OpenRaiser/Intern-Atlas huggingface dataset:
Refining Similarity Matrices to Cluster Attributed Networks Accurately
Venue: arXiv cs.SI 2020
Year: 2020

As a result of the recent popularity of social networks and the increase in the number of research papers published across all fields, attributed networks consisting of relationships between objects, such as humans and the papers, that have attributes are becoming increasingly large. Therefore, various studies for clustering attributed networks into sub-networks are being actively conducted. When clustering attributed networks using spectral clustering, the clustering accuracy is strongly affected by the quality of the similarity matrices, which are input into spectral clustering and represent the similarities between pairs of objects. In this paper, we aim to increase the accuracy by refining the matrices before applying spectral clustering to them. We verify the practicability of our proposed method by comparing the accuracy of spectral clustering with similarity matrices before and after refining them.
NwQM: A neural quality assessment framework for Wikipedia
Venue: arXiv cs.SI 2020
Year: 2020

Millions of people irrespective of socioeconomic and demographic backgrounds, depend on Wikipedia articles everyday for keeping themselves informed regarding popular as well as obscure topics. Articles have been categorized by editors into several quality classes, which indicate their reliability as encyclopedic content. This manual designation is an onerous task because it necessitates profound knowledge about encyclopedic language, as well navigating circuitous set of wiki guidelines. In this paper we propose Neural wikipedia QualityMonitor (NwQM), a novel deep learning model which accumulates signals from several key information sources such as article text, meta data and images to obtain improved Wikipedia article representation. We present comparison of our approach against a plethora of available solutions and show 8% improvement over state-of-the-art approaches with detailed ablation studies.
InstantEmbedding: Efficient Local Node Representations
Venue: arXiv cs.LG 2020
Year: 2020

In this paper, we introduce InstantEmbedding, an efficient method for generating single-node representations using local PageRank computations. We theoretically prove that our approach produces globally consistent representations in sublinear time. We demonstrate this empirically by conducting extensive experiments on real-world datasets with over a billion edges. Our experiments confirm that InstantEmbedding requires drastically less computation time (over 9,000 times faster) and less memory (by over 8,000 times) to produce a single node's embedding than traditional methods including DeepWalk, node2vec, VERSE, and FastRP. We also show that our method produces high quality representations, demonstrating results that meet or exceed the state of the art for unsupervised representation learning on tasks like node classification and link prediction.
On Factor Left Prime Factorization Problems for Multivariate Polynomial Matrices
Venue: arXiv cs.SC 2020
Year: 2020

This paper is concerned with factor left prime factorization problems for multivariate polynomial matrices without full row rank. We propose a necessary and sufficient condition for the existence of factor left prime factorizations of a class of multivariate polynomial matrices, and then design an algorithm to compute all factor left prime factorizations if they exist. We implement the algorithm on the computer algebra system Maple, and two examples are given to illustrate the effectiveness of the algorithm. The results presented in this paper are also true for the existence of factor right prime factorizations of multivariate polynomial matrices without full column rank.
Design and Implementation of User-Friendly and Low-Cost Multiple-Application System for Smart City Using Microcontrollers
Venue: arXiv cs.CY 2020
Year: 2020

Our proposed system has seven main contributions, i.e., Smart street lights, Smart home, Bio-metric door and home security system, Intelligent traffic lights management and road security system, Private and smart parking, Intelligent accident management system and Smart information display/ notice board system. Our prototypes / products employ Arduino UNO board, Node MCU, Ultrasonic sensor, Fingerprint module, Servo motors, GSM, GPS, LEDs, Flame Sensor, Bluetooth and Wi-Fi module etc. We are very confident that our proposed systems are efficient, reliable, and cost-effective and can be easily tested and implemented on a large scale under real conditions.
Understanding bias in facial recognition technologies
Venue: arXiv cs.CY 2020
Year: 2020

Over the past couple of years, the growing debate around automated facial recognition has reached a boiling point. As developers have continued to swiftly expand the scope of these kinds of technologies into an almost unbounded range of applications, an increasingly strident chorus of critical voices has sounded concerns about the injurious effects of the proliferation of such systems. Opponents argue that the irresponsible design and use of facial detection and recognition technologies (FDRTs) threatens to violate civil liberties, infringe on basic human rights and further entrench structural racism and systemic marginalisation. They also caution that the gradual creep of face surveillance infrastructures into every domain of lived experience may eventually eradicate the modern democratic forms of life that have long provided cherished means to individual flourishing, social solidarity and human self-creation. Defenders, by contrast, emphasise the gains in public safety, security and efficiency that digitally streamlined capacities for facial identification, identity verification and trait characterisation may bring. In this explainer, I focus on one central aspect of this debate: the role that dynamics of bias and discrimination play in the development and deployment of FDRTs. I examine how historical patterns of discrimination have made inroads into the design and implementation of FDRTs from their very earliest moments. And, I explain the ways in which the use of biased FDRTs can lead distributional and recognitional injustices. The explainer concludes with an exploration of broader ethical questions around the potential proliferation of pervasive face-based surveillance infrastructures and makes some recommendations for cultivating more responsible approaches to the development and governance of these technologies.
MARS-Gym: A Gym framework to model, train, and evaluate Recommender Systems for Marketplaces
Venue: arXiv cs.IR 2020
Year: 2020

