OpenRaiser/Intern-Atlas huggingface dataset:
GPSPiChain-Blockchain based Self-Contained Family Security System in Smart Home
Venue: arXiv cs.CR 2021
Year: 2021

With advancements in technology, personal computing devices are better adapted for and further integrated into people's lives and homes. The integration of technology into society also results in an increasing desire to control who and what has access to sensitive information, especially for vulnerable people including children and the elderly. With blockchain coming in to the picture as a technology that can revolutionise the world, it is now possible to have an immutable audit trail of locational data over time. By controlling the process through inexpensive equipment in the home, it is possible to control whom has access to such personal data. This paper presents a blockchain based family security system for tracking the location of consenting family members' smart phones. The locations of the family members' smart phones are logged and stored in a private blockchain which can be accessed through a node installed in the family home on a computer. The data for the whereabouts of family members stays within the family unit and does not go to any third party. The system is implemented in a small scale (one miner and two other nodes) and the technical feasibility is discussed along with the limitations of the system. Further research will cover the integration of the system into a smart home environment, and ethical implementations of tracking, especially of vulnerable people, using the immutability of blockchain.
A Bayesian social platform for inclusive and evidence-based decision making
Venue: arXiv cs.SI 2021
Year: 2021

Against the backdrop of a social media reckoning, this paper seeks to demonstrate the potential of social tools to build virtuous behaviours online. We must assume that human behaviour is flawed, the truth can be elusive, and as communities we must commit to mechanisms to encourage virtuous social digital behaviours. Societies that use social platforms should be inclusive, responsive to evidence, limit punitive actions and allow productive discord and respectful disagreement. Social media success, we argue, is in the hypothesis. Documents are valuable to the degree that they are evidence in service of, or to challenge an idea for a purpose. We outline how a Bayesian social platform can facilitate virtuous behaviours to build evidence-based collective rationality. The chapter outlines the epistemic architecture of the platform's algorithms and user interface in conjunction with explicit community management to ensure psychological safety. The BetterBeliefs platform rewards users who demonstrate epistemically virtuous behaviours and exports evidence-based propositions for decision-making. A Bayesian social network can make virtuous ideas powerful.
MATCH: An MPI Fault Tolerance Benchmark Suite
Venue: arXiv cs.DC 2021
Year: 2021

MPI has been ubiquitously deployed in flagship HPC systems aiming to accelerate distributed scientific applications running on tens of hundreds of processes and compute nodes. Maintaining the correctness and integrity of MPI application execution is critical, especially for safety-critical scientific applications. Therefore, a collection of effective MPI fault tolerance techniques have been proposed to enable MPI application execution to efficiently resume from system failures. However, there is no structured way to study and compare different MPI fault tolerance designs, so to guide the selection and development of efficient MPI fault tolerance techniques for distinct scenarios. To solve this problem, we design, develop, and evaluate a benchmark suite called MATCH to characterize, research, and comprehensively compare different combinations and configurations of MPI fault tolerance designs. Our investigation derives useful findings: (1) Reinit recovery in general performs better than ULFM recovery; (2) Reinit recovery is independent of the scaling size and the input problem size, whereas ULFM recovery is not; (3) Using Reinit recovery with FTI checkpointing is a highly efficient fault tolerance design. MATCH code is available at https://github.com/kakulo/MPI- FT- Bench.
Segmenting two-dimensional structures with strided tensor networks
Venue: arXiv cs.CV 2021
Year: 2021

Tensor networks provide an efficient approximation of operations involving high dimensional tensors and have been extensively used in modelling quantum many-body systems. More recently, supervised learning has been attempted with tensor networks, primarily focused on tasks such as image classification. In this work, we propose a novel formulation of tensor networks for supervised image segmentation which allows them to operate on high resolution medical images. We use the matrix product state (MPS) tensor network on non-overlapping patches of a given input image to predict the segmentation mask by learning a pixel-wise linear classification rule in a high dimensional space. The proposed model is end-to-end trainable using backpropagation. It is implemented as a Strided Tensor Network to reduce the parameter complexity. The performance of the proposed method is evaluated on two public medical imaging datasets and compared to relevant baselines. The evaluation shows that the strided tensor network yields competitive performance compared to CNN-based models while using fewer resources. Additionally, based on the experiments we discuss the feasibility of using fully linear models for segmentation tasks.
Why Security Defects Go Unnoticed during Code Reviews? A Case-Control Study of the Chromium OS Project
Venue: arXiv cs.SE 2021
Year: 2021

