Miracle-dz/UniFAR-FTUs huggingface dataset:
A Bandit Framework for Optimal Selection of Reinforcement Learning Agents

Deep Reinforcement Learning has been shown to be very successful in complex games, e.g. Atari or Go. These games have clearly defined rules, and hence allow simulation. In many practical applications, however, interactions with the environment are costly and a good simulator of the environment is not available. Further, as environments differ by application, the optimal inductive bias (architecture, hyperparameters, etc.) of a reinforcement agent depends on the application. In this work, we propose a multi-arm bandit framework that selects from a set of different reinforcement learning agents to choose the one with the best inductive bias. To alleviate the problem of sparse rewards, the reinforcement learning agents are augmented with surrogate rewards. This helps the bandit framework to select the best agents early, since these rewards are smoother and less sparse than the environment reward. The bandit has the double objective of maximizing the reward while the agents are learning and selecting the best agent after a finite number of learning steps. Our experimental results on standard environments show that the proposed framework is able to consistently select the optimal agent after a finite number of steps, while collecting more cumulative reward compared to selecting a sub-optimal architecture or uniformly alternating between different agents.
Acceleration via Symplectic Discretization of High-Resolution Differential Equations

We study first-order optimization methods obtained by discretizing ordinary differential equations (ODEs) corresponding to Nesterov's accelerated gradient methods (NAGs) and Polyak's heavy-ball method. We consider three discretization schemes: an explicit Euler scheme, an implicit Euler scheme, and a symplectic scheme. We show that the optimization algorithm generated by applying the symplectic scheme to a high-resolution ODE proposed by Shi et al. [2018] achieves an accelerated rate for minimizing smooth strongly convex functions. On the other hand, the resulting algorithm either fails to achieve acceleration or is impractical when the scheme is implicit, the ODE is low-resolution, or the scheme is explicit.
Consistency models in distributed systems: A survey on definitions, disciplines, challenges and applications

The replication mechanism resolves some challenges with big data such as data durability, data access, and fault tolerance. Yet, replication itself gives birth to another challenge known as the consistency in distributed systems. Scalability and availability are the challenging criteria on which the replication is based upon in distributed systems which themselves require the consistency. Consistency in distributed computing systems has been employed in three different applicable fields, such as system architecture, distributed database, and distributed systems. Consistency models based on their applicability could be sorted from strong to weak. Our goal is to propose a novel viewpoint to different consistency models utilized in the distributed systems. This research proposes two different categories of consistency models. Initially, consistency models are categorized into three groups of data-centric, client-centric and hybrid models. Each of which is then grouped into three subcategories of traditional, extended, and novel consistency models. Consequently, the concepts and procedures are expressed in mathematical terms, which are introduced in order to present our models' behavior without implementation. Moreover, we have surveyed different aspects of challenges with respect to the consistency i.e., availability, scalability, security, fault tolerance, latency, violation, and staleness, out of which the two latter i.e. violation and staleness, play the most pivotal roles in terms of consistency and trade-off balancing. Finally, the contribution extent of each of the consistency models and the growing need for them in distributed systems are investigated.
A Blockchain-based Self-tallying Voting Scheme in Decentralized IoT

The Internet of Things (IoT) is experiencing explosive growth and has gained extensive attention from academia and industry in recent years. Most of the existing IoT infrastructures are centralized, in which the presence of a cloud server is mandatory. However, centralized frameworks suffer from the issues of unscalability and single-point-of-failure. Consequently, decentralized IoT has been proposed by taking advantage of the emerging technology of Blockchain. Voting systems are widely adopted in IoT, such as a leader election in wireless sensor networks. Self-tallying voting systems are alternatives to traditional centralized voting systems in decentralized IoT since the traditional ones are not suitable for such scenarios. Unfortunately, self-tallying voting systems inherently suffer from fairness issues, such as adaptive and abortive issues caused by malicious voters. In this paper, we introduce a framework of self-tallying systems in decentralized IoT based on Blockchain. We propose a concrete construction and prove the proposed system satisfies all the security requirements including fairness, dispute-freeness and maximal ballot secrecy. The implementations on mobile phones demonstrate the practicability of our system.
Prediction of Dashed Café Wall illusion by the Classical Receptive Field Model

