Reviewerly/exHarmony huggingface dataset:
Effect of Pruning on Catastrophic Forgetting in Growing Dual Memory Networks

Grow-when-required networks such as the Growing Dual-Memory (GDM) networks possess a dynamic network structure, expanding to accommodate new neurons in response to learning novel concepts. Over time, it may be necessary to prune obsolete neurons and/or neural connections to meet performance or resource limitations. GDM networks utilize an age-based pruning strategy, whereby older neurons and neural connections that have not been activated recently are removed. Catastrophic forgetting occurs when knowledge learned by the networks in previous learning iterations is lost due to being overwritten by newer learning iterations, or to the pruning process. In this work, we investigate catastrophic forgetting in GDM networks in response to different pruning strategies. The age-based pruning method was shown to significantly sparsify the GDM network topology while improving the networks ability to recall newly acquired concepts with a slight decrease in performance with respect to older knowledge. A significance-based pruning method was tested as a replacement for the age-based pruning, but was not as effective at pruning even though it performed better at recalling older knowledge.
Adversarial Action Data Augmentation for Similar Gesture Action Recognition

Human gestures are unique for recognizing and describing human actions, and video-based human action recognition techniques are effective solutions to varies real-world applications, such as surveillance, video indexing, and human-computer interaction. Most existing video human action recognition approaches either using handcraft features from the frames or deep learning models such as convolutional neural networks (CNN) and recurrent neural networks (RNN); however, they have mostly overlooked the similar gestures between different actions when processing the frames into the models. The classifiers suffer from similar features extracted from similar gestures, which are unable to classify the actions in the video streams. In this paper, we propose a novel framework with generative adversarial networks (GAN) to generate the data augmentation for similar gesture action recognition. The contribution of our work is tri-fold: 1) we proposed a novel action data augmentation framework (ADAF) to enlarge the differences between the actions with very similar gestures; 2) the framework can boost the classification performance either on similar gesture action pairs or the whole dataset; 3) experiments conducted on both KTH and UCF101 datasets show that our data augmentation framework boost the performance on both similar gestures actions as well as the whole dataset compared with baseline methods such as 2DCNN and 3DCNN.
Confidential Boosting with Random Linear Classifiers for Outsourced User-Generated Data

User-generated data is crucial to predictive modeling in many applications. With a web/mobile/wearable interface, a data owner can continuously record data generated by distributed users and build various predictive models from the data to improve its operations, services, and revenue. Due to the large size and evolving nature of users data, a data owner may rely on public cloud service providers (Cloud) for storage and computation scalability. Exposing sensitive user-generated data and advanced analytic models to Cloud raises privacy concerns. We present a confidential learning framework, SecureBoost, for data owners that want to learn predictive models from aggregated user-generated data but offload the storage and computational burden to Cloud without having to worry about protecting the sensitive data. SecureBoost allows users to submit encrypted or randomly masked data to designated Cloud directly. Our framework utilizes random linear classifiers (RLCs) as the base classifiers in the boosting framework to dramatically simplify the design of the proposed confidential protocols, yet still preserve the model quality. A Cryptographic Service Provider (CSP) is used to assist the Cloud’s processing, reducing the complexity of the protocol constructions. We present two constructions of SecureBoost: HE+GC and SecSh+GC, using combinations of homomorphic encryption, garbled circuits, and random masking to achieve both security and efficiency. For a boosted model, Cloud learns only the RLCs and the CSP learns only the weights of the RLCs. Finally, the data owner collects the two parts to get the complete model. We conduct extensive experiments to understand the quality of the RLC-based boosting and the cost distribution of the constructions. Our results show that SecureBoost can efficiently learn high-quality boosting models from protected user-generated data.
Systematic Literature Review on Decision-Making of Requirement Engineering from Agile Software Development

This paper presents a systematic literature review of agile software development at decision making method for requirement engineering. Presently, agile software development method is operated to cope with requirements that changes dynamically. This study seeks to find out and discuss what types of method that have been exploited for decision making on managing feasible requirements and challenges of decision making in agile software development. Papers reviewed in this study are published from 2017 to present. Resulting 8 papers that have been identified of presenting decision making methods. Using these papers, 11 methods and 7 challenges of decision making identified. This study contributes a review of requirement management and engineering by providing decision making methods on agile software development and the challenges of decision making for requirement engineering.
A Model Based on Siamese Neural Network for Online Transaction Fraud Detection

