Reviewerly/exHarmony huggingface dataset:
Social User Interest Mining

he abundance of user generated content on social networks pro-vides the opportunity to build models that are able to accurately and effectively extract, mine and predict users' interests with the hopes of enabling more effective user engagement, better quality delivery of appropriate services and higher user satisfaction. While traditional methods for building user profiles relied on AI-based preference elicitation techniques that could have been considered to be intrusive and undesirable by the users, more recent advances are focused on a non-intrusive yet accurate way of determining users' interests and preferences. In this tutorial, we cover five important aspects related to the effective mining of user interests: (1) we introduce the information sources that are used for extracting user interests, (2) various types of user interest profiles that have been proposed in the literature, (3) techniques that have been adopted or proposed for mining user interests, (4) the scalability and re-source requirements of the state of the art methods, and finally (5)the evaluation methodologies that are adopted in the literature for validating the appropriateness of the mined user interest profiles.We also introduce existing challenges, open research question and exciting opportunities for further work.
Towards optimal path computation in a simplicial complex

Computing optimal path in a configuration space is fundamental to solving motion planning problems in robotics and autonomy. Graph-based search algorithms have been widely used to that end, but they suffer from drawbacks. We present an algorithm for computing the shortest path through a metric simplicial complex that can be used to construct a piece-wise linear discrete model of the configuration manifold. In particular, given an undirected metric graph, G, which is constructed as a discrete representation of an underlying configuration manifold (a larger “continuous” space typically of dimension greater than one), we consider the Rips complex, [Formula: see text], associated with it. Such a complex, and hence shortest paths in it, represent the underlying metric space more closely than what the graph does. Our algorithm requires only a local connectivity-based description of an abstract graph, [Formula: see text], and a cost/length function, [Formula: see text], as inputs. No global information such as an embedding or a global coordinate chart is required. The local nature of the proposed algorithm makes it suitable for configuration spaces of arbitrary topology, geometry, and dimension. We not only develop the search algorithm for computing shortest distances, but we also present a path reconstruction algorithm for explicitly computing the shortest paths through the simplicial complex. The complexity of the presented algorithm is comparable with that of Dijkstra’s search, but, as the results presented in this paper demonstrate, the shortest paths obtained using the proposed algorithm represent the geodesic paths in the original metric space significantly more closely.
Deepfovea

Recent advances in head-mounted displays (HMDs) provide new levels of immersion by delivering imagery straight to human eyes. The high spatial and temporal resolution requirements of these displays pose a tremendous challenge for real-time rendering and video compression. Since the eyes rapidly decrease in spatial acuity with increasing eccentricity, providing high resolution to peripheral vision is unnecessary. Upcoming VR displays provide real-time estimation of gaze, enabling gaze-contingent rendering and compression methods that take advantage of this acuity falloff. In this setting, special care must be given to avoid visible artifacts such as a loss of contrast or addition of flicker.
Tweakable TWINE: Building a Tweakable Block Cipher on Generalized Feistel Structure

Tweakable block cipher (TBC) is an extension of conventional block cipher. We study how to build a TBC based on generalized Feistel structure (GFS), a classical block cipher construction. While known dedicated TBC proposals are based on substitution-permutation network (SPN), GFS has not been used for building TBC. In particular, we take 64-bit GFS block cipher TWINE and try to make it tweakable with a minimum change. To find a best one from a large number of candidates, we performed a comprehensive search with a help of mixed integer linear programming (MILP) solver. As a result, our proposal Tweakable TWINE is quite efficient, has the same number of rounds as TWINE with extremely simple tweak schedule.
Depth-Awareness in a System for Mixed-Reality Aided Surgical Procedures

Computer-assisted surgery is a trending topic in research, with many different approaches which aim at supporting surgeons in the operating room. Existing surgical planning and navigation solutions are often considered to be distracting, unintuitive or hard to interpret. In this work, we address this issue with an approach based on mixed reality devices like Microsoft HoloLens. We assess the depth sensing capabilities of Microsoft HoloLens, and the potential benefit they could bring to computer-assisted surgery applications.
Introduction to Multimodal Scene Understanding

