Reviewerly/exHarmony huggingface dataset:
Visual Object Categorization Based on Hierarchical Shape Motifs Learned From Noisy Point Cloud Decompositions

Object shape is a key cue that contributes to the semantic understanding of objects. In this work we focus on the categorization of real-world object point clouds to particular shape types. Therein surface description and representation of object shape structure have significant influence on shape categorization accuracy, when dealing with real-world scenes featuring noisy, partial and occluded object observations. An unsupervised hierarchical learning procedure is utilized here to symbolically describe surface characteristics on multiple semantic levels. Furthermore, a constellation model is proposed that hierarchically decomposes objects. The decompositions are described as constellations of symbols (shape motifs) in a gradual order, hence reflecting shape structure from local to global, i.e., from parts over groups of parts to entire objects. The combination of this multi-level description of surfaces and the hierarchical decomposition of shapes leads to a representation which allows to conceptualize shapes. An object discrimination has been observed in experiments with seven categories featuring instances with sensor noise, occlusions as well as inter-category and intra-category similarities. Experiments include the evaluation of the proposed description and shape decomposition approach, and comparisons to Fast Point Feature Histograms, a Vocabulary Tree and a neural network-based Deep Learning method. Furthermore, experiments are conducted with alternative datasets which analyze the generalization capability of the proposed approach.
Melody slot machine

We developed an interactive music system called the "Melody Slot Machine, " which provides an experience of manipulating a music performance. The melodies used in the system are divided into multiple segments, and each segment has multiple variations of melodies. By turning the dials manually, users can switch the variations of melodies freely. When you pull the slot lever, the melody of all segments rotates, and melody segments are randomly selected. Since the performer displayed in a hologram moves in accordance with the selected variation of melody, users can enjoy the feeling of manipulating the performance.
Model Criticism in Latent Space

Model criticism is usually carried out by assessing if replicated data generated under the fitted model looks similar to the observed data, see e.g. Gelman, Carlin, Stern, and Rubin (2004, p. 165). This paper presents a method for latent variable models by pulling back the data into the space of latent variables, and carrying out model criticism in that space. Making use of a model's structure enables a more direct assessment of the assumptions made in the prior and likelihood. We demonstrate the method with examples of model criticism in latent space applied to factor analysis, linear dynamical systems and Gaussian processes.
A Security Model for Web-Based Fuzzy-Logic Direct Torque Control of Induction Motor Drive

A web-based fuzzy-logic direct torque control of induction motor (IM) simulation program in a distributed client/server architecture and its implementation steps are discussed in this paper. The client program is a web page developed in java software, which accesses remotely simulated IM dynamics program by executing it in the server through the internet. The proposed IM simulation program offers a convenient remotely accessible which can work on any computer platform and simulation environment, implemented in a distributed client/server architecture, for a standalone motor simulation programs. This architecture has two major parts: graphical user interface (GUI) program in the client side and simulation engine in the server side. In the client side, the GUI program is developed in java software typically run in any computer platforms in client, allowing us to access the simulation program in the server using any browser and to send the data’s to the server, and to retrieve/exhibit the outputs from the server using different sets of parameters and configurations.
Evaluating Ancient Sundanese Glyph Recognition Using Convolutional Neural Network

Abstract Handwriting recognition is still a real challenge in classification tasks. Not as in modern documents, the isolated glyph images in ancient document have various random noises, non uniform background color, and smudge. Convolution neural network (CNN) is one of successful method in pattern recognition and machine learning to classify the objects. The evaluation of some CNN architectures with several different convolution layers classifying the isolated glyph image are presented in this paper. The experimental study is tested on 60 classes of glyph from the ancient Sundanese dataset that published in ICDAR 2017. Beside, the batch normalization is also investigated to measure the performance of the learning process. The results shown that the recognition rate was affected by multi convolutional layers, multi fully connected layer and batch normalization. Based on the experimental study, model 8C2F could achieve 86.15% of recognition rate.
On Max-min Fair Resource Allocation for Distributed Job Execution

In modern data intensive computing, it is increasingly common for jobs to be executed in a distributed fashion across multiple machine clusters or datacenters to take advantage of data locality. This paper studies fair resource allocation among jobs requiring distributed execution. We extend conventional max-min fairness for resource allocation in a single machine or machine cluster to distributed job execution over multiple sites and define Aggregate Max-min Fairness (AMF) which requires the aggregate resource allocation across all sites to be max-min fair. We show that AMF satisfies the properties of Pareto efficiency, envy-freeness and strategy-proofness, but it does not necessarily satisfy the sharing incentive property. We propose an enhanced version of AMF to guarantee the sharing incentive property. We present algorithms to compute AMF allocations and propose an add-on to optimize the job completion times under AMF. Experimental results show that compared with a baseline which simply requires the resource allocation at each site to be max-min fair, AMF performs significantly better in balancing resource allocation and in job completion time, particularly when the workload distribution of jobs among sites is highly skewed.
Game of Streaming Players

