OpenRaiser/Intern-Atlas huggingface dataset:
Security Analysis of Big Data on Internet of Things
Venue: arXiv cs.CR 2018
Year: 2018

The volume of data exchange in this network is ascending, by expansing of internet of things and its increasing familiarity in recent years. By increasing of requests for joining to the network and taking advantage of its services, necessity to maintain the privacy and security is felt more than ever and the whole thing has changed into a challenge. Keeping the security of the users in small networks seems to be simpler and the threats more predictable. As the users of the network which are sensors, the volume of data increases as well. As a result the routers of the network and network servers has become a seriously challenging task to control and maintain the security for this volume of data. We analyze these challenges in this paper. This paper aims to maintain the security of the users and present procedures to dominate the problems and express the strategies to overcome them. Some methods such as using both encryption function and secure protocol are proposed. Other method to overcome over big data challenges is smart design of communication protocol. Finally, we point out some future challenges of big-data on the internet of thing network.
Analysis and Improvement of a Lightweight Anonymous Authentication Protocol for Mobile Pay-TV Systems (Full text)
Venue: arXiv cs.CR 2018
Year: 2018

For many years, the pay-TV system has attracted a lot of users. Users have recently expressed the desire to use mobile TV or mobile payment via anonymous protocols. The mobile users have also received their services over cellular communications networks. Each mobile device receives services from each head end systems. With increasing numbers of users and the expansion of Internet, user's privacy has become crucial important. When a device leaves the head end system's range, it must receive services from another head end system. In this paper, we review Chen et al's scheme and we highlight some weaknesses, including privilege insider attack and user traceability attack. Finally, we alleviate the scheme and analyze the alleviated scheme using both heuristic and formal methods.
Power of Ensemble Diversity and Randomization for Energy Aggregation
Venue: arXiv cs.SY 2018
Year: 2018

We study an ensemble of diverse (inhomogeneous) thermostatically controlled loads aggregated to provide the demand response (DR) services in a district-level energy system. Each load in the ensemble is assumed to be equipped with a random number generator switching heating/cooling on or off with a Poisson rate, $r$, when the load leaves the comfort zone. Ensemble diversity is modeled through inhomogeneity/disorder in the deterministic dynamics of loads. Approached from the standpoint of statistical physics, the ensemble represents a non-equilibrium system driven away from its natural steady state by the DR. The ability of the ensemble to recover by mixing faster to the steady state after its DR's use is advantageous. The trade-off between the level of the aggregator's control, commanding the devices to lower the rate $r$, and the phase-space-oscillatory deterministic dynamics is analyzed. We discover that there exists a critical value, $r_c$, corresponding to both the most efficient mixing and the bifurcation point where the ensemble transitions from the oscillatory relaxation at $r>r_c$ to the pure relaxation at $r<r_c$. Then, we study the effect of the load diversity, investigating four different disorder probability distributions (DPDs) ranging from the case of the Gaussian DPD to the case of the uniform with finite support DPD. Demonstrating resemblance to the similar question of the effectiveness of Landau damping in plasma physics, we show that stronger regularity of the DPD around its maximum results in faster mixing. Our theoretical analysis is supported by extensive numerical validation, which also allows us to access the effect of the ensemble's finite size.
Cycle-of-Learning for Autonomous Systems from Human Interaction
Venue: arXiv cs.AI 2018
Year: 2018

We discuss different types of human-robot interaction paradigms in the context of training end-to-end reinforcement learning algorithms. We provide a taxonomy to categorize the types of human interaction and present our Cycle-of-Learning framework for autonomous systems that combines different human-interaction modalities with reinforcement learning. Two key concepts provided by our Cycle-of-Learning framework are how it handles the integration of the different human-interaction modalities (demonstration, intervention, and evaluation) and how to define the switching criteria between them.
Wavelet Video Coding Algorithm Based on Energy Weighted Significance Probability Balancing Tree
Venue: arXiv cs.MM 2018
Year: 2018

This work presents a 3-D wavelet video coding algorithm. By analyzing the contribution of each biorthogonal wavelet basis to reconstructed signal's energy, we weight each wavelet subband according to its basis energy. Based on distribution of weighted coefficients, we further discuss a 3-D wavelet tree structure named \textbf{significance probability balancing tree}, which places the coefficients with similar probabilities of being significant on the same layer. It is implemented by using hybrid spatial orientation tree and temporal-domain block tree. Subsequently, a novel 3-D wavelet video coding algorithm is proposed based on the energy-weighted significance probability balancing tree. Experimental results illustrate that our algorithm always achieves good reconstruction quality for different classes of video sequences. Compared with asymmetric 3-D orientation tree, the average peak signal-to-noise ratio (PSNR) gain of our algorithm are 1.24dB, 2.54dB and 2.57dB for luminance (Y) and chrominance (U,V) components, respectively. Compared with temporal-spatial orientation tree algorithm, our algorithm gains 0.38dB, 2.92dB and 2.39dB higher PSNR separately for Y, U, and V components. In addition, the proposed algorithm requires lower computation cost than those of the above two algorithms.
Implications of Integrated CPU-GPU Processors on Thermal and Power Management Techniques
Venue: arXiv cs.AR 2018
Year: 2018

