Reviewerly/exHarmony huggingface dataset:
Dual-Mode NIZKs from Obfuscation

Two standard security properties of a non-interactive zero-knowledge (NIZK) scheme are soundness and zero-knowledge. But while standard NIZK systems can only provide one of those properties against unbounded adversaries, dual-mode NIZK systems allow to choose dynamically and adaptively which of these properties holds unconditionally. The only known dual-mode NIZK schemes are Groth-Sahai proofs (which have proved extremely useful in a variety of applications), and the FHE-based NIZK constructions of Canetti et al. and Peikert et al, which are concurrent and independent to this work. However, all these constructions rely on specific algebraic settings. Here, we provide a generic construction of dual-mode NIZK systems for all of NP. The public parameters of our scheme can be set up in one of two indistinguishable ways. One way provides unconditional soundness, while the other provides unconditional zero-knowledge. Our scheme relies on subexponentially secure indistinguishability obfuscation and subexponentially secure one-way functions, but otherwise only on comparatively mild and generic computational assumptions. These generic assumptions can be instantiated under any one of the DDH, $$k$$-LIN, DCR, or QR assumptions. As an application, we reduce the required assumptions necessary for several recent obfuscation-based constructions of multilinear maps. Combined with previous work, our scheme can be used to construct multilinear maps from obfuscation and a group in which the strong Diffie-Hellman assumption holds. We also believe that our work adds to the understanding of the construction of NIZK systems, as it provides a conceptually new way to achieve dual-mode properties.
Leveraging Linked Open Data to Automatically Answer Arabic Questions

The interchangeably connected Web technologies and the advancements that accompany the semantic web content's leaps, have raised many challenges in the results' retrieval process especially for the Arabic Language.This research targets an important, yet insufficiently precedent, area in using Linked Open Data (LOD) for Automatic Question Answering systems in the Arabic Language.The significance of work presented, comes from its ability to overcome many challenges in querying Arabic content.Some of these challenges are: (a) bridging the gap between natural language and linked data by mapping users' queries to a standard semantic web query language such as SPARQL, (b) facilitating multilingual access to semantic data, and (c) maintaining the quality of data.Another challenging aspect was the lack of related work and publicly available resources for Arabic Question Answering Systems over Linked Data, despite the vastly growing Arabic corpus on the web.This paper presents a novel approach that targets Automatic Arabic Questions' Answering Systems whilst bypassing many featured challenges in the field.A hybrid approach that evaluates the effectiveness of using LOD to automatically answer Arabic questions is developed.The approach is developed to map users' questions in Modern Standard Arabic, to a standard query language for LOD (i.e.SPARQL) through: (i) extracting entities from questions and linking them over the web using Named-Entity Recognition and Disambiguation (NER/NED), and (ii) extracting properties among extracted named entities using a dependency parsing approach integrated with Wikidata ontology.To evaluate our proposed system, an Arabic questions dataset was created including: (a) Question body in Arabic language, (b) Question type, (c) SPARQL Query formulation, and (d) Question answer.Evaluation results are promising with a Precision of 84%, a Recall of 81.3%, and an F-Measure of 82.8%.
A robust multimedia traffic SDN-Based management system using patterns and models of QoE estimation with BRNN

Nowadays, network infrastructures such as Software Defined Networks (SDN) achieve a huge computational power. This allows to add a high processing on the network nodes. In this paper, a multimedia traffic management system is presented. This system is based on estimation models of Quality of Experience (QoE) and also on the traffic patterns classification. In order to achieve this, a QoE estimation method has been modeled. This method allows for classifying the multimedia traffic from multimedia transmission patterns. In order to do this, the SDN controller gathers statistics from the network. The patterns used have been defined from a lineal combination of objective QoE measurements. The model has been defined by Bayesian regularized neural networks (BRNN). From this model, the system is able to classify several kind of traffic according to the quality perceived by the users. Then, a model has been developed to determine which video characteristics need to be changed to provide the user with the best possible quality in the critical moments of the transmission. The choice of these characteristics is based on the quality of service (QoS) parameters, such as delay, jitter, loss rate and bandwidth. Moreover, it is also based on subpatterns defined by clusters from the dataset and which represents network and video characteristics. When a critical network situation is given, the model selects, by using network parameters as entries, the subpattern with the most similar network condition. The minimum Euclidean distance between these entries and the network parameters of the subpatters is calculated to perform this selection. Both models work together to build a reliable multimedia traffic management system perfectly integrated into current network infrastructures, which is able to classify the traffic and solve critical situations changing the video characteristics, by using the SDN architecture.
Step by Step Towards Effective Human Activity Recognition: A Balance between Energy Consumption and Latency in Health and Wellbeing Applications