A fundamental goal of computer vision is to discover the semantic information within a given scene, commonly referred to as scene understanding. The overall goal is to find a mapping to derive semantic information from sensor data, which is an extremely challenging task, partially due to the ambiguities in the appearance of the data. However, the majority of the scene understanding tasks tackled so far are mainly involving visual modalities only. In this book, we aim at providing an overview of recent advances in algorithms and applications that involve multiple sources of information for scene understanding. In this context, deep learning models are particularly suitable for combining multiple modalities and, as a matter of fact, many contributions are dealing with such architectures to take benefit of all data streams and obtain optimal performances. We conclude this book's introduction by a concise description of the rest of the chapters therein contained. They are focused at providing an understanding of the state-of-the-art, open problems, and future directions related to multimodal scene understanding as a scientific discipline.
Robust Facial Alignment with Internal Denoising Auto-Encoder

The development of facial alignment models is growing rapidly thanks to the availability of large facial landmarked datasets and powerful deep learning models. However, important challenges still remain for facial alignment models to work on images under extreme conditions, such as severe occlusions or large variations in pose and illumination. Current attempts to overcome this limitation have mainly focused on building robust feature extractors with the assumption that the model will be able to discard the noise and select only the meaningful features. However, such an assumption ignores the importance of understanding the noise that characterizes unconstrained images, which has been shown to benefit computer vision models if used appropriately on the learning strategy. Thus, in this paper we investigate the introduction of specialized modules for noise detection and removal, in combination with our state-of-the-art facial alignment module and show that this leads to improved robustness both to synthesized noise and in-the-wild conditions. The proposed model is built by combining two major subnetworks: internal image denoiser (based on the Auto-Encoder architecture) and facial landmark localiser (based on the inception-resnet architecture). Our results on the 300-W and Menpo datasets show that our model can effectively handle different types of synthetic noise, which also leads to enhanced robustness in real-world unconstrained settings, reaching top state-of-the-art accuracy.
Three dimensional colorization based image/video reconstruction from white-dominant RGBW pattern images

RGBW (RGB-White) CFAs (Color Filter Arrays) which contain white pixels as well as RGB pixels have been actively proposed, which is due to the higher SNR (Signal to Noise Ratio) characteristics of the white pixels. The higher SNR characteristics of the white pixels results in the reconstruction of color images with higher resolutions than those reconstructed with widely used RGB CFAs, especially in low illumination environments. Recently, we proposed a new RGBW demosaicking method which uses an RGBW CFA where the white pixels cover 75% of the CFA, and which reconstructs the color image from the RGBW pattern image by use of the colorization method. The use of a large number of white pixels, and the use of colorization as an interpolation method made it possible to reconstruct a color image with high SNR values. In this paper, we extend this method to reconstruct video sequences by extending the colorization method to the 3-D domain, i.e., we propose a 3-D color reconstruction method which takes an RGBW pattern video sequence as the input and produces a reconstructed color video sequence. We implement the colorization as a 3-D diffusion instead of solving an overall colorization matrix, since the computation is high for the 3-D domain. The 3-D diffusion flow stops at temporal edges, which prevents the false colorizing of moving objects. Furthermore, the 3-D diffusion flow also eliminates the incoherence between colors of neighboring frames, which eliminates the flickering artifact. One major difficulty in extending to the temporal axis is that the noise in the few color seeds can propagate throughout the 3-D domain resulting in many false colors. This is especially important in the case when the image/video is taken in low illumination, since the energy of the thermal noise becomes relatively large compared to the light energy making the RGBW pattern image/video very noisy. To prevent the propagation of false colors caused by the noise in the color seeds, we use the 3-D stacked white channels as a guidance for denoising, so that the denoising takes into account only the color seeds that belong to the same object region in the 3-D space. The stacked white channels also guide the 3-D diffusion throughout the 3-D space. Experimental results show that the image/video reconstructed by the proposed method has better visual quality than those reconstructed by conventional RGB/RGBW CFA based methods. Therefore, the proposed method can be used in surveillance camera applications to recover important information from noisy video sequences taken in very low illumination environment.
Bounded Fuzzy Possibilistic Method

This paper introduces the Bounded Fuzzy Possibilistic Method (BFPM) by addressing several issues that previous clustering/classification methods have not considered. In fuzzy clustering, the data object's membership values should sum to 1. Hence, any data object may obtain full membership in at most one cluster. Possibilistic clustering methods remove this restriction. However, BFPM differs from previous fuzzy and possibilistic clustering approaches by allowing the membership function to take larger values with respect to all clusters. Furthermore, in BFPM, a data object can have full membership in multiple clusters or even in all clusters. BFPM relaxes the boundary conditions (restrictions) in membership assignment. The assigned boundaries satisfy the necessity of obtaining full memberships and overcome the issues with conventional methods (overlapping). The functionality of the proposed method has been proved in geometry, set theory, and other disciplines, when learning methods should provide the important arithmetic operations on different domains. Weighted Feature Distance function (WFD) is also used to cover diversity. Validity and comparison indexes have been applied to evaluate the accuracy of BFPM. Conventional fuzzy and possibilistic methods are compared with BFPM and BFPM-WFD, in terms of accuracy, fuzzification constant, different norms, and covering diversity and overlapping. The results show that the proposed algorithms perform better than other fuzzy and possibilistic methods.
Medicine Recognition from Colors and Text