Heterogeneous processors with architecturally different cores (CPU and GPU) integrated on the same die lead to new challenges and opportunities for thermal and power management techniques because of shared thermal/power budgets between these cores. In this paper, we show that new parallel programming paradigms (e.g., OpenCL) for CPU-GPU processors create a tighter coupling between the workload, the thermal/power management unit and the operating system. Using detailed thermal and power maps of the die from infrared imaging, we demonstrate that in contrast to traditional multi-core CPUs, heterogeneous processors exhibit higher coupled behavior for dynamic voltage and frequency scaling and workload scheduling, in terms of their effect on performance, power, and temperature. Further, we show that by taking the differences in core architectures and relative proximity of different computing cores on the die into consideration, better scheduling schemes could be implemented to reduce both the power density and peak temperature of the die. The findings presented in the paper can be used to improve thermal and power efficiency of heterogeneous CPU-GPU processors.
Revisiting Relative Indicators and Provisional Truths
Venue: arXiv cs.DL 2018
Year: 2018

Following discussions in 2010 and 2011, scientometric evaluators have increasingly abandoned relative indicators in favor of comparing observed with expected citation ratios. The latter method provides parameters with error values allowing for the statistical testing of differences in citation scores. A further step would be to proceed to non-parametric statistics (e.g., the top-10%) given the extreme skewness (non-normality) of the citation distributions. In response to a plea for returning to relative indicators in the previous issue of this newsletter, we argue in favor of further progress in the development of citation impact indicators.
Fair Marketplace for Secure Outsourced Computations
Venue: arXiv cs.CR 2018
Year: 2018

The cloud computing paradigm offers clients ubiquitous and on demand access to a shared pool of computing resources, enabling the clients to provision scalable services with minimal management effort. Such a pool of resources, however, is typically owned and controlled by a single service provider, making it a single-point-of-failure. This paper presents Kosto - a framework that provisions a fair marketplace for secure outsourced computations, wherein the pool of computing resources aggregates resources offered by a large cohort of independent compute nodes. Kosto protects the confidentiality of clients' inputs as well as the integrity of the outsourced computations and their results using trusted hardware's enclave execution, in particular Intel SGX. Furthermore, Kosto warrants fair exchanges between the clients' payments for the execution of an outsourced computations and the compute nodes' work in servicing the clients' requests. Empirical evaluation on the prototype implementation of Kosto shows that performance overhead incurred by enclave execution is as small as 3% for computation-intensive operations, and 1.5x for IO-intensive operations.
PCR-Pro: 3D Sparse and Different Scale Point Clouds Registration and Robust Estimation of Information Matrix For Pose Graph SLAM
Venue: arXiv cs.RO 2018
Year: 2018

For both indoor and outdoor environments, we propose an efficient and novel method for different scales and sparse 3D point clouds registration that cannot be handled by the current popular ICP approaches. Our algorithm efficiently detects the scale difference between point clouds and uses the keyframes to estimate the relative pose for calculating the scale difference. The algorithm applies a filter and computes the final transformation which coverages to a global minimum. The good estimation of transform and scale helps in the calculation of the covariance matrix using a closed form solution efficiently. This covariance between point clouds helps in the estimation of information matrix for pose-graph SLAM.
Evaluating Fuzz Testing
Venue: arXiv cs.CR 2018
Year: 2018

Fuzz testing has enjoyed great success at discovering security critical bugs in real software. Recently, researchers have devoted significant effort to devising new fuzzing techniques, strategies, and algorithms. Such new ideas are primarily evaluated experimentally so an important question is: What experimental setup is needed to produce trustworthy results? We surveyed the recent research literature and assessed the experimental evaluations carried out by 32 fuzzing papers. We found problems in every evaluation we considered. We then performed our own extensive experimental evaluation using an existing fuzzer. Our results showed that the general problems we found in existing experimental evaluations can indeed translate to actual wrong or misleading assessments. We conclude with some guidelines that we hope will help improve experimental evaluations of fuzz testing algorithms, making reported results more robust.
Identifying the sentiment styles of YouTube's vloggers
Venue: arXiv cs.CL 2018
Year: 2018

