OpenRaiser/Intern-Atlas huggingface dataset:
Empirical Evaluations of Seed Set Selection Strategies for Predictive Coding
Venue: arXiv cs.IR 2019
Year: 2019

Training documents have a significant impact on the performance of predictive models in the legal domain. Yet, there is limited research that explores the effectiveness of the training document selection strategy - in particular, the strategy used to select the seed set, or the set of documents an attorney reviews first to establish an initial model. Since there is limited research on this important component of predictive coding, the authors of this paper set out to identify strategies that consistently perform well. Our research demonstrated that the seed set selection strategy can have a significant impact on the precision of a predictive model. Enabling attorneys with the results of this study will allow them to initiate the most effective predictive modeling process to comb through the terabytes of data typically present in modern litigation. This study used documents from four actual legal cases to evaluate eight different seed set selection strategies. Attorneys can use the results contained within this paper to enhance their approach to predictive coding.
Smart Radio Environments Empowered by AI Reconfigurable Meta-Surfaces: An Idea Whose Time Has Come
Venue: arXiv cs.IT 2019
Year: 2019

Future wireless networks are expected to constitute a distributed intelligent wireless communications, sensing, and computing platform, which will have the challenging requirement of interconnecting the physical and digital worlds in a seamless and sustainable manner. Currently, two main factors prevent wireless network operators from building such networks: 1) the lack of control of the wireless environment, whose impact on the radio waves cannot be customized, and 2) the current operation of wireless radios, which consume a lot of power because new signals are generated whenever data has to be transmitted. In this paper, we challenge the usual "more data needs more power and emission of radio waves" status quo, and motivate that future wireless networks necessitate a smart radio environment: A transformative wireless concept, where the environmental objects are coated with artificial thin films of electromagnetic and reconfigurable material (that are referred to as intelligent reconfigurable meta-surfaces), which are capable of sensing the environment and of applying customized transformations to the radio waves. Smart radio environments have the potential to provide future wireless networks with uninterrupted wireless connectivity, and with the capability of transmitting data without generating new signals but recycling existing radio waves. This paper overviews the current research efforts on smart radio environments, the enabling technologies to realize them in practice, the need of new communication-theoretic models for their analysis and design, and the long-term and open research issues to be solved towards their massive deployment. In a nutshell, this paper is focused on discussing how the availability of intelligent reconfigurable meta-surfaces will allow wireless network operators to redesign common and well-known network communication paradigms.
Solving the Steiner Tree Problem in Graphs using Physarum-inspired Algorithms
Venue: arXiv cs.ET 2019
Year: 2019

Some biological experiments show that the tubular structures of Physarum polycephalum are often analogous to those of Steiner trees. Therefore, the emerging Physarum-inspired Algorithms (PAs) have the potential of computing Steiner trees. In this paper, we propose two PAs to solve the Steiner Tree Problem in Graphs (STPG). We apply some widely-used artificial and real-world VLSI design instances to evaluate the performance of our PAs. The experimental results show that: 1) for instances with hundreds of vertices, our first PA can find feasible solutions with an average error of 0.19%, while the Genetic Algorithm (GA), the Discrete Particle Swarm Optimization (DPSO) algorithm and a widely-used Steiner tree approximation algorithm: the Shortest Path Heuristic (SPH) algorithm can only find feasible solutions with an average error above 4.96%; and 2) for larger instances with up to tens of thousands of vertices, where our first PA, GA and DPSO are too slow to be used, our second PA can find feasible solutions with an average error of 3.69%, while SPH can only find feasible solutions with an average error of 6.42%. These experimental results indicate that PAs can compute Steiner trees, and it may be preferable to apply our PAs to solve STPG in some cases.
Biasing MCTS with Features for General Games
Venue: arXiv cs.AI 2019
Year: 2019

This paper proposes using a linear function approximator, rather than a deep neural network (DNN), to bias a Monte Carlo tree search (MCTS) player for general games. This is unlikely to match the potential raw playing strength of DNNs, but has advantages in terms of generality, interpretability and resources (time and hardware) required for training. Features describing local patterns are used as inputs. The features are formulated in such a way that they are easily interpretable and applicable to a wide range of general games, and might encode simple local strategies. We gradually create new features during the same self-play training process used to learn feature weights. We evaluate the playing strength of an MCTS player biased by learnt features against a standard upper confidence bounds for trees (UCT) player in multiple different board games, and demonstrate significantly improved playing strength in the majority of them after a small number of self-play training games.
The CASE Dataset of Candidate Spaces for Advert Implantation
Venue: arXiv cs.CV 2019
Year: 2019