Peer code review has been found to be effective in identifying security vulnerabilities. However, despite practicing mandatory code reviews, many Open Source Software (OSS) projects still encounter a large number of post-release security vulnerabilities, as some security defects escape those. Therefore, a project manager may wonder if there was any weakness or inconsistency during a code review that missed a security vulnerability. Answers to this question may help a manager pinpointing areas of concern and taking measures to improve the effectiveness of his/her project's code reviews in identifying security defects. Therefore, this study aims to identify the factors that differentiate code reviews that successfully identified security defects from those that missed such defects. With this goal, we conduct a case-control study of Chromium OS project. Using multi-stage semi-automated approaches, we build a dataset of 516 code reviews that successfully identified security defects and 374 code reviews where security defects escaped. The results of our empirical study suggest that the are significant differences between the categories of security defects that are identified and that are missed during code reviews. A logistic regression model fitted on our dataset achieved an AUC score of 0.91 and has identified nine code review attributes that influence identifications of security defects. While time to complete a review, the number of mutual reviews between two developers, and if the review is for a bug fix have positive impacts on vulnerability identification, opposite effects are observed from the number of directories under review, the number of total reviews by a developer, and the total number of prior commits for the file under review.
Improving Automated Visual Fault Detection by Combining a Biologically Plausible Model of Visual Attention with Deep Learning
Venue: arXiv cs.CV 2021
Year: 2021

It is a long-term goal to transfer biological processing principles as well as the power of human recognition into machine vision and engineering systems. One of such principles is visual attention, a smart human concept which focuses processing on a part of a scene. In this contribution, we utilize attention to improve the automatic detection of defect patterns for wafers within the domain of semiconductor manufacturing. Previous works in the domain have often utilized classical machine learning approaches such as KNNs, SVMs, or MLPs, while a few have already used modern approaches like deep neural networks (DNNs). However, one problem in the domain is that the faults are often very small and have to be detected within a larger size of the chip or even the wafer. Therefore, small structures in the size of pixels have to be detected in a vast amount of image data. One interesting principle of the human brain for solving this problem is visual attention. Hence, we employ here a biologically plausible model of visual attention for automatic visual inspection. We propose a hybrid system of visual attention and a deep neural network. As demonstrated, our system achieves among other decisive advantages an improvement in accuracy from 81% to 92%, and an increase in accuracy for detecting faults from 67% to 88%. Hence, the error rates are reduced from 19% to 8%, and notably from 33% to 12% for detecting a fault in a chip. These results show that attention can greatly improve the performance of visual inspection systems. Furthermore, we conduct a broad evaluation, identifying specific advantages of the biological attention model in this application, and benchmarks standard deep learning approaches as an alternative with and without attention. This work is an extended arXiv version of the original conference article published in "IECON 2020", which has been extended regarding visual attention.
Euclidean Affine Functions and Applications to Calendar Algorithms
Venue: arXiv cs.DS 2021
Year: 2021

We study properties of Euclidean affine functions (EAFs), namely those of the form $f(r) = (α\cdot r + β)/δ$, and their closely related expression $\mathring{f}(r) = (α\cdot r + β)\%δ$, where $r$, $α$, $β$ and $δ$ are integers, and where $/$ and $\%$ respectively denote the quotient and remainder of Euclidean division. We derive algebraic relations and numerical approximations that are important for the efficient evaluation of these expressions in modern CPUs. Since simple division and remainder are particular cases of EAFs (when $α= 1$ and $β= 0$), the optimisations proposed in this paper can also be appplied to them. Such expressions appear in some of the most common tasks in any computer system, such as printing numbers, times and dates. We use calendar calculations as the main application example because it is richer with respect to the number of EAFs employed. Specifically, the main application presented in this article relates to Gregorian calendar algorithms. We will show how they can be implemented substantially more efficiently than is currently the case in widely used C, C++, C# and Java open source libraries. Gains in speed of a factor of two or more are common.
Normalized Convolution Upsampling for Refined Optical Flow Estimation
Venue: arXiv cs.CV 2021
Year: 2021

Optical flow is a regression task where convolutional neural networks (CNNs) have led to major breakthroughs. However, this comes at major computational demands due to the use of cost-volumes and pyramidal representations. This was mitigated by producing flow predictions at quarter the resolution, which are upsampled using bilinear interpolation during test time. Consequently, fine details are usually lost and post-processing is needed to restore them. We propose the Normalized Convolution UPsampler (NCUP), an efficient joint upsampling approach to produce the full-resolution flow during the training of optical flow CNNs. Our proposed approach formulates the upsampling task as a sparse problem and employs the normalized convolutional neural networks to solve it. We evaluate our upsampler against existing joint upsampling approaches when trained end-to-end with a a coarse-to-fine optical flow CNN (PWCNet) and we show that it outperforms all other approaches on the FlyingChairs dataset while having at least one order fewer parameters. Moreover, we test our upsampler with a recurrent optical flow CNN (RAFT) and we achieve state-of-the-art results on Sintel benchmark with ~6% error reduction, and on-par on the KITTI dataset, while having 7.5% fewer parameters (see Figure 1). Finally, our upsampler shows better generalization capabilities than RAFT when trained and evaluated on different datasets.
Towards reliable and transparent vaccine phase III trials with smart contracts
Venue: arXiv cs.CR 2021
Year: 2021

