Reviewerly/exHarmony huggingface dataset:
Entropy-Constrained Training of Deep Neural Networks

Motivated by the Minimum Description Length (MDL) principle, we first derive an expression for the entropy of a neural network which measures its complexity explicitly in terms of its bit-size. Then, we formalize the problem of neural network compression as an entropy-constrained optimization objective. This objective generalizes many of the currently proposed compression techniques in the literature, in that pruning or reducing the cardinality of the weight elements can be seen as special cases of entropy reduction methods. Furthermore, we derive a continuous relaxation of the objective, which allows us to minimize it using gradient-based optimization techniques. Finally, we show that we can reach compression results, which are competitive with those obtained using state-of-the-art techniques, on different network architectures and data sets, e.g. achieving×71 compression gains on a VGG-like architecture.
Sequence Labeling With Deep Gated Dual Path CNN

Sequence labeling, such as part-of-speech (POS) tagging, named entity recognition (NER), text chunking, is a classic task in natural language processing. Most existing neural networks models for sequence labeling are based on recurrent neural networks. Recently, convolutional neural networks have been proposed to replace the recurrent components for sequence labeling. However, they are usually shallow compared to deep convolutional networks that achieve start-of-the-art performance in other fields. Due to the vanishing gradient problem, these models usually can not work well when simply increasing the number of layers. In this paper, we propose using deep CNN architecture in sequence labeling, which can capture a large context through stacked convolutions. To reduce the vanishing gradient problem, the proposed method incorporates gated linear units, residual connections, and dense connections. Experimental results on three sequence labeling tasks show that the proposed model can achieve competitive performance to the RNN-based state-of-the-art method while maintaining 2.41 × faster speed, even with up to 10 convolutional layers.
Detection of Violent Extremists in Social Media

The ease of use of the Internet has enabled violent extremists such as the Islamic State of Iraq and Syria (ISIS) to easily reach large audience, build personal relationships and increase recruitment. Social media are primarily based on the reports they receive from their own users to mitigate the problem. Despite efforts of social media in suspending many accounts, this solution is not guaranteed to be effective, because not all extremists are caught this way, or they can simply return with another account or migrate to other social networks. In this paper, we design an automatic detection scheme that using as little as three groups of information related to usernames, profile, and textual content of users, determines whether or not a given username belongs to an extremist user. We first demonstrate that extremists are inclined to adopt usernames that are similar to the ones that their like-minded have adopted in the past. We then propose a detection framework that deploys features which are highly indicative of potential online extremism. Results on a real-world ISIS-related dataset from Twitter demonstrate the effectiveness of the methodology in identifying extremist users.
Design fictions for learning: A method for supporting students in reflecting on technology in Human-Computer Interaction courses

Design fictions describe non-existing prototype devices and services, encouraging reflection on technology matters. However, until now most of the fictional design work has been carried out either by “experts” to foster critical thinking within the Human-Computer Interaction (HCI) community, or by user groups to mostly define requirements for creating novel devices. In this article, we aim to use design fictions as a method for supporting students in thinking of the assumptions and consequences of emerging technologies. We report a multi-year experience in using fictional design in the context of academic education to show that such method can be employed to both teach fundamental elements of technology design and HCI and, at the same time, elicit a critical thinking, helping students reflect on the ramifications of their creations and their role as designers. We discuss the methodological implications, pointing out the opportunities this method opens as well as its weaknesses. Finally, we propose a series of methodological suggestions addressed to facilitate the use of design fictions as a “tool for reflection.”
FigureNet : A Deep Learning model for Question-Answering on Scientific Plots

Deep Learning has managed to push boundaries in a wide variety of tasks. One area of interest is to tackle problems in reasoning and understanding, with an aim to emulate human intelligence. In this work, we describe a deep learning model that addresses the reasoning task of question-answering on categorical plots. We introduce a novel architecture FigureNet, that learns to identify various plot elements, quantify the represented values and determine a relative ordering of these statistical values. We test our model on the FigureQA dataset which provides images and accompanying questions for scientific plots like bar graphs and pie charts, augmented with rich annotations. Our approach outperforms the state-of-the-art Relation Networks baseline by approximately 7% on this dataset, with a training time that is over an order of magnitude lesser.
Deep Representation Learning for Code Smells Detection using Variational Auto-Encoder