Recommender Systems are especially challenging for marketplaces since they must maximize user satisfaction while maintaining the healthiness and fairness of such ecosystems. In this context, we observed a lack of resources to design, train, and evaluate agents that learn by interacting within these environments. For this matter, we propose MARS-Gym, an open-source framework to empower researchers and engineers to quickly build and evaluate Reinforcement Learning agents for recommendations in marketplaces. MARS-Gym addresses the whole development pipeline: data processing, model design and optimization, and multi-sided evaluation. We also provide the implementation of a diverse set of baseline agents, with a metrics-driven analysis of them in the Trivago marketplace dataset, to illustrate how to conduct a holistic assessment using the available metrics of recommendation, off-policy estimation, and fairness. With MARS-Gym, we expect to bridge the gap between academic research and production systems, as well as to facilitate the design of new algorithms and applications.
Croatian public companies for energy distribution and supply: integration of information subsystems
Venue: arXiv cs.CY 2020
Year: 2020

This research is about integration of information subsystems from:information system procurement, financial information system, information system security, technical information systems and legal information systems, and about their mutual dependence and close connections in Croatian public companies for energy distribution and supply. Also, herewe research the main goals of procurement information system which must be achieved in every organization because procurement process takes place in every public organization. Based on the model of the business technology matrix their processes can be executed by other companies engaged in similar activities. This research paper describes the timing of the sub processes, also. The timing of sub processes needs to be reduced as much as possible to achieve the planned results at the exit of the sub processes so that the costs of running sub-processes are equal to or lower than they had been so far, but with a higher quality output. We discuss possible threats to the information systems organization, and how to protect electronic information in the process of restoration of electronic data base for the financial information system. At the end the research paper we explain the advantages and disadvantages of cloud computing, information security in the event of possible applications of computing in the clouds and activities for technical information system in the flow diagram.
UAV Trajectory and Communication Co-design: Flexible Path Discretization and Path Compression
Venue: arXiv cs.IT 2020
Year: 2020

The performance optimization of UAV communication systems requires the joint design of UAV trajectory and communication efficiently. To tackle the challenge of infinite design variables arising from the continuous-time UAV trajectory optimization, a commonly adopted approach is by approximating the UAV trajectory with piecewise-linear path segments in three-dimensional (3D) space. However, this approach may still incur prohibitive computational complexity in practice when the UAV flight period/distance becomes long, as the distance between consecutive waypoints needs to be kept sufficiently small to retain high approximation accuracy. To resolve this fundamental issue, we propose in this paper a new and general framework for UAV trajectory and communication co-design. First, we propose a flexible path discretization scheme that optimizes only a number of selected waypoints (designable waypoints) along the UAV path for complexity reduction, while all the designable and non-designable waypoints are used in calculating the approximated communication utility along the UAV trajectory for ensuring high trajectory discretization accuracy. Next, given any number of designable waypoints, we propose a novel path compression scheme where the UAV 3D path is first decomposed into three one-dimensional (1D) sub-paths and each sub-path is then approximated by superimposing a number of selected basis paths weighted by their corresponding path coefficients, thus further reducing the path design complexity. Finally, we provide a case study on UAV trajectory design for aerial data harvesting from distributed ground sensors, and numerically show that the proposed schemes can significantly reduce the UAV trajectory design complexity yet achieve favorable rate performance as compared to conventional path/time discretization schemes.
Contextual Pattern Matching
Venue: arXiv cs.DS 2020
Year: 2020

