Reviewerly/exHarmony huggingface dataset:
Non-linear contrast stretching with optimizations

The primary objective of enhancement is to improve the contrast an image, that the outcome image is more appropriate than the original image for the given application. One of the simplest, computationally effective and most used empirical algorithms that may improve overall contrast is the class (linear stretching and non-linear stretching) of stretching methods. However, linear and non-linear stretching suffer from several issues, for instance, a low-contrast effect by organizing intensities or an over-brightness effect by super-imposing intensities. The goal of this paper is to present new techniques for image contrast enhancement: (1) a bi-non-linear contrast-stretching algorithm, (2) the optimized combination of linear contrast and non-linear contrast stretching algorithms, and (3) the optimized combination of a linear contrast, a non-linear contrast stretching and a local histogram equalization algorithm. Computer simulations on publicly available Thermal Focus Image Database and the Tufts Face Database show that the proposed methods increase the dynamic image range and demonstrate a significantly improved global and local contrast by taking the most exquisite details and edges. In addition, the simulation results show that the proposed method well correlates with subjective evaluations of image quality. The presented concept is useful in guiding the future design of cutting-edge image enhancement methods.
Implementing perceptron models with qubits

We propose a method for learning a quantum probabilistic model of a perceptron. By considering a cross entropy between two density matrices we can learn a model that takes noisy output labels into account while learning. Although some work has been done that aims to utilize the curious properties of quantum systems to build a quantum perceptron, these proposals rely on the ad hoc introduction of a classical cost function for the optimization procedure. We demonstrate the usage of a quantum probabilistic model by considering a quantum equivalent of the classical log-likelihood, which allows for both a quantum model and training procedure. We show that this allows us to better capture noisiness in data compared to a classical perceptron. By considering entangled qubits we can learn nonlinear separation boundaries, such as xor.
User Interface Modelling for SIBI (Sistem Isyarat Bahasa Indonesia/Indonesian Sign Language System) learning applications using the User-Centered Design Method

Sign Language is a type of language used to help deaf children to communicate. The Indonesian government has created their standardization system for sign language called SIBI (Sistem Isyarat Bahasa Indonesia). Deaf children have characteristics such as lack of memory ability. Therefore they need media for repetition in learning. The actual mobile-based learning could support recurrence in the subject is already available, but not all the designs on mobile technology are suitable for children's needs, proven by testing the existing application using USE Questionnaire which obtained a usability average of 58%, which mean that the app could not provide convenience and satisfaction for users. Therefore, to create a user interface that suits the needs of the user, the User-Centered Design (UCD) method is used. The usability testing conducted for the prototype application and the result is 90% with an excellent category. If the result of the usability is an excellent value, then the app could be used very easy and satisfied for user.
Real-Time Arm Gesture Recognition Using 3D Skeleton Joint Data

In this paper we present an approach towards real-time hand gesture recognition using the Kinect sensor, investigating several machine learning techniques. We propose a novel approach for feature extraction, using measurements on joints of the extracted skeletons. The proposed features extract angles and displacements of skeleton joints, as the latter move into a 3D space. We define a set of gestures and construct a real-life data set. We train gesture classifiers under the assumptions that they shall be applied and evaluated to both known and unknown users. Experimental results with 11 classification approaches prove the effectiveness and the potential of our approach both with the proposed dataset and also compared to state-of-the-art research works.
3D Face Reprentation and Reconstruction with Multi-scale Graph Convolutional Autoencoders

Effective representation and reconstruction for human faces are very important in many applications. Existing linear representation methods cannot reconstruct high quality 3D faces with details, while the newest non-linear representation method is less suitable for real shapes since spectral decompositions are unstable across different graphs. To address these problems, we propose a multi-scale graph convolutional autoencoder for face representation and reconstruction. Our autoencoder uses graph convolution, which is easily trained for the data with graph structures and can be used for other deformable models. Our model can also be used for variational training to generate high quality face shapes. Experimental results demonstrate that our model can generate more plausible, complex, and stable 3D shapes, and achieves higher quality face reconstruction compared with state-of-the-art methods.
Mapping Distributional Semantics to Property Norms with Deep Neural Networks

Word embeddings have been very successful in many natural language processing tasks, but they characterize the meaning of a word/concept by uninterpretable “context signatures”. Such a representation can render results obtained using embeddings difficult to interpret. Neighboring word vectors may have similar meanings, but in what way are they similar? That similarity may represent a synonymy, metonymy, or even antonymy relation. In the cognitive psychology literature, in contrast, concepts are frequently represented by their relations with properties. These properties are produced by test subjects when asked to describe important features of concepts. As such, they form a natural, intuitive feature space. In this work, we present a neural-network-based method for mapping a distributional semantic space onto a human-built property space automatically. We evaluate our method on word embeddings learned with different types of contexts, and report state-of-the-art performances on the widely used McRae semantic feature production norms.
Data Analytics Towards Predictive Maintenance for Industrial Ovens

