OpenRaiser/Intern-Atlas huggingface dataset:
Optimizing Neural Network Weights using Nature-Inspired Algorithms
Venue: arXiv cs.LG 2021
Year: 2021

This study aims to optimize Deep Feedforward Neural Networks (DFNNs) training using nature-inspired optimization algorithms, such as PSO, MTO, and its variant called MTOCL. We show how these algorithms efficiently update the weights of DFNNs when learning from data. We evaluate the performance of DFNN fused with optimization algorithms using three Wisconsin breast cancer datasets, Original, Diagnostic, and Prognosis, under different experimental scenarios. The empirical analysis demonstrates that MTOCL is the most performing in most scenarios across the three datasets. Also, MTOCL is comparable to past weight optimization algorithms for the original dataset, and superior for the other datasets, especially for the challenging Prognostic dataset.
Testing DBMS Performance with Mutations
Venue: arXiv cs.DB 2021
Year: 2021

Because database systems are the critical component of modern data-intensive applications, it is important to ensure that they operate correctly. To this end, developers extensively test these systems to eliminate bugs that negatively affect functionality. In addition to functional bugs, however, there is another important class of bugs: performance bugs. These bugs negatively affect the response time of a database system and can therefore affect the overall performance of the system. Despite their impact on end-user experience, performance bugs have received considerably less attention than functional bugs. In this paper, we present AMOEBA, a system for automatically detecting performance bugs in database systems. The core idea behind AMOEBA is to construct query pairs that are semantically equivalent to each other and then compare their response time on the same database system. If the queries exhibit a significant difference in their runtime performance, then the root cause is likely a performance bug in the system. We propose a novel set of structure and predicate mutation rules for constructing query pairs that are likely to uncover performance bugs. We introduce feedback mechanisms for improving the efficacy and computational efficiency of the tool. We evaluate AMOEBA on two widely-used DBMSs, namely PostgreSQL and CockroachDB. AMOEBA has discovered 20 previously-unknown performance bugs, among which developers have already confirmed 14 and fixed 4.
Approximation Algorithms for the NFV Service Distribution Problem
Venue: arXiv cs.NI 2021
Year: 2021

Distributed cloud networking builds on network functions virtualization (NFV) and software defined networking (SDN) to enable the deployment of network services in the form of elastic virtual network functions (VNFs) instantiated over general purpose servers at distributed cloud locations. We address the design of fast approximation algorithms for the NFV service distribution problem (NSDP), whose goal is to determine the placement of VNFs, the routing of service flows, and the associated allocation of cloud and network resources that satisfy client demands with minimum cost. We show that in the case of load-proportional costs, the resulting fractional NSDP can be formulated as a multi-commodity-chain flow problem on a cloud augmented graph, and design a queue-length based algorithm, named QNSD, that provides an O(ε) approximation in time O(1/ε). We then address the case in which resource costs are a function of the integer number of allocated resources and design a variation of QNSD that effectively pushes for flow consolidation into a limited number of active resources to minimize overall cloud network cost.
Towards Automatic Sizing for PPE with a Point Cloud Based Variational Autoencoder
Venue: arXiv cs.LG 2021
Year: 2021

Sizing and fitting of Personal Protective Equipment (PPE) is a critical part of the product creation process; however, traditional methods to do this type of work can be labor intensive and based on limited or non-representative anthropomorphic data. In the case of PPE, a poor fit can jeopardize an individual's health and safety. In this paper we present an unsupervised machine learning algorithm that can identify a representative set of exemplars, individuals that can be utilized by designers as idealized sizing models. The algorithm is based around a Variational Autoencoder (VAE) with a Point-Net inspired encoder and decoder architecture trained on Human point-cloud data obtained from the CEASAR dataset. The learned latent space is then clustered to identify a specified number of sizing groups. We demonstrate this technique on scans of human faces to provide designers of masks and facial coverings a reference set of individuals to test existing mask styles.
A Need-finding Study for Understanding Text Entry in Smartphone App Usage
Venue: arXiv cs.HC 2021
Year: 2021

Text entry makes up about one-fourth of the smartphone interaction events, and is known to be challenging and difficult. However, there has been little study about the characteristics of text entry in the context of smartphone app usage. In this paper, we present a mixed-method in-situ study conducted in 2016 with 17 active smartphone users to better understand text entry in smartphone app usage. Our results show 80% of text was entered into communication apps, with different apps exhibiting distinct usage patterns. We found that structured data such as URLs and email addresses are rarely typed but instead are auto-completed or replaced with search, copy-and-paste is rarely used, and sessions of smartphone usage with text entry involve more apps and last longer. We conclude with a discussion about the implications on the development of systems to better support mobile interaction.
Changes from the Trenches: Should We Automate Them?
Venue: arXiv cs.SE 2021
Year: 2021

