OpenRaiser/Intern-Atlas huggingface dataset:
Predictive Maintenance -- Bridging Artificial Intelligence and IoT
Venue: arXiv cs.LG 2021
Year: 2021

This paper highlights the trends in the field of predictive maintenance with the use of machine learning. With the continuous development of the Fourth Industrial Revolution, through IoT, the technologies that use artificial intelligence are evolving. As a result, industries have been using these technologies to optimize their production. Through scientific research conducted for this paper, conclusions were drawn about the trends in Predictive Maintenance applications with the use of machine learning bridging Artificial Intelligence and IoT. These trends are related to the types of industries in which Predictive Maintenance was applied, the models of artificial intelligence were implemented, mainly of machine learning and the types of sensors that are applied through the IoT to the applications. Six sectors were presented and the production sector was dominant as it accounted for 54.54% of total publications. In terms of artificial intelligence models, the most prevalent among ten were the Artificial Neural Networks, Support Vector Machine and Random Forest with 27.84%, 17.72% and 13.92% respectively. Finally, twelve categories of sensors emerged, of which the most widely used were the sensors of temperature and vibration with percentages of 60.71% and 46.42% correspondingly.
Multi-sequence Spreading Random Access (MSRA) for Compressive Sensing-based Grant-free Communication
Venue: arXiv cs.IT 2021
Year: 2021

The performance of grant-free random access (GF-RA) is limited by the number of accessible random access resources (RRs) due to the absence of collision resolution. Compressive sensing (CS)-based RA schemes scale up the RRs at the expense of increased non-orthogonality among transmitted signals. This paper presents the design of multi-sequence spreading random access (MSRA) which employs multiple spreading sequences to spread the different symbols of a user as opposed to the conventional schemes in which a user employs the same spreading sequence for each symbol. We show that MSRA provides code diversity, enabling the multi-user detection (MUD) to be modeled into a well-conditioned multiple measurement vector (MMV) CS problem. The code diversity is quantified by the decrease in the average Babel mutual coherence among the spreading sequences. Moreover, we present a two-stage active user detection (AUD) scheme for both wideband and narrowband implementation. Our theoretical analysis shows that with MSRA activity misdetection falls exponentially while the size of GF-RA frame is increased. Finally, the simulation results show that about 82% increase in utilization of RRs, i.e., more active users, is supported by MSRA than the conventional schemes while achieving the RA failure rate lower bound set by random access collision.
Your Classifier can Secretly Suffice Multi-Source Domain Adaptation
Venue: arXiv cs.LG 2021
Year: 2021

Multi-Source Domain Adaptation (MSDA) deals with the transfer of task knowledge from multiple labeled source domains to an unlabeled target domain, under a domain-shift. Existing methods aim to minimize this domain-shift using auxiliary distribution alignment objectives. In this work, we present a different perspective to MSDA wherein deep models are observed to implicitly align the domains under label supervision. Thus, we aim to utilize implicit alignment without additional training objectives to perform adaptation. To this end, we use pseudo-labeled target samples and enforce a classifier agreement on the pseudo-labels, a process called Self-supervised Implicit Alignment (SImpAl). We find that SImpAl readily works even under category-shift among the source domains. Further, we propose classifier agreement as a cue to determine the training convergence, resulting in a simple training algorithm. We provide a thorough evaluation of our approach on five benchmarks, along with detailed insights into each component of our approach.
The Effectiveness of Morphology-aware Segmentation in Low-Resource Neural Machine Translation
Venue: arXiv cs.CL 2021
Year: 2021

This paper evaluates the performance of several modern subword segmentation methods in a low-resource neural machine translation setting. We compare segmentations produced by applying BPE at the token or sentence level with morphologically-based segmentations from LMVR and MORSEL. We evaluate translation tasks between English and each of Nepali, Sinhala, and Kazakh, and predict that using morphologically-based segmentation methods would lead to better performance in this setting. However, comparing to BPE, we find that no consistent and reliable differences emerge between the segmentation methods. While morphologically-based methods outperform BPE in a few cases, what performs best tends to vary across tasks, and the performance of segmentation methods is often statistically indistinguishable.
Software Engineering for IoT-Driven Data Analytics Applications
Venue: arXiv cs.SE 2021
Year: 2021

