Reviewerly/exHarmony huggingface dataset:
A New Robust Pupil Detection Algorithm for Eye Tracking Based Human-Computer Interface

In this paper a new pupil detection algorithm appropriate for development of head mounted eye tracking interfaces is presented. The robustness of the proposed algorithm is given by its low running time and the ability to accurately provide the coordinates of the pupil center even under real-world scenarios like variable illumination and high noise conditions. The proposed algorithm is based on an adaptive quantitative binarization technique with a feedback-loop controlled threshold. The pupil center is detected by applying the Least Square Fit to Ellipse procedure to the processed eye image. The performance of this algorithm is given by a 5 pixels detection rate of 93%, obtained for the eye images from two representative databases.
Near Real-Time Object Recognition for Pepper Based on Deep Neural Networks Running on a Backpack

The main goal of this work is to provide Pepper with a near real-time object recognition system based on deep neural networks. The proposed system is based on YOLO (You Only Look Once), a deep neural network that is able to detect and recognize objects robustly and at a high speed. In addition, considering that YOLO cannot be run in the Pepper’s internal computer in near real-time, we propose to use a Backpack for Pepper, which holds a Jetson TK1 card and a battery. By using this card, Pepper is able to robustly detect and recognize objects in images of $$320 \times 320$$ pixels at about 5 frames per second.
E2E Verifiable Borda Count Voting System without Tallying Authorities

An end-to-end verifiable (E2E) voting system enables candidates, voters and observers to monitor the integrity of an election process and verify the results without relying on trusted systems. In this paper, we propose a DRE-based Borda count e-voting system called DRE-Borda. The proposed system is E2E verifiable without involving any tallying authorities. Furthermore, it outputs only the total score a candidate gets without revealing any other information such as the breakdown of scores with respect to different ranks. This reduces the information leakage from the tallying result to the minimum, hence effectively preventing Italian attacks. When the DRE machine is completely compromised, the integrity of the tallying result is still preserved and what an adversary can learn from a compromised machine is strictly limited to the partial tally at the time of compromise.
Selective Poisoning Attack on Deep Neural Network to Induce Fine-Grained Recognition Error

Deep neural networks (DNNs) provide good performance for image recognition, speech recognition, and pattern recognition. However, a poisoning attack is a serious threat to DNN's security. The poisoning attack is a method to reduce the accuracy of DNN by adding malicious training data during DNN training process. In some situations such as a military, it may be necessary to drop only a chosen class of accuracy in the model. For example, if an attacker does not allow only nuclear facilities to be selectively recognized, it may be necessary to intentionally prevent UAV from correctly recognizing nuclear-related facilities. In this paper, we propose a selective poisoning attack that reduces the accuracy of only chosen class in the model. The proposed method reduces the accuracy of a chosen class in the model by training malicious training data corresponding to a chosen class, while maintaining the accuracy of the remaining classes. For experiment, we used tensorflow as a machine learning library and MNIST and CIFAR10 as datasets. Experimental results show that the proposed method can reduce the accuracy of the chosen class to 43.2% and 55.3% in MNIST and CIFAR10, while maintaining the accuracy of the remaining classes.
CUT ELIMINATION IN HYPERSEQUENT CALCULUS FOR SOME LOGICS OF LINEAR TIME

Abstract This is a sequel article to [10] where a hypersequent calculus (HC) for some temporal logics of linear frames including Kt4.3 and its extensions for dense and serial flow of time was investigated in detail. A distinctive feature of this approach is that hypersequents are noncommutative, i.e., they are finite lists of sequents in contrast to other hypersequent approaches using sets or multisets. Such a system in [10] was proved to be cut-free HC formalization of respective logics by means of semantical argument. In this article we present an equivalent variant of this calculus for which a constructive syntactical proof of cut elimination is provided.
A New 360 Camera Design for Multi Format VR Experiences

We present a new 360 camera design for creating 360 videos for immersive VR experiences. We place eight fish-eye lenses on a circle. Four interlaced fish-eye lenses are slightly re-oriented up in order to cover the scene above. To the best of our knowledge, our camera has the smallest diameter of any existing stereo multi-lens rig on the market. Our camera can be used to create 2D, 3D and 6DoF multi-format 360 videos. Due to its compact design, the minimum safe distance of our new camera is very short (approximately 30cm). This allows users to create special intimate immersive experiences. We also propose to characterize the camera design using the fractal ratio of the distance of adjacent view points and interpupillary distance. While most early camera designs have fractal ratio or =1, our camera has the fractal ratio . Moreover, with adjustable rendering interpupillary distance, our camera can be used to flexibly control the interpupillary distance for creating 3D 360 videos. Our camera design has high fault tolerance and it can continue operating properly even in the event of the failure of some individual lenses.
Part-based visual tracking via structural support correlation filter

