OpenRaiser/Intern-Atlas huggingface dataset:
Evaluating the Red Belly Blockchain
Venue: arXiv cs.CR 2018
Year: 2018

In this paper, we present the most extensive evaluation of blockchain system to date. To achieve scalability across servers in more than 10 countries located on 4 different continents, we drastically revisited Byzantine fault tolerant blockchains and verification of signatures. The resulting blockchain, called the Red Belly Blockchain (RBBC), commits more than a hundred thousand transactions issued by permissionless nodes. These transactions are grouped into blocks within few seconds through a partially synchronous consensus run by permissioned nodes. It prevents double spending by guaranteeing that a unique block is decided at any given index of the chain in a deterministic way by all participants. We compared the performance of RBBC against traditional Byzantine fault tolerant alternatives and more recent randomized solutions. In the same geo-distributed environment with low-end machines, we noticed two interesting comparisons: (i) the RBBC throughput scales to hundreds of machines whereas the classic 3-step leader-based BFT state machine used by consortium blockchains cannot scale to 40 identically configured nodes; (ii) RBBC guarantees transaction finality in 3 seconds and experiences a third of the latency that randomized-based solutions like HoneyBadgerBFT can offer. This empirical evaluation demonstrates that blockchain scalability can be achieved without sacrificing security.
Estimating the Relative Speed of RF Jammers in VANETs
Venue: arXiv cs.CR 2018
Year: 2018

Vehicular Ad-Hoc Networks (VANETs) aim at enhancing road safety and providing a comfortable driving environment by delivering early warning and infotainment messages to the drivers. Jamming attacks, however, pose a significant threat to their performance. In this paper, we propose a novel Relative Speed Estimation Algorithm (RSEA) of a moving interfering vehicle that approaches a Transmitter ($Tx$) - Receiver ($Rx$) pair, that interferes with their Radio Frequency (RF) communication by conducting a Denial of Service (DoS) attack. Our scheme is completely sensorless and passive and uses a pilot-based received signal without hardware or computational cost in order to, firstly, estimate the combined channel between the transmitter - receiver and jammer - receiver and secondly, to estimate the jamming signal and the relative speed between the jammer - receiver using the RF Doppler shift. Moreover, the relative speed metric exploits the Angle of Projection (AOP) of the speed vector of the jammer in the axis of its motion in order to form a two-dimensional representation of the geographical area. This approach can effectively be applied both for a jamming signal completely unknown to the receiver and for a jamming signal partly known to the receiver. Our speed estimator method is proven to have quite accurate performance, with a Mean Absolute Error (MAE) value of approximately $10\%$ compared to the optimal zero MAE value under different jamming attack scenarios.
Do altmetrics work for assessing research quality?
Venue: arXiv cs.DL 2018
Year: 2018

Alternative metrics (aka altmetrics) are gaining increasing interest in the scientometrics community as they can capture both the volume and quality of attention that a research work receives online. Nevertheless, there is limited knowledge about their effectiveness as a mean for measuring the impact of research if compared to traditional citation-based indicators. This work aims at rigorously investigating if any correlation exists among indicators, either traditional (i.e. citation count and h-index) or alternative (i.e. altmetrics) and which of them may be effective for evaluating scholars. The study is based on the analysis of real data coming from the National Scientific Qualification procedure held in Italy by committees of peers on behalf of the Italian Ministry of Education, Universities and Research.
Rate-Distortion-Perception Tradeoff of Variable-Length Source Coding for General Information Sources
Venue: arXiv cs.IT 2018
Year: 2018

Blau and Michaeli recently introduced a novel concept for inverse problems of signal processing, that is, the perception-distortion tradeoff. We introduce their tradeoff into the rate distortion theory of variable-length lossy source coding in information theory, and clarify the tradeoff among information rate, distortion and perception for general information sources. We also discuss the fixed-length coding with average distortion criterion that was missing in the previous letter.
DCI: Discriminative and Contrast Invertible Descriptor
Venue: arXiv cs.CV 2018
Year: 2018

Local feature descriptors have been widely used in fine-grained visual object search thanks to their robustness in scale and rotation variation and cluttered background. However, the performance of such descriptors drops under severe illumination changes. In this paper, we proposed a Discriminative and Contrast Invertible (DCI) local feature descriptor. In order to increase the discriminative ability of the descriptor under illumination changes, a Laplace gradient based histogram is proposed. A robust contrast flipping estimate is proposed based on the divergence of a local region. Experiments on fine-grained object recognition and retrieval applications demonstrate the superior performance of DCI descriptor to others.
Gender bias in academic recruitment
Venue: arXiv cs.DL 2018
Year: 2018

