POS) tags, Type Dependencies (TDs) and semantic roles from the input text specification to populate use case elements. It further makes use of the questionnaire-based approach to develop the remaining unpopulated parts of the use case template. The paper demonstrates the applicability of the proposed approach by applying both industry and research-level case studies. The results highlight that the generated output is correct, consistent, non-redundant and complete, and helpful to use case developers in further analysis and documentation.
Using augmented reality to train students to visualize three-dimensional drawings of mortise–tenon joints in furniture carpentry

Furniture carpentry uses spatial structure and geometry, e.g. mortise–tenon joints, which require cognitive judgments of hidden views and spatial understanding. For the furniture carpentry novice, the idea of spatial geometry in the mortise–tenon joint is difficult to understand because furniture carpentry drawings are usually two-dimensional (2-D) graphics. A great deal of cognitive effort and furniture design drawing practice is required to accurately visualize these 2-D objects as three-dimensional (3-D). Novices unfamiliar with 3-D spatial concepts often make mortise–tenon joint errors when making furniture. Augmented reality (AR) has helped train carpentry novices to understand the conceptual manifestations of 3-D space. In this study, we recruited 40 freshmen for a basic general carpentry class with an Experimental (n = 20) and a Control (n = 20) group. Both groups were asked to make, using their hands and a wood saw, mortise–tenon joints at three levels of difficulty: preliminary, mid-level, and advanced. The Experimental group was given AR training; the Control group was not. After the three phases had been completed, we found that the AR training helped Experimental group students improve their 3-D visualization and indirectly improved their grasp of furniture carpentry skills and of the complicated mortise–tenon joint structure.
As-planar-as-possible depth map estimation

We propose an approach to compute dense disparity maps that takes the characteristics of man-made environments into account. The key contribution is to generate a piecewise planar disparity map while preventing the oversimplification problem in non-planar regions. To achieve this, we decompose the stereo matching problem into three sequential subproblems: initial disparity map estimation, plane hypotheses generation, and global optimization with plane hypotheses. After finding an initial disparity map, we find local and global plane hypotheses from the disparity map through segmentation-based local plane fitting, agglomerative hierarchical clustering, and energy-based multi-model fitting techniques. We then estimate a disparity map that is a mixture of over-parameterized and scalar disparity values while identifying unreliable pixels in an energy minimization framework; disparity values in planar regions are parameterized as a plane, disparity values in non-planar regions are represented as scalar, and unreliable pixels are marked as outliers. As a post-processing step, we perturb assigned plane parameters as well as scalar disparity values. We experimentally verify the proposed method using publicly available benchmarks and various stereo matching algorithms.
Exploiting Diversity in Android TLS Implementations for Mobile App Traffic Classification

Network traffic classification is an important tool for network administrators in enabling monitoring and service provisioning. Traditional techniques employed in classifying traffic do not work well for mobile app traffic due to lack of unique signatures. Encryption renders this task even more difficult since packet content is no longer available to parse. More recent techniques based on statistical analysis of parameters such as packet-size and arrival time of packets have shown promise; such techniques have been shown to classify traffic from a small number of applications with a high degree of accuracy. However, we show that when employed to a large number of applications, the performance falls short of satisfactory. In this paper, we propose a novel set of bit-sequence based features which exploit differences in randomness of data generated by different applications. These differences originating due to dissimilarities in encryption implementations by different applications leave footprints on the data generated by them. We validate that these features can differentiate data encrypted with various ciphers (89% accuracy) and key-sizes (83% accuracy). Our evaluation shows that such features can not only differentiate traffic originating from different categories of mobile apps (90% accuracy), but can also classify 175 individual applications with 95% accuracy.
Aggregated Dendrograms for Visual Comparison between Many Phylogenetic Trees

We address the visual comparison of multiple phylogenetic trees that arises in evolutionary biology, specifically between one reference tree and a collection of dozens to hundreds of other trees. We abstract the domain questions of phylogenetic tree comparison as tasks to look for supporting or conflicting evidence for hypotheses that requires inspection of both topological structure and attribute values at different levels of detail in the tree collection. We introduce the new visual encoding idiom of aggregated dendrograms to concisely summarize the topological relationships between interactively chosen focal subtrees according to biologically meaningful criteria, and provide a layout algorithm that automatically adapts to the available screen space. We design and implement the ADView system, which represents trees at multiple levels of detail across multiple views: the entire collection, a subset of trees, an individual tree, specific subtrees of interest, and the individual branch level. We benchmark the algorithms developed for ADView, compare its information density to previous work, and demonstrate its utility for quickly gathering evidence about biological hypotheses through usage scenarios with data from recently published phylogenetic analysis and case studies of expert use with real-world data, drawn from a summative interview study.
Novel approaches towards slope and slant correction for tri-script handwritten word images