To better deal with the partial occlusion issue and improve their efficiency of part-based and support vector machines (SVM) based trackers, we propose a novel part-based structural support correlation filter tracking method, which absorbs the strong discriminative ability from SVM and the excellent property of part-based tracking methods which is less sensitive to partial occlusion. Then, our proposed model can learn the support correlation filter of each part jointly by a star structure model, which preserves the spatial layout structure among parts and tolerates outliers of parts. In addition, our model introduces inter-frame consistencies of local parts to mitigate the drift problem. Finally, our model can accurately estimate the scale changes of object by the relative distance change among reliable parts. The extensive empirical evaluations on three benchmark datasets: OTB2015, TempleColor128 and VOT2015 demonstrate that the proposed method achieves comparable performance against several state-of-the-art trackers and runs in real time.
Voronoi tree models for distribution-preserving sampling and generation

Abstract We propose a method based on recursive binary Voronoi trees to learn a nonparametric model of the distribution underlying a given dataset. The obtained model can be used as a general tool both to extract good samples from the original dataset (e.g., for batch selection, bagging, or sample size reduction) or to generate new synthetic ones, also in a conditional fashion (e.g., to deal with imbalanced sets or to reconstruct corrupted points). In order to ensure that the distribution of the new sets, either sampled or generated, follows closely that of the original dataset, we design all the procedures according to a specific measure of distance between distributions. The use of binary recursive Voronoi structures enables the proposed algorithms to be simple, efficient and able to adapt to the shape of the original dataset. Simulation tests showcase the good performance and flexibility of the approach in various learning contexts.
A Generalised Multi-Attribute Task Sequencing Approach for Robotics Optical Inspection Systems

Robot programming usually consists of four steps: (1) task planning; (2) task sequencing; (3) path planning and (4) motion planning. Task (2) and (3-4) are strongly coupled. For example, the optimal robot path, which is function of the robot kinematics, relies on the pre-defined schedule of tasks, whose sequencing is computed based on the assumption that the travelling “cost” from one task to the next is only driven by the Euclidean distance in Cartesian space. Current methods tends to decouple the problem and sequentially compute the task sequencing in the T-space, and then compute the robot path by solving the inverse kinematics in the C-space. However, those approaches suffer the capability to reach a global optimum. This paper aims at developing a novel approach which integrates some of the key computational requirements of the path planning in the early stage of the task sequencing. Multi-attribute objectives are introduced to take into account: robot pose and reachability, data quality, obstacles avoidance, overall cycle time. The paper introduces a novel multi-attribute approach to find the optimized task sequencing via candidate poses solving inverse kinematics in the T-space. This is based on the core idea to combine T-space and C-space. The proposed solution has been tested on a vision-based inspection robot system with application to automotive body assembly systems. Results could however impact a wider area, from navigation systems, game and graph theory, to autonomous driving systems.
A Novel Clustering Algorithm Based on Fully-Informed Particle Swarm

Data Clustering partitions a set of unlabeled data into several homogenous groups. Here, a new clustering method based on fully-informed particle swarm (FIPS) named FIPS-based clustering algorithm is introduced. Fully-informed particle swarm, as a generalization of standard PSO, applies different neighborhood topologies, as such, in this study we propose three topologies to be employed in clustering algorithm: all topology, distance topology and ring topology. All and ring topologies have been already used for optimization problems, however, distance topology is proposed in this study for clustering algorithm. For the purpose of evaluation, FIPS-based clustering algorithm is implemented for different problems along with canonical particle swarm optimization clustering technique using aforementioned neighborhood topologies. According to the obtained results, FIPS-based clustering algorithm with distance topology can provide much better results than canonical PSO clustering method in majority of the studied problems.
Mapping the social landscape through social media

Being a habitat of the global village, every place has established connections through the strength and power of social media, piercing through the political boundaries. Social media is a digital platform, where people across the world can interact. This has a number of advantages of being universal, anonymous, easy accessibility, indirect interaction, gathering and sharing information when compared with direct interaction. The easy access to social networking sites (SNSs) such as Facebook, Twitter and blogs has brought about unprecedented opportunities for citizens to voice their opinions loaded with emotions/sentiments. Furthermore, social media can influence human thoughts. A recent incident of public importance had presented an opportunity to map the sentiments, involved around it. Sentiments were extracted from tweets for a week. These sentiments were classified as positive, negative and neutral and were mapped in geographic information system (GIS) environment. It was found that the number of tweets diminished by 91% over a week from 25 August 2017 to 31 August 2017. Maximum tweets emerged from places near the origin of the case (Haryana, Delhi and Punjab). The trend of sentiments was found to be – neutral (47.4%), negative (30%) and positive (22.6%). Interestingly, tweets were also coming from unexpected places such as United States, United Kingdom and West Asia. The result can also be used to assess the spatial distribution of digital penetration in India. The highest concentration was found to be around metropolitan cities, that is, Mumbai, Delhi and lowest in North East India and Jammu &amp; Kashmir indicating the penetration of SNSs.
Evolving One-Dimensional Deep Convolutional Neural Network: A Swarm based Approach