It is well known that women are underrepresented in the academic systems of many countries. Gender discrimination is one of the factors that could contribute to this phenomenon. This study considers a recent national academic recruitment campaign in Italy, examining whether women are subject to more or less bias than men. The findings show that no gender-related differences occur among the candidates who benefit from positive bias, while among those candidates affected by negative bias, the incidence of women is lower than that of men. Among the factors that determine success in a competition for an academic position, the number of the applicant's career years in the same university as the committee members assumes greater weight for male candidates than for females. Being of the same gender as the committee president is also a factor that assumes greater weight for male applicants. On the other hand, for female applicants, the presence of a full professor in the same university with the same family name as the candidate assumes greater weight than for male candidates.
Hierarchical Fuzzy Opinion Networks: Top-Down for Social Organizations and Bottom-Up for Election
Venue: arXiv cs.MA 2018
Year: 2018

A fuzzy opinion is a Gaussian fuzzy set with the center representing the opinion and the standard deviation representing the uncertainty about the opinion, and a fuzzy opinion network is a connection of a number of fuzzy opinions in a structured way. In this paper, we propose: (a) a top-down hierarchical fuzzy opinion network to model how the opinion of a top leader is penetrated into the members in social organizations, and (b) a bottom-up fuzzy opinion network to model how the opinions of a large number of agents are agglomerated layer-by-layer into a consensus or a few opinions in the social processes such as an election. For the top-down hierarchical fuzzy opinion network, we prove that the opinions of all the agents converge to the leaders opinion, but the uncertainties of the agents in different groups are generally converging to different values. We demonstrate that the speed of convergence is greatly improved by organizing the agents in a hierarchical structure of small groups. For the bottom-up hierarchical fuzzy opinion network, we simulate how a wide spectrum of opinions are negotiating and summarizing with each other in a layer-by-layer fashion in some typical situations.
Boundary-Conforming Finite Element Methods for Twin-Screw Extruders: Unsteady - Temperature-Dependent - Non-Newtonian Simulations
Venue: arXiv cs.CE 2018
Year: 2018

We present a boundary-conforming space-time finite element method to compute the flow inside co-rotating, self-wiping twin-screw extruders. The mesh update is carried out using the newly developed Snapping Reference Mesh Update Method (SRMUM). It allows to compute time-dependent flow solutions inside twin-screw extruders equipped with conveying screw elements without any need for re-meshing and projections of solutions - making it a very efficient method. We provide cases for Newtonian and non-Newtonian fluids in 2D and 3D, that show mesh convergence of the solution as well as agreement to experimental results. Furthermore, a complex, unsteady and temperature-dependent 3D test case with multiple screw elements illustrates the potential of the method also for industrial applications.
Effect of implementation of improved methods of the life cycle stages organisation to the online community management
Venue: arXiv cs.CY 2018
Year: 2018

This paper presents the current problem of investigation of the effect of implementation of improved methods of the life cycle stages organisation to online community management. The online community life cycle is the sum of the stages of organisation and development of online community. The current approaches of scientific researches of social processes within the WWW are analysed. The types of life cycles are distinguished and implemented in the online community management. The algorithm of life cycle stages of the online community is designed. The total quality of the life cycle stages execution and quality of the life cycle stages execution of Lviv Polytechnic online community from 2013 till 2016. Currently, the appropriate study and development of community management hardware and software provoke the most interest because online communities are a widespread and popular phenomenon, and the existing management software for them is imperfect and not complex.
Distributed dual vigilance fuzzy adaptive resonance theory learns online, retrieves arbitrarily-shaped clusters, and mitigates order dependence
Venue: arXiv cs.NE 2018
Year: 2018

This paper presents a novel adaptive resonance theory (ART)-based modular architecture for unsupervised learning, namely the distributed dual vigilance fuzzy ART (DDVFA). DDVFA consists of a global ART system whose nodes are local fuzzy ART modules. It is equipped with the distinctive features of distributed higher-order activation and match functions, using dual vigilance parameters responsible for cluster similarity and data quantization. Together, these allow DDVFA to perform unsupervised modularization, create multi-prototype clustering representations, retrieve arbitrarily-shaped clusters, and control its compactness. Another important contribution is the reduction of order-dependence, an issue that affects any agglomerative clustering method. This paper demonstrates two approaches for mitigating order-dependence: preprocessing using visual assessment of cluster tendency (VAT) or postprocessing using a novel Merge ART module. The former is suitable for batch processing, whereas the latter can be used in online learning. Experimental results in the online learning mode carried out on 30 benchmark data sets show that DDVFA cascaded with Merge ART statistically outperformed the best other ART-based systems when samples were randomly presented. Conversely, they were found to be statistically equivalent in the offline mode when samples were pre-processed using VAT. Remarkably, performance comparisons to non-ART-based clustering algorithms show that DDVFA (which learns incrementally) was also statistically equivalent to the non-incremental (offline) methods of DBSCAN, single linkage hierarchical agglomerative clustering (HAC), and k-means, while retaining the appealing properties of ART. Links to the source code and data are provided. Considering the algorithm's simplicity, online learning capability, and performance, it is an ideal choice for many agglomerative clustering applications.
Machine Translation: A Literature Review
Venue: arXiv cs.CL 2018
Year: 2018