Code changes constitute one of the most important features of software evolution. Studying them can provide insights into the nature of software development and also lead to practical solutions - recommendations and automations of popular changes for developers. In our work, we developed a tool called PythonChangeMiner that allows to discover code change patterns in the histories of Python projects. We validated the tool and then employed it to discover patterns in the dataset of 120 projects from four different domains of software engineering. We manually categorized patterns that occur in more than one project from the standpoint of their structure and content, and compared different domains and patterns in that regard. We conducted a survey of the authors of the discovered changes: 82.9% of them said that they can give the change a name and 57.9% expressed their desire to have the changes automated, indicating the ability of the tool to discover valuable patterns. Finally, we interviewed 9 members of a popular integrated development environment (IDE) development team to estimate the feasibility of automating the discovered changes. It was revealed that independence from the context and high precision made a pattern a better candidate for automation. The patterns received mainly positive reviews and several were ranked as very likely for automation.
Explainable Machine Learning with Prior Knowledge: An Overview
Venue: arXiv cs.LG 2021
Year: 2021

This survey presents an overview of integrating prior knowledge into machine learning systems in order to improve explainability. The complexity of machine learning models has elicited research to make them more explainable. However, most explainability methods cannot provide insight beyond the given data, requiring additional information about the context. We propose to harness prior knowledge to improve upon the explanation capabilities of machine learning models. In this paper, we present a categorization of current research into three main categories which either integrate knowledge into the machine learning pipeline, into the explainability method or derive knowledge from explanations. To classify the papers, we build upon the existing taxonomy of informed machine learning and extend it from the perspective of explainability. We conclude with open challenges and research directions.
Rule Augmented Unsupervised Constituency Parsing
Venue: arXiv cs.CL 2021
Year: 2021

Recently, unsupervised parsing of syntactic trees has gained considerable attention. A prototypical approach to such unsupervised parsing employs reinforcement learning and auto-encoders. However, no mechanism ensures that the learnt model leverages the well-understood language grammar. We propose an approach that utilizes very generic linguistic knowledge of the language present in the form of syntactic rules, thus inducing better syntactic structures. We introduce a novel formulation that takes advantage of the syntactic grammar rules and is independent of the base system. We achieve new state-of-the-art results on two benchmarks datasets, MNLI and WSJ. The source code of the paper is available at https://github.com/anshuln/Diora_with_rules.
GAN pretraining for deep convolutional autoencoders applied to Software-based Fingerprint Presentation Attack Detection
Venue: arXiv cs.LG 2021
Year: 2021

The need for reliable systems to determine fingerprint presentation attacks grows with the rising use of the fingerprint for authentication. This work presents a new approach to single-class classification for software-based fingerprint presentation attach detection. The described method utilizes a Wasserstein GAN to apply transfer learning to a deep convolutional autoencoder. By doing so, the autoencoder could be pretrained and finetuned on the LivDet2021 Dermalog sensor dataset with only 1122 bona fide training samples. Without making use of any presentation attack samples, the model could archive an average classification error rate of 16.79%. The Wasserstein GAN implemented to pretrain the autoencoders weights can further be used to generate realistic-looking artificial fingerprint patches. Extensive testing of different autoencoder architectures and hyperparameters led to coarse architectural guidelines as well as multiple implementations which can be utilized for future work.
RFID-based Article-to-Fixture Predictions in Real-World Fashion Stores
Venue: arXiv cs.IR 2021
Year: 2021

In recent years, Radio Frequency Identification (RFID) technology has been applied to improve numerous processes, such as inventory management in retail stores. However, automatic localization of RFID-tagged goods in stores is still a challenging problem. To address this issue, we equip fixtures (e.g., shelves) with reference tags and use data we collect during RFID-based stocktakes to map articles to fixtures. Knowing the location of goods enables the implementation of several practical applications, such as automated Money Mapping (i.e., a heat map of sales across fixtures). Specifically, we conduct controlled lab experiments and a case-study in two fashion retail stores to evaluate our article-to-fixture prediction approaches. The approaches are based on calculating distances between read event time series using DTW, and clustering of read events using DBSCAN. We find that, read events collected during RFID-based stocktakes can be used to assign articles to fixtures with an accuracy of more than 90%. Additionally, we conduct a pilot to investigate the challenges related to the integration of such a localization system in the day-to-day business of retail stores. Hence, in this paper we present an exploratory venture into novel and practical RFID-based applications in fashion retails stores, beyond the scope of stock management.
A Lightweight Deep Network for Efficient CSI Feedback in Massive MIMO Systems
Venue: arXiv cs.IT 2021
Year: 2021