This paper proposes a new approach for evolving One-Dimensional Convolutional Neural Network (1D-CNN). A Particle Swarm Optimization (PSO) algorithm was incorporated to evolve the network layers and parameters. The proposed approach was evaluated over a real-world rainfall prediction application. It was compared to a trial and error approach and to the first version of the official rainfall prediction system used by the Bureau of Meteorology in Australia. Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and Pearson correlation (r) statistical measurements were calculated to measure the performance of each approach. The performance of the proposed approach was promising compared to existing state-of-the art methods. This proposed framework can be easily extended and deployed to other applications including machine vision.
Fast and high precision spatial carrier frequency phase shifting algorithm based on the second order difference

A spatial carrier phase shifting algorithm based on the second-order difference (SCFPS-SD) is proposed. By numerically calculating the first and second order difference of one spatial carrier frequency interferogram (SCFI) and then using a normalization algorithm, the high accuracy of phase extraction will be obtained rapidly. Based on the original differential extraction phase (SCFPS based on derivative method, SCFPS-DM) method, the proposed SCFPS-SD algorithm does not need to calculate the carrier frequency in advance and the achieved result is not affected by the accuracy of the extraction carrier frequency algorithm. Compared with current SCFPS algorithm, the proposed algorithm reveals obvious advantage in calculation speed and precision even if the sample height, carrier frequency and noise level are greatly changed. Both advantages and effectiveness of the proposed SCFPS-SD algorithm can be demonstrated by simulation and experiment.
Curriculum Learning in Sentiment Analysis

This work deals with curriculum learning for deep learning models for the sentiment analysis task. We design a new way of curriculum learning for text data. We reorder the training dataset to introduce the simpler examples first. We estimate the difficulty of the examples by measuring the length of the sentences. The simple examples are supposed to be shorter. We also experiment with measuring the frequency of the words, which is a technique designed by earlier researchers. We attempt to evaluate changes in the overall accuracy of the models using both curriculum learning techniques. Our experiments do not show an increase in accuracy for any of the methods. Nevertheless, we reach a new state of the art in the sentiment analysis for Czech as a by-product of our effort.
Takagi-Sugeno Discrete Fuzzy Modeling: an IoT Controlled ABS for UAV

Drones are increasingly technologically present in many sectors: they are also able to cover, at a low price, all the roles that were previously covered by satellites in low orbit (LEO). Their serendipity is remarkable and well known to all: the only limit is the continuous need to keep the weight low in order to have a good operative range. To do this it is necessary to move part of the electronic hardware, from the flying platform to the ground, providing the aircraft with only the necessary data, relieving it of the burden of calculation. This paper provides a basic calculation for Takagi-Sugeno fuzzy logic control of a simplified ABS of an UAV in order to increase the effect of the brakes.
Efficient, Lightweight, Coordinate-Based Network for Image Super Resolution

Deep learning approaches have recently proven their effectiveness in the task of image Super Resolution (SR). In most cases, very deep structures have been adopted to increase the models’ performance, leading to neural networks with a high parameter count that require large computational resources. In this paper, we propose an efficient, lightweight model, which leverages the benefits of recursive architectures. The structure of our network is based on progressive reconstruction, which strengthens the information flow by taking advantage of dense and residual connections. Moreover, since SR is a problem that involves spatial representations and transformations, we exploit the pixel position information to reinforce the reconstruction task. To achieve that, we use the Coordinate Convolutional layer, which exploits coordinate information allowing the network to learn the translation dependency required by the SR task. We show that the proposed method performs favorably compared to lightweight state-of-the-art methods on public benchmark datasets.
Thai Comments Sentiment Analysis on Social Networks with Deep Learning Approach

Sentiment analysis is a kind of a Natural Language Processing (NLP) tasks in text classification used to determine the sentiment polarity from any given document. In this research, two problems were solved. The first problem is the word ambiguous manipulation. Another task is an automatic sentiment classification. This work shows our process of Thai online document cleaning to attack the uncertainty in Thai text. This work also includes attempts to find the appropriate deep learning algorithm to classify the sentiment of Thai online document by comparing the performance among several approaches. The results show that a model using the bi-directional gated recurrent unit (GRU) with attention mechanism yields the best performance in sentiment classification.
Answering the Call of the Wild?