Internet of Things Driven Data Analytics (IoT-DA) has the potential to excel data-driven operationalisation of smart environments. However, limited research exists on how IoT-DA applications are designed, implemented, operationalised, and evolved in the context of software and system engineering life-cycle. This article empirically derives a framework that could be used to systematically investigate the role of software engineering (SE) processes and their underlying practices to engineer IoT-DA applications. First, using existing frameworks and taxonomies, we develop an evaluation framework to evaluate software processes, methods, and other artefacts of SE for IoT-DA. Secondly, we perform a systematic mapping study to qualitatively select 16 processes (from academic research and industrial solutions) of SE for IoT-DA. Thirdly, we apply our developed evaluation framework based on 17 distinct criterion (a.k.a. process activities) for fine-grained investigation of each of the 16 SE processes. Fourthly, we apply our proposed framework on a case study to demonstrate development of an IoT-DA healthcare application. Finally, we highlight key challenges, recommended practices, and the lessons learnt based on framework's support for process-centric software engineering of IoT-DA. The results of this research can facilitate researchers and practitioners to engineer emerging and next-generation of IoT-DA software applications.
An Efficient Simulation of Quantum Secret Sharing
Venue: arXiv cs.CR 2021
Year: 2021

In quantum cryptography, quantum secret sharing $(QSS)$ is a fundamental primitive. $QSS$ can be used to create complex and secure multiparty quantum protocols. Existing $QSS$ protocols are either at the $(n, n)$ threshold $2$ level or at the $(t, n)$ threshold $d$ level with a trusted player, where $n$ denotes the number of players and $t$ denotes the threshold number of players. Here, we propose a secure $d$-level $QSS$ protocol for sharing a secret with efficient simulation. This protocol is more secure, flexible, and practical as compared to the existing $QSS$ protocols: $(n, n)$ threshold $2$-level and $(t,n)$ threshold $d$-level with a trusted player. Further, it does not disclose any information about the secret to players. Its security analysis shows that the intercept-resend, intercept, entangle-measure, forgery, collision and collusion attacks are not possible in this protocol.
On the Impossibility of Post-Quantum Black-Box Zero-Knowledge in Constant Rounds
Venue: arXiv cs.CR 2021
Year: 2021

We investigate the existence of constant-round post-quantum black-box zero-knowledge protocols for $\mathbf{NP}$. As a main result, we show that there is no constant-round post-quantum black-box zero-knowledge argument for $\mathbf{NP}$ unless $\mathbf{NP}\subseteq \mathbf{BQP}$. As constant-round black-box zero-knowledge arguments for $\mathbf{NP}$ exist in the classical setting, our main result points out a fundamental difference between post-quantum and classical zero-knowledge protocols. Combining previous results, we conclude that unless $\mathbf{NP}\subseteq \mathbf{BQP}$, constant-round post-quantum zero-knowledge protocols for $\mathbf{NP}$ exist if and only if we use non-black-box techniques or relax certain security requirements such as relaxing standard zero-knowledge to $ε$-zero-knowledge. Additionally, we also prove that three-round and public-coin constant-round post-quantum black-box $ε$-zero-knowledge arguments for $\mathbf{NP}$ do not exist unless $\mathbf{NP}\subseteq \mathbf{BQP}$.
Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges
Venue: arXiv cs.LG 2021
Year: 2021

Interpretability in machine learning (ML) is crucial for high stakes decisions and troubleshooting. In this work, we provide fundamental principles for interpretable ML, and dispel common misunderstandings that dilute the importance of this crucial topic. We also identify 10 technical challenge areas in interpretable machine learning and provide history and background on each problem. Some of these problems are classically important, and some are recent problems that have arisen in the last few years. These problems are: (1) Optimizing sparse logical models such as decision trees; (2) Optimization of scoring systems; (3) Placing constraints into generalized additive models to encourage sparsity and better interpretability; (4) Modern case-based reasoning, including neural networks and matching for causal inference; (5) Complete supervised disentanglement of neural networks; (6) Complete or even partial unsupervised disentanglement of neural networks; (7) Dimensionality reduction for data visualization; (8) Machine learning models that can incorporate physics and other generative or causal constraints; (9) Characterization of the "Rashomon set" of good models; and (10) Interpretable reinforcement learning. This survey is suitable as a starting point for statisticians and computer scientists interested in working in interpretable machine learning.
Study of Design of Rate-Compatible Polar Codes Based on Non-Uniform Channel Polarization
Venue: arXiv cs.IT 2021
Year: 2021

We propose a novel scheme for rate-compatible arbitrary-length polar code construction for the additive white Gaussian noise (AWGN) channel. The proposed scheme is based on the concept of non-uniform channel polarization. The original polar codes can only be designed with code lengths that are powers of two. Puncturing, shortening and extension are three strategies to obtain arbitrary code lengths and code rates for polar codes. There are other ways to design codes with arbitrary length but which have encoding and decoding with higher complexity such as multi-kernel, concatenated codes and specific constructions for Belief propagation (BP) or Successive Cancellation (SC) decoding. In general, the quality of the projected bit channels by these arbitrary-length techniques differs from that of the original bit channels, which can greatly affect the performance of the constructed polar codes. The proposed Non-Uniform Polarization based on Gaussian Approximation (NUPGA) is an efficient construction technique for rate-compatible arbitrary-length polar codes, which chooses the best channels (i.e., selects the positions of the information bits) by re-polarization of the codeword with desired length. A generalization of the Gaussian Approximation is devised for both polarization and re-polarization processes. We also present shortening and extension techniques for design polar codes. Simulations verify the effectiveness of the proposed NUPGA designs against existing rate-compatible techniques.
Semantic 3D Map Change Detection and Update based on Smartphone Visual Positioning System
Venue: arXiv cs.RO 2021
Year: 2021