People of hazy or blurred vision or the elderly people finds it way too challenging just to identify the pills if they are out of the box or packet. And various pills of various shapes, size, texture, color comes with a diverse set of medicinal components. It creates confusion among pills of same color and shape to identify based on a specific texture. For visually impaired people, even if they configure the shape of the pill, the color information and the texts imprinted on the pill remains unknown to them. In this paper, the splitting processes of a dataset according to the number of colors and the texts imprinted on the pills, will be described. Initially the color information were extracted by segmenting pill region from pill image and then some statistical measurements i.e. Kurtosis and skewness, are calculated for probability distributions generated from the image histograms. Thus figuring out the how many colors the pill surface consists of. For the text recognition, the probable text region is detected for an error free text detection. For high quality image data, the reference images from NLM RxIMAGE database has been utilized. The overall accuracy of the proposed system for number of color determination is 95.6% and text recognition accuracy is 81.32%.
Blockchain-Based Publicly Verifiable Cloud Storage

Cloud storage adoption, due to the growing popularity of IoT solutions, is steadily on the rise, and ever more critical to services and businesses. In light of this trend, customers of cloud-based services are increasingly reliant, and their interests correspondingly at stake, on the good faith and appropriate conduct of providers at all times, which can be misplaced considering that data is the "new gold", and malicious interests on the provider side may conjure to misappropriate, alter, hide data, or deny access. A key to this problem lies in identifying and designing protocols to produce a trail of all interactions between customers and providers, at the very least, and make it widely available, auditable and its contents therefore provable. This work introduces preliminary results of this research activity, in particular including scenarios, threat models, architecture, interaction protocols and security guarantees of the proposed blockchain-based solution.
Evolvable Hardware Design of Digital Circuits Based on Adaptive Genetic Algorithm

Evolvable hardware (EHW) is a thriving area of research which uses the genetic algorithm (GA) to construct novel circuits without manual engineering. GA has been widely implemented using software but have not gained an appreciable edge because of the huge computation time involved and easy to fall into local optimum. This has been a major hindrance to real-time applications. In order to improve GA, this paper proposes an adaptive algorithm for adjusting the probability value of genetic operator, called adaptive genetic algorithm (AGA). Adding an elite strategy to the algorithm further accelerates the convergence speed of the algorithm. In addition, a complete hardware evolution system based on Field Programmable SoCs (FPSoCs) for design of digital circuits is developed. The experimental results show that the proposed algorithm can accelerate convergence, reduce the generation of evolutionary circuits, and increase the success rate of evolution.
Fling - A Fluent API Generator.

We present the first general and practical solution of the fluent API problem - an algorithm, that given a deterministic language (equivalently, LR(k), k >= 0 language) encodes it in an unbounded parametric polymorphism type system employing only a polynomial number of types. The theoretical result is accompanied by an actual tool Fling - a fluent API compiler-compiler in the venue of YACC, tailored for embedding DSLs in Java.
Efficient Generation of Multiple Sketch Styles Using a Single Network

In the real world, different artists draw sketches of the same person with different artistic styles both in texture and shape.Our goal is to synthesize realistic face sketches of different styles while retaining the input face identity, only using a single network.To achieve this, we employ a modified conditional GAN with a target style label as input.Our method is capable of synthesizing multiple sketch styles even though it is based on a single network.Sketches created by our method show sketch quality comparable to the state-of-the-art sketch synthesis methods that use multiple networks.
Aid for ALS Patient Using ALS Specs and IOT

Amyotrophic Lateral Sclerosis is a disease which affects the brain and spinal cord nerve cell which lead to losing voluntarily control over arm, legs, and face. Detecting pupils and eye movements of a person can be an important method to help Amyotrophic Lateral Sclerosis (ALS) patients to communicate with others. Encompass with smart appliances and devices an infrastructure can be developed by connecting them with the Internet of Things (IoT), which can be controlled with a tablet. This paper proposes an eye movement tracking and head motion sensor system connected to an IOT infrastructure to ease the daily endeavor of ALS patient. A calibration is to be done at the very first time of the use and can be calibrated any time later when required. The proposed system will help the ALS patient to communicate with others by using their eye movements which will lead them to live an independent life.
Improvement of K-means algorithm based on density