In Industry 4.0, predictive maintenance aims to improve both production and maintenance efficiency. The interconnected machines and IoT devices produce a variety of data that enable the early detection of anomalies and failures by predictive analytic algorithms. Predictive analytics can also reduce the machines downtimes and decrease the production of faulty products. This paper introduces predictive analytics for industrial ovens and their application in a real-world's oven used by a leading medical devices manufacturer. Two distinct approaches are presented in this work. A technique based on existing sensors for oven failure prediction based on monitoring and log data; and a technique based on deployed sensors for fault diagnosis based on acoustic data. Deep learning techniques have been applied on existing sensor and event log data, especially temperature monitoring, whereas an outlier detection analysis were implemented on acoustic sensor measurements. Both analytics methods create a complete solution able to detect early oven failures from their root.
Lifted Temporal Maximum Expected Utility

The dynamic junction tree algorithm (LDJT) efficiently answers exact filtering and prediction queries for temporal probabilistic relational models by building and then reusing a first-order cluster representation of a knowledge base for multiple queries and time steps. To also support sequential online decision making, we extend the underling model of LDJT with action and utility nodes, resulting in parameterised probabilistic dynamic decision models, and introduce meuLDJT to efficiently solve the exact lifted temporal maximum expected utility problem, while also answering marginal queries efficiently.
The juridical spheres for digital forensics and electronic evidence in the insecure electronic world

Abstract With so much information collected from so many places and stored in so many others, the forensic value of digital evidence has exploded globally. This ranges from evidence relating to traditional crimes to that for new cybercrimes. The ability to use digital forensics, computational policing analytics and electronic evidence of all types, and the protections from misuse, are governed by the laws of national jurisdictions. Where those laws intersect in transnational cases may be harmonized or conflict‐ridden. As digital forensics is an interdisciplinary activity, both juridical and technical coordination between nations are essential for effective transnational law enforcement. This requires the application of forensic technology conforms to the legal rules of the jurisdictions involved. We examine legal aspects of this evolving data paradigm and issues relating to legal‐technical compliance. We speculate as to how regulation may evolve for the future. This article is categorized under: Digital and Multimedia Science &gt; Cyber Threat Intelligence Digital and Multimedia Science &gt; Mobile Forensics Digital and Multimedia Science &gt; IoT Forensics Jurisprudence and Regulatory Oversight &gt; Communication Across Science and Law
Crypto‐coin hierarchies: social contestation in blockchain networks

Abstract In this article, I discuss the role of crypto‐coins (CCs) as an explicit response to the 2008 economic crisis. Drawing on networked social movement theory, I outline the tension between blockchain based currencies promoted as horizontal markets of technological innovation and the persistence of hierarchical authority within these networks. By examining the assumptions about authority and regulation that underpin the creation of CCs I argue that the cryptographic responses to the financial crisis understate the persistence of hierarchies in their claims to upset traditional monetary authority. As global networks, blockchain technology faces political challenges from regulators, hegemonic market actors and adherents devoted to particular CCs. Consequently, I draw parallels between CCs and networked social movements to argue that the emergence and proliferation of ‘altcoins’ presents a series of political challenges to the framing of CCs as disruptive technology. The innovative character of CCs relies on tacit and explicit assumptions about politics that social movement theory can help to explain. By examining the altcoins through the concepts of resource mobilization, framing and identity formation, I argue that the dynamics of offline social hierarchies persist and are magnified in the world of CCs.
Skin detection in image and video founded in clustering and region growing

Region growing is defined as a procedure of finding regions containing user defined objects of interest. Growing region is a vital phase for various image processing applications. Growing region in images has been very challenging as it is the base for further image analysis, interpretation, and classification. Region growing varies for different purpose of aim. However, the identified region are widely used for various domain-skin detection, detect object in image, hand gesture detection, etc. In this paper, the main concentration is to defining region of interest from an image based on skin detection. A clustering method was used. Skin detection can be used as a preprocessing step for several applications included but not limited to various Human Computer Interaction (HCI) tasks. However, skin detection is a challenging problem due to sparse variations of skin tone of human. Skin tone can be confused with background color, attire color, ethnicity, individual characteristics-age, sex, body parts, makeup, hair color, presence of non-human objects, and camera calibration. Besides that, lightning conditions also plays a vital role. Researchers have been working tirelessly for an efficient skin detection method but those are not beyond limitations. Various approach including pixel wise threshold for various color spaces, segmentation, face and hand detection based approaches are proposed. But it still lacks from a method which can be applied for all types of skin detection. In this paper, a novel skin detection method is proposed which is free from any manual threshold values and automatically define number of clusters.
Conversational engagement and mobile technology use