Human activity recognition (HAR) is a classification process that is used for recognizing human motions. A comprehensive review of currently considered approaches in each stage of HAR, as well as the influence of each HAR stage on energy consumption and latency is presented in this paper. It highlights various methods for the optimization of energy consumption and latency in each stage of HAR that has been used in literature and was analyzed in order to provide direction for the implementation of HAR in health and wellbeing applications. This paper analyses if and how each stage of the HAR process affects energy consumption and latency. It shows that data collection and filtering and data segmentation and classification stand out as key stages in achieving a balance between energy consumption and latency. Since latency is only critical for real-time HAR applications, the energy consumption of sensors and devices stands out as a key challenge for HAR implementation in health and wellbeing applications. Most of the approaches in overcoming challenges related to HAR implementation take place in the data collection, filtering and classification stages, while the data segmentation stage needs further exploration. Finally, this paper recommends a balance between energy consumption and latency for HAR in health and wellbeing applications, which takes into account the context and health of the target population.
Geo-LiM: a new geo-lithological map for Central Europe (Germany, France, Switzerland, Austria, Slovenia, and Northern Italy) as a tool for the estimation of atmospheric CO<sub>2</sub> consumption

Formulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom.
A Non-negative Factorization Approach to Node Pooling in Graph Convolutional Neural Networks

The paper discusses a pooling mechanism to induce subsampling in graph structured data and introduces it as a component of a graph convolutional neural network. The pooling mechanism builds on the Non-Negative Matrix Factorization (NMF) of a matrix representing node adjacency and node similarity as adaptively obtained through the vertices embedding learned by the model. Such mechanism is applied to obtain an incrementally coarser graph where nodes are adaptively pooled into communities based on the outcomes of the non-negative factorization. The empirical analysis on graph classification benchmarks shows how such coarsening process yields significant improvements in the predictive performance of the model with respect to its non-pooled counterpart.
Improved Bilateral Filtering for a Gaussian Pyramid Structure-Based Image Enhancement Algorithm

To address the problem of unclear images affected by occlusion from fog, we propose an improved Retinex image enhancement algorithm based on the Gaussian pyramid transformation. Our algorithm features bilateral filtering as a replacement for the Gaussian function used in the original Retinex algorithm. Operation of the technique is as follows. To begin, we deduced the mathematical model for an improved bilateral filtering function based on the spatial domain kernel function and the pixel difference parameter. The input RGB image was subsequently converted into the Hue Saturation Intensity (HSI) color space, where the reflection component of the intensity channel was extracted to obtain an image whose edges were retained and are not affected by changes in brightness. Following reconversion to the RGB color space, color images of this reflection component were obtained at different resolutions using Gaussian pyramid down-sampling. Each of these images was then processed using the improved Retinex algorithm to improve the contrast of the final image, which was reconstructed using the Laplace algorithm. Results from experiments show that the proposed algorithm can enhance image contrast effectively, and the color of the processed image is in line with what would be perceived by a human observer.
Hierarchical Memcapacitive Reservoir Computing Architecture

The quest for novel computing architectures is currently driven by (1) machine learning applications and (2) the need to reduce power consumption. To address both needs, we present a novel hierarchical reservoir computing architecture that relies on energy-efficient memcapacitive devices. Reservoir computing is a new brain-inspired machine learning architecture that typically relies on a monolithic, i.e., unstructured, network of devices. We use memcapacitive devices to perform the computations because they do not consume static power. Our results show that hierarchical memcapacitive reservoir computing device networks have a higher kernel quality, outperform monolithic reservoirs by 10%, and reduce the power consumption by a factor of 3.4× on our benchmark tasks. The proposed new architecture is relevant for building novel, adaptive, and power-efficient neuromorphic hardware with applications in embedded systems, the Internet-of-Things, and robotics.
Drift-detection Based Incremental Ensemble for Reacting to Different Kinds of Concept Drift

Data stream mining has attracted attention in recent years due to its wide range of applications. Concept drift is a great challenge for learning data streams. The existing algorithms are generally designed for a particular type of concept drift. However, real-world data stream applications are always complex combinations of many types of concept drift. In this paper, We proposed a data-stream ensemble classifier for reacting to different types of concept drift, called Drift-detection based Incremental Ensemble (DIE). DIE combines the operators of concept-drift detection and component update mechanism to handle concept drift. In the chunk-based framework, a drift detector is used to monitor the dynamics of data distribution. When a concept drift is triggered, DIE uses the alternative tree of Hoeffding Adaptive Tree to replace the old one, rather than just updating the weights of ensemble members, which can enhance the ability of the model to deal with sudden drift. We also present a component update mechanism to adjust previous ensemble members using the latest examples. Thus, DIE is suitable for handling slow drift. Experimental studies demonstrate the effectiveness of DIE in dealing with different kinds of concept drift.
A Study on Data Profiling: Focusing on Attribute Value Quality Index