The dramatic growth of HTTP adaptive streaming (HAS) traffic represents a practical challenge for service providers in satisfying the demand from their customers. Achieving this in a network where multiple players share the network capacity has so far proved hard because of the bandwidth competition among the HAS players. This competition is exacerbated by the bandwidth overestimation that is introduced due to the isolated and selfish behavior of the HAS players. Each player strives individually to select the maximum bitrate without considering the co-existing players or network resource dynamics. As a result, the HAS players suffer from video quality instability, quality unfairness, and network underutilization or oversubscription, and the players observe a poor quality of experience (QoE). To address this issue, we propose a fully distributed game theory and consensus-based collaborative adaptive bitrate solution for shared network environments, termed Game Theory and consensus-based Approach for Cooperative HAS delivery systems (GTAC). Our solution consists of two-stage games that run in parallel during a streaming session. We extensively evaluate GTAC on a broad set of trace-driven and real-world experiments. Results show that GTAC enhances the viewer QoE by up to 22%, presentation quality stability by up to 24%, fairness by at least 31%, and network utilization by 28% compared to the well-known schemes.
A pipeline to improve compressed image quality

This paper presents a new image restoration pipeline performing especially well on noisy and compressed images. Most images are corrupted by noise. The signal to noise ratio (SNR) level increases with the pixel intensity value, which makes the denoising process especially challenging in dark areas of the images. Moreover, these areas are more likely to be highly compressed since they have low signal variations. In this paper, we take into account compression by introducing a pre-processing step restituting the instrument noise. Then we propose a denoising and deconvolution step optimally parametrized since the instrument response (noise and Modulation Transfer Function) is known. We achieve better restoration than classical algorithms on satellite imagery. This improvement in image quality is shown on two kinds of application: pansharpening and 3D restitution.
Simultaneous Adversarial Training - Learn from Others’ Mistakes

Adversarial examples are maliciously tweaked images that can easily fool machine learning techniques, such as neural networks, but they are normally not visually distinguishable for human beings. One of the main approaches to solve this problem is to retrain the networks using those adversarial examples, namely adversarial training. However, standard adversarial training might not actually change the decision boundaries but cause the problem of gradient masking, resulting in a weaker ability to generate adversarial examples [39]. Therefore, it cannot alleviate the problem of black-box attacks, where adversarial examples generated from other networks can transfer to the targeted one. In order to reduce the problem of black-box attacks, we propose a novel method that allows two networks to learn from each others' adversarial examples and become resilient to black-box attacks. We also combine this method with a simple domain adaptation to further improve the performance.
De-anonymizing bitcoin networks

The anonymity and de-anonymity of blockchain and Bitcoin have always been a hot topic in blockchain related research. Since Bitcoin was created by Nakamoto in 2009, it has, to some extent, deviated from its currency attribute as a trading medium but instead turned into an object for financial investment and operations. In this paper, the power-law distribution that the Bitcoin network obeys is given, while traditional de-anonymous methods such as clustering fail to satisfy it. Therefore, considering the profit-oriented characteristics of Bitcoin traders in such occasion, we put forward a deanonymous heuristic approach that recognizes and analyzes the behavioral patterns of financial High-Frequency Transactions(HFT), with real-time exchange rate of Bitcoin involved. Basing on the heuristic approach, finally we established the de-anonymous method that matches the activity information of the IP with the transaction records in blockchain. Experiments on IP matching method are applied to the actual data. It turns out that similar behavioral pattern between IP and transaction records are shown, which indicates the superiority of IP matching method.
Effects of Parametric Uncertainties in Cascaded Open Quantum Harmonic Oscillators and Robust Generation of Gaussian Invariant States

This paper is concerned with the generation of Gaussian invariant states in cascades of open quantum harmonic oscillators governed by linear quantum stochastic differential equations. We carry out infinitesimal perturbation analysis of the covariance matrix for the invariant Gaussian state of such a system and the related purity functional subject to inaccuracies in the energy and coupling matrices of the subsystems. This leads to the problem of balancing the state-space realizations of the component oscillators through symplectic similarity transformations in order to minimize the mean square sensitivity of the purity functional to small random perturbations of the parameters. This results in a quadratic optimization problem with an effective solution in the case of cascaded one-mode oscillators, which is demonstrated by a numerical example. The findings of the paper are applicable to robust state generation in quantum stochastic networks.
Multi-robot Cooperation Strategy in a Partially Observable Markov Game Using Enhanced Deep Deterministic Policy Gradient