The present work investigated effects of mobility constraints on gesturing and other aspects of conversational engagement when using mobile technology. Based on studies of non-verbal communication and interaction quality, we compared mobile and static forms of video call in structured interviews. Using real-time motion capture and content coding of recordings as well as self-reports, several data sources were considered: hand velocity as an indicator of gesturing, observed levels of engagement and remembered conversational content. Gesturing, in contrast to other indicators of engagement, was reduced under high mobility constraints and was more pronounced under low constraints. Gesturing was further positively related to observed engagement. Additionally, lower constraints resulted in more memories of conversational content. These findings emphasise the relevance of mobility constraints and bodily movement during mediated interaction and call for further model development in computer-mediated communication. Potential for application lies in interactive scenarios such as remote interviewing, testimonials, and relationship maintenance.
Multi-party quantum summation with a single d-level quantum system

A multi-party quantum summation with a single [Formula: see text]-level quantum system is proposed, in which a semi-honest third party (TP) is employed. Only sequential communication of a single quantum [Formula: see text]-level system (for any prime [Formula: see text]) is required in the presented protocol, resulting in advantages in scalability. In addition, it can be realized with state-of-the-art technology. Due to the semi-honest TP, the protocol is not only secure against outside attacks, but also secure against participant attacks.
A Multi-demand Negotiation Model with Fuzzy Concession Strategies

This paper proposes a multi-demand negotiation model in discrete domain. To make the bargaining more realistic, we equip our negotiating agents with fuzzy concession strategies modelled by fuzzy rules. Specifically, while keeping the preference on agent’s demands unchanged during the course of negotiation, negotiating agents keep can give up several demands in each round at their will. In addition, we experimentally studied our fuzzy negotiation model and reveal the relationships among various factors involved and different concession strategies.
Eye Tracking as a Method of Neuromarketing for Attention Research—An Empirical Analysis Using the Online Appointment Booking Platform from Mercedes-Benz

A good user experience can only be achieved by knowing and understanding users’ wishes, needs and cognitive abilities. The aim of this study is to find out how the user of the online appointment booking interacts with the interface as well as the visualizations. The focus is on the question of whether the new interface of online service appointment booking leads to their success and can attract their attention. In fact, our study’s aim is to check, whether the new and more expensive customer website is considered to be more user-friendly and helpful than the older and cheaper version. To answer this question, an empirical eye tracking study was conducted on the old and updated website of the automobile manufacturer Mercedes-Benz of Daimler AG. Through the eye tracking study, it is possible to record and analyze the viewing directions of subjects in order to get insights into the cognitive processes of the subjects. Therefore, 20 study participants were recruited. The observations of the subjects and evaluations of the eye tracking data have shown that there are still some shortcomings in the usability of the service appointment booking. Aspects such as the scanpaths and heatmaps could demonstrate the superiority of the old website.
Load-balancing distributed outer joins through operator decomposition

High-performance data analytics largely relies on being able to efficiently execute various distributed data operators such as distributed joins. So far, large amounts of join methods have been proposed and evaluated in parallel and distributed environments. However, most of them focus on inner joins, and there is little published work providing the detailed implementations and analysis of outer joins. In this work, we present POPI (Partial Outer join & Partial Inner join), a novel method to load-balance large parallel outer joins by decomposing them into two operations: a large outer join over data that does not present significant skew in the input and an inner join over data presenting significant skew. We present the detailed implementation of our approach and show that POPI is implementable over a variety of architectures and underlying join implementations. Moreover, our experimental evaluation over a distributed memory platform also demonstrates that the proposed method is able to improve outer join performance under varying data skew and present excellent load-balancing properties, compared to current approaches.
Guiding gaze

We evaluate subtle, emotionally-driven models of eye movement animation. Two models are tested, reading and face scanning, each based on recorded gaze transition probabilities. For reading, simulated emotional mood is governed by the probability density function that varies word advancement, i.e., re-fixations, forward, or backward skips. For face scanning, gaze behavior depends on task (gender or emotion discrimination) or the facial emotion portrayed. Probability density functions in both cases are derived from empirically observed transitions that significantly alter viewing behavior, captured either during mood-induced reading or during scanning faces expressing different emotions. A perceptual study shows that viewers can distinguish between reading and face scanning eye movements. However, viewers could not gauge the emotional valence of animated eye motion. For animation, our contribution shows that simulated emotionally-driven viewing behavior is too subtle to be discerned, or it needs to be exaggerated to be effective.
Towards Automated Boundary Value Testing with Program Derivatives and Search