Accurate localization and 3D maps are increasingly needed for various artificial intelligence based IoT applications such as augmented reality, intelligent transportation, crowd monitoring, robotics, etc. This article proposes a novel semantic 3D map change detection and update based on a smartphone visual positioning system (VPS) for the outdoor and indoor environments. The proposed method presents an alternate solution to SLAM for map update in terms of efficiency, cost, availability, and map reuse. Building on existing 3D maps of recent years, a system is designed to use artificial intelligence to identify high-level semantics in images for positioning and map change detection. Then, a virtual LIDAR that estimates the depth of objects in the 3D map is used to generate a compact point cloud to update changes in the scene. We present an excellent performance of localization with respect to other state-of-the-art smartphone positioning solutions to accurately update semantic 3D maps. It is shown that the proposed solution can position users within 1.9m, and update objects with an average error of 2.1m.
Reference-Aided Part-Aligned Feature Disentangling for Video Person Re-Identification
Venue: arXiv cs.CV 2021
Year: 2021

Recently, video-based person re-identification (re-ID) has drawn increasing attention in compute vision community because of its practical application prospects. Due to the inaccurate person detections and pose changes, pedestrian misalignment significantly increases the difficulty of feature extraction and matching. To address this problem, in this paper, we propose a \textbf{R}eference-\textbf{A}ided \textbf{P}art-\textbf{A}ligned (\textbf{RAPA}) framework to disentangle robust features of different parts. Firstly, in order to obtain better references between different videos, a pose-based reference feature learning module is introduced. Secondly, an effective relation-based part feature disentangling module is explored to align frames within each video. By means of using both modules, the informative parts of pedestrian in videos are well aligned and more discriminative feature representation is generated. Comprehensive experiments on three widely-used benchmarks, i.e. iLIDS-VID, PRID-2011 and MARS datasets verify the effectiveness of the proposed framework. Our code will be made publicly available.
Fault Prediction based on Software Metrics and SonarQube Rules. Machine or Deep Learning?
Venue: arXiv cs.SE 2021
Year: 2021

Background. Developers spend more time fixing bugs and refactoring the code to increase the maintainability than developing new features. Researchers investigated the code quality impact on fault-proneness focusing on code smells and code metrics. Objective. We aim at advancing fault-inducing commit prediction based on SonarQube considering the contribution provided by each rule and metric. Method. We designed and conducted a case study among 33 Java projects analyzed with SonarQube and SZZ to identify fault-inducing and fault-fixing commits. Moreover, we investigated fault-proneness of each SonarQube rule and metric using Machine and Deep Learning models. Results. We analyzed 77,932 commits that contain 40,890 faults and infected by more than 174 SonarQube rules violated 1,9M times, on which there was calculated 24 software metrics available by the tool. Compared to machine learning models, deep learning provide a more accurate fault detection accuracy and allowed us to accurately identify the fault-prediction power of each SonarQube rule. As a result, fourteen of the 174 violated rules has an importance higher than 1\% and account for 30\% of the total fault-proneness importance, while the fault proneness of the remaining 165 rules is negligible. Conclusion. Future works might consider the adoption of timeseries analysis and anomaly detection techniques to better and more accurately detect the rules that impact fault-proneness.
Expected Extinction Times of Epidemics with State-Dependent Infectiousness
Venue: arXiv cs.SI 2021
Year: 2021

We model an epidemic where the per-person infectiousness in a network of geographic localities changes with the total number of active cases. This would happen as people adopt more stringent non-pharmaceutical precautions when the population has a larger number of active cases. We show that there exists a sharp threshold such that when the curing rate for the infection is above this threshold, the mean time for the epidemic to die out is logarithmic in the initial infection size, whereas when the curing rate is below this threshold, the mean time for epidemic extinction is infinite. We also show that when the per-person infectiousness goes to zero asymptotically as a function of the number of active cases, the mean extinction times all have the same asymptote independent of network structure. Simulations bear out these results, while also demonstrating that if the per-person infectiousness is large when the epidemic size is small (i.e., the precautions are lax when the epidemic is small and only get stringent after the epidemic has become large), it might take a very long time for the epidemic to die out. We also provide some analytical insight into these observations.
Escaping the Time Pit: Pitfalls and Guidelines for Using Time-Based Git Data
Venue: arXiv cs.SE 2021
Year: 2021