Detecting code smells is an important research problem in the software maintenance. It assists the subsequent steps of the refactoring process so as to improve the quality of the software system. However, most of existing approaches have been limited to the use of structural information. There have been few researches to detect code smells using semantic information although its proven effectiveness in many software engineering problems. In addition, they do not capture entirely the semantic embedded in the source code. This paper attempts to fill this gap by proposing a semantic-based approach that detects bad smells which are scattered at different levels of granularity in the source code. To this end, we use an Abstract Syntax Tree with a Variational Auto-Encoder in the detection of three code smells. The code smells are Blob, Feature Envy and Long Method. We have performed our experimental evaluation on nine open-source projects and the results have achieved a considerable overall accuracy. To further evaluate the performance of our approach, we compare our results with a state-of-the-art method on the same publicly available dataset.
Minimization of Gini Impurity: NP-completeness and Approximation Algorithm via Connections with the k-means Problem

The Gini impurity is a very popular criterion to select attributes during decision trees construction. In the problem of finding a partition with minimum weighted Gini impurity (PMWGP), the one faced during the construction of decision trees, a set of vectors must be partitioned into k different clusters such that the partition's overall Gini impurity is minimized. We show that PMWGP is APX-hard for arbitrary k and admits a randomized PTAS when the number of clusters is fixed. These results significantly improve the current knowledge on the problem. The key idea to obtain these results is to explore connections between PMWGP and the geometric k-means clustering problem.
The Limits of Expert Text Entry Speed on Mobile Keyboards with Autocorrect

Improving mobile keyboard typing speed increases in value as more tasks move to a mobile setting. Autocorrect reduces the time it takes to manually fix typing errors, which results in typing speed increase. However, recent user studies uncovered an unexplored side-effect: participants' aversion to typing errors despite autocorrect. We present a computational model of typing on keyboards with autocorrect, which enables precise study of expert typists' aversion to typing errors on such keyboards. Unlike empirical typing studies that last days, our model evaluates this phenomenon for any autocorrect accuracy in seconds. We show that typists' aversion to typing errors imposes a limit on upper bound typing speeds, even for highly accurate autocorrect. Our findings motivate future keyboard designs that reduce typists' aversion to typing errors to increase typing speeds.
Security Smells in Smart Contracts

The popularity of blockchain technology encourages organizations to use more blockchain features in mission-critical processes such as trading, access control, and computational public safety. Automation of processes with smart contracts is one of these features that significantly enlarge the scope of a blockchain implementation. Smart contracts help automate business processes by modeling business activities on the distributed ledger. Smart contracts are significantly different from other programs from a defect fixing and security issue handling perspective. The opportunity of fixing such issues is only available in the narrow window before registering the contract on to the blockchain. After a smart contract becomes a part of the chain, it is not possible to update or fix any issues. This distinct nature of smart contracts makes it essential to detect the program issues early on by paying attention to security smells. Security smells are clues that point to a deeper problem in the programming space. In this study, we review the literature and identify vulnerabilities that programmers and beneficiaries of smart contracts must avoid. We explain these security smells and categorize them based on their nature. We also review the applications that detect these vulnerabilities and provide information about their approach and coverage. Our main contribution is the evaluation of smart contracts as a platform or aid for mission-critical applications such as access control platforms. We conducted this evaluation by identifying the issues related to smart contracts and informing the reader about the problem, challenges, and techniques. We conclude by defining future directions for our research.
Style transfer-based image synthesis as an efficient regularization technique in deep learning

These days deep learning is the fastest-growing area in the field of Machine Learning. Convolutional Neural Networks are currently the main tool used for the image analysis and classification purposes. Although great achievements and perspectives, deep neural networks and accompanying learning algorithms have some relevant challenges to tackle. In this paper, we have focused on the most frequently mentioned problem in the field of machine learning, that is relatively poor generalization abilities. Partial remedies for this are regularization techniques e.g. dropout, batch normalization, weight decay, transfer learning, early stopping and data augmentation. In this paper we have focused on data augmentation. We propose to use a method based on a neural style transfer, which allows to generate new unlabeled images of high perceptual quality that combine the content of a base image with the appearance of another one. In a proposed approach, the newly created images are described with pseudo-labels, and then used as a training dataset. Real, labeled images are divided into the validation and test set. We validated proposed method on a challenging skin lesion classification case study. Four representative neural architectures are examined. Obtained results show the strong potential of the proposed approach.
Managing authentication and authorization in distributed science gateway middleware