Machine translation (MT) plays an important role in benefiting linguists, sociologists, computer scientists, etc. by processing natural language to translate it into some other natural language. And this demand has grown exponentially over past couple of years, considering the enormous exchange of information between different regions with different regional languages. Machine Translation poses numerous challenges, some of which are: a) Not all words in one language has equivalent word in another language b) Two given languages may have completely different structures c) Words can have more than one meaning. Owing to these challenges, along with many others, MT has been active area of research for more than five decades. Numerous methods have been proposed in the past which either aim at improving the quality of the translations generated by them, or study the robustness of these systems by measuring their performance on many different languages. In this literature review, we discuss statistical approaches (in particular word-based and phrase-based) and neural approaches which have gained widespread prominence owing to their state-of-the-art results across multiple major languages.
A Systematic Review of Tracing Solutions in Software Product Lines
Venue: arXiv cs.SE 2018
Year: 2018

Software Product Lines are large-scale, multi-unit systems that enable massive, customized production. They consist of a base of reusable artifacts and points of variation that provide the system with flexibility, allowing generating customized products. However, maintaining a system with such complexity and flexibility could be error prone and time consuming. Indeed, any modification (addition, deletion or update) at the level of a product or an artifact would impact other elements. It would therefore be interesting to adopt an efficient and organized traceability solution to maintain the Software Product Line. Still, traceability is not systematically implemented. It is usually set up for specific constraints (e.g. certification requirements), but abandoned in other situations. In order to draw a picture of the actual conditions of traceability solutions in Software Product Lines context, we decided to address a literature review. This review as well as its findings is detailed in the present article.
DeCoILFNet: Depth Concatenation and Inter-Layer Fusion based ConvNet Accelerator
Venue: arXiv cs.DC 2018
Year: 2018

Convolutional Neural Networks (CNNs) are rapidly gaining popularity in varied fields. Due to their increasingly deep and computationally heavy structures, it is difficult to deploy them on energy constrained mobile applications. Hardware accelerators such as FPGAs have come up as an attractive alternative. However, with the limited on-chip memory and computation resources of FPGA, meeting the high memory throughput requirement and exploiting the parallelism of CNNs is a major challenge. We propose a high-performance FPGA based architecture - Depth Concatenation and Inter-Layer Fusion based ConvNet Accelerator - DeCoILFNet which exploits the intra-layer parallelism of CNNs by flattening across depth and combines it with a highly pipelined data flow across the layers enabling inter-layer fusion. This architecture significantly reduces off-chip memory accesses and maximizes the throughput. Compared to a 3.5GHz hexa-core Intel Xeon E7 caffe-implementation, our 120MHz FPGA accelerator is 30X faster. In addition, our design reduces external memory access by 11.5X along with a speedup of more than 2X in the number of clock cycles compared to state-of-the-art FPGA accelerators.
v-SVR Polynomial Kernel for Predicting the Defect Density in New Software Projects
Venue: arXiv cs.SE 2018
Year: 2018

An important product measure to determine the effectiveness of software processes is the defect density (DD). In this study, we propose the application of support vector regression (SVR) to predict the DD of new software projects obtained from the International Software Benchmarking Standards Group (ISBSG) Release 2018 data set. Two types of SVR (e-SVR and v-SVR) were applied to train and test these projects. Each SVR used four types of kernels. The prediction accuracy of each SVR was compared to that of a statistical regression (i.e., a simple linear regression, SLR). Statistical significance test showed that v-SVR with polynomial kernel was better than that of SLR when new software projects were developed on mainframes and coded in programming languages of third generation
Risk-Aware Resource Allocation for URLLC: Challenges and Strategies with Machine Learning
Venue: arXiv cs.NI 2018
Year: 2018

