OpenRaiser/Intern-Atlas huggingface dataset:
A Comprehensive guide to Bayesian Convolutional Neural Network with Variational Inference
Venue: arXiv cs.LG 2019
Year: 2019

Artificial Neural Networks are connectionist systems that perform a given task by learning on examples without having prior knowledge about the task. This is done by finding an optimal point estimate for the weights in every node. Generally, the network using point estimates as weights perform well with large datasets, but they fail to express uncertainty in regions with little or no data, leading to overconfident decisions. In this paper, Bayesian Convolutional Neural Network (BayesCNN) using Variational Inference is proposed, that introduces probability distribution over the weights. Furthermore, the proposed BayesCNN architecture is applied to tasks like Image Classification, Image Super-Resolution and Generative Adversarial Networks. The results are compared to point-estimates based architectures on MNIST, CIFAR-10 and CIFAR-100 datasets for Image CLassification task, on BSD300 dataset for Image Super Resolution task and on CIFAR10 dataset again for Generative Adversarial Network task. BayesCNN is based on Bayes by Backprop which derives a variational approximation to the true posterior. We, therefore, introduce the idea of applying two convolutional operations, one for the mean and one for the variance. Our proposed method not only achieves performances equivalent to frequentist inference in identical architectures but also incorporate a measurement for uncertainties and regularisation. It further eliminates the use of dropout in the model. Moreover, we predict how certain the model prediction is based on the epistemic and aleatoric uncertainties and empirically show how the uncertainty can decrease, allowing the decisions made by the network to become more deterministic as the training accuracy increases. Finally, we propose ways to prune the Bayesian architecture and to make it more computational and time effective.
Consensus Mechanism Design based on Structured Directed Acyclic Graphs
Venue: arXiv cs.DC 2019
Year: 2019

We introduce a structure for the directed acyclic graph (DAG) and a mechanism design based on that structure so that peers can reach consensus at large scale based on proof of work (PoW). We also design a mempool transaction assignment method based on the DAG structure to render negligible the probability that a transaction being processed by more than one miners. The result is a significant scale-up of the capacity without sacrificing security and decentralization.
A Security Framework for Cloud Data Storage(CDS) Based on Agent
Venue: arXiv cs.CR 2019
Year: 2019

The Cloud has become a new Information Technology(IT) model for delivering resources such as computing and storage to customers on demand, it provides both high flexibility and resources use. However we are gaining these advantages at the cost of high security threats, which presents the major brake for the migration towards Cloud Computing. Cloud Data Storage(CDS) is one of the Cloud services, it allows users to store their data in the Cloud, this service is very useful for companies and individuals, but data security remains the problem which makes customers worried about their data that reside in the Cloud. In this paper, we propose a framework of security to ensure the CDS, which is based on agents, it contains three layers: Cloud Provider layer, Customer layer and Trusted Third Party(TTP) layer.
Swarm coordination of mini-UAVs for target search using imperfect sensors
Venue: arXiv cs.MA 2019
Year: 2019

Unmanned Aerial Vehicles (UAVs) have a great potential to support search tasks in unstructured environments. Small, lightweight, low speed and agile UAVs, such as multi-rotors platforms can incorporate many kinds of sensors that are suitable for detecting object of interests in cluttered outdoor areas. However, due to their limited endurance, moderate computing power, and imperfect sensing, mini-UAVs should be into groups using swarm coordination algorithms to perform tasks in a scalable, reliable and robust manner. In this paper a biologically-inspired mechanisms is adopted to coordinate drones performing target search with imperfect sensors. In essence, coordination can be achieved by combining stigmergic and flocking behaviors. Stigmergy occurs when a drone releases digital pheromone upon sensing of a potential target. Such pheromones can be aggregated and diffused between flocking drones, creating a spatiotemporal attractive potential field. Flocking occurs, as an emergent effect of alignment, separation and cohesion, where drones self organise with similar heading and dynamic arrangement as a group. The emergent coordination of drones relies on the alignment of stigmergy and flocking strategies. This paper reports on the design of the novel swarming algorithm, reviewing different strategies and measuring their performance on a number of synthetic and real-world scenarios.
Report of the Third Global Experimentation for Future Internet (GEFI 2018) Workshop
Venue: arXiv cs.NI 2019
Year: 2019