Deep reinforcement learning (DRL) has been applied to solve challenging problems in robotic domains. However, since non-stationary of the environment and the difficulty of long-term interaction between robots, traditional DRL is poorly suitable for multi-robot. Thus, an enhanced deep deterministic policy gradient algorithm is proposed in this study to explore the application of DRL in multi-robot domains. The algorithm ensures a cooperation strategy for multi-robot, which merely uses partially observed state of each robot, named a partially observable Markov game, realize global optimality in executing process. It is achieved by eliminating non-stationary of the environment in training process and a centralized critic for decentralized multi-robot. Simulations with increasingly complex environments are performed to validate the effectiveness of the proposed algorithm.
An effective algorithm for learning single occurrence regular expressions with interleaving

The advantages offered by the presence of a schema are numerous. However, many XML documents in practice are not accompanied by a (valid) schema, making schema inference an attractive research problem. The fundamental task in XML schema learning is inferring restricted subclasses of regular expressions. Most previous work either lacks support for interleaving or only has limited support for interleaving. In this paper, we first propose a new subclass Single Occurrence Regular Expressions with Interleaving (SOIRE), which has unrestricted support for interleaving. Then, based on single occurrence automaton and maximum independent set, we propose an algorithm iSOIRE to infer SOIREs. Finally, we further conduct a series of experiments on real datasets to evaluate the effectiveness of our work, comparing with both ongoing learning algorithms in academia and industrial tools in real-world. The results reveal the practicability of SOIRE and the effectiveness of iSOIRE, showing the high preciseness and conciseness of our work.
An Inter-Peer Communication Mechanism Based Water Cycle Algorithm

As a nature inspired metaheuristic algorithm, Water Cycle Algorithm (WCA) has been applied to some real-world problems for its excellent optimization performance. However, in standard WCA, each individual only learns information from a higher level individual but lacks communication among inter peers, which leads to the loss of some important information. In order to address this problem, an inter-peer communication mechanism based Water Cycle Algorithm (IPCWCA) is presented in this paper. In IPCWCA, besides getting information from higher level individual, each stream and river communicate with one of their peers to increase the diversity of whole population and enhance the efficiency of optimization. To explore the efficiency of IPCWCA, other four heuristic algorithms are involved to test on eight benchmark functions. Experimental results show that IPCWCA performs better on solving different types of problems compared with other four algorithms.
A new vehicular blackbox architecture based on searchable encryption

Blackboxes are being increasingly used in the vehicular context to store and transmit information related to safety, security and many other applications. The plethora of sensors available at the different parts of the vehicle can provide enriched gathering of the data related to these applications. Nonetheless, to support multiple use cases, the blackbox must be accessible by various actors (e.g. vehicle owner, insurance company, law enforcement authorities). This raises significant challenges regarding the privacy of the data collected and stored in the blackbox. In fact, these data can often lead to tracing back accurate facts about the behaviour of the owner of the vehicle. To cope with this problem, we propose a new blackbox architecture supporting searchable encryption. This feature allows multiple users who are not able to decipher the content of the blackbox to validate properties such as path traceback and velocity. To illustrate the implementation of the proposed technique in practice, we discuss a case study related to post-accident processing by insurance companies.
Smart recommendation of personalized cloud music service based on mental model

Cloud music service has become one of the popular internet services in today's people's life. Meanwhile, the artistic experience brought by music to audience is greatly affected by the audience's psychological preferences as well as their mood status under different environment scenes, and therefore those personalized requirements should be considered in the recommendations of cloud music service for improving the audience's satisfactions and searching efficiency. This paper firstly conducted a neural experiment to analyze the audience's satisfactions and their concerned factors of cloud music services, and on this basis studied the mental model to describe audience's psychological preferences and music choice habits from the intelligent mining of their behavioral data. Finally, the smart recommendation method which embedded emotional intelligence with rational intelligence based on the audience's mental model was proposed for personalized cloud music services.
Sparse over-complete patch matching