Establishing users’ identities and determining their permissions before they access research infrastructure resources are key features of science gateways. With many science gateways now relying on general purpose gateway platform services, the challenges of managing identity-derived features have expanded to include network-based authentication and authorization scenarios that connect science gateway tenants, science gateway platform middleware, and third party identity provider services, including campus identity management systems. This paper examines both architectural and implementation considerations for integrating these services. We provide a summary case study that further shows how end-to-end authentication and authorization can be provided between gateways, campus authentication systems, science gateway middleware, and campus computing resources. We conclude with observations on lifecycle management of third party components in science gateway platform services, which is an important consideration for both selection of new technologies and transitioning from older systems.
Electronic Voting System using Biometrics, Raspberry Pi and TFT module

The “Vote from Anywhere” system makes use of advanced technologies so that everyone can take advantage of their voting right. In this voting system, the voter can enter aadhaar card number on LCD display connected to raspberry pi. After that voter is asked to scan his fingerprint, which is compared with the fingerprint library present in the database this is retrieved from aadhaar database. The admin can login to the web portal by using his/her user id and password to insert candidate's information before election. Admin can update and delete candidate's information. Also we have used encryption techniques to provide more secure voting. So, when voter votes for a candidate, the vote count is incremented and encrypted before giving it to central database and user disable request is given to system to avoid multiple voting.
Object Detection in Underwater Image by Detecting Edges using Adaptive Thresholding

Identification of natural and artificial objects in under water images is necessary in order to explore the underwater life because of its various application potentials. Degradation of underwater image because of scattering and absorption in underwater condition makes the objects in under water images diminish their perceivability, the image enhancement is therefore necessary in the detection of the underwater objects to improve the appearance of objects. Our method deals with this problem which enhances the image by stretching the contrast and segments it along with extracting boundaries to find the object using adaptive thresholding with Sobel operator. The experimental results of the proposed method on underwater images has performed better than Canny, Prewitt and Hule et al. [3] edge detection based object detection method both by perceiving visually and by subjective criteria, i.e., PSNR and SSIM.
ZCOMP

Deep Neural Networks (DNNs) are becoming the prevalent approach in computer vision, machine learning, natural language processing, and speech recognition applications. Although DNNs are perceived as compute-intensive tasks, they also apply intense pressure on the capacity and bandwidth of the memory hierarchy, primarily due to the large intermediate data communicated across network layers. Prior work on hardware DNN accelerators leverages the cross-layer data sparsity via fully-customized datapaths. However, dynamically compressing/expanding such data is a challenging task for general-purpose multi-processors with virtual memory and hardware-managed coherent cache hierarchies.
Mapless Collaborative Navigation for a Multi-Robot System Based on the Deep Reinforcement Learning

Compared with the single robot system, a multi-robot system has higher efficiency and fault tolerance. The multi-robot system has great potential in some application scenarios, such as the robot search, rescue and escort tasks, and so on. Deep reinforcement learning provides a potential framework for multi-robot formation and collaborative navigation. This paper mainly studies the collaborative formation and navigation of multi-robots by using the deep reinforcement learning algorithm. The proposed method improves the classical Deep Deterministic Policy Gradient (DDPG) to address the single robot mapless navigation task. We also extend the single-robot Deep Deterministic Policy Gradient algorithm to the multi-robot system, and obtain the Parallel Deep Deterministic Policy Gradient (PDDPG). By utilizing the 2D lidar sensor, the group of robots can accomplish the formation construction task and the collaborative formation navigation task. The experiment results in a Gazebo simulation platform illustrates that our method is capable of guiding mobile robots to construct the formation and keep the formation during group navigation, directly through raw lidar data inputs.
QSSA: Quantum Evolutionary Salp Swarm Algorithm for Mechanical Design

Salp swarm algorithm is a new meta-heuristic algorithm which has excellent advantages for solving the multidimensional optimization problem.In this paper, a hybrid model assisted evolutionary algorithm for solving engineering design optimization problems is proposed and investigated.The purpose of the optimizer is to improve the potential shortcomings of the basic salp swarm optimization, including trapping in local or deceptive optima easily.On the one hand, quantum behavior can increase an individual's searchability, which can promote the overall optimization trend.On the other hand, the elite oppositionbased learning strategy is used to enhance the diversity of the population.Besides, mutation mechanism is introduced to prevent the individuals of the population from being in stagnation behavior.The proposed quantum-behaved and wavelet mutation salp swarm algorithm (QSSA) is applied on twenty-three benchmark functions and three basic constrained engineering problems.Experimental results demonstrate that the algorithm has excellent solution quality, and it can overcome the defect of the low convergence rate.
Detection of Age-Induced Makeup Attacks on Face Recognition Systems Using Multi-Layer Deep Features