The third Global Experimentation for Future Internet (GEFI 2018) workshop was held October 25-26, 2018 in Tokyo, Japan, hosted by the University of Tokyo. A total of forty-four participants attended, representing Belgium, Brazil, China, Denmark, France, Ireland, Japan, the Republic of Korea, and the United States. The workshop employed a mixed format of presentations and open group discussions to advance multi-national coordination and interoperation of research infrastructure for advanced networking and computer science research. Major topic areas included: softwareization and virtualization of radios and networks; testbed support for networking experiments; EdgeNet; a federated testbed of elastic optical networks; and reproducibility in experimentation. Workshop goals included both the formulation of specific new research collaborations and strategies for coordination and interoperation of research testbeds. Workshop outcomes include a variety of new and ongoing collaborative efforts, ranging from an agreement to pursue the development of optical "white boxes" in support of elastic optical testbeds to the identification of strategies for effective use of open-source software and hardware platforms in future research infrastructure.
Polynomial-time Capacity Calculation and Scheduling for Half-Duplex 1-2-1 Networks
Venue: arXiv cs.IT 2019
Year: 2019

This paper studies the 1-2-1 half-duplex network model, where two half-duplex nodes can communicate only if they point `beams' at each other; otherwise, no signal can be exchanged or interference can be generated. The main result of this paper is the design of two polynomial-time algorithms that: (i) compute the approximate capacity of the 1-2-1 half-duplex network and, (ii) find the network schedule optimal for the approximate capacity. The paper starts by expressing the approximate capacity as a linear program with an exponential number of constraints. A core technical component consists of building a polynomial-time separation oracle for this linear program, by using algorithmic tools such as perfect matching polytopes and Gomory-Hu trees.
DASPS: A Database for Anxious States based on a Psychological Stimulation
Venue: arXiv cs.CV 2019
Year: 2019

Anxiety affects human capabilities and behavior as much as it affects productivity and quality of life. It can be considered as the main cause of depression and suicide. Anxious states are easily detectable by humans due to their acquired cognition, humans interpret the interlocutor's tone of speech, gesture, facial expressions and recognize their mental state. There is a need for non-invasive reliable techniques that performs the complex task of anxiety detection. In this paper, we present DASPS database containing recorded Electroencephalogram (EEG) signals of 23 participants during anxiety elicitation by means of face-to-face psychological stimuli. EEG signals were captured with Emotiv Epoc headset as it's a wireless wearable low-cost equipment. In our study, we investigate the impact of different parameters, notably: trial duration, feature type, feature combination and anxiety levels number. Our findings showed that anxiety is well elicited in 1 second. For instance, stacked sparse autoencoder with different type of features achieves 83.50% and 74.60% for 2 and 4 anxiety levels detection, respectively. The presented results prove the benefits of the use of a low-cost EEG headset instead of medical non-wireless devices and create a starting point for new researches in the field of anxiety detection.
Spectral Approach to Verifying Non-linear Arithmetic Circuits
Venue: arXiv cs.SC 2019
Year: 2019

This paper presents a fast and effective computer algebraic method for analyzing and verifying non-linear integer arithmetic circuits using a novel algebraic spectral model. It introduces a concept of algebraic spectrum, a numerical form of polynomial expression; it uses the distribution of coefficients of the monomials to determine the type of arithmetic function under verification. In contrast to previous works, the proof of functional correctness is achieved by computing an algebraic spectrum combined with a local rewriting of word-level polynomials. The speedup is achieved by propagating coefficients through the circuit using And-Inverter Graph (AIG) datastructure. The effectiveness of the method is demonstrated with experiments including standard and Booth multipliers, and other synthesized non-linear arithmetic circuits up to 1024 bits containing over 12 million gates.
PFML-based Semantic BCI Agent for Game of Go Learning and Prediction
Venue: arXiv cs.AI 2019
Year: 2019

