Reviewerly/exHarmony huggingface dataset:
Towards continual reinforcement learning through evolutionary meta-learning

In continual learning, an agent is exposed to a changing environment, requiring it to adapt during execution time. While traditional reinforcement learning (RL) methods have shown impressive results in various domains, there has been less progress in addressing the challenge of continual learning. Current RL approaches do not allow the agent to adapt during execution but only during a dedicated training phase. Here we study the problem of continual learning in a 2D bipedal walker domain, in which the legs of the walker grow over its lifetime, requiring the agent to adapt. The introduced approach combines neuroevolution, to determine the starting weights of a deep neural network, and a version of deep reinforcement learning that is continually running during execution time. The proof-of-concept results show that the combined approach gives a better generalisation performance when compared to evolution or reinforcement learning alone. The hybridization of reinforcement learning and evolution opens up exciting new research directions for continually learning agents that can benefit from suitable priors determined by an evolutionary process.
Touch Analysis: An Empirical Evaluation of Machine Learning Classification Algorithms on Touch Data

Our research aims at classifying individuals based on their unique interactions on the touchscreen-based smartphones. In this research, we use 'TouchAnalytics' dataset, which include 41 subjects and 30 different behavioral features. Furthermore, we derived new features from the raw data to improve the overall authentication performance. Previous research has already been done on the TouchAnalytics dataset with the state-of-the-art classifiers, including Support Vector Machine (SVM) and k-nearest neighbor (kNN) and achieved equal error rates (EERs) between 0% to 4%. In this paper, we propose a Deep Neural Net (DNN) architecture to classify the individuals correctly. When we combine the new features with the existing ones, SVM and k-NN achieved the classification accuracies of 94.7% and 94.6%, respectively. This research explored seven other classifiers and out of them, decision tree and our proposed DNN classifiers resulted in the highest accuracies with 100%. The others included: Logistic Regression (LR), Linear Discriminant Analysis (LDA), Gaussian Naive Bayes (NB), Neural Network, and VGGNet with the accuracy scores of 94.7%, 95.9%, 31.9%, 88.8%, and 96.1%, respectively.
A Verifiable $(t, n)$ Threshold Quantum State Sharing Against Denial Attack

To detect frauds by a dealer or some participants, researchers have proposed verifiable threshold quantum state sharing (VTQSTS) schemes. However, the existing VTQSTS schemes either have lowcomputation efficiency or weak security. In this paper, a verifiable (t, n) threshold quantum state sharing against denial attack (VTQSTS-ADA) scheme is proposed to overcome the above limitations, in which the dealer Alice encodes the quantum secret sequence into two quantum message sequences and a quantum signature sequence. First, for each participant in the authorization subset, the received quantum signature sequence is validated by a quantum verification algorithm, and then a unitary transformation with a rotation key is performed on the received quantum message sequence; next, a new quantum signature sequence is generated by a quantum signature algorithm with the signature key. Each participant sends the new message sequences and signature sequence to the next participant. The original quantum secret sequence can be reconstructed when t(t ≤ n) participants collaborate. The security analysis shows that the proposed VTQSTS-ADA scheme can detect the fraud instances raised by the dealer and participants and provide stronger security such as resisting denial attacks and man-in-middle attacks compared with the existing schemes. The performance analysis shows that the proposed VTQSTS-ADA scheme outperforms the earlier VTQSTS scheme and is comparable to the recent scheme.
Comparison on user experience of mid-air gesture interaction and traditional remotes control

Previous studies have explored mid-air gestures as a new interaction mode. However, very little is known about the user experience of using gestures for control and why it is better than traditional way. In this study, we invited users to use both gestures and a remote to control TV in a home environment setting and we assessed user experience in these two conditions. Results showed that while gesture control put more mental stress and cognitive load on users, it could improve the overall experience, especially in terms of unrestraint and efficiency, compared to remote control. Based on study results, discussion on user experience of mid-air gestures interaction and further study on a natural interactive experience evaluation mode were provided.
UMGAN: Generative adversarial network for image unmosaicing using perceptual loss

Image mosaicing conceals sensitive parts of an image. The objective of this work is to recover hidden semantic structure under mosaiced parts, especially focusing on facial images. While recent image completion methods based on deep learning have shown promising results on recovering damaged parts in an image, they have not addressed the problem of image unmosaicing. We present a Generative Adversarial Network (GAN) approach to image unmosaicing called UMGAN, which is an image-to-image translation method. We have found that exploiting perceptual loss together with low level l1 loss and high level Structural SIMilarity (SSIM) loss is quite effective to attain visually plausible and semantically consistent results. We have evaluated our method on the CelebA and MIT-CBCL image datasets and achieved better perceptual results than state-of-the-art image completion methods.
Semi-Supervised Neural Machine Translation via Marginal Distribution Estimation