With the rapid development of Internet finance, the volume of online transactions increases gradually, but the risk of exposure is increasing, and fraud is emerging. Because of the characteristics of online transaction, such as large volume, high frequency and fast update speed. In addition, online transaction data has the problems of unbalanced positive and negative sample and sparse timing of transaction data. Most of the existing methods to solve data imbalance are sampled, but this method will change the dataset’s distribution, which is not conducive to improving the generalization ability of the model. There are some timing characteristics of online transaction data, and the common fraud detection model does not take the problem into account in the design of the model. Based on the problems, this paper puts forward the siamese neural network structure based on CNN and LSTM, uses the siamese neural network structure to solve the problem of sample imbalance in online transaction and uses the LSTM structure to make model memory user's transaction information, in order to better detect the fraudulent transaction. The model presented in this paper is verified in real B2C transaction data, and its precision and recall reach about 95% and 96%, respectively.
Sentiment Analysis of Code-Switched Tunisian Dialect: Exploring RNN-Based Techniques

With the increasing use of social networks and the multilingualism that characterizes the Internet in general and the social media in particular, an increasing number of recent research works on Sentiment Analysis and Opinion Mining are tackling the analysis of informal textual content, which includes language alternation, known as code-switching. To date, very little work has addressed in particular, the analysis social media of the Tunisian dialect, which is characterized both by a frequent occurring of code-switching and by a double script (Arabic and Latin) when written on the social media. Our study aims to explore and compare various classification models based on RNNs (Recurrent Neural Networks), precisely on LSTM (Long Short-Term Memory) neural networks.
Language Modeling through Long-Term Memory Network

Recurrent Neural Networks (RNN), Long Short-Term Memory Networks (LSTM), and Memory Networks which contain memory are popularly used to learn patterns in sequential data. Sequential data has long sequences that hold relationships. RNN can handle long sequences but suffers from the vanishing and exploding gradient problems. While LSTM and other memory networks address this problem, they are not capable of handling long sequences (50 or more data points long sequence patterns). Language modelling requiring learning from longer sequences are affected by the need for more information in memory. This paper introduces Long Term Memory network (LTM), which can tackle the exploding and vanishing gradient problems and handles long sequences without forgetting. LTM is designed to scale data in the memory and gives a higher weight to the input in the sequence. LTM avoid overfitting by scaling the cell state after achieving the optimal results. The LTM is tested on Penn treebank dataset, and Text8 dataset and LTM achieves test perplexities of 83 and 82 respectively. 650 LTM cells achieved a test perplexity of 67 for Penn treebank, and 600 cells achieved a test perplexity of 77 for Text8. LTM achieves state of the art results by only using ten hidden LTM cells for both datasets.
GLMB Tracker with Partial Smoothing

In this paper, we introduce a tracking algorithm based on labeled Random Finite Sets (RFS) and Rauch-Tung-Striebel (RTS) smoother via a Generalized Labeled Multi-Bernoulli (GLMB) multi-scan estimator to track multiple objects in a wide range of tracking scenarios. In the forward filtering stage, we use the GLMB filter to generate a set of labels and the association history between labels and the measurements. In the trajectory-estimating stage, we apply a track management strategy to eliminate tracks with short lifespan compared to a threshold value. Subsequently, we apply the information of trajectories captured from the forward GLMB filtering stage to carry out standard forward filtering and RTS backward smoothing on each estimated trajectory. For the experiment, we implement the tracker with standard GLMB filter, the hybrid track-before-detect (TBD) GLMB filter, and the GLMB filter with objects spawning. The results show improvements in tracking performance for all implemented trackers given negligible extra computational effort compared to standard GLMB filters.
Probabilities of False Alarm and Detection for the First-Order Cyclostationarity Test: Application to Modulation Classification

Automatic modulation classification is one of the most challenging problems of cognitive radio and has significant commercial and military application. This letter focuses on blind modulation classification of first-order cyclostationary signals under a Gaussian channel. A detailed theoretical analysis of the first-order cyclostationarity test has been performed. We present the theoretical expressions of the probability of false alarm Pfa and the probability of detection Pd as a function of the selected threshold for the first-order cyclostationarity test based constant false alarm rate detector. The Pd is presented for only AM and M-FSK signals, which are the only signals that possess first-order cyclostationary features. Theoretical formulations presented in this letter are validated by extensive simulation results.
Accurate binocular stereo underwater measurement method

Light changes its direction of propagation before entering a camera enclosed in a waterproof housing owing to refraction, which means that perspective imaging models in the air cannot be directly used underwater. In this article, we propose an accurate binocular stereo measurement system in an underwater environment. First, based on the physical underwater imaging model without approximation and Tsai’s calibration method, the proposed system is calibrated to acquire the extrinsic parameters, as the internal parameters can be pre-calibrated in air. Then, based on the calibrated camera parameters, an image correction method is proposed to convert the underwater images to air images. Thus, the epipolar constraint can be used to search the matching point directly. The experimental results show that the proposed method in this article can effectively eliminate the effect of refraction in the binocular vision and the measurement accuracy can be compared with the measurement result in air.
A new improved firefly clustering algorithm for SMC-PHD filter