This paper presents a semantic brain computer interface (BCI) agent with particle swarm optimization (PSO) based on a Fuzzy Markup Language (FML) for Go learning and prediction applications. Additionally, we also establish an Open Go Darkforest (OGD) cloud platform with Facebook AI research (FAIR) open source Darkforest and ELF OpenGo AI bots. The Japanese robot Palro will simultaneously predict the move advantage in the board game Go to the Go players for reference or learning. The proposed semantic BCI agent operates efficiently by the human-based BCI data from their brain waves and machine-based game data from the prediction of the OGD cloud platform for optimizing the parameters between humans and machines. Experimental results show that the proposed human and smart machine co-learning mechanism performs favorably. We hope to provide students with a better online learning environment, combining different kinds of handheld devices, robots, or computer equipment, to achieve a desired and intellectual learning goal in the future.
Popular SQL Server Database Encryption Choices
Venue: arXiv cs.DB 2019
Year: 2019

This article gives an overview of different database encryption choices in SQL Server. Which one works best in which situation. In today's world Data is more crucial than the expensive hardware cost. No one wants their personal data to be comprised. Same for business houses as well and they also do not want their data to be inappropriately handled to go out of the business. To help protect the public rights and safety, recently this year, the European Union had come up with strict rules and regulation of GDPR (General Data Protection Regulation).
Challenges in Covert Wireless Communications with Active Warden on AWGN channels
Venue: arXiv cs.CR 2019
Year: 2019

Covert wireless communication or low probability of detection (LPD) communication that employs the noise or jamming signals as the cover to hide user's information can prevent a warden Willie from discovering user's transmission attempts. Previous work on this problem has typically assumed that the warden is static and has only one antenna, often neglecting an active warden who can dynamically adjust his/her location to make better statistic tests. In this paper, we analyze the effect of an active warden in covert wireless communications on AWGN channels and find that, having gathered samples at different places, the warden can easily detect Alice's transmission behavior via a trend test, and the square root law is invalid in this scenario. Furthermore, a more powerful warden with multiple antennas is harder to be deceived, and Willie's detection time can be greatly shortened.
An Optimal Linear Error Correcting Delivery Scheme for Coded Caching with Shared Caches
Venue: arXiv cs.IT 2019
Year: 2019

Classical coded caching setting avails each user to have one dedicated cache. This is generalized to a more general shared cache scheme and the exact expression for the worst case rate was derived in [E. Parrinello, A. Unsal, P. Elia, "Fundamental Limits of Caching in Heterogeneous Networks with Uncoded Prefetching," available on arXiv:1811.06247 [cs.IT], Nov. 2018]. For this case, an optimal linear error correcting delivery scheme is proposed and an expression for the peak rate is established for the same. Furthermore, a new delivery scheme is proposed, which gives an improved rate for the case when the demands are not distinct.
Scheduling in distributed systems: A cloud computing perspective
Venue: arXiv cs.DC 2019
Year: 2019

Scheduling is essentially a decision-making process that enables resource sharing among a number of activities by determining their execution order on the set of available resources. The emergence of distributed systems brought new challenges on scheduling in computer systems, including clusters, grids, and more recently clouds. On the other hand, the plethora of research makes it hard for both newcomers researchers to understand the relationship among different scheduling problems and strategies proposed in the literature, which hampers the identification of new and relevant research avenues. In this paper we introduce a classification of the scheduling problem in distributed systems by presenting a taxonomy that incorporates recent developments, especially those in cloud computing. We review the scheduling literature to corroborate the taxonomy and analyze the interest in different branches of the proposed taxonomy. Finally, we identify relevant future directions in scheduling for distributed systems.
Joint Power Splitting and Relay Selection in Energy-Harvesting Communications for IoT Networks
Venue: arXiv cs.IT 2019
Year: 2019