Transforming a vaccine concept into a real vaccine product is a complicated process and includes finding suitable antigens and regulatory, technical, and manufacturing obstacles. A relevant issue within this scope is the clinical trial process. Monitoring and ensuring the integrity of trial data using the traditional system is not always feasible. The search for a vaccine against the coronavirus SARS-CoV-2 illustrates this situation. The scientific credibility of findings from several vaccines' clinical trials contributed to distorted perceptions concerning the benefits and risks of the drug. This scenario is ideal for applying technologies such as Blockchain and Smart Contracts in healthcare issues. This paper proposes a protocol based on Smart Contracts, named VaccSC, to enable transparency, accounting, and confidentiality to Phase III of vaccine experiments. The protocol was implemented in Solidity language, and results show that the VaccSC enables double-blindness, randomization, and the auditability of clinical data, even in the presence of dishonest participants.
Doping: A technique for efficient compression of LSTM models using sparse structured additive matrices
Venue: arXiv cs.LG 2021
Year: 2021

Structured matrices, such as those derived from Kronecker products (KP), are effective at compressing neural networks, but can lead to unacceptable accuracy loss when applied to large models. In this paper, we propose the notion of doping -- addition of an extremely sparse matrix to a structured matrix. Doping facilitates additional degrees of freedom for a small number of parameters, allowing them to independently diverge from the fixed structure. To train LSTMs with doped structured matrices, we introduce the additional parameter matrix while slowly annealing its sparsity level. However, we find that performance degrades as we slowly sparsify the doping matrix, due to co-matrix adaptation (CMA) between the structured and the sparse matrices. We address this over dependence on the sparse matrix using a co-matrix dropout regularization (CMR) scheme. We provide empirical evidence to show that doping, CMA and CMR are concepts generally applicable to multiple structured matrices (Kronecker Product, LMF, Hybrid Matrix Decomposition). Additionally, results with doped kronecker product matrices demonstrate state-of-the-art accuracy at large compression factors (10 - 25x) across 4 natural language processing applications with minor loss in accuracy. Doped KP compression technique outperforms previous state-of-the art compression results by achieving 1.3 - 2.4x higher compression factor at a similar accuracy, while also beating strong alternatives like pruning and low-rank methods by a large margin (8% or more). Additionally, we show that doped KP can be deployed on commodity hardware using the current software stack and achieve 2.5 - 5.5x inference run-time speed-up over baseline.
Tight lower bounds for Dynamic Time Warping
Venue: arXiv cs.LG 2021
Year: 2021

Dynamic Time Warping (DTW) is a popular similarity measure for aligning and comparing time series. Due to DTW's high computation time, lower bounds are often employed to screen poor matches. Many alternative lower bounds have been proposed, providing a range of different trade-offs between tightness and computational efficiency. LB Keogh provides a useful trade-off in many applications. Two recent lower bounds, LB Improved and LB Enhanced, are substantially tighter than LB Keogh. All three have the same worst case computational complexity - linear with respect to series length and constant with respect to window size. We present four new DTW lower bounds in the same complexity class. LB Petitjean is substantially tighter than LB Improved, with only modest additional computational overhead. LB Webb is more efficient than LB Improved, while often providing a tighter bound. LB Webb is always tighter than LB Keogh. The parameter free LB Webb is usually tighter than LB Enhanced. A parameterized variant, LB Webb Enhanced, is always tighter than LB Enhanced. A further variant, LB Webb*, is useful for some constrained distance functions. In extensive experiments, LB Webb proves to be very effective for nearest neighbor search.
indicnlp@kgp at DravidianLangTech-EACL2021: Offensive Language Identification in Dravidian Languages
Venue: arXiv cs.CL 2021
Year: 2021

The paper presents the submission of the team indicnlp@kgp to the EACL 2021 shared task "Offensive Language Identification in Dravidian Languages." The task aimed to classify different offensive content types in 3 code-mixed Dravidian language datasets. The work leverages existing state of the art approaches in text classification by incorporating additional data and transfer learning on pre-trained models. Our final submission is an ensemble of an AWD-LSTM based model along with 2 different transformer model architectures based on BERT and RoBERTa. We achieved weighted-average F1 scores of 0.97, 0.77, and 0.72 in the Malayalam-English, Tamil-English, and Kannada-English datasets ranking 1st, 2nd, and 3rd on the respective tasks.
On the Equilibrium Elicitation of Markov Games Through Information Design
Venue: arXiv cs.MA 2021
Year: 2021