Neural machine translation (NMT) heavily relies on parallel bilingual corpora for training. Since large-scale, high-quality parallel corpora are usually costly to collect, it is appealing to exploit monolingual corpora to improve NMT. Inspired by the law of total probability, which connects the probability of a given target-side monolingual sentence to the conditional probability of translating from a source sentence to the target one, we propose to explicitly exploit this connection and help the training procedure of NMT models using monolingual data. The key technical challenge of this approach is that there are exponentially many source sentences for a target monolingual sentence while computing the sum of the conditional probability given each possible source sentence. We address this challenge by leveraging the reverse translation model (target-to-source translation model) to sample several mostly likely source-side sentences and avoid enumerating all possible candidate source sentences. Then we propose two different methods to leverage the law of total probability, including marginal distribution regularization and likelihood maximization of monolingual corpora. Experiment results on English-French and German-English tasks demonstrate that our methods achieve significant improvement over several strong baselines.
Manual Annotations on Depth Maps for Human Pose Estimation

Few works tackle the Human Pose Estimation on depth maps. Moreover, these methods usually rely on automatically annotated datasets, and these annotations are often imprecise and unreliable, limiting the achievable accuracy using this data as ground truth. For this reason, in this paper we propose an annotation refinement tool of human poses, by means of body joints, and a novel set of fine joint annotations for the Watch-n-Patch dataset, which has been collected with the proposed tool. Furthermore, we present a fully-convolutional architecture that performs the body pose estimation directly on depth maps. The extensive evaluation shows that the proposed architecture outperforms the competitors in different training scenarios and is able to run in real-time.
Research on KNN Algorithm in Malicious PDF Files Classification under Adversarial Environment

Traditional machine learning classifiers are usually based on the same distribution of training and testing sets, and only pay attention to the accuracy of the classifier, when attackers change the data distribution, the usability of the model is reduced. A method on how to improve the robustness of the KNN classifier is proposed. Firstly, the gradient descent attack method is used to attack the KNN algorithm. Secondly, add the adversarial samples generated by the gradient descent attack to the training set to train a new KNN classifier. Finally, compare the robustness of the improved classifier and the initial classifier by simulating different attack strengths. The experimental results show that adding the adversarial samples to the KNN classifier can effectively improve the performance of the classifier against the evasion attacks.
Application of Similarity-Based Image Steganography Method to Computerized Tomography Images

With the rapid development of technology, computers have got in every area of our lives. One of these areas is the area of health. Medical records are now stored in computer environments and can be accessed if needed. Nowadays, thanks to internet technologies, access to information has been facilitated, and this has brought security problems with malicious users. Using computed tomography images in this study, doctor reviews are hidden in these images. For this purpose, similarity based least significant bit method was used. While hiding the reviews of the doctor, three different messages were concealed with the capacity of hiding, applied directly to the hiding and NOT to the reviews to be hidden. The mean square error and peak signal to noise ratio metrics were used as performance evaluation criteria. As a result, the peak signal to noise ratio value of the lowest message hiding capacity (961 bytes) was found as 71.4408, applied to the original reviews, while the peak signal to noise ratio value was found to be 71.0473, applied to the inverted reviews.
People Counting in Elevator Car Based on Computer Vision

To detect the number of passengers in an elevator based on computer vision technology, image preprocessing and human contour detection algorithms were proposed. After the target image was edge-detected and morphologically eroded, a binary image containing only human targets was obtained. The edges of the image were detected using canny operator, it was most suitable for the edge detection of the human body contour in elevator. Person counting was mainly performed by using morphological mark outline connection area counting method. According to the algorithm flow chart, the real elevator monitoring video was tested and verified. The number of people with fewer numbers (less than 10 persons) was highly accurate.
Reversible Pebble Games for Reducing Qubits in Hierarchical Quantum Circuit Synthesis

Hierarchical reversible logic synthesis can find quantum circuits for large combinational functions. The price for a better scalability compared to functional synthesis approaches is the requirement for many additional qubits to store temporary results of the hierarchical input representation. However, implementing a quantum circuit with large number of qubits is a major hurdle. In this paper, we demonstrate and establish how reversible pebble games can be used to reduce the number of stored temporary results, thereby reducing the qubit count. Our proposed algorithm can be constrained with number of qubits, which is aimed to meet. Experimental studies show that the qubit count can be significantly reduced (by up to 63.2%) compared to the state-of-the-art algorithms, at the cost of additional gate count.
Cognitive Action Laws: The Case of Visual Features