The Café Wall illusion is one of a class of tilt illusions where lines that are parallel appear to be tilted. We demonstrate that a simple Differences of Gaussian model provides an explanatory mechanism for the illusory tilt perceived in a family of Café Wall illusion generalizes to the dashed versions of Café Wall. Our explanation models the visual mechanisms in low-level stages and the lateral inhibition of simple cells that can reveal tilt cues in Geometrical distortion illusions such as Tile illusions particularly Café Wall illusions. For this, we simulate the activations of the retinal/cortical simple cells in responses to these patterns based on a Classical Receptive Field (CRF) model (referred to as Vis-CRF) to explain tilt effects in these illusions. Previously, it was assumed that all these visual experiences of tilt arise from the orientation selectivity properties described for more complex cortical cells. An estimation of an overall tilt angle perceived in these illusions is based on the integration of the local tilts detected by simple cells which is presumed to be a key mechanism utilized by the complex cells to create our final perception of tilt.
MOTS: Multi-Object Tracking and Segmentation

This paper extends the popular task of multi-object tracking to multi-object tracking and segmentation (MOTS). Towards this goal, we create dense pixel-level annotations for two existing tracking datasets using a semi-automatic annotation procedure. Our new annotations comprise 65,213 pixel masks for 977 distinct objects (cars and pedestrians) in 10,870 video frames. For evaluation, we extend existing multi-object tracking metrics to this new task. Moreover, we propose a new baseline method which jointly addresses detection, tracking, and segmentation with a single convolutional network. We demonstrate the value of our datasets by achieving improvements in performance when training on MOTS annotations. We believe that our datasets, metrics and baseline will become a valuable resource towards developing multi-object tracking approaches that go beyond 2D bounding boxes. We make our annotations, code, and models available at https://www.vision. rwth-aachen.de/page/mots.
Efficient Power Theft Detection for Residential Consumers Using Mean Shift Data Mining Knowledge Discovery Process

Energy theft constitutes an issue of great importance for electricity operators. The attempt to detect and reduce non-technical losses is a challenging task due to insufficient inspection methods. With the evolution of advanced metering infrastructure (AMI) in smart grids, a more complicated status quo in energy theft has emerged and many new technologies are being adopted to solve the problem. In order to identify illegal residential consumers, a computational method of analyzing and identifying electricity consumption patterns of consumers based on data mining techniques has been presented. Combining principal component analysis (PCA) with mean shift algorithm for different power theft scenarios, we can now cope with the power theft detection problem sufficiently. The overall research has shown encouraging results in residential consumers power theft detection that will help utilities to improve the reliability, security and operation of power network.
Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning

We propose a unified mechanism for achieving coordination and communication in Multi-Agent Reinforcement Learning (MARL), through rewarding agents for having causal influence over other agents' actions. Causal influence is assessed using counterfactual reasoning. At each timestep, an agent simulates alternate actions that it could have taken, and computes their effect on the behavior of other agents. Actions that lead to bigger changes in other agents' behavior are considered influential and are rewarded. We show that this is equivalent to rewarding agents for having high mutual information between their actions. Empirical results demonstrate that influence leads to enhanced coordination and communication in challenging social dilemma environments, dramatically increasing the learning curves of the deep RL agents, and leading to more meaningful learned communication protocols. The influence rewards for all agents can be computed in a decentralized way by enabling agents to learn a model of other agents using deep neural networks. In contrast, key previous works on emergent communication in the MARL setting were unable to learn diverse policies in a decentralized manner and had to resort to centralized training. Consequently, the influence reward opens up a window of new opportunities for research in this area.
Forward-backward-forward methods with variance reduction for stochastic variational inequalities

We develop a new stochastic algorithm with variance reduction for solving pseudo-monotone stochastic variational inequalities. Our method builds on Tseng's forward-backward-forward (FBF) algorithm, which is known in the deterministic literature to be a valuable alternative to Korpelevich's extragradient method when solving variational inequalities over a convex and closed set governed by pseudo-monotone, Lipschitz continuous operators. The main computational advantage of Tseng's algorithm is that it relies only on a single projection step and two independent queries of a stochastic oracle. Our algorithm incorporates a variance reduction mechanism and leads to almost sure (a.s.) convergence to an optimal solution. To the best of our knowledge, this is the first stochastic look-ahead algorithm achieving this by using only a single projection at each iteration.
Machine Learning With Feature Selection Using Principal Component Analysis for Malware Detection: A Case Study