In this paper, we consider an energy-harvesting (EH) relay system consisting of a source, a destination, and multiple EH decode-and-forward (DF) relays that can harvest the energy from their received radio signals. A power-splitting ratio is employed at an EH DF relay to characterize a trade-off between an energy used for decoding the source signal received at the relay and the remaining energy harvested for retransmitting the decode outcome. We propose an optimal power splitting and relay selection (OPS-RS) framework and also consider the conventional equal power splitting and relay selection (EPS-RS) for comparison purposes. We derive closed-form expressions of outage probability for both the OPS-RS and EPS-RS schemes and characterize their diversity gains through an asymptotic outage analysis in the high signal-to-noise ratio region. We further examine an extension of our OPS-RS framework to an energy-harvesting battery (EHB) enabled cooperative relay scenario, where the EH relays are equipped with batteries used to store their harvested energies. We propose an amplify-and-forward (AF) based EHB-OPS-RS and a DF based EHB-OPS-RS schemes for AF and DF relay networks, respectively. Numerical results show that the proposed OPS-RS always outperforms the EPS-RS in terms of outage probability. Moreover, the outage probabilities of AF and DF based EHB-OPS-RS schemes are much smaller than that of OPS-RS and EPS-RS methods, demonstrating the benefit of exploiting the batteries in EH relays. Additionally, the DF based EHB-OPS-RS substantially outperforms the AF based EHB-OPS-RS and such an outage advantage becomes more significant, as the number of EH relays increases.
Risk of Cascading Blackouts Given Correlated Component Outages
Venue: arXiv cs.CE 2019
Year: 2019

Cascading blackouts typically occur when nearly simultaneous outages occur in k out of N components in a power system, triggering subsequent failures that propagate through the network and cause significant load shedding. While large cascades are rare, their impact can be catastrophic, so quantifying their risk is important for grid planning and operation. A common assumption in previous approaches to quantifying such risk is that the $k$ initiating component outages are statistically independent events. However, when triggered by a common exogenous cause, initiating outages may actually be correlated. Here, copula analysis is used to quantify the impact of correlation of initiating outages on the risk of cascading failure. The method is demonstrated on two test cases; a 2383-bus model of the Polish grid under varying load conditions and a synthetic 10,000-bus model based on the geography of the Western US. The large size of the Western US test case required development of new approaches for bounding an estimate of the total number of N-3 blackout-causing contingencies. The results suggest that both risk of cascading failure, and the relative contribution of higher order contingencies, increase as a function of spatial correlation in component failures.
A Framework for Analyzing Fog-Cloud Computing Cooperation Applied to Information Processing of UAVs
Venue: arXiv cs.RO 2019
Year: 2019

Unmanned aerial vehicles (UAVs) are a relatively new technology. Their application can often involve complex and unseen problems. For instance, they can work in a cooperative-based environment under the supervision of a ground station to speed up critical decision-making processes. However, the amount of information exchanged among the aircraft and ground station is limited by high distances, low bandwidth size, restricted processing capability, and energy constraints. These drawbacks restrain large-scale operations such as large area inspections. New distributed state-of-the-art processing architectures, such as fog computing, can improve latency, scalability, and efficiency to meet time constraints via data acquisition, processing, and storage at different levels. Under these amendments, this research work proposes a mathematical model to analyze distribution-based UAVs topologies and a fog-cloud computing framework for large-scale mission and search operations. The tests have successfully predicted latency and other operational constraints, allowing the analysis of fog-computing advantages over traditional cloud-computing architectures.
Context Aware Machine Learning
Venue: arXiv cs.LG 2019
Year: 2019