This paper proposes a theory for understanding perceptual learning processes within the general framework of laws of nature. Artificial neural networks are regarded as systems whose connections are Lagrangian variables, namely, functions depending on time. They are used to minimize the cognitive action, an appropriate functional index that measures the agent interactions with the environment. The cognitive action contains a potential and a kinetic term that nicely resemble the classic formulation of regularization in machine learning. A special choice of the functional index, which leads to the fourth-order differential equations-Cognitive Action Laws (CAL) - exhibits a structure that mirrors classic formulation of machine learning. In particular, unlike the action of mechanics, the stationarity condition corresponds with the global minimum. Moreover, it is proven that typical asymptotic learning conditions on the weights can coexist with the initialization provided that the system dynamics is driven under a policy referred to as information overloading control. Finally, the theory is experimented for the problem of feature extraction in computer vision.
An Evaluation Framework for Interactive Recommender Systems

Traditional recommender systems present a relatively static list of recommendations to a user where the feedback is typically limited to an accept/reject or a rating model. However, these simple modes of feedback may only provide limited insights as to why a user likes or dislikes an item and what aspects of the item the user has considered. Interactive recommender systems present an opportunity to engage the user in the process by allowing them to interact with the recommendations, provide feedback and impact the results in real-time. Evaluation of the impact of the user interaction typically requires an extensive user study which is time consuming and gives researchers limited opportunities to tune their solutions without having to conduct multiple rounds of user feedback. Additionally, user experience and design aspects can have a significant impact on the user feedback which may result in not necessarily assessing the quality of some of the underlying algorithmic decisions in the overall solution. As a result, we present an evaluation framework which aims to simulate the users interacting with the recommender. We formulate metrics to evaluate the quality of the interactive recommenders which are outputted by the framework once simulation is completed. While simulation alone is not sufficient to evaluate a complete solution, the results can be useful to help researchers tune their solution before moving to the user study stage.
Cuckoo Search Algorithm for Border Reconstruction of Medical Images with Rational Curves

Border reconstruction is a key technology in medical image processing, where it is applied to identify and separate different tissues, organs, and tumors in diagnostic procedures. The classical approaches for this problem are based on either linear or polynomial functions to describe the border of the region of interest. However, little effort has been devoted to the more powerful case of rational functions, which extend the polynomial case by including extra degrees of freedom (the weights). As a consequence, rational functions are more difficult to compute. In this paper, we solve the problem by applying a nature-inspired swarm intelligence method called cuckoo search algorithm. The method is applied to two illustrative examples of medical images with satisfactory results.
ALVEC: Auto-scaling by Lotka Volterra elastic cloud: A QoS aware non linear dynamical allocation model

Measurement of the dynamic elasticity of resource allocation in cloud computing continues to be a relevant problem in the related literature. Yet, there is scant evidence on determining the dynamic scaling quotient in such operations. Elasticity is defined as the ability to adapt to the changing workloads by provisioning and de-provisioning of Cloud resources and scaling is essential for maintaining elasticity in resource allocation. We propose ALVEC, as a model of resource allocation in Cloud data centers (Sarkar et al. , 2016) [7], [16], to address dynamic allocation by auto-tuning the model parameters. The proposed model, governed by a coupled differential equation known as Lotka Volterra (LV), fares better for management of Service Level Agreement (SLA) and Quality of Services (QoS). We show evidence of true elasticity both in theoretical and numerical applications. Additionally, we show that ALVEC as an example of unsupervised resource allocation, is able to predict the future load and allocate virtual machines efficiently.
Topic Grouper: An Agglomerative Clustering Approach to Topic Modeling

We introduce Topic Grouper as a complementary approach in the field of probabilistic topic modeling. Topic Grouper creates a disjunctive partitioning of the training vocabulary in a stepwise manner such that resulting partitions represent topics. Topic generation is based on a simple probabilistic model and agglomerative clustering, where clusters are formed as sets of words from the vocabulary. The resulting binary tree of topics may act as a containment hierarchy typically with more general topics towards the root of tree and more specific topics towards the leaves. As opposed to other topic modeling approaches, Topic Grouper avoids the need for hyper parameter optimizations. As part of an evaluation, we show that Topic Grouper has reasonable predictive power but also a reasonable complexity. It can deal well with stop words and function words. Also, it can handle topic distributions, where some topics are more frequent than others. We present examples of computed topics which appear as conclusive and coherent.
Individualism of particles in particle swarm optimization