This work considers a novel information design problem and studies how the craft of payoff-relevant environmental signals solely can influence the behaviors of intelligent agents. The agents' strategic interactions are captured by an incomplete-information Markov game, in which each agent first selects one environmental signal from multiple signal sources as additional payoff-relevant information and then takes an action. There is a rational information designer (designer) who possesses one signal source and aims to control the equilibrium behaviors of the agents by designing the information structure of her signals sent to the agents. An obedient principle is established which states that it is without loss of generality to focus on the direct information design when the information design incentivizes each agent to select the signal sent by the designer, such that the design process avoids the predictions of the agents' strategic selection behaviors. We then introduce the design protocol given a goal of the designer referred to as obedient implementability (OIL) and characterize the OIL in a class of obedient perfect Bayesian Markov Nash equilibria (O-PBME). A new framework for information design is proposed based on an approach of maximizing the optimal slack variables. Finally, we formulate the designer's goal selection problem and characterize it in terms of information design by establishing a relationship between the O-PBME and the Bayesian Markov correlated equilibria, in which we build upon the revelation principle in classic information design in economics. The proposed approach can be applied to elicit desired behaviors of multi-agent systems in competing as well as cooperating settings and be extended to heterogeneous stochastic games in the complete- and the incomplete-information environments.
Fault-Tolerant Distance Labeling for Planar Graphs
Venue: arXiv cs.DS 2021
Year: 2021

In fault-tolerant distance labeling we wish to assign short labels to the vertices of a graph $G$ such that from the labels of any three vertices $u,v,f$ we can infer the $u$-to-$v$ distance in the graph $G\setminus \{f\}$. We show that any directed weighted planar graph (and in fact any graph in a graph family with $O(\sqrt{n})$-size separators, such as minor-free graphs) admits fault-tolerant distance labels of size $O(n^{2/3})$. We extend these labels in a way that allows us to also count the number of shortest paths, and provide additional upper and lower bounds for labels and oracles for counting shortest paths.
Max-Min Fair Hybrid Precoding for Multi-group Multicasting in Millimeter-Wave Channel
Venue: arXiv cs.IT 2021
Year: 2021

The potential of using millimeter-wave (mmWave) to encounter the current bandwidth shortage has motivated packing more antenna elements in the same physical size which permits the advent of massive multiple-input-multiple-output (MIMO) for mmWave communication. However, with increasing number of antenna elements, the ability of allocating a single RF-chain per antenna becomes infeasible and unaffordable. As a cost-effective alternative, the design of hybrid precoding has been considered where the limited-scattering signals are captured by a high-dimensional RF precoder realized by an analog phase-shifter network followed by a low-dimensional digital precoder at baseband. In this paper, the max-min fair problem is considered to design a low-complexity hybrid precoder for multi-group multicasting systems in mmWave channels. The problem is non-trivial due to two main reasons: the original max-min problem for multi-group multicasting for a fully-digital precoder is non-convex, and the analog precoder places constant modules constraint which restricts the feasible set of the precoders in the design problem. Therefore, we consider a low complexity hybrid precoder design to tackle and benefit from the mmWave channel structure. Each analog beamformer was designed to maximize the minimum matching component for users within a given group. Once obtained, the digital precoder was attained by solving the max-min problem of the equivalent channel.
Energy and Spectral Efficiency Balancing Algorithm for Energy Saving in LTE Downlinks
Venue: arXiv cs.NI 2021
Year: 2021

In wireless network communication environments, Spectral Efficiency (SE) and Energy Efficiency (EE) are among the major indicators used for evaluating network performance. However, given the high demand for data rate services and the exponential growth of energy consumption, SE and EE continue to elicit increasing attention in academia and industries. Consequently, a study of the trade-off between these metrics is imperative. In contrast with existing works, this study proposes an efficient SE and EE trade-off algorithm for saving energy in downlink Long Term Evolution (LTE) networks to concurrently optimize SE and EE while considering battery life at the Base Station (BS). The scheme is formulated as a Multi-objective Optimization Problem (MOP) and its Pareto optimal solution is examined. In contrast with other algorithms that prolong battery life by considering the idle state of a BS, thereby increasing average delay and energy consumption, the proposed algorithm prolongs battery life by adjusting the initial and final states of a BS to minimize the average delay and the energy consumption. Similarly, the use of an omni-directional antenna to spread radio signals to the user equipment in all directions causes high interference and low spatial reuse. We propose using a directional antenna instead of an omni-directional antenna by transmitting signals in one direction which results in no or low interference and high spatial reuse. The proposed scheme has been extensively evaluated through simulation, where simulation results prove that the proposed scheme is efficiently able to decrease the average response delay, improve SE, and minimize energy consumption.