Cyber security threats have been growing significantly in both volume and sophistication over the past decade. This poses great challenges to malware detection without considerable automation. In this paper, we have proposed a novel approach by extending our recently suggested artificial neural network (ANN) based model with feature selection using the principal component analysis (PCA) technique for malware detection. The effectiveness of the approach has been successfully demonstrated with the application in PDF malware detection. A varying number of principal components is examined in the comparative study. Our evaluation shows that the model with PCA can significantly reduce feature redundancy and learning time with minimum impact on data information loss, as confirmed by both training and testing results based on around 105,000 real-world PDF documents. Of the evaluated models using PCA, the model with 32 principal feature components exhibits very similar training accuracy to the model using the 48 original features, resulting in around 33% dimensionality reduction and 22% less learning time. The testing results further confirm the effectiveness and show that the model is able to achieve 93.17% true positive rate (TPR) while maintaining the same low false positive rate (FPR) of 0.08% as the case when no feature selection is applied, which significantly outperforms all evaluated seven well known commercial antivirus (AV) scanners of which the best scanner only has a TPR of 84.53%.
Vcash: A Novel Reputation Framework for Identifying Denial of Traffic Service in Internet of Connected Vehicles

Trust management of the Internet of Connected Vehicles has been a hot topic in recent years with the rapid development of UGV technologies. However, existing resolutions based on trustworthiness verification among vehicles make the traffic event transmission quite inefficient. In this article, we assume that the deployed roadside units (RSUs) can provide efficient communication between any pair of RSU and vehicle and propose vehicle cash (Vcash), a reputation framework for identifying denial of traffic service, to resolve the trustworthiness problem in the application level of the Internet of Connected Vehicles. In our reputation framework, every vehicle communicates with the RSU directly for traffic event verification, and spreads verified traffic event notification. We borrow the idea of market trading, and set up trading rules to restrict the malicious vehicle’s spread of false message, and to encourage vehicles to contribute to the traffic event monitoring and verification. To evaluate the effectiveness of our reputation framework, we conduct a simulation experimental. Our experiment results indicate that our proposal manages to avoid bogus event spread, and a vehicle in our framework has to contribute to the traffic event detection to normally employ the traffic service.
IMPACT: Interval-based Multi-pass Proteomic Alignment with Constant Traceback

Darwin is a genomics co-processor that achieved a 15000x acceleration on long read assembly through innovative hardware and algorithm co-design. Darwins algorithms and hardware implementation were specifically designed for DNA analysis pipelines. This paper analyzes the feasibility of applying Darwins algorithms to the problem of protein sequence alignment. In addition to a behavioral analysis of Darwin when aligning proteins, we propose an algorithmic improvement to Darwins alignment algorithm, GACT, in the form of a multi-pass variant that increases its accuracy on protein sequence alignment. Concretely, our proposed multi-pass variant of GACT achieves on average 14\% better alignment scores.
An Algorithm Unrolling Approach to Deep Blind Image Deblurring

Blind image deblurring remains a topic of enduring interest. Learning based approaches, especially those that employ neural networks have emerged to complement traditional model based methods and in many cases achieve vastly enhanced performance. That said, neural network approaches are generally empirically designed and the underlying structures are difficult to interpret. In recent years, a promising technique called algorithm unrolling has been developed that has helped connect iterative algorithms such as those for sparse coding to neural network architectures. However, such connections have not been made yet for blind image deblurring. In this paper, we propose a neural network architecture based on this idea. We first present an iterative algorithm that may be considered as a generalization of the traditional total-variation regularization method in the gradient domain. We then unroll the algorithm to construct a neural network for image deblurring which we refer to as Deep Unrolling for Blind Deblurring (DUBLID). Key algorithm parameters are learned with the help of training images. Our proposed deep network DUBLID achieves significant practical performance gains while enjoying interpretability at the same time. Extensive experimental results show that DUBLID outperforms many state-of-the-art methods and in addition is computationally faster.
Verifiable Smart Contract Portability

With the advent of blockchain technologies, the idea of decentralized applications has gained traction. Smart contracts permit the implementation of application logic to foster distributed systems that are capable of removing intermediaries. Hereby, lock in effects originating from isolated data storage and central authorities are mitigated. Yet, smart contracts deployed to a ledger generate dependencies on the underlying blockchain. Over time, requirements regarding contract execution may detach from the utilized blockchain due to contradicting incentives and security or performance issues. To avoid a novel form of lock in effect towards a host blockchain, we introduce a concept for smart contract portability that permits any user to migrate contract logic and state between blockchains in a flexible and verifiable manner. As the Ethereum Virtual Machine (EVM) is supported by a multitude of blockchain implementations, it poses a common execution environment for smart contracts. We provide a toolbox that facilitates smart contract portability between EVM-compatible blockchains without trust requirements in the entity executing the migration process. To prove the concept’s soundness, we transfer token contracts based on the ERC20 standard as well as applications containing dependencies to other smart contracts. Our evaluation shows the validity of ported applications including their current states.
WarpFlow: Exploring Petabytes of Space-Time Data