Aiming at the difficulty of extracting multi-target states in SMC-PHD filter, this paper proposes a SMC-PHD multi-target tracking method based on the improved firefly clustering algorithm. This method uses the improved firefly clustering algorithm to extract the peaks of the updated PHD function. Firstly, select the particles equal to the number of targets to form firefly individuals, introduce attraction and movement mechanism of the firefly algorithm, use the optimal firefly guiding other fireflies’ movement, and undergo combination and mutation operation for the optimal firefly. Search the optimal solution in the whole solution space and obtain multi-target states. The experimental results show that the improved algorithm has more stable peak extraction ability than K-Means clustering algorithm in SMC-PHD filter.
mQRCode

Quick response (QR) codes are becoming pervasive due to their rapid readability and the popularity of smartphones with built-in cameras. QR codes are also gaining importance in the retail sector as a convenient mobile payment method. However, researchers have concerns regarding the security of QR codes, which leave users susceptible to financial loss or private information leakage. In this study, we addressed this issue by developing a novel QR code (called mQRCode), which exploits patterns presenting a specific spatial frequency as a form of camouflage. When the targeted receiver holds a camera in a designated position (e.g., directly in front at a distance of 30 cm from the camouflaged QR code), the original QR code is revealed in form of a Moire pattern. From any other position, only the camouflaged QR code can be seen. In experiments, the decryption rate of mQRCode was > 98.6% within 10.2 frames via a multi-frame decryption method. The decryption rate for cameras positioned 20° off axis or > 10cm away from the designated location dropped to 0%, indicating that mQRCode is robust against attacks.
Reversible Data Hiding in Encrypted Images Using POB Number System

Prediction-error expansion (PEE) methods usually involve the shifting process between peak points and zero points in order to vacate room for embedding.This occurrence will inevitably impose a troublesome because overflow/underflow issue after data embedding may occur, in turn, it dramatically increases the amount of auxiliary information to be transmitted or map information to be embedded.Besides, PEE-based reversible data hiding in encrypted images (RDHEI) methods usually adopt a block-size encryption strategy, but the correlation reservation of intra-block for subsequently embedding may leave opportunities for potential attacks.To deal with these issues, we proposed a novel RDHEI method with the functionality of embedding synchronized with re-encryption, which is well carried out by a data hider using a permutation ordered binary (POB) number system.Unlike conventional PEE-based RDHEI techniques, this scheme takes advantage of two peak points for embedding, but thoroughly abandons the former idea of shifting process.Experimental results verify the superiority of the proposed method, which not only may extend the applications of PEE-based RDHEI schemes substantially, but also vastly extend POB number application scenarios in practice.
Dual Adaptive Pyramid Network for Cross-Stain Histopathology Image Segmentation

Supervised semantic segmentation normally assumes the test data being in a similar data domain as the training data. However, in practice, the domain mismatch between the training and unseen data could lead to a significant performance drop. Obtaining accurate pixel-wise label for images in different domains is tedious and labor intensive, especially for histopathology images. In this paper, we propose a dual adaptive pyramid network (DAPNet) for histopathological gland segmentation adapting from one stain domain to another. We tackle the domain adaptation problem on two levels: (1) the image-level considers the differences of image color and style; (2) the feature-level addresses the spatial inconsistency between two domains. The two components are implemented as domain classifiers with adversarial training. We evaluate our new approach using two gland segmentation datasets with H&E and DAB-H stains respectively. The extensive experiments and ablation study demonstrate the effectiveness of our approach on the domain adaptive segmentation task. We show that the proposed approach performs favorably against other state-of-the-art methods.
Secure and Hassle-Free EVM Through Deep Learning Based Face Recognition

The primary right of voting in the elections is the fundamental yardstick of a democratic citizen. During the modern era, Electronic Voting machine (EVM) has been introduced which has marked a significant change in the conventional voting system in India replacing the ballot papers and boxes which were used earlier. Previously, the ballot papers used to consume a lot of time, due to the malpractices like booth-capturing and ballot-box stuffing, leading to more disputes and delayed results announcement. In this paper, we propose a EVM system which captures the facial image of a voter through a deep CNN based face recognizer, verifies it with the pre-captured images in the database, the result being positive, assumes the voter is a valid one, asks him to cast his vote for a political party. After voting, the facial alignment of the voter gets deleted from the system, ensuring the voter to vote for only once. This article describes the methodology adopted towards building the system and compares the performance of the face-recognizer (99.1%) against a baseline.
Algebraic Bayesian Networks: Naïve Frequentist Approach to Local Machine Learning Based on Imperfect Information from Social Media and Expert Estimates