To fully exploit the advantages of massive multiple-input multiple-output (m-MIMO), accurate channel state information (CSI) is required at the transmitter. However, excessive CSI feedback for large antenna arrays is inefficient and thus undesirable in practical applications. By exploiting the inherent correlation characteristics of complex-valued channel responses in the angular-delay domain, we propose a novel neural network (NN) architecture, namely ENet, for CSI compression and feedback in m-MIMO. Even if the ENet processes the real and imaginary parts of the CSI values separately, its special structure enables the network trained for the real part only to be reused for the imaginary part. The proposed ENet shows enhanced performance with the network size reduced by nearly an order of magnitude compared to the existing NN-based solutions. Experimental results verify the effectiveness of the proposed ENet.
Trimming Feature Extraction and Inference for MCU-based Edge NILM: a Systematic Approach
Venue: arXiv cs.LG 2021
Year: 2021

Non-Intrusive Load Monitoring (NILM) enables the disaggregation of the global power consumption of multiple loads, taken from a single smart electrical meter, into appliance-level details. State-of-the-Art approaches are based on Machine Learning methods and exploit the fusion of time- and frequency-domain features from current and voltage sensors. Unfortunately, these methods are compute-demanding and memory-intensive. Therefore, running low-latency NILM on low-cost, resource-constrained MCU-based meters is currently an open challenge. This paper addresses the optimization of the feature spaces as well as the computational and storage cost reduction needed for executing State-of-the-Art (SoA) NILM algorithms on memory- and compute-limited MCUs. We compare four supervised learning techniques on different classification scenarios and characterize the overall NILM pipeline's implementation on a MCU-based Smart Measurement Node. Experimental results demonstrate that optimizing the feature space enables edge MCU-based NILM with 95.15% accuracy, resulting in a small drop compared to the most-accurate feature vector deployment (96.19%) while achieving up to 5.45x speed-up and 80.56% storage reduction. Furthermore, we show that low-latency NILM relying only on current measurements reaches almost 80% accuracy, allowing a major cost reduction by removing voltage sensors from the hardware design.
On the use of feature-maps and parameter control for improved quality-diversity meta-evolution
Venue: arXiv cs.NE 2021
Year: 2021

In Quality-Diversity (QD) algorithms, which evolve a behaviourally diverse archive of high-performing solutions, the behaviour space is a difficult design choice that should be tailored to the target application. In QD meta-evolution, one evolves a population of QD algorithms to optimise the behaviour space based on an archive-level objective, the meta-fitness. This paper proposes an improved meta-evolution system such that (i) the database used to rapidly populate new archives is reformulated to prevent loss of quality-diversity; (ii) the linear transformation of base-features is generalised to a feature-map, a function of the base-features parametrised by the meta-genotype; and (iii) the mutation rate of the QD algorithm and the number of generations per meta-generation are controlled dynamically. Experiments on an 8-joint planar robot arm compare feature-maps (linear, non-linear, and feature-selection), parameter control strategies (static, endogenous, reinforcement learning, and annealing), and traditional MAP-Elites variants, for a total of 49 experimental conditions. Results reveal that non-linear and feature-selection feature-maps yield a 15-fold and 3-fold improvement in meta-fitness, respectively, over linear feature-maps. Reinforcement learning ranks among top parameter control methods. Finally, our approach allows the robot arm to recover a reach of over 80% for most damages and at least 60% for severe damages.
Characterizing the Anti-Vaxxers' Reply Behavior on Social Media
Venue: arXiv cs.SI 2021
Year: 2021

Although the online campaigns of anti-vaccine advocates, or anti-vaxxers, severely threaten efforts for herd immunity, their reply behavior--the form of directed messaging that can be sent beyond follow-follower relationships--remains poorly understood. Here, we examined the characteristics of anti-vaxxers' reply behavior on Twitter to attempt to comprehend their characteristics of spreading their beliefs in terms of interaction frequency, content, and targets. Among the results, anti-vaxxers more frequently conducted reply behavior with other clusters, especially neutral accounts. Anti-vaxxers' replies were significantly more toxic than those from neutral accounts and pro-vaxxers, and their toxicity, in particular, was higher with regard to the rollout of vaccines. Anti-vaxxers' replies were more persuasive than the others in terms of the emotional aspect, rather than linguistical styles. The targets of anti-vaxxers' replies tend to be accounts with larger numbers of followers and posts, including accounts that relate to health care or represent scientists, policy-makers, or media figures or outlets. We discussed how their reply behaviors are effective in spreading their beliefs, as well as possible countermeasures to restrain them. These findings should prove useful for pro-vaxxers and platformers to promote trusted information while reducing the effect of vaccine disinformation.
Polyjuice: High-Performance Transactions via Learned Concurrency Control
Venue: arXiv cs.DB 2021
Year: 2021