WarpFlow is a fast, interactive data querying and processing system with a focus on petabyte-scale spatiotemporal datasets and Tesseract queries. With the rapid growth in smartphones and mobile navigation services, we now have an opportunity to radically improve urban mobility and reduce friction in how people and packages move globally every minute-mile, with data. WarpFlow speeds up three key metrics for data engineers working on such datasets -- time-to-first-result, time-to-full-scale-result, and time-to-trained-model for machine learning.
Beyond horizon graphs: space efficient time series visualization with composite visual mapping

Restricted screen space is a limit to visualization of time series regardless of the medium. To address this challenge, we introduce new space-efficient visual designs for time series based on an approach similar to the well established Horizon Graph, namely "composite" visual mapping. In this approach, each data attribute is decomposed into two components and then each component is mapped onto a separate visual channel. Our visual designs consist in different combinations ofgeometric and optical visual channels. We compare our propositions with Horizon Graph across different chart heights and we measure accuracy and speed of users in a discrimination and estimation task. Our results show that although Horizon Graph perform best at larger chart heights, our propositions demonstrate same levels of accuracy in small chart heights. Moreover, at least one of our propositions has significant advantage over Horizon Graphs in terms of speed. Based on our findings, we propose design guidelines for using composite visual mapping and combinations of optical visual channels in limited vertical screen resolution settings.
An Information Gain Ratio based Discovery of User Similarity in Sina Blog Community

Researchers have already found that the interaction data on Blogs bulletin board and social network are the reflection of human and human society. It can be used to build a user profile and tell the researchers what a network community is composed of. In previous studies we have noticed that users who are friends in real life tend to interact with each other with high frequency and the characteristics of their blogs shares more similarity, compared with that of strangers. It brings the idea that can we predict the behavior as well as personal value of a total new comer by the friends around him/her? In this paper we proposed a method to classify the sub-community groups into even smaller and more alike ones by applying information gain ratio based decision tree to users' tags as well as behavior patterns. First we recognize the social network groups by community discover algorithm with Sina blog-oriented community definition and then further analyze the topology of the groups by frequency subtree mining to discover the real closely collected small groups. Then we collect the users' tags by text mining and assign every user a 10 words long tag set. Finally we use the information gain ratio decision tree to catalogue users into even smaller groups with greater similarity. Results show that our method has a higher accuracy.
A model of exploratory search for evaluating its systems & applications

Current evaluation methods of exploratory search systems are still incomplete as they are not fully based on a suitable model of the exploratory search process: as such they cannot be used to determine if they effectively support exploratory search behaviors and tasks. Aiming to elaborate evaluation methods based on an appropriate model of exploratory search, we propose in this paper a model of the exploratory search process compliant with the acknowledged exploratory search characteristics, and we present a first evaluation of this model.
On Continuous Speech Recognition of Indian English

Indian English (IE) derives from British English, but they differ in many aspects. It varies from speech to word, and is a typical variety of English. At present, it is hard to see researches on continuous speech recognition (CSR) of lesser-known English varieties such as Indian English. Indian English has developed some distinctive features of its own with phonological features being the most remarkable. Compared with British English (or American English) which has a large amount of annotated speech data, IE is a relatively low-resourced language. What's more, the performance of existing English CSR systems perform unsatisfactorily when dealing with IE spontaneous conversations. To date, CSR of low-resourced languages (minority languages, dialects and varieties of a certain language with relatively few annotated speech data) performs unsatisfactorily. This paper takes Indian English for example, focuses on CSR of IE under low-resourced conditions, extracts acoustic features and trains acoustic models with different methods, explores the effective method in recognizing low-resourced languages. Firstly, we employ MFCC and PLP to extract features respectively and choose the GMM-HMM acoustic model to build the baseline system. Secondly, it comes up with another 6 acoustic models while using BLSTM-RNN and TDNN neural network algorithms. Then the models are tested on the test set and results are analyzed in detail. Thirdly, we use an acoustic model of American English trained by a large number of annotated data which is then transferred to the optimization of the acoustic model of Indian English. Finally, the decoding test is carried out on the test set and generates the experimental analysis. According to the experimental results, we find that the performances of the two neural network recognition systems are both improved with transfer learning technology while the BLSTM-RNN is more remarkable.