The task of model learning arises in algebraic Bayesian networks as one of the probabilistic graphical models. Several approaches to machine learning of algebraic Bayesian networks are known. This research is dedicated to the algorithm of machine learning of algebraic Bayesian network represented by a knowledge pattern on missing data. Besides this algorithm, some examples of machine learning on artificial and real data from social media are considered.
A Novel Methodology to Improve Cooling Efficiency at Data Centers

Data centers are mission-critical infrastructures. There are strict service level requirements and standards imposed on operators and maintainers to ensure reliable run-the-clock operation. In the context of thermal management and data hall environmental control, the formation of hot and cold spots around server cabinets are especially undesirable, and can result in overheating, lifespan reductions, and performance throttling in the former and condensation damage in the latter. In this paper, we present a comprehensive multi-pronged methodology in data center environmental control, comprising computational fluid dynamics (CFD) simulation-aided predictive design first-stage approach, and a complementing Internet of Things (IoT) reactive management system that autonomously monitors and regulates fluctuations in thermal parameters. The novel hybrid methodology is demonstrated on various test scenarios derived from real-world context, and prototypes of the IoT system have been experimentally validated. The approach is shown to be efficient in eliminating unfavourable environmental variations and provides an enhanced understanding of common design problems and respective mitigation measures.
Adaptive-Length Coding of Image Data for Low-Cost Approximate Storage

In the past few years, ever-increasing amounts of image data have been generated by users globally, and these images are routinely stored in cold storage systems in compressed formats. This article investigates the use of approximate storage that leverages the use of cheaper, lower reliability memories that can have higher error rates. Since traditional JPEG-based schemes based on variable-length coding are extremely sensitive to error, the direct use of approximate storage results in severe quality degradation. We propose an error-resilient adaptive-length coding (ALC) scheme that divides all symbols into two classes, based on their frequency of occurrence, where each class has a fixed-length codeword. This provides a balance between the reliability of fixed-length coding schemes, which have a high storage overhead, and the storage-efficiency of Huffman coding schemes, which show high levels of error on low-reliability storage platforms. Further, we use data partitioning to determine which bits are stored in approximate or reliable storage to lower the overall cost of storage. We show that ALC can be used with general non-volatile storage, and can substantially reduce the total cost compared to traditional JPEG-based storage.
Short Paper: I Can’t Believe It’s Not Stake! Resource Exhaustion Attacks on PoS

We present a new resource exhaustion attack affecting several chain-based proof-of-stake cryptocurrencies, and in particular Qtum, a top 30 cryptocurrency by market capitalization ($300M as of Sep ’18). In brief, these cryptocurrencies do not adequately validate the proof-of-stake before allocating resources to data received from peers. An attacker can exploit this vulnerability, even without any stake at all, simply by connecting to a victim and sending malformed blocks, which the victim stores on disk or in RAM, eventually leading to a crash. We demonstrate and benchmark the attack through experiments attacking our own node on the Qtum main network; in our experiment we are able to fill the victim’s RAM at a rate of 2MB per second, or the disk at a rate of 6MB per second. We have begun a responsible disclosure of this vulnerability to appropriate development teams. Our disclosure includes a Docker-based reproducibility kit using the Python-based test framework. This problem has gone unnoticed for several years. Although the attack can be mitigated, this appears to require giving up optimizations enjoyed by proof-of-work cryptocurrencies, underscoring the difficulty in implementing and deploying chain-based proof-of-stake.
Mask R-CNN for Object Detection in Multitemporal SAR Images

The following topics are dealt with: geophysical image processing; remote sensing; image classification; time series; learning (artificial intelligence); feature extraction; radar imaging; synthetic aperture radar; image resolution; convolutional neural nets.
Singularity Structures and Impacts on Parameter Estimation in Finite Mixtures of Distributions

Singularities of a statistical model are the elements of the model's parameter space which make the corresponding Fisher information matrix degenerate. These are the points for which estimation techniques such as the maximum likelihood estimator and standard Bayesian procedures do not admit the root-$n$ parametric rate of convergence. We propose a general framework for the identification of singularity structures of the parameter space of finite mixtures, and study the impacts of the singularity structures on minimax lower bounds and rates of convergence for the maximum likelihood estimator over a compact parameter space. Our study makes explicit the deep links between model singularities, parameter estimation convergence rates and minimax lower bounds, and the algebraic geometry of the parameter space for mixtures of continuous distributions. The theory is applied to establish concrete convergence rates of parameter estimation for finite mixture of skew-normal distributions. This rich and increasingly popular mixture model is shown to exhibit a remarkably complex range of asymptotic behaviors which have not been hitherto reported in the literature.