Slope and slant correction of offline handwritten word images are two of the major pre-processing steps in document image processing, because these reduce the variations in writing, thereby make further processing of the same much easier. This paper presents novel slope and slant correction methods that are applied in three different script handwritten words namely Devanagari, Bangla and Roman. The language dependency and the computational complexity of state-of-the-art approaches towards the word level slope and slant correction are addressed here. A new technique for approximate core region detection is introduced here for skew detection and then linear regression is recursively applied to de-skew the word image. Whereas, in case of slant correction, a novel cost function over the vertical projection of de-skewed image is designed and optimized to fix the uniform slant angle of text words. A new benchmarked database is developed herein to evaluate the proposed methods both quantitatively and qualitatively. Comparison of the performances by our methods with some existing slope and slant correction methods reveals that our methods are more accurate and faster.
Applying graph matching techniques to enhance reuse of plant design information

This article investigates how graph matching can be applied to process plant design data in order to support the reuse of previous designs. A literature review of existing graph matching algorithms is performed, and a group of algorithms is chosen for further testing. A use case from early phase plant design is presented. A methodology for addressing the use case is proposed, including graph simplification algorithms and node similarity measures, so that existing graph matching algorithms can be applied in the process plant domain. The proposed methodology is evaluated empirically on an industrial case consisting of design data from several pulp and paper plants.
PrivIdEx: Privacy Preserving and Secure Exchange of Digital Identity Assets.

User's digital identity information has privacy and security requirements. Privacy requirements include confidentiality of the identity information itself, anonymity of those who verify and consume a user's identity information and unlinkability of online transactions which involve a user's identity. Security requirements include correctness, ownership assurance and prevention of counterfeits of a user's identity information. Such privacy and security requirements, although conflicting, are critical for identity management systems enabling the exchange of users' identity information between different parties during the execution of online transactions. Addressing all such requirements, without a centralized party managing the identity exchange transactions, raises several challenges. This paper presents a decentralized protocol for privacy preserving exchange of users' identity information addressing such challenges. The proposed protocol leverages advances in blockchain and zero knowledge proof technologies, as the main building blocks. We provide prototype implementations of the main building blocks of the protocol and assess its performance and security.
Co-Design of a 3D Virtual Campus for Synchronous Distance Teaching Based on Student Satisfaction: Experience at the University of Almería (Spain)

This study presents the main advances made in recent years by an innovative teaching group at the University of Almeria, who are attempting to incorporate alternative spaces for teaching–learning interaction. This group is developing a new line of study to obtain the main vectors for creating an accessible 3D virtual campus on the Second Life platform, recognized as a multi-user virtual environment (MUVE). The goal of this work was to measure the satisfaction and perception of a group of university students. To do this, a co-design approach based on student opinions by relating methodological procedures with an adaptation of the Kano model usually used for product design has been followed. The results found by a panel of postgraduate students in the second stage of the educational project, suggest that the synchronous distance teaching proposal developed can effectively facilitate conciliation of family, work and personal life with online teaching–learning. In the case of undergraduate students with class attendance, the use of the 3D virtual campus is perceived as something more complementary.
Movie Question Answering via Textual Memory and Plot Graph

Movies provide us with a mass of visual content as well as attracting stories. Existing methods have illustrated that understanding movie stories through only visual content is still a hard problem. In this paper, for answering questions about movies, we introduce a new dataset called PlotGraphs, as external knowledge. The dataset contains massive graph-based information of movies. In addition, we put forward a model that can utilize movie clip, subtitle, and graph-based external knowledge. The model contains two main parts: a layered memory network (LMN) and a plot graph representation network (PGRN). In particular, the LMN can represent frame-level and clip-level movie content by the fixed word memory module and the adaptive subtitle memory module, respectively. And the plot graph representation network can represent the entire graph. We first extract words and sentences from the training movie subtitles and then the hierarchically formed movie representations, which are learned from LMN. At the same time, the PGRN can represent the semantic information and the relationships in the graph. We conduct extensive experiments on the MovieQA dataset and the PlotGraphs dataset. With only visual content as inputs, the LMN with frame-level representation obtains a large performance improvement. When incorporating subtitles into LMN to form the clip-level representation, we achieve the state-of-the-art performance on the online evaluation task of “Video+Subtitles.” After the integration of external knowledge, the performance of the model consisting of the LMN and the PGRN is further improved. The good performance successfully demonstrates that the external knowledge and the proposed model are effective for movie understanding.