Vlogs provide a rich public source of data in a novel setting. This paper examined the continuous sentiment styles employed in 27,333 vlogs using a dynamic intra-textual approach to sentiment analysis. Using unsupervised clustering, we identified seven distinct continuous sentiment trajectories characterized by fluctuations of sentiment throughout a vlog's narrative time. We provide a taxonomy of these seven continuous sentiment styles and found that vlogs whose sentiment builds up towards a positive ending are the most prevalent in our sample. Gender was associated with preferences for different continuous sentiment trajectories. This paper discusses the findings with respect to previous work and concludes with an outlook towards possible uses of the corpus, method and findings of this paper for related areas of research.
Scalable and Efficient Virtual Memory Sharing in Heterogeneous SoCs with TLB Prefetching and MMU-Aware DMA Engine
Venue: arXiv cs.AR 2018
Year: 2018

Shared virtual memory (SVM) is key in heterogeneous systems on chip (SoCs), which combine a general-purpose host processor with a many-core accelerator, both for programmability and to avoid data duplication. However, SVM can bring a significant run time overhead when translation lookaside buffer (TLB) entries are missing. Moreover, allowing DMA burst transfers to write SVM traditionally requires buffers to absorb transfers that miss in the TLB. These buffers have to be overprovisioned for the maximum burst size, wasting precious on-chip memory, and stall all SVM accesses once they are full, hampering the scalability of parallel accelerators. In this work, we present our SVM solution that avoids the majority of TLB misses with prefetching, supports parallel burst DMA transfers without additional buffers, and can be scaled with the workload and number of parallel processors. Our solution is based on three novel concepts: To minimize the rate of TLB misses, the TLB is proactively filled by compiler-generated Prefetching Helper Threads, which use run-time information to issue timely prefetches. To reduce the latency of TLB misses, misses are handled by a variable number of parallel Miss Handling Helper Threads. To support parallel burst DMA transfers to SVM without additional buffers, we add lightweight hardware to a standard DMA engine to detect and react to TLB misses. Compared to the state of the art, our work improves accelerator performance for memory-intensive kernels by up to 4x and by up to 60% for irregular and regular memory access patterns, respectively.
Vulnerable Open Source Dependencies: Counting Those That Matter
Venue: arXiv cs.SE 2018
Year: 2018

BACKGROUND: Vulnerable dependencies are a known problem in today's open-source software ecosystems because OSS libraries are highly interconnected and developers do not always update their dependencies. AIMS: In this paper we aim to present a precise methodology, that combines the code-based analysis of patches with information on build, test, update dates, and group extracted from the very code repository, and therefore, caters to the needs of industrial practice for correct allocation of development and audit resources. METHOD: To understand the industrial impact of the proposed methodology, we considered the 200 most popular OSS Java libraries used by SAP in its own software. Our analysis included 10905 distinct GAVs (group, artifact, version) when considering all the library versions. RESULTS: We found that about 20% of the dependencies affected by a known vulnerability are not deployed, and therefore, they do not represent a danger to the analyzed library because they cannot be exploited in practice. Developers of the analyzed libraries are able to fix (and actually responsible for) 82% of the deployed vulnerable dependencies. The vast majority (81%) of vulnerable dependencies may be fixed by simply updating to a new version, while 1% of the vulnerable dependencies in our sample are halted, and therefore, potentially require a costly mitigation strategy. CONCLUSIONS: Our case study shows that the correct counting allows software development companies to receive actionable information about their library dependencies, and therefore, correctly allocate costly development and audit resources, which is spent inefficiently in case of distorted measurements.
Edge Caching for Cache Intensity under Probabilistic Delay Constraint
Venue: arXiv cs.IT 2018
Year: 2018

In order to reduce the latency of data delivery, one of techniques is to cache the popular contents at the base stations (BSs) i.e. edge caching. However, the technique of caching at edge can only reduce the backhaul delay, other techniques such as BS densification will also need to be considered to reduce the fronthaul delay. In this work, we study the trade-offs between BS densification and cache size under delay constraint at a typical user (UE). For this, we use the downlink SINR coverage probability and throughput obtained based on stochastic geometrical analysis. The network deployment of BS and cache storage is introduced as a minimization problem of the product of the BS intensity and cache size which we refer to the product of \tit{cache intensity}' under probabilistic delay constraint. We examine the cases when (i) either BS intensity or the cache size is held fixed, and (ii) when both BS intensity and the cache size are vary. For the case when both BS intensity and the cache size are variable, the problem become nonconvex and we convert into a geometric programing which we solve it analytically.
MACNet: Multi-scale Atrous Convolution Networks for Food Places Classification in Egocentric Photo-streams
Venue: arXiv cs.CV 2018
Year: 2018