We propose a principle for exploring context in machine learning models. Starting with a simple assumption that each observation may or may not depend on its context, a conditional probability distribution is decomposed into two parts: context-free and context-sensitive. Then by employing the log-linear word production model for relating random variables to their embedding space representation and making use of the convexity of natural exponential function, we show that the embedding of an observation can also be decomposed into a weighted sum of two vectors, representing its context-free and context-sensitive parts, respectively. This simple treatment of context provides a unified view of many existing deep learning models, leading to revisions of these models able to achieve significant performance boost. Specifically, our upgraded version of a recent sentence embedding model not only outperforms the original one by a large margin, but also leads to a new, principled approach for compositing the embeddings of bag-of-words features, as well as a new architecture for modeling attention in deep neural networks. More surprisingly, our new principle provides a novel understanding of the gates and equations defined by the long short term memory model, which also leads to a new model that is able to converge significantly faster and achieve much lower prediction errors. Furthermore, our principle also inspires a new type of generic neural network layer that better resembles real biological neurons than the traditional linear mapping plus nonlinear activation based architecture. Its multi-layer extension provides a new principle for deep neural networks which subsumes residual network (ResNet) as its special case, and its extension to convolutional neutral network model accounts for irrelevant input (e.g., background in an image) in addition to filtering.
From Plots to Endings: A Reinforced Pointer Generator for Story Ending Generation
Venue: arXiv cs.CL 2019
Year: 2019

We introduce a new task named Story Ending Generation (SEG), whic-h aims at generating a coherent story ending from a sequence of story plot. Wepropose a framework consisting of a Generator and a Reward Manager for thistask. The Generator follows the pointer-generator network with coverage mech-anism to deal with out-of-vocabulary (OOV) and repetitive words. Moreover, amixed loss method is introduced to enable the Generator to produce story endingsof high semantic relevance with story plots. In the Reward Manager, the rewardis computed to fine-tune the Generator with policy-gradient reinforcement learn-ing (PGRL). We conduct experiments on the recently-introduced ROCStoriesCorpus. We evaluate our model in both automatic evaluation and human evalua-tion. Experimental results show that our model exceeds the sequence-to-sequencebaseline model by 15.75% and 13.57% in terms of CIDEr and consistency scorerespectively.
Dialog System Technology Challenge 7
Venue: arXiv cs.CL 2019
Year: 2019

This paper introduces the Seventh Dialog System Technology Challenges (DSTC), which use shared datasets to explore the problem of building dialog systems. Recently, end-to-end dialog modeling approaches have been applied to various dialog tasks. The seventh DSTC (DSTC7) focuses on developing technologies related to end-to-end dialog systems for (1) sentence selection, (2) sentence generation and (3) audio visual scene aware dialog. This paper summarizes the overall setup and results of DSTC7, including detailed descriptions of the different tracks and provided datasets. We also describe overall trends in the submitted systems and the key results. Each track introduced new datasets and participants achieved impressive results using state-of-the-art end-to-end technologies.
Color Recognition for Rubik's Cube Robot
Venue: arXiv cs.CV 2019
Year: 2019

In this paper, we proposed three methods to solve color recognition of Rubik's cube, which includes one offline method and two online methods. Scatter balance \& extreme learning machine (SB-ELM), a offline method, is proposed to illustrate the efficiency of training based method. We also point out the conception of color drifting which indicates offline methods are always ineffectiveness and can not work well in continuous change circumstance. By contrast, dynamic weight label propagation is proposed for labeling blocks color by known center blocks color of Rubik's cube. Furthermore, weak label hierarchic propagation, another online method, is also proposed for unknown all color information but only utilizes weak label of center block in color recognition. We finally design a Rubik's cube robot and construct a dataset to illustrate the efficiency and effectiveness of our online methods and to indicate the ineffectiveness of offline method by color drifting in our dataset.