With the advent of faster internet services and growth of multimedia content, we observe a massive growth in the number of online videos. The users generate these video contents at an unprecedented rate, owing to the use of smart-phones and other hand-held video capturing devices. This creates immense potential for the advertising and marketing agencies to create personalized content for the users. In this paper, we attempt to assist the video editors to generate augmented video content, by proposing candidate spaces in video frames. We propose and release a large-scale dataset of outdoor scenes, along with manually annotated maps for candidate spaces. We also benchmark several deep-learning based semantic segmentation algorithms on this proposed dataset.
Improving Machine Hearing on Limited Data Sets
Venue: arXiv cs.SD 2019
Year: 2019

Convolutional neural network (CNN) architectures have originated and revolutionized machine learning for images. In order to take advantage of CNNs in predictive modeling with audio data, standard FFT-based signal processing methods are often applied to convert the raw audio waveforms into an image-like representations (e.g. spectrograms). Even though conventional images and spectrograms differ in their feature properties, this kind of pre-processing reduces the amount of training data necessary for successful training. In this contribution we investigate how input and target representations interplay with the amount of available training data in a music information retrieval setting. We compare the standard mel-spectrogram inputs with a newly proposed representation, called Mel scattering. Furthermore, we investigate the impact of additional target data representations by using an augmented target loss function which incorporates unused available information. We observe that all proposed methods outperform the standard mel-transform representation when using a limited data set and discuss their strengths and limitations. The source code for reproducibility of our experiments as well as intermediate results and model checkpoints are available in an online repository.
Multi-hop Byzantine Reliable Broadcast with Honest Dealer Made Practical
Venue: arXiv cs.DC 2019
Year: 2019

We revisit Byzantine tolerant reliable broadcast with honest dealer algorithms in multi-hop networks. To tolerate Byzantine faulty nodes arbitrarily spread over the network, previous solutions require a factorial number of messages to be sent over the network if the messages are not authenticated (e.g. digital signatures are not available). We propose modifications that preserve the safety and liveness properties of the original unauthenticated protocols, while highly decreasing their observed message complexity when simulated on several classes of graph topologies, potentially opening to their employment.
A unified spectra analysis workflow for the assessment of microbial contamination of ready to eat green salads: Comparative study and application of non-invasive sensors
Venue: arXiv cs.LG 2019
Year: 2019

The present study provides a comparative assessment of non-invasive sensors as means of estimating the microbial contamination and time-on-shelf (i.e. storage time) of leafy green vegetables, using a novel unified spectra analysis workflow. Two fresh ready-to-eat green salads were used in the context of this study for the purpose of evaluating the efficiency and practical application of the presented workflow: rocket and baby spinach salads. The employed analysis workflow consisted of robust data normalization, powerful feature selection based on random forests regression, and selection of the number of partial least squares regression coefficients in the training process by estimating the knee-point on the explained variance plot. Training processes were based on microbiological and spectral data derived during storage of green salad samples at isothermal conditions (4, 8 and 12C), whereas testing was performed on data during storage under dynamic temperature conditions (simulating real-life temperature fluctuations in the food supply chain). Since an increasing interest in the use of non-invasive sensors in food quality assessment has been made evident in recent years, the unified spectra analysis workflow described herein, by being based on the creation/usage of limited sized featured sets, could be very useful in food-specific low-cost sensor development.
Generative Models For Deep Learning with Very Scarce Data
Venue: arXiv cs.LG 2019
Year: 2019

The goal of this paper is to deal with a data scarcity scenario where deep learning techniques use to fail. We compare the use of two well established techniques, Restricted Boltzmann Machines and Variational Auto-encoders, as generative models in order to increase the training set in a classification framework. Essentially, we rely on Markov Chain Monte Carlo (MCMC) algorithms for generating new samples. We show that generalization can be improved comparing this methodology to other state-of-the-art techniques, e.g. semi-supervised learning with ladder networks. Furthermore, we show that RBM is better than VAE generating new samples for training a classifier with good generalization capabilities.
Levelling the Playing Field: A Comprehensive Comparison of Visual Place Recognition Approaches under Changing Conditions
Venue: arXiv cs.CV 2019
Year: 2019

In recent years there has been significant improvement in the capability of Visual Place Recognition (VPR) methods, building on the success of both hand-crafted and learnt visual features, temporal filtering and usage of semantic scene information. The wide range of approaches and the relatively recent growth in interest in the field has meant that a wide range of datasets and assessment methodologies have been proposed, often with a focus only on precision-recall type metrics, making comparison difficult. In this paper we present a comprehensive approach to evaluating the performance of 10 state-of-the-art recently-developed VPR techniques, which utilizes three standardized metrics: (a) Matching Performance b) Matching Time c) Memory Footprint. Together this analysis provides an up-to-date and widely encompassing snapshot of the various strengths and weaknesses of contemporary approaches to the VPR problem. The aim of this work is to help move this particular research field towards a more mature and unified approach to the problem, enabling better comparison and hence more progress to be made in future research.
Exploratory studies of human gait changes using depth cameras and considering measurement errors
Venue: arXiv cs.HC 2019
Year: 2019