Ecological validity is a term commonly used in several disciplines to refer to the fact that in a research study, the methods, the materials, and the settings must approximate the real world, i.e. what happens in everyday life. Variability modeling is no exception, it has striven for this form of validity by looking at two main sources, industrial projects and open source projects. Despite their unquestionable value, industrial projects inherently pose limitations; for instance, in terms of open access or results replication, which are two important tenets for any scientific endeavor. In this paper, we present our first findings on the use of open source projects in variability modeling research, and identify trends and avenues for further research.
Analysis of the fruitless cycle of Pollard's rho method based attack for solving ECDLP over Barreto-Naehrig curves

This paper focuses on the computational security of elliptic curve discrete logarithm (ECDLP), which can evaluate by an actual attack. The authors apply Pollard's rho method to a Barreto-Naehrig curve with minimum parameters for solving an ECDLP. An analysis of the structure for all combinations of a select of seed and starting points are given. Then, we propose the method for selecting a set of efficient seed points for a random walk for the rho method.
Dealing With C's Original Sin

In the very early days of C, the compiler written by Dennis Ritchie and supplied with the UNIX operating system entirely defined the language. As the number of users and C implementations grew, however, so too did the need for a language standard-a contract between users and implementers about what should and should not count as C. This effort began in 1983 with the formation of a committee tasked with producing "an unambiguous and machine-independent definition of the language C" and led to the ANSI C Standard in 1989.1 In retrospect, it was not until this date, 17 years after the first compiler, when C's most notorious language feature slithered into the world: <i>undefined behavior</i>.
Deep Anchored Convolutional Neural Networks

Convolutional Neural Networks (CNNs) have been proven to be extremely successful at solving computer vision tasks. State-of-the-art methods favor such deep network architectures for its accuracy performance, with the cost of having massive number of parameters and high weights redundancy. Previous works have studied how to prune such CNNs weights. In this paper, we go to another extreme and analyze the performance of a network stacked with a single convolution kernel across layers, as well as other weights sharing techniques. We name it Deep Anchored Convolutional Neural Network (DACNN). Sharing the same kernel weights across layers allows to reduce the model size tremendously, more precisely, the network is compressed in memory by a factor of L, where L is the desired depth of the network, disregarding the fully connected layer for prediction. The number of parameters in DACNN barely increases as the network grows deeper, which allows us to build deep DACNNs without any concern about memory costs. We also introduce a partial shared weights network (DACNN-mix) as well as an easy-plug-in module, coined regulators, to boost the performance of our architecture. We validated our idea on 3 datasets: CIFAR-10, CIFAR-100 and SVHN. Our results show that we can save massive amounts of memory with our model, while maintaining a high accuracy performance.
Holistic Blockchain Approach to Foster Trust, Privacy and Security in IoT Based Ambient Assisted Living Environment

The application of blockchains techniques in the Internet of Things (IoT) is gaining much attention with new solutions proposed in diverse areas of the IoT. Conventionally IoT systems are designed to follow the centralised paradigm where security and privacy control is vested on a "trusted" third-party. This design leaves the user at the mercy of a sovereign broker and in addition, susceptible to several attacks. The implicit trust and the inferred reliability of centralised systems have been challenged recently following several privacy violations and personal data breaches. Consequently, there is a call for more secure decentralised systems that allows for finer control of user privacy while providing secure communication. Propitiously, the blockchain holds much promise and may provide the necessary framework for the design of a secure IoT system that guarantees fine-grained user privacy in a trustless manner. In this paper, we propose a holistic blockchain-based decentralised model for Ambient Assisted Living (AAL) environment. The nodes in our proposed model utilize smart contracts to define interaction rules while working collaboratively to contribute storage and computing resources. Based on the blockchain technique, our proposed model promotes trustless interaction and enhanced user's privacy through the blockchain - Interplanetary File System (IPFS) alliance. The proposed model also addresses the shortfall of storage constraints exhibited in many IoT systems.
Differential Evolution with Better and Nearest Option for Function Optimization

Differential evolution(DE) is a conventional algorithm with fast convergence speed. However, DE may be trapped in local optimal solution easily. Many researchers devote themselves to improving DE. In our previously work, whale swarm algorithm have shown its strong searching performance due to its niching based mutation strategy. Based on this fact, we propose a new DE algorithm called DE with Better and Nearest option (NbDE). In order to evaluate the performance of NbDE, NbDE is compared with several meta-heuristic algorithms on nine classical benchmark test functions with different dimensions. The results show that NbDE outperforms other algorithms in convergence speed and accuracy.