The research on indexing repetitive string collections has focused on the same search problems used for regular string collections, though they can make little sense in this scenario. For example, the basic pattern matching query "list all the positions where pattern $P$ appears" can produce huge outputs when $P$ appears in an area shared by many documents. All those occurrences are essentially the same. In this paper we propose a new query that can be more appropriate in these collections, which we call {\em contextual pattern matching}. The basic query of this type gives, in addition to $P$, a context length $\ell$, and asks to report the occurrences of all {\em distinct} strings $XPY$, with $|X|=|Y|=\ell$. While this query is easily solved in optimal time and linear space, we focus on using space related to the repetitiveness of the text collection and present the first solution of this kind. Letting $\ovr$ be the maximum of the number of runs in the BWT of the text $T[1..n]$ and of its reverse, our structure uses $O(\ovr\log(n/\ovr))$ space and finds the $c$ contextual occurrences $XPY$ of $(P,\ell)$ in time $O(|P| + c \log n)$. We give other space/time tradeoffs as well, for compressed and uncompressed indexes.
Underwater Augmented Reality for improving the diving experience in submerged archaeological sites
Venue: arXiv cs.HC 2020
Year: 2020

The Mediterranean Sea has a vast maritime heritage which exploitation is made difficult because of the many limitations imposed by the submerged environment. Archaeological diving tours, in fact, suffer from the impossibility to provide underwater an exhaustive explanation of the submerged remains. Furthermore, low visibility conditions, due to water turbidity and biological colonization, sometimes make very confusing for tourists to find their way around in the underwater archaeological site. To this end, the paper investigates the feasibility and potentials of the underwater Augmented Reality (UWAR) technologies developed in the iMARECulture project for improving the experience of the divers that visit the Underwater Archaeological Park of Baiae (Naples). In particular, the paper presents two UWAR technologies that adopt hybrid tracking techniques to perform an augmented visualization of the actual conditions and of a hypothetical 3D reconstruction of the archaeological remains as appeared in the past. The first one integrates a marker-based tracking with inertial sensors, while the second one adopts a markerless approach that integrates acoustic localization and visual-inertial odometry. The experimentations show that the proposed UWAR technologies could contribute to have a better comprehension of the underwater site and its archaeological remains.
Exploiting Spectral Augmentation for Code-Switched Spoken Language Identification
Venue: arXiv cs.CL 2020
Year: 2020

Spoken language Identification (LID) systems are needed to identify the language(s) present in a given audio sample, and typically could be the first step in many speech processing related tasks such as automatic speech recognition (ASR). Automatic identification of the languages present in a speech signal is not only scientifically interesting, but also of practical importance in a multilingual country such as India. In many of the Indian cities, when people interact with each other, as many as three languages may get mixed. These may include the official language of that province, Hindi and English (at times the languages of the neighboring provinces may also get mixed during these interactions). This makes the spoken LID task extremely challenging in Indian context. While quite a few LID systems in the context of Indian languages have been implemented, most such systems have used small scale speech data collected internally within an organization. In the current work, we perform spoken LID on three Indian languages (Gujarati, Telugu, and Tamil) code-mixed with English. This task was organized by the Microsoft research team as a spoken LID challenge. In our work, we modify the usual spectral augmentation approach and propose a language mask that discriminates the language ID pairs, which leads to a noise robust spoken LID system. The proposed method gives a relative improvement of approximately 3-5% in the LID accuracy over a baseline system proposed by Microsoft on the three language pairs for two shared tasks suggested in the challenge.
Multi-class segmentation under severe class imbalance: A case study in roof damage assessment
Venue: arXiv cs.CV 2020
Year: 2020

The task of roof damage classification and segmentation from overhead imagery presents unique challenges. In this work we choose to address the challenge posed due to strong class imbalance. We propose four distinct techniques that aim at mitigating this problem. Through a new scheme that feeds the data to the network by oversampling the minority classes, and three other network architectural improvements, we manage to boost the macro-averaged F1-score of a model by 39.9 percentage points, thus achieving improved segmentation performance, especially on the minority classes.
Emergent Jaw Predominance in Vocal Development through Stochastic Optimization
Venue: arXiv cs.SD 2020
Year: 2020

Infant vocal babbling strongly relies on jaw oscillations, especially at the stage of canonical babbling, which underlies the syllabic structure of world languages. In this paper, we propose, model and analyze an hypothesis to explain this predominance of the jaw in early babbling. This hypothesis states that general stochastic optimization principles, when applied to learning sensorimotor control, automatically generate ordered babbling stages with a predominant exploration of jaw movements in early stages. The reason is that those movements impact the auditory effects more than other articulators. In previous computational models, such general principles were shown to selectively freeze and free degrees of freedom in a model reproducing the proximo-distal development observed in infant arm reaching. The contribution of this paper is to show how, using the same methods, we are able to explain such patterns in vocal development. We present three experiments. The two first ones show that the recruitment order of articulators emerging from stochastic optimization depends on the target sound to be achieved but that on average the jaw is largely chosen as the first recruited articulator. The third experiment analyses in more detail how the emerging recruitment order is shaped by the dynamics of the optimization process.
Fast Generation of Unlabelled Free Trees using Weight Sequences
Venue: arXiv cs.DS 2020
Year: 2020