This research aims to quantify human walking patterns through depth cameras to (1) detect walking pattern changes of a person with and without a motion-restricting device or a walking aid, and to (2) identify distinct walking patterns from different persons of similar physical attributes. Microsoft Kinect devices, often used for video games, were used to provide and track coordinates of 25 different joints of people over time to form a human skeleton. Then multiple machine learning (ML) models were applied to the SE datasets from ten college-age subjects - five males and five females. In particular, ML models were applied to classify subjects into two categories: normal walking and abnormal walking (i.e. with motion-restricting devices). The best ML model (K-nearest neighborhood) was able to predict 97.3% accuracy using 10-fold cross-validation. Finally, ML models were applied to classify five gait conditions: walking normally, walking while wearing the ankle brace, walking while wearing the ACL brace, walking while using a cane, and walking while using a walker. The best ML model was again the K-nearest neighborhood performing at 98.7% accuracy rate.
Distributed off-Policy Actor-Critic Reinforcement Learning with Policy Consensus
Venue: arXiv cs.LG 2019
Year: 2019

In this paper, we propose a distributed off-policy actor critic method to solve multi-agent reinforcement learning problems. Specifically, we assume that all agents keep local estimates of the global optimal policy parameter and update their local value function estimates independently. Then, we introduce an additional consensus step to let all the agents asymptotically achieve agreement on the global optimal policy function. The convergence analysis of the proposed algorithm is provided and the effectiveness of the proposed algorithm is validated using a distributed resource allocation example. Compared to relevant distributed actor critic methods, here the agents do not share information about their local tasks, but instead they coordinate to estimate the global policy function.
Proofware: Proof of Useful Work Blockchain Consensus Protocol for Decentralized Applications
Venue: arXiv cs.DC 2019
Year: 2019

In a blockchain system, consensus protocol as an incentive and security mechanism, is to ensure the participants to build the block honestly and effectively. There are different consensus protocols for blockchain, like Proof of work (PoW), Proof of Stake (PoS), Proof of Space (PoSpace), Proof of Activities etc. But most of these consensus protocols are not designed for doing some useful jobs for society because of too much competition and scalability limitation. Massive electric power and computing resources, including CPU, RAM, storage and sensors have been wasted to run blockchain network based on these consensus protocols. Current frameworks and middleware for building decentralised applications (dApps) are largely limited to simple and less useful jobs. In this paper, we present Proofware which is designed for developers to build their dApps easily with existing public/crowd-based computing resources. Under Proofware, developers can develop and test their own Proof of Useful Work (PoUW) consensus protocols. Also, rather than depending on a centralised accounting system, each dApp has an embedded currency system to keep the whole incentive system decentralised, fair, transparent, stable and sustainable. Based on Proofware, we have built a crowd based video sharing application, called OurTube, as a case study. By the OurTube example, it has shown Proofware significantly improves the productivity to build crowd-based computing system with the features of cost-effectiveness, anti-censorship, elasticity and financial sustainability.
Conditional Capacity and Transmit Signal Design for SWIPT Systems with Multiple Nonlinear Energy Harvesting Receivers
Venue: arXiv cs.IT 2019
Year: 2019

We study information-theoretic limits for simultaneous wireless information and power transfer (SWIPT) systems employing nonlinear radio frequency (RF) energy harvesting (EH) receivers (Rxs). In particular, we consider a SWIPT system with one transmitter that broadcasts a common signal to an information decoding (ID) Rx and multiple EH Rxs. Owing to the nonlinearity of the EH Rxs' circuitry, the efficiency of wireless power transfer depends on the waveform of the transmitted signal. We aim to answer the following fundamental question: What is the optimal input distribution of the transmit signal waveform that maximizes the information transfer rate at the ID Rx conditioned on individual minimum required direct-current (DC) powers to be harvested at the EH Rxs? Specifically, we study the conditional capacity problem of a SWIPT system impaired by additive white Gaussian noise subject to average-power (AP) and peak power (PP) constraints at the transmitter and nonlinear EH constraints at the EH Rxs. To this end, we develop a novel nonlinear EH model that captures the saturation of the harvested DC power by taking into account the reverse breakdown current of the rectifying diode. We derive a novel semiclosed-form expression for the harvested DC power, which simplifies to closed form for low input RF powers. The derived analytical expressions are shown to closely match circuit simulation results. We solve the conditional capacity problem for real- and complex-valued signalling and prove that the optimal input distribution that maximizes the rate-energy (R-E) region is unique and discrete with a finite number of mass points. Furthermore, we show that the boundary of the R-E region saturates for high PP constraints due to the saturation of the harvested DC power for high input RF powers. In addition, we devise a suboptimal input distribution whose R-E tradeoff performance is close to optimal.
Blockchain and its Potential in Education
Venue: arXiv cs.CY 2019
Year: 2019