Supporting ultra-reliable low-latency communications (URLLC) is a major challenge of 5G wireless networks. Stringent delay and reliability requirements need to be satisfied for both scheduled and non-scheduled URLLC traffic to enable a diverse set of 5G applications. Although physical and media access control layer solutions have been investigated to satisfy only scheduled URLLC traffic, there is a lack of study on enabling transmission of non-scheduled URLLC traffic, especially in coexistence with the scheduled URLLC traffic. Machine learning (ML) is an important enabler for such a co-existence scenario due to its ability to exploit spatial/temporal correlation in user behaviors and use of radio resources. Hence, in this paper, we first study the coexistence design challenges, especially the radio resource management (RRM) problem and propose a distributed risk-aware ML solution for RRM. The proposed solution benefits from hybrid orthogonal/non-orthogonal radio resource slicing, and proactively regulates the spectrum needed for satisfying delay/reliability requirement of each URLLC traffic type. A case study is introduced to investigate the potential of the proposed RRM in serving coexisting URLLC traffic types. The results further provide insights on the benefits of leveraging intelligent RRM, e.g. a 75% increase in data rate with respect to the conservative design approach for the scheduled traffic is achieved, while the 99.99% reliability of both scheduled and nonscheduled traffic types is satisfied.
Conversational Intent Understanding for Passengers in Autonomous Vehicles
Venue: arXiv cs.CL 2018
Year: 2018

Understanding passenger intents and extracting relevant slots are important building blocks towards developing a contextual dialogue system responsible for handling certain vehicle-passenger interactions in autonomous vehicles (AV). When the passengers give instructions to AMIE (Automated-vehicle Multimodal In-cabin Experience), the agent should parse such commands properly and trigger the appropriate functionality of the AV system. In our AMIE scenarios, we describe usages and support various natural commands for interacting with the vehicle. We collected a multimodal in-cabin data-set with multi-turn dialogues between the passengers and AMIE using a Wizard-of-Oz scheme. We explored various recent Recurrent Neural Networks (RNN) based techniques and built our own hierarchical models to recognize passenger intents along with relevant slots associated with the action to be performed in AV scenarios. Our experimental results achieved F1-score of 0.91 on utterance-level intent recognition and 0.96 on slot extraction models.
A novel 3D display based on micro-volumetric scanning and real time reconstruction of holograms principle
Venue: arXiv cs.GR 2018
Year: 2018

The present study proposes a novel 3D display contains a micro-volumetric scanning system (MVS) and a real time reconstruction hologram system (RTRH).
Residual Attention based Network for Hand Bone Age Assessment
Venue: arXiv cs.CV 2018
Year: 2018

Computerized automatic methods have been employed to boost the productivity as well as objectiveness of hand bone age assessment. These approaches make predictions according to the whole X-ray images, which include other objects that may introduce distractions. Instead, our framework is inspired by the clinical workflow (Tanner-Whitehouse) of hand bone age assessment, which focuses on the key components of the hand. The proposed framework is composed of two components: a Mask R-CNN subnet of pixelwise hand segmentation and a residual attention network for hand bone age assessment. The Mask R-CNN subnet segments the hands from X-ray images to avoid the distractions of other objects (e.g., X-ray tags). The hierarchical attention components of the residual attention subnet force our network to focus on the key components of the X-ray images and generate the final predictions as well as the associated visual supports, which is similar to the assessment procedure of clinicians. We evaluate the performance of the proposed pipeline on the RSNA pediatric bone age dataset and the results demonstrate its superiority over the previous methods.
Avoiding a Tragedy of the Commons in the Peer Review Process
Venue: arXiv cs.CY 2018
Year: 2018

Peer review is the foundation of scientific publication, and the task of reviewing has long been seen as a cornerstone of professional service. However, the massive growth in the field of machine learning has put this community benefit under stress, threatening both the sustainability of an effective review process and the overall progress of the field. In this position paper, we argue that a tragedy of the commons outcome may be avoided by emphasizing the professional aspects of this service. In particular, we propose a rubric to hold reviewers to an objective standard for review quality. In turn, we also propose that reviewers be given appropriate incentive. As one possible such incentive, we explore the idea of financial compensation on a per-review basis. We suggest reasonable funding models and thoughts on long term effects.
Preventing wireless deauthentication attacks over 802.11 Networks
Venue: arXiv cs.CR 2018
Year: 2018

The objective of this paper is to investigate a special Denial of Service (DoS) attack against 802.11 wireless networks. This attack is known as the Deauthentication / Disassociation attack which is launched against 802.11-based wireless networks. When a client needs to disconnect from the wireless access point, it sends special frames known as deauthentication or disassociation frames. Due to being encrypted, these frames do not require an authenticated user. Hence, an attacker can craft these frames and send them to the access point in such a way that the access point assumes the frames to be coming from the client and not the attacker. In this paper, an efficient solution is proposed to prevent deauthentication attack by using a session management system used to then verify deauthentication frames.