Many software engineering research papers rely on time-based data (e.g., commit timestamps, issue report creation/update/close dates, release dates). Like most real-world data however, time-based data is often dirty. To date, there are no studies that quantify how frequently such data is used by the software engineering research community, or investigate sources of and quantify how often such data is dirty. Depending on the research task and method used, including such dirty data could affect the research results. This paper presents the first survey of papers that utilize time-based data, published in the Mining Software Repositories (MSR) conference series. Out of the 690 technical track and data papers published in MSR 2004--2020, we saw at least 35% of papers utilized time-based data. We then used the Boa and Software Heritage infrastructures to help identify and quantify several sources of dirty commit timestamp data. Finally we provide guidelines/best practices for researchers utilizing time-based data from Git repositories.
BigCarl: Mining frequent subnets from a single large Petri net
Venue: arXiv cs.DB 2021
Year: 2021

While there have been lots of work studying frequent subgraph mining, very rare publications have discussed frequent subnet mining from more complicated data structures such as Petri nets. This paper studies frequent subnets mining from a single large Petri net. We follow the idea of transforming a Petri net in net graph form and to mine frequent sub-net graphs to avoid high complexity. Technically, we take a minimal traversal approach to produce a canonical label of the big net graph. We adapted the maximal independent embedding set approach to the net graph representation and proposed an incremental pattern growth (independent embedding set reduction) way for discovering frequent sub-net graphs from the single large net graph, which are finally transformed back to frequent subnets. Extensive performance studies made on a single large Petri net, which contains 10K events, 40K conditions and 30 K arcs, showed that our approach is correct and the complexity is reasonable.
Neighbor Embedding Variational Autoencoder
Venue: arXiv cs.LG 2021
Year: 2021

Being one of the most popular generative framework, variational autoencoders(VAE) are known to suffer from a phenomenon termed posterior collapse, i.e. the latent variational distributions collapse to the prior, especially when a strong decoder network is used. In this work, we analyze the latent representation of collapsed VAEs, and proposed a novel model, neighbor embedding VAE(NE-VAE), which explicitly constraints the encoder to encode inputs close in the input space to be close in the latent space. We observed that for VAE variants that report similar ELBO, KL divergence or even mutual information scores may still behave quite differently in the latent organization. In our experiments, NE-VAE can produce qualitatively different latent representations with majority of the latent dimensions remained active, which may benefit downstream latent space optimization tasks. NE-VAE can prevent posterior collapse to a much greater extent than it's predecessors, and can be easily plugged into any autoencoder framework, without introducing addition model components and complex training routines.
Self adversarial attack as an augmentation method for immunohistochemical stainings
Venue: arXiv cs.CV 2021
Year: 2021

It has been shown that unpaired image-to-image translation methods constrained by cycle-consistency hide the information necessary for accurate input reconstruction as imperceptible noise. We demonstrate that, when applied to histopathology data, this hidden noise appears to be related to stain specific features and show that this is the case with two immunohistochemical stainings during translation to Periodic acid- Schiff (PAS), a histochemical staining method commonly applied in renal pathology. Moreover, by perturbing this hidden information, the translation models produce different, plausible outputs. We demonstrate that this property can be used as an augmentation method which, in a case of supervised glomeruli segmentation, leads to improved performance.
Characterising Communities of Twitter Users Who Posted Vaccines Related Tweets by Information Sources
Venue: arXiv cs.SI 2021
Year: 2021

Objective: We formed community structure based on web page credibility and we measured the types of information for characterizing communities of tweeter users who posted about tweets related to vaccine. Methods: We performed the experiment on only Twitter data (tweets) regarding vaccine. The duration of data collection was between 17 January 2017 and 14 March 2018. We formulated cluster based on the information on its contents and sources it resides (i.e., website domains). We only focused the topics which were related to vaccine. To detect the structure and network of community, we applied Louvain community algorithm along with Random walks called Info map method over vaccines related tweeter user. We defined the communities based on various measures derived from the information shared by Twitter users. Representations and visualizations of the communities based on these derived measures help the public health organization to make understand the possible cause of the rejecting the safety and efficacy of vaccines. Results: To analyse people perception over social media, we downloaded 6,591,566 tweets. We distinguished 1,860,662 users who were posting related to vaccine. We applied Louvain community detection algorithm along with DMM values and we found 192 communities. It also produced higher alignment values as long as the number of topics were low. With the increase of topics number, the alignment becomes lower. The total tweets were divided into two parts based on their characterizes. The first category contained 163,148 (57.16%) tweets which were based as evidence and advocacy and the second category contained 6244 (2.19%) tweets which were based on the experiences and opinion. We observed 4548 users were posting about experiential tweets about vaccine and of them 3449 users (75.84%) were posted evidence and advocacy related.