The particle swarm optimization (PSO) method is an effective, nature-inspired, computational algorithm for optimization problems. However, the influence of individuals’ cultural orientations is neglected in particle swarms. Individualist and collectivist orientations have an important influence on optimization. In order to improve the performance of PSO in nature, particularly with respect to the balance of exploitation and exploration in the search process, and in view of the inherent characteristics of particles, a few particles in the swarm was regarded as having an individualistic orientation, which may be beneficial to group creativity from a cultural psychology perspective. The particles holding individualistic orientations were named individualism particles (I-particles). To simulate the divisive and unruly features of I-particles, a random term was introduced to the velocity updating formula to simulate the creative behavior. The experiment was performed with and without I-particles in the PSO algorithm. The presence of I-particles produced better performance in terms of solution accuracy and convergence speed. Furthermore, when added to one of the PSO variants, the I-particles could also contribute to improve the performance of the PSO variant. Further, even for complex CEC2013 benchmark functions, good results were achieved in most problems when I-particles were added to the PSO. Besides, the study is not focused on a PSO variant algorithm, but on the nature of the PSO. Thus, The findings of the nature indicated that I-particles in a particle swarm might be regarded as a supplement to the basic structure of PSO.
Application Development and Testing Based on ISO 9126 Framework

Abstract Software quality testing is one of the stages in SDLC (systems development life cycle). Software or application testing can’t ensure quality free of bugs or errors, but can provide trust and assurance to the application at some level. Software of freight forwarding, from the development, implementation and testing of this application can achieve some desired objectives by the user that is to help transportation services become more effective and efficient, providing convenience in terms of data and information process. With the use of the application compared with the process of recording manually is facilitate the decision makers to be able to monitor transactions and reports in a short time. To design and implement reliable Information Systems (IS) of freight forwarding and avoid bugs or errors, comprehensive testing is required based on the ISO 9126 method, which includes attributes such as aspects of functionality and Maintainability. Testing an application or software is an activity to check if the actual results match the expected results and to ensure that the application is bug-free. IS of freight forwarding services is appropriate and able to become software in the desired by the user consisting of input, process, and output.
Implementation of FPGA-Based Barker’s-Like Codes

Pseudorandom code sequences with a low level of side lobe autocorrelation function have been used in radars, communication systems and information security. The results of the studies have shown that there are no Barker’s signals with a length greater than 13, for which the value of the side lobe autocorrelation function doesn’t exceed one. Consequently, in many cases, code sequences with length greater than 13 are used with the minimum possible value of the side petals level.The article provides an overview of the synthesis of Barker-like sequence with the arbitrary length using the ideal ring bundles and an example of implementation by means of FPGA, their testing, and evaluation of FPGA hardware resources that are required for this. FPGA EP3C16F484 of the Cyclone III family has been used in the implementation process. The development has been carried out in the VHDL hardware programming language in the development environment of Quartus II with the using of libraries of the development environment.
Engineering Methods for Differentially Private Histograms: Efficiency Beyond Utility

Publishing histograms with <inline-formula><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> - <i>differential privacy</i> has been studied extensively in the literature. Existing schemes aim at maximizing the <i>utility</i> of the published data, while previous experimental evaluations analyze the privacy/utility trade-off. In this paper, we provide the first experimental evaluation of differentially private methods that goes beyond utility, emphasizing also on another important aspect, namely <i>efficiency</i> . Towards this end, we first observe that all existing schemes are comprised of a small set of common blocks. We then optimize and choose the best implementation for each block, determine the combinations of blocks that capture the entire literature, and propose novel block combinations. We qualitatively assess the quality of the schemes based on the skyline of efficiency and utility, i.e., based on whether a method is dominated on both aspects or not. Using exhaustive experiments on four real datasets with different characteristics, we conclude that there are always trade-offs in terms of utility and efficiency. We demonstrate that the schemes derived from our novel block combinations provide the best trade-offs for time critical applications. Our work can serve as a guide to help practitioners <i>engineer</i> a differentially private histogram scheme depending on their application requirements.
Ratios of ordered points of point processes with regularly varying intensity measures

We study limiting properties of ratios of ordered points of point processes whose intensity measures have regularly varying tails, giving a systematic treatment which points the way to “large-trimming” properties of extremal processes and a variety of applications. Our point process approach facilitates a connection with the negative binomial process of Gregoire (1984) and consequently to certain generalised versions of the Poisson–Dirichlet distribution.
Why blockchains need the law

Governance issues limit blockchains' ability to evolve and face unforeseen challenges. It seems possible to argue that this impasse is because most blockchains lack meta-rules. This work considers blockchains as a socio-technical system of rules, in order to draw a comparison with legal systems. Following the comparison, one finds that most blockchains lack what, in legal theory, are considered secondary rules. That is, the meta-rule of the system.