In view of the shortcomings of the traditional K-means method, the D-K-means algorithm is proposed in this paper. The algorithm adopts the concept of density number. The point set of high density number is extracted from the original data set as a new training set, and the point in the point set of the high density number is selected as the initial cluster center point. Then, using the method of geometric center points to update the cluster center points at high density points until convergence conditions are reached. Experiments show that the method can effectively avoid the local optimal situation of K-means clustering algorithm. On the other hand, the iterative number of iteration in the clustering process is reduced, and the stability and accuracy of clustering are improved.
Automated recovery of damaged audio files using deep neural networks

In this paper, we propose two methods to recover damaged audio files using deep neural networks. The presented audio file recovery methods differ from the conventional file carving-based recovery method because the former restore lost data, which are difficult to recover with the latter method. This research suggests that recovery tasks, which are essential yet very difficult or very time consuming, can be automated with the proposed recovery methods using deep neural networks. We apply feed-forward and Long Short Term Memory neural networks for the tasks. The experimental results show that deep neural networks can distinguish speech signals from non-speech signals, and can also identify the encoding methods of the audio files at the level of bits. This leads to successful recovery of the damaged audio files, which are otherwise difficult to recover using the conventional file-carving-based methods.
The Modeling and Diagnostic Features in the Computer Systems of the Heart Rhythm Analysis with the Increased Informativeness

The work concerns the development of the new approach to the modeling and analysis of human heart rhythm in computer systems of cardiodiagnostics. This approach is based on the new mathematical model of cardiosignals in the form of conditional cyclic random process, that, unlike the known models, takes into account the double stochasticity in the time structure of cardiosignals, namely, it takes into consideration the stochasticity of its morphologic structure and stochasticity of its rhythmic structure. It was shown, that the use of random rhythm function for the analysis of heart rhythm allows to take into account much thoroughly the information about its time structure, that is the way to increase the accuracy and reliability of heart rhythm diagnostics. As a mathematical model of heart rhythm, the use of vector of random variables was justified. This vector describes the set of counting of discrete random rhythm function, that is named as the rhythmcardiogram with the increased resolution. There were justified the new diagnostic features of heart rhythm analysis in the form of the parameters of cumulative distribution functions and the matrix of correlation coefficients of rhythmcardiogram with the increased resolution, that increased the informativeness of the analysis of heart rhythm in the computer systems of functional diagnostics of human heart state.
An Analysis of Local and Global Solutions to Address Big Data Imbalanced Classification: A Case Study with SMOTE Preprocessing

Addressing the huge amount of data continuously generated is an important challenge in the Machine Learning field. The need to adapt the traditional techniques or create new ones is evident. To do so, distributed technologies have to be used to deal with the significant scalability constraints due to the Big Data context. In many Big Data applications for classification, there are some classes that are highly underrepresented, leading to what is known as the imbalanced classification problem. In this scenario, learning algorithms are often biased towards the majority classes, treating minority ones as outliers or noise. Consequently, preprocessing techniques to balance the class distribution were developed. This can be achieved by suppressing majority instances (undersampling) or by creating minority examples (oversampling). Regarding the oversampling methods, one of the most widespread is the SMOTE algorithm, which creates artificial examples according to the neighborhood of each minority class instance. In this work, our objective is to analyze the SMOTE behavior in Big Data as a function of some key aspects such as the oversampling degree, the neighborhood value and, specially, the type of distributed design (local vs. global).
GPS: Group People Segmentation with Detailed Part Inference

Noticeable progress has been witnessed in general object detection, semantic segmentation and instance segmentation, while parsing a group of people is still a challenging task for human-centric visual understanding due to severe occlusion and various poses. In this paper, we present a new large-scale dataset named “GPS (Group People Segmentation)” to boost academical study and technology development. GPS contains 14000 elaborately annotated images with 20 fine-grained semantic category labels related to human, divided into two sub-datasets corresponding to indoor and outdoor scenes involving various poses, occlusion and background. We further propose a novel GPSNet for group people segmentation. GPSNet consists of a new “Adjusted RoI Align” module to adjust position of detected person and align RoI features, such that the network does not need to fit various positions of each person. A fusion of global and local features is also employed to refine parsing results. Compared with baseline methods, GPSNet achieves the best performance on GPS Dataset.