In the era of the Fourth Industrial Revolution, companies are focusing on securing artificial intelligence (AI) technology to enhance their competitiveness via machine learning, which is the core technology of AI, and to allow computers to acquire a high level of quality data through self-learning. Securing good-quality big data is becoming a very important asset for companies to enhance their competitiveness. The volume of digital information is expected to grow rapidly around the world, reaching 90 zettabytes (ZB) by 2020. It is very meaningful to present the value quality index on each data attribute as it may be desirable to evaluate the data quality for a user with regard to whether the data is suitable for use from the user’s point of view. As a result, this allows the user to determine whether they would take the data or not based on the data quality index. In this study, we propose a quality index calculation model with structured and unstructured data, as well as a calculation method for the attribute value quality index (AVQI) and the structured data value quality index (SDVQI).
Using Behavioural Programming with Solver, Context, and Deep Reinforcement Learning for Playing a Simplified RoboCup-Type Game

We describe four scenario-based implementations of controllers for a player in a simplified RoboCup-type game. All four implementations are based on the behavioural programming (BP) approach. We first describe a simple controller for the player using the state-of-the-art BPjs tool and then show how it can be extended in various ways. The first extension is based on a version of BP where the Z3 SMT solver is used to provide mechanisms for richer composition of modules within the BP model. This allows for modules with higher cohesion and lower coupling. It also allows incrementality: we could use the scenarios we developed for the challenge of MDETOOLS'18 and extend the model to handle the new system. The second extension of BP demonstrated in this paper is a set of idioms for subjecting model components to context. One of the differences between this year's challenge and the challenge we dealt with last year is that following the ball is not the only task that a player needs to handle, there is much more to care for. We demonstrate how we used the idioms for handling context to parametrize scenarios like "go to a target" in a dynamic and natural fashion such that modelers can efficiently specify reusable components similar to the way modern user manuals for advanced products are written. Lastly, in an attempt to make the instructions to the robot even more natural, we demonstrate a third extension based on deep reinforcement learning. Towards substantiating the observation that it is easier to explain things to an intelligent agent than to dumb compiler, we demonstrate how the combination of BP and deep reinforcement learning (DRL) allows for giving abstract instructions to the robot and for teaching it to follow them after a short training session.
Comparison of Classification Algorithms to Improve Smart Fire Alarm System Performance

The following topics are dealt with: learning (artificial intelligence); pattern classification; cryptographic protocols; government data processing; Bayes methods; data mining; patient monitoring; medical signal processing; support vector machines; data analysis.
Superpixel Segmentation of Breast Cancer Pathology Images Based on Features Extracted from the Autoencoder

In order to identify the breast cancer region, it is necessary to discriminate the pathological image of breast cancer pixel by pixel. This is a very huge work for machine learning. Therefore, the preprocessing of superpixel segmentation of breast cancer pathology images is necessary for reducing the number of pixels that need to be discriminated. In this paper: 1. We have trained serveral kinds of autoencoder networks and evaluated their performance in images clustering. 2. In order to enhance the image clustering effect, we adopt the clustering loss which is defined in Deep Embedded Clustering (DEC). 3. In order to enhance the ability of neural networks of extracting features, we added inception-like block, Sequeeze and Excitation (SE) block to the network. 4. We improved the performance of current Simple Linear Iterative Clustering (SLIC) algorithm to achieve superpixel segmentation of high-dimensional features.
Consistency of Fuzzy If-Then Rules for Control System

Fuzzy If-Then rules are frequently used to describe the conditional statements that consist of fuzzy logic. It is closely connected with fuzzy inference process which is formulated from fuzzy logic operators and fuzzy If-Then rules. To define consistency of given rules enables decision makers to select more important criteria. However, researches on consistency for fuzzy control systems are still scarce. This paper describes some preliminary investigations on consistency of fuzzy If-Then rule based on control systems. Some numerical examples related to this subject are characterized in our research.
A deterministic rewrite system for the probabilistic λ-calculus

Abstract In this paper we present an operational semantics for the ‘call-by-name’ probabilistic λ -calculus, whose main feature is to use only deterministic relations and to have no constraint on the reduction strategy. The calculus enjoys similar properties to the usual λ -calculus. In particular we prove it to be confluent, and we prove a standardisation theorem.
Extraction and Identification of Manipuri and Mizo Texts from Scene and Document Images