The proposed paper presents a literature review regarding the status of integrating the dynamic blockchain technology in the educational field. Blockchain is a relatively new technology and the same is its implementation in education. The emerging need in this area of research, which still is in its infancy, is justified by the possible use cases; some of these cases are in piloting phase, while others have already been adopted by educational institutions. This paper focuses on extending knowledge about blockchain and on identifying the benefits, risks and the associated challenges regarding the successful implementation of blockchain-based solutions in the field of education, fully in line with standards and guidelines for quality assurance.
A Novel Independent RNN Approach to Classification of Seizures against Non-seizures
Venue: arXiv cs.LG 2019
Year: 2019

In current clinical practices, electroencephalograms (EEG) are reviewed and analyzed by trained neurologists to provide supports for therapeutic decisions. Manual reviews can be laborious and error prone. Automatic and accurate seizure/non-seizure classification methods are desirable. A critical challenge is that seizure morphologies exhibit considerable variabilities. In order to capture essential seizure features, this paper leverages an emerging deep learning model, the independently recurrent neural network (IndRNN), to construct a new approach for the seizure/non-seizure classification. This new approach gradually expands the time scales, thereby extracting temporal and spatial features from the local time duration to the entire record. Evaluations are conducted with cross-validation experiments across subjects over the noisy data of CHB-MIT. Experimental results demonstrate that the proposed approach outperforms the current state-of-the-art methods. In addition, we explore how the segment length affects the classification performance. Thirteen different segment lengths are assessed, showing that the classification performance varies over the segment lengths, and the maximal fluctuating margin is more than 4%. Thus, the segment length is an important factor influencing the classification performance.
Commitment Nets in Software Process Improvement
Venue: arXiv cs.SE 2019
Year: 2019

Several studies have revealed the fact that nearly two-thirds of all software process improvement (SPI) efforts have failed or have at least fallen short of expectations. Literature and practice have shown that commitment to SPI at all organizational levels is essential for the success of any SPI endeavor. A research model for studying the existence, development and interplay of SPI-related commitment is introduced in this paper. This study suggests that software organizations operate through strategic, operational and personal commitment nets. These nets consist of actors, drivers, concerns, actions, commitment, and outcomes. The commitment nets model is applied in a study of four industrial SPI initiatives. The results from two of these cases are reported here. The results show that SPI is driven through the formation and reformation of commitment nets. The contents of strategic, operational and personal commitment nets are laid out and implications are discussed.
Speed and Energy Optimised Quasi-Delay-Insensitive Block Carry Lookahead Adder
Venue: arXiv cs.AR 2019
Year: 2019

We present a new asynchronous quasi-delay-insensitive (QDI) block carry lookahead adder with redundancy carry (BCLARC) realized using delay-insensitive dual-rail data encoding and 4-phase return-to-zero (RTZ) and 4-phase return-to-one (RTO) handshaking. The proposed QDI BCLARC is found to be faster and energy-efficient than the existing asynchronous adders which are QDI and non-QDI (i.e., relative-timed). Compared to existing asynchronous adders corresponding to various architectures such as ripple carry adder (RCA), conventional carry lookahead adder (CCLA), carry select adder (CSLA), BCLARC, and hybrid BCLARC-RCA, the proposed BCLARC is found to be faster and more energy-optimised. The cycle time (CT), which is the sum of forward and reverse latencies, governs the speed; and the product of average power dissipation and cycle time viz. the power-cycle time product (PCTP) defines the low power/energy efficiency. For a 32-bit addition, the proposed QDI BCLARC achieves the following average reductions in design metrics over its counterparts when considering RTZ and RTO handshaking: i) 20.5% and 19.6% reductions in CT and PCTP respectively compared to an optimum QDI early output RCA, ii) 16.5% and 15.8% reductions in CT and PCTP respectively compared to an optimum relative-timed RCA, iii) 32.9% and 35.9% reductions in CT and PCTP respectively compared to an optimum uniform input-partitioned QDI early output CSLA, iv) 47.5% and 47.2% reductions in CT and PCTP respectively compared to an optimum QDI early output CCLA, v) 14.2% and 27.3% reductions in CT and PCTP respectively compared to an optimum QDI early output BCLARC, and vi) 12.2% and 11.6% reductions in CT and PCTP respectively compared to an optimum QDI early output hybrid BCLARC-RCA. The adders were implemented using a 32/28nm CMOS technology.