A natural and often used strategy when testing software is to use input values at boundaries, i.e. where behavior is expected to change the most, an approach often called boundary value testing or analysis (BVA). Even though this has been a key testing idea for long it has been hard to clearly define and formalize. Consequently, it has also been hard to automate. In this research note we propose one such formalization of BVA by, in a similar way as to how the derivative of a function is defined in mathematics, considering (software) program derivatives. Critical to our definition is the notion of distance between inputs and outputs which we can formalize and then quantify based on ideas from Information theory. However, for our (black-box) approach to be practical one must search for test inputs with specific properties. Coupling it with search-based software engineering is thus required and we discuss how program derivatives can be used as and within fitness functions. This brief note does not allow a deeper, empirical investigation but we use a simple illustrative example throughout to introduce the main ideas. By combining program derivatives with search, we thus propose a practical as well as theoretically interesting technique for automated boundary value (analysis and) testing.
A deep learning approach for robust head pose independent eye movements recognition from videos

Recognizing eye movements is important for gaze behavior understanding like in human communication analysis (human-human or robot interactions) or for diagnosis (medical, reading impairments). In this paper, we address this task using remote RGB-D sensors to analyze people behaving in natural conditions. This is very challenging given that such sensors have a normal sampling rate of 30 Hz and provide low-resolution eye images (typically 36×60 pixels), and natural scenarios introduce many variabilities in illumination, shadows, head pose, and dynamics. Hence gaze signals one can extract in these conditions have lower precision compared to dedicated IR eye trackers, rendering previous methods less appropriate for the task. To tackle these challenges, we propose a deep learning method that directly processes the eye image video streams to classify them into fixation, saccade, and blink classes, and allows to distinguish irrelevant noise (illumination, low-resolution artifact, inaccurate eye alignment, difficult eye shapes) from true eye motion signals. Experiments on natural 4-party interactions demonstrate the benefit of our approach compared to previous methods, including deep learning models applied to gaze outputs.
Particle Swarm Optimization Based Feature Selection for Face Recognition

Dimensionality of extracted feature vectors is one of the main problems in pattern recognition area. In this paper, feature selection using particle swarm optimization (PSO) is proposed and evaluated on face recognition problem. Feature selection can help to reduce the dimensionality of the feature vectors and in chorus maintain the quality of the reserved features. For evaluation purpose, Experiments carried out using two well-known face databases. Performance of the PSO approach in terms of accuracy, specificity and sensitivity show that PSO approach gives high performance compare to other algorithms such as principal component analysis (PCA). The PSO approach can be furtherly studied and generalized for different pattern recognition applications.
Hierarchical Attribute-Based Signatures: Short Keys and Optimal Signature Length

With Attribute-based Signatures (ABS) users can simultaneously sign messages and prove compliance of their attributes, issued by designated attribute authorities, with some verification policy. Neither signer’s identity nor possessed attributes are leaked during the verification process, making ABS schemes a handy tool for applications requiring privacy-preserving authentication. Earlier ABS schemes lacked support for hierarchical delegation of attributes (across tiers of attribute authorities down to the signers), a distinct property that has made traditional PKIs more scalable and widely adoptable. This changed recently with the introduction of Hierarchical ABS (HABS) schemes, where support for attribute delegation was proposed in combination with stronger privacy guarantees for the delegation paths (path anonymity) and new accountability mechanisms allowing a dedicated tracing authority to identify these paths (path traceability) and the signer, along with delegated attributes, if needed. Yet, current HABS construction is generic with inefficient delegation process resulting in sub-optimal signature lengths of order $$O(k^{2}|\varPsi |)$$ where $$\varPsi $$ is the policy size and k the height of the hierarchy. This paper proposes a direct HABS construction in bilinear groups that significantly improves on these bounds and satisfies the original security and privacy requirements. At the core of our HABS scheme is a new delegation process based on the length-reducing homomorphic trapdoor commitments to group elements for which we introduce a new delegation technique allowing step-wise commitments to additional elements without changing the length of the original commitment and its opening. While also being of independent interest, this technique results in shorter HABS keys and achieves the signature-length growth of $$O(k|\varPsi |)$$ which is optimal due to the path-traceability requirement.