The content inside an image is exceptionally compelling. As such, text within an image can be of special interest and compared to other semantic contents, it tends to be effectively extracted. Text detection within an image is the task of detecting and localizing the portion of an image that contains the text information. Manipuri and Mizo are respectively the lingua francas of two neighboring northeastern states of Manipur and Mizoram in India. While Manipuri, is currently written using Meetei Mayek script and Bengali script, Mizo is written in Roman script with circumflex accent added to the vowels. In this work, we report the task of text detection in natural scene images and document images in Manipuri and Mizo. We made a comparative study between Maximally Stable Extremal Regions (MSER) coupled with Stroke Width Transform (SWT) and Efficient and Accurate Scene Text Detector (EAST) for the text detection. The detected text portion of both the languages is subjected to Optical Character Recognition (OCR) and a post OCR processing of spelling correction. In our experiment of the text detection, EAST outperformed the other method.
In-Vehicle Network Intrusion Detection and Explanation Using Density Ratio Estimation

Most modern vehicles are equipped with Electronic Control Units (ECUs) and they are interconnected by Controller Area Network (CAN), a widely used communication protocol for in-vehicle networks. Recent studies have demonstrated that injecting malicious packets into in-vehicle networks can cause unintended behaviors of vehicles, and such a security threat is considered as an urgent issue to address in the industry. In this paper, we tackle with the task of detecting injected malicious packets as well as the task of explaining these injected packets, that is, to find which parts of the injected packets are essential in the attack. In contrast with the previous approaches using statistical anomaly detection techniques to find anomalous contents, our approach employs change detection which can detect small changes of frequencies of non-anomalous contents. Especially, our change detection approach is based on a density ratio estimation method using a neural network classifier; therefore, we can interpret the detection results using recent techniques to interpret decisions by deep neural networks. Our experimental results using real CAN packet datasets collected from an actual vehicle facing several kinds of attacks show the advantage of the proposed approach over anomaly detection based methods.
Combining DWT/KLT for Secure Transfer of Color Images

The idea of the proposed work is to contribute to the secure transfer, medical color images, through the proposal of a digital watermarking scheme. The idea of this approach is to contribute in the medical domain. The watermark to be inserted is the diagnosis own to patient's. The tested images are the medical endoscopic images. The principal idea turns on ameliorate of the principal constraints constituted in all watermarked system such as the imperceptibility and the robustness of the watermarking system. Experimental results show that the proposed scheme is robust against most common attacks including JPEG 2000 compression, low-pass filter, rotation and Salt & Pepper noise.
Augmented Reality Technique Assists Target Language Learning

Technology assists learning and teaching covers a great deal of research and yet there are relatively few studies suggesting paths how language instructors help L2 learners cope with learning by implementing augmented reality techniques. Thus, the purpose of this study was to investigate whether the implementation of augmented reality technique helps increasing EFL learners’ learning effectiveness and motivation. The research took place at a private university in the northern part of Taiwan. 216 freshmen participated in the study. Mixed method was applied in the study. The questionnaire of augmented reality technique assists learning L2 effectiveness and satisfaction were implemented at the first section of the data collection. In addition, the use of semi-structure interview and field note techniques were carried out to be the main method of gathering qualitative research data. 12 Participants were randomly selected and asked to reflect on how technology operates in the English learning context and how AR technique creates the model of interactive learning environments after participants’ learning practices. The results indicated that implementing augmented reality technique was a vital strategy for producing independent thinkers and learners.
What Synthesis Is Missing: Depth Adaptation Integrated With Weak Supervision for Indoor Scene Parsing

Scene Parsing is a crucial step to enable autonomous systems to understand and interact with their surroundings. Supervised deep learning methods have made great progress in solving scene parsing problems, however, come at the cost of laborious manual pixel-level annotation. Synthetic data as well as weak supervision have been investigated to alleviate this effort. Nonetheless, synthetically generated data still suffers from severe domain shift while weak labels often lack precision. Moreover, most existing works for weakly supervised scene parsing are limited to salient foreground objects. The aim of this work is hence twofold: Exploit synthetic data where feasible and integrate weak supervision where necessary. More concretely, we address this goal by utilizing depth as transfer domain because its synthetic-to-real discrepancy is much lower than for color. At the same time, we perform weak localization from easily obtainable image level labels and integrate both using a novel contour-based scheme. Our approach is implemented as a teacher-student learning framework to solve the transfer learning problem by generating a pseudo ground truth. Using only depth-based adaptation, this approach already outperforms previous transfer learning approaches on the popular indoor scene parsing SUN RGB-D dataset. Our proposed two-stage integration more than halves the gap towards fully supervised methods when compared to previous state-of-the-art in transfer learning.