Makeup is a simple and easy instrument that can alter the appearance of a person's face, and hence, create a presentation attack on face recognition (FR) systems. These attacks, especially the ones mimicking ageing, are difficult to detect due to their close resemblance with genuine (non-makeup) appearances. Makeups can also degrade the performance of recognition systems and of various algorithms that use human face as an input. The detection of facial makeups is an effective prohibitory measure to minimize these problems. This work proposes a deep learning-based presentation attack detection (PAD) method to identify facial makeups. We propose the use of a convolutional neural network (CNN) to extract features that can distinguish between presentations with age-induced facial makeups (attacks), and those without makeup (bona-fide). These feature descriptors, based on shape and texture cues, are constructed from multiple intermediate layers of a CNN. We introduce a new dataset AIM (Age Induced Makeups) consisting of 200+ video presentations of old-age makeups and bona-fide, each. Our experiments indicate makeups in AIM result in 14% decrease in the median matching scores of a recent CNN-based FR system. We demonstrate accuracy of the proposed PAD method where 93% presentations in the AIM dataset are correctly classified. In additional testing, it also outperforms existing methods of detection of generic makeups. A simple score-level fusion, performed on the classification scores of shape- and texture-based features, can further improve the accuracy of the proposed makeup detector.
EVA: Efficient Versatile Auditing Scheme for IoT-Based Datamarket in Jointcloud

Cloud storage offers convenient outsourcing services to users, and it serves as a basic platform to drive Internet-of-Things (IoT) where massive devices are connected to the cloud storage and interact with each other. However, cloud storage is more than a data warehouse. In the literature, data market was proposed as a novel model to empower IoT, where data are circulated as merchandise in the digital marketplace with financial activities. When storing IoT data in cloud storage, security and efficiency rules should be applied. Meanwhile, data dynamics is counted as a critical factor to the feasibility of datamarket as data are supposed to be manipulated through circulation and exploitation for IoT. Another issue is the single-point-of-failure (SPoF) of cloud server in which the initiative of jointcloud was suggested. Since providing data security, efficiency, and dynamics simultaneously is challenging, in this article, we propose a versatile auditing scheme (EVA) as a solution to problems. Our proposal ensures that data are securely, efficiently, and dynamically stored in the jointcloud meanwhile supported by data trades via blockchain. We give a comprehensive security analysis based on our security definitions and experiments to support our claims. The evidence has shown that our EVA is efficient for processing large files when proper parameters are chosen.
LVC-Net: Medical Image Segmentation with Noisy Label Based on Local Visual Cues

CNN-based deep architecture has been successfully applied to medical image semantic segmentation task because of its effective feature learning mechanism. However, due to the lack of semantic guidance, such supervised learning model may be susceptible to annotation noise. In order to address this problem, we propose a novel medical image segmentation algorithm based on automatic label error correction. Firstly, local visual saliency regions, namely the Local Visual Cues (LVCs), are captured from low-level feature channels. Then, a deformable spatial transformation module is integrated into our LVC-Net to build visual connections between the predictions and LVCs. By combining noisy labels with image LVCs, a novel loss function is proposed based on their intrinsic spatial relationship. Our method can effectively suppress the influence of label noise by utilizing potential visual guidance during the learning process, thereby generate better semantic segmentation results. Comparative experiment on hip x-ray image segmentation task demonstrate that our algorithm achieves significant improvement over state-of-the-arts in the presences of noisy label.
Eye landmarks detection via weakly supervised learning

Extensive eye researches provide good results when images are captured under constrained environment. However, the accuracy of eye landmarks detection depends on explicit bounding-box of eye regions and drops severely in non-ideal conditions. This paper has proposed a novel weakly supervised eye landmarks detection algorithm with object detection and recurrent learning modules. The former is combined with faster R-CNN and is competent to detect bounding-box of facial components and initial positions of the eye. The recurrent module is employed for eye landmarks refinement using the initial eye shape. The proposed algorithm can augment training data effectively and our specific format data consist of supervised and weakly supervised samples. Supervised samples have ground truth of bounding-boxes, corresponding classification labels and eye landmarks coordinates while weakly supervised data does not have eye landmarks information. Despite trained on facial images, the proposed method can detect eyes in severely occluded or local view of facial images without prerequisites of face alignment. Further experiments are performed on our supervised testing set and some public datasets. Their results demonstrate the robustness and effectiveness of the proposed method.