Concurrency control algorithms are key determinants of the performance of in-memory databases. Existing algorithms are designed to work well for certain workloads. For example, optimistic concurrency control (OCC) is better than two-phase-locking (2PL) under low contention, while the converse is true under high contention. To adapt to different workloads, prior works mix or switch between a few known algorithms using manual insights or simple heuristics. We propose a learning-based framework that instead explicitly optimizes concurrency control via offline training to maximize performance. Instead of choosing among a small number of known algorithms, our approach searches in a "policy space" of fine-grained actions, resulting in novel algorithms that can outperform existing algorithms by specializing to a given workload. We build Polyjuice based on our learning framework and evaluate it against several existing algorithms. Under different configurations of TPC-C and TPC-E, Polyjuice can achieve throughput numbers higher than the best of existing algorithms by 15% to 56%.
Going Beyond Second Screens: Applications for the Multi-display Intelligent Living Room
Venue: arXiv cs.HC 2021
Year: 2021

This work aims to investigate how the amenities offered by Intelligent Environments can be used to shape new types of useful, exciting and fulfilling experiences while watching sports or movies. Towards this direction, two ambient media players were developed aspiring to offer live access to secondary information via the available displays of an Intelligent Living Room, and to appropriately exploit the technological equipment so as to support natural interaction. Expert-based evaluation experiments revealed some factors that can influence the overall experience significantly, without hindering the viewers' immersion to the main media.
FRaGenLP: A Generator of Random Linear Programming Problems for Cluster Computing Systems
Venue: arXiv cs.DC 2021
Year: 2021

The article presents and evaluates a scalable FRaGenLP algorithm for generating random linear programming problems of large dimension $n$ on cluster computing systems. To ensure the consistency of the problem and the boundedness of the feasible region, the constraint system includes $2n+1$ standard inequalities, called support inequalities. New random inequalities are generated and added to the system in a manner that ensures the consistency of the constraints. Furthermore, the algorithm uses two likeness metrics to prevent the addition of a new random inequality that is similar to one already present in the constraint system. The algorithm also rejects random inequalities that cannot affect the solution of the linear programming problem bounded by the support inequalities. The parallel implementation of the FRaGenLP algorithm is performed in C++ through the parallel BSF-skeleton, which encapsulates all aspects related to the MPI-based parallelization of the program. We provide the results of large-scale computational experiments on a cluster computing system to study the scalability of the FRaGenLP algorithm.
Compressing Deep CNNs using Basis Representation and Spectral Fine-tuning
Venue: arXiv cs.CV 2021
Year: 2021

We propose an efficient and straightforward method for compressing deep convolutional neural networks (CNNs) that uses basis filters to represent the convolutional layers, and optimizes the performance of the compressed network directly in the basis space. Specifically, any spatial convolution layer of the CNN can be replaced by two successive convolution layers: the first is a set of three-dimensional orthonormal basis filters, followed by a layer of one-dimensional filters that represents the original spatial filters in the basis space. We jointly fine-tune both the basis and the filter representation to directly mitigate any performance loss due to the truncation. Generality of the proposed approach is demonstrated by applying it to several well known deep CNN architectures and data sets for image classification and object detection. We also present the execution time and power usage at different compression levels on the Xavier Jetson AGX processor.
A Localization and Navigation Method for an In-pipe Robot in Water Distribution System through Wireless Control towards Long-Distance Inspection
Venue: arXiv cs.RO 2021
Year: 2021

In this paper, we propose an operation procedure for our previously developed in-pipe robotic system that is used for water quality monitoring in water distribution systems (WDS). The proposed operation procedure synchronizes a developed wireless communication system that is suitable for harsh environments of soil, water, and rock with a multi-phase control algorithm. The new wireless control algorithm facilitates smart navigation and near real-time wireless data transmission during operation for our in-pipe robot in WDS. The smart navigation enables the robot to pass through different configurations of the pipeline with long inspection capability with a battery in which is mounted on the robot. To this end, we have divided the operation procedure into five steps that assign a specific motion control phase and wireless communication task to the robot. We describe each step and the algorithm associated with that step in this paper. The proposed robotic system defines the configuration type in each pipeline with the pre-programmed pipeline map that is given to the robot before the operation and the wireless communication system. The wireless communication system includes some relay nodes that perform bi-directional communication in the operation procedure. The developed wireless robotic system along with operation procedure facilitates localization and navigation for the robot toward long-distance inspection in WDS.