First-person (wearable) camera continually captures unscripted interactions of the camera user with objects, people, and scenes reflecting his personal and relational tendencies. One of the preferences of people is their interaction with food events. The regulation of food intake and its duration has a great importance to protect against diseases. Consequently, this work aims to develop a smart model that is able to determine the recurrences of a person on food places during a day. This model is based on a deep end-to-end model for automatic food places recognition by analyzing egocentric photo-streams. In this paper, we apply multi-scale Atrous convolution networks to extract the key features related to food places of the input images. The proposed model is evaluated on an in-house private dataset called "EgoFoodPlaces". Experimental results shows promising results of food places classification recognition in egocentric photo-streams.
Modelling Langford's Problem: A Viewpoint for Search
Venue: arXiv cs.AI 2018
Year: 2018

The performance of enumerating all solutions to an instance of Langford's Problem is sensitive to the model and the search strategy. In this paper we compare the performance of a large variety of models, all derived from two base viewpoints. We empirically show that a channelled model with a static branching order on one of the viewpoints offers the best performance out of all the options we consider. Surprisingly, one of the base models proves very effective for propagation, while the other provides an effective means of stating a static search order.
dpMood: Exploiting Local and Periodic Typing Dynamics for Personalized Mood Prediction
Venue: arXiv cs.HC 2018
Year: 2018

Mood disorders are common and associated with significant morbidity and mortality. Early diagnosis has the potential to greatly alleviate the burden of mental illness and the ever increasing costs to families and society. Mobile devices provide us a promising opportunity to detect the users' mood in an unobtrusive manner. In this study, we use a custom keyboard which collects keystrokes' meta-data and accelerometer values. Based on the collected time series data in multiple modalities, we propose a deep personalized mood prediction approach, called {\pro}, by integrating convolutional and recurrent deep architectures as well as exploring each individual's circadian rhythm. Experimental results not only demonstrate the feasibility and effectiveness of using smart-phone meta-data to predict the presence and severity of mood disturbances in bipolar subjects, but also show the potential of personalized medical treatment for mood disorders.
Internet of Nano-Things, Things and Everything: Future Growth Trends
Venue: arXiv cs.NI 2018
Year: 2018

The current statuses and future promises of the Internet of Things (IoT), Internet of Everything (IoE) and Internet of Nano-Things (IoNT) are extensively reviewed and a summarized survey is presented. The analysis clearly distinguishes between IoT and IoE, which are wrongly considered to be the same by many commentators. After evaluating the current trends of advancement in the fields of IoT, IoE and IoNT, this paper identifies the 21 most significant current and future challenges as well as scenarios for the possible future expansion of their applications. Despite possible negative aspects of these developments, there are grounds for general optimism about the coming technologies. Certainly, many tedious tasks can be taken over by IoT devices. However, the dangers of criminal and other nefarious activities, plus those of hardware and software errors, pose major challenges that are a priority for further research. Major specific priority issues for research are identified.
An Adaptive Conversational Bot Framework
Venue: arXiv cs.CL 2018
Year: 2018

How can we enable users to heavily specify criteria for database queries in a user-friendly way? This paper describes a general framework of a conversational bot that extracts meaningful information from user's sentences, that asks subsequent questions to complete missing information, and that adjusts its questions and information-extraction parameters for later conversations depending on users' behavior. Additionally, we provide a comparison of existing tools and give novel techniques to implement such framework. Finally, we exemplify the framework with a bot to query movies in a database, whose code is available for Microsoft employees.
Early Lessons from a Voice-Only Interface for Finding Movies
Venue: arXiv cs.HC 2018
Year: 2018

The current generation of streaming media players often allow users to speak commands (e.g., users can change the TV channel by pressing a button and saying "ESPN"). However, these devices typically support a narrow range of control- and search-oriented commands, and do not support deeper recommendation or exploration queries. To study spoken natural language interactions with recommenders, we have built MovieLens TV, a movie recommender system with no input modalities except voice. In this poster, we describe MovieLens TV, with a focus on lessons learned building a prototype system around an off-the-shelf Amazon Echo.
Dropout with Tabu Strategy for Regularizing Deep Neural Networks
Venue: arXiv cs.LG 2018
Year: 2018

Dropout has proven to be an effective technique for regularization and preventing the co-adaptation of neurons in deep neural networks (DNN). It randomly drops units with a probability $p$ during the training stage of DNN. Dropout also provides a way of approximately combining exponentially many different neural network architectures efficiently. In this work, we add a diversification strategy into dropout, which aims at generating more different neural network architectures in a proper times of iterations. The dropped units in last forward propagation will be marked. Then the selected units for dropping in the current FP will be kept if they have been marked in the last forward propagation. We only mark the units from the last forward propagation. We call this new technique Tabu Dropout. Tabu Dropout has no extra parameters compared with the standard Dropout and also it is computationally cheap. The experiments conducted on MNIST, Fashion-MNIST datasets show that Tabu Dropout improves the performance of the standard dropout.