In this paper, we introduce a new representation for ordered trees, the weight sequence representation. We then use this to construct new representations for both rooted trees and free trees, namely the canonical weight sequence representation. We construct algorithms for generating the weight sequence representations for all rooted and free trees of order n, and then add a number of modifications to improve the efficiency of the algorithms. Python implementations of the algorithms incorporate further improvements by using generators to avoid having to store the long lists of trees returned by the recursive calls, as well as caching the lists for rooted trees of small order, thereby eliminating many of the recursive calls. We further show how the algorithm can be modifed to generate adjacency list and adjacency matrix representations for free trees. We compared the run-times of our Python implementation for generating free trees with the Python implementation of the well-known WROM algorithm taken from NetworkX. The implementation of our algorithm is over four times as fast as the implementation of the WROM algorithm. The run-times for generating adjacency lists and matrices are somewhat longer than those for weight sequences, but are still over three times as fast as the corresponding implementations of the WROM algorithm.
Against the Others! Detecting Moral Outrage inSocial Media Networks
Venue: arXiv cs.SI 2020
Year: 2020

Online firestorms on Twitter are seemingly arbitrarily occurring outrages towards people, companies, media campaigns and politicians. Moral outrages can create an excessive collective aggressiveness against one single argument, one single word, or one action of a person resulting in hateful speech. With a collective "against the others" the negative dynamics often start. Using data from Twitter, we explored the starting points of several firestorm outbreaks. As a social media platform with hundreds of millions of users interacting in real-time on topics and events all over the world, Twitter serves as a social sensor for online discussions and is known for quick and often emotional disputes. The main question we pose in this article, is whether we can detect the outbreak of a firestorm. Given 21 online firestorms on Twitter, the key questions regarding the anomaly detection are: 1) How can we detect the changing point? 2) How can we distinguish the features that cause a moral outrage? In this paper we examine these challenges developing a method to detect the point of change systematically spotting on linguistic cues of tweets. We are able to detect outbreaks of firestorms early and precisely only by applying linguistic cues. The results of our work can help detect negative dynamics and may have the potential for individuals, companies, and governments to mitigate hate in social media networks.
Adaptive Deep Forest for Online Learning from Drifting Data Streams
Venue: arXiv cs.LG 2020
Year: 2020

Learning from data streams is among the most vital fields of contemporary data mining. The online analysis of information coming from those potentially unbounded data sources allows for designing reactive up-to-date models capable of adjusting themselves to continuous flows of data. While a plethora of shallow methods have been proposed for simpler low-dimensional streaming problems, almost none of them addressed the issue of learning from complex contextual data, such as images or texts. The former is represented mainly by adaptive decision trees that have been proven to be very efficient in streaming scenarios. The latter has been predominantly addressed by offline deep learning. In this work, we attempt to bridge the gap between these two worlds and propose Adaptive Deep Forest (ADF) - a natural combination of the successful tree-based streaming classifiers with deep forest, which represents an interesting alternative idea for learning from contextual data. The conducted experiments show that the deep forest approach can be effectively transformed into an online algorithm, forming a model that outperforms all state-of-the-art shallow adaptive classifiers, especially for high-dimensional complex streams.
Temperature check: theory and practice for training models with softmax-cross-entropy losses
Venue: arXiv cs.LG 2020
Year: 2020

The softmax function combined with a cross-entropy loss is a principled approach to modeling probability distributions that has become ubiquitous in deep learning. The softmax function is defined by a lone hyperparameter, the temperature, that is commonly set to one or regarded as a way to tune model confidence after training; however, less is known about how the temperature impacts training dynamics or generalization performance. In this work we develop a theory of early learning for models trained with softmax-cross-entropy loss and show that the learning dynamics depend crucially on the inverse-temperature $β$ as well as the magnitude of the logits at initialization, $||β{\bf z}||_{2}$. We follow up these analytic results with a large-scale empirical study of a variety of model architectures trained on CIFAR10, ImageNet, and IMDB sentiment analysis. We find that generalization performance depends strongly on the temperature, but only weakly on the initial logit magnitude. We provide evidence that the dependence of generalization on $β$ is not due to changes in model confidence, but is a dynamical phenomenon. It follows that the addition of $β$ as a tunable hyperparameter is key to maximizing model performance. Although we find the optimal $β$ to be sensitive to the architecture, our results suggest that tuning $β$ over the range $10^{-2}$ to $10^1$ improves performance over all architectures studied. We find that smaller $β$ may lead to better peak performance at the cost of learning stability.