Image patch matching, which is the process of identifying corresponding patches across images, has been used as a subroutine for many computer vision and image processing tasks. State -of-the-art patch matching techniques take image patches as input to a convolutional neural network to extract the patch features and evaluate their similarity. Our aim in this paper is to improve on the state of the art patch matching techniques by observing the fact that a sparse-overcomplete representation of an image posses statistical properties of natural visual scenes which can be exploited for patch matching. We propose a new paradigm which encodes image patch details by encoding the patch and subsequently using this sparse representation as input to a neural network to compare the patches. As sparse coding is based on a generative model of natural image patches, it can represent the patch in terms of the fundamental visual components from which it has been composed of, leading to similar sparse codes for patches which are built from similar components. Once the sparse coded features are extracted, we employ a fully-connected neural network, which captures the non-linear relationships between features, for comparison. We have evaluated our approach using the Liberty and Notredame subsets of the popular UBC patch dataset and set a new benchmark outperforming all state-of-the-art patch matching techniques for these datasets.
Empirical Study of Test Driven Development with Scrum

Now days, Agile development methodologies are becoming popular in various IT industries. This methodology is combination of different stages in repetitive and incremental manner. Its main focus is to increase the adaptability in process, which in turn increases customer satisfaction. There are various development frameworks in agile methodology like Scrum and Kanban. There is also another programming practice known as Test Driven Development. It starts with developing test for a feature, before its implementation. It is also known as test first programming. The main objective of this paper is to adopt an approach in which TDD can be merged with Scrum to add benefits of TDD in Scrum and also to provide a comprehensive review of this approach.
Homomorphic Encryption for Privacy-Preserving Genome Sequences Search

Genome sequence search is useful, for example, in clinical applications where a care provider needs to select a treatment option for a patient based on the exact kind of cancer the patient might have. Homomorphic encryption is a desirable technology to be used for this application because it is non-interactive. However, privacy-preserving genome sequence search using homomorphic encryption has been a practical challenge because of scalability issues driven by the depth of computations that need to be supported for privacy-preserving genome sequence search. In this paper, we build off of earlier privacy-preserving genome sequence search results to design, implement and compare two approaches to a client-server style system for privacy-preserving genome sequence search. There is a myriad of options and design trade-offs associated with the application of homomorphic encryption in this domain driven, for example, by choices in data encoding, scheme selection, and even encryption software library. We particularly focus on the use of the BGV and BFV homomorphic encryption schemes provided by the HElib and PALISADE open-source homomorphic encryption software libraries. Our results show that using the BFV-based approach in PALISADE provides optimal results for this application over our sample data.
Removing Personally Identifiable Information from Shared Dataset for Keystroke Authentication Research

Research on keystroke dynamics has the good potential to offer continuous authentication that complements conventional authentication methods in combating insider threats and identity theft before more harm can be done to the genuine users. Unfortunately, the large amount of data required by free-text keystroke authentication often contain personally identifiable information, or PII, and personally sensitive information, such as a user's first name and last name, username and password for an account, bank card numbers, and social security numbers. As a result, there are privacy risks associated with keystroke data that must be mitigated before they are shared with other researchers. We conduct a systematic study to remove PII's from a recent large keystroke dataset. We find substantial amounts of PII's from the dataset, including names, usernames and passwords, social security numbers, and bank card numbers, which, if leaked, may lead to various harms to the user, including personal embarrassment, blackmails, financial loss, and identity theft. We thoroughly evaluate the effectiveness of our detection program for each kind of PII. We demonstrate that our PII detection program can achieve near perfect recall at the expense of losing some useful information (lower precision). Finally, we demonstrate that the removal of PII's from the original dataset has only negligible impact on the detection error tradeoff of the free-text authentication algorithm by Gunetti and Picardi. We hope that this experience report will be useful in informing the design of privacy removal in future keystroke dynamics based user authentication systems.
Credibility Assessment of Complex Simulation Models Using Cloud Models to Represent and Aggregate Diverse Evaluation Results

Comprehensive and objective credibility assessments of complex simulation models are crucial to the successful application of models and simulation results in various critical evaluation and decision making problems. However, the credibility assessment of a complex simulation model usually encounters many challenges, involves the measurements and evaluations of hundreds of qualitative and quantitative indicators, and requires the integration of heterogeneous results. Therefore, cloud models which can describe both fuzziness and randomness are adopted to represent and aggregate diverse evaluation results of various qualitative and quantitative indicators. Then, crisp values, interval numbers, statistical data and linguistic terms can all be represented and aggregated by normal cloud models. The main advantages of our methods are that diverse evaluation results of various indicators can be represented and aggregated, and uncertainties associated with these results of leaf indicators can be preserved and propagated into the final assessment result. A missile simulation model credibility assessment example is presented to illustrate the proposed methods.