OpenRaiser/Intern-Atlas huggingface dataset:
Boxer: Interactive Comparison of Classifier Results
Venue: arXiv cs.HC 2020
Year: 2020

Machine learning practitioners often compare the results of different classifiers to help select, diagnose and tune models. We present Boxer, a system to enable such comparison. Our system facilitates interactive exploration of the experimental results obtained by applying multiple classifiers to a common set of model inputs. The approach focuses on allowing the user to identify interesting subsets of training and testing instances and comparing performance of the classifiers on these subsets. The system couples standard visual designs with set algebra interactions and comparative elements. This allows the user to compose and coordinate views to specify subsets and assess classifier performance on them. The flexibility of these compositions allow the user to address a wide range of scenarios in developing and assessing classifiers. We demonstrate Boxer in use cases including model selection, tuning, fairness assessment, and data quality diagnosis.
CPARR: Category-based Proposal Analysis for Referring Relationships
Venue: arXiv cs.CV 2020
Year: 2020

The task of referring relationships is to localize subject and object entities in an image satisfying a relationship query, which is given in the form of \texttt{<subject, predicate, object>}. This requires simultaneous localization of the subject and object entities in a specified relationship. We introduce a simple yet effective proposal-based method for referring relationships. Different from the existing methods such as SSAS, our method can generate a high-resolution result while reducing its complexity and ambiguity. Our method is composed of two modules: a category-based proposal generation module to select the proposals related to the entities and a predicate analysis module to score the compatibility of pairs of selected proposals. We show state-of-the-art performance on the referring relationship task on two public datasets: Visual Relationship Detection and Visual Genome.
Smartphone camera based pointer
Venue: arXiv cs.HC 2020
Year: 2020

Large screen displays are omnipresent today as a part of infrastructure for presentations and entertainment. Also powerful smartphones with integrated camera(s) are ubiquitous. However, there are not many ways in which smartphones and screens can interact besides casting the video from a smartphone. In this paper, we present a novel idea that turns a smartphone into a direct virtual pointer on the screen using the phone's camera. The idea and its implementation are simple, robust, efficient and fun to use. Besides the mathematical concepts of the idea we accompany the paper with a small javascript project (www.mobiletvgames.com) which demonstrates the possibility of the new interaction technique presented as a massive multiplayer game in the HTML5 framework.
Automating Cutting Planes is NP-Hard}
Venue: arXiv cs.CC 2020
Year: 2020

We show that Cutting Planes (CP) proofs are hard to find: Given an unsatisfiable formula $F$, 1) It is NP-hard to find a CP refutation of $F$ in time polynomial in the length of the shortest such refutation; and 2)unless Gap-Hitting-Set admits a nontrivial algorithm, one cannot find a tree-like CP refutation of $F$ in time polynomial in the length of the shortest such refutation. The first result extends the recent breakthrough of Atserias and Müller (FOCS 2019) that established an analogous result for Resolution. Our proofs rely on two new lifting theorems: (1) Dag-like lifting for gadgets with many output bits. (2) Tree-like lifting that simulates an $r$-round protocol with gadgets of query complexity $O(\log r)$ independent of input length.
Crosstalk Noise based Configurable Computing: A New Paradigm for Digital Electronics
Venue: arXiv cs.ET 2020
Year: 2020

The past few decades have seen exponential growth in capabilities of digital electronics primarily due to the ability to scale Integrated Circuits (ICs) to smaller dimensions while attaining power and performance benefits. That scalability is now being challenged due to the lack of scaled transistor performance and also manufacturing complexities [1]-[5]. In addition, the growing cyber threat in fabless manufacturing era poses a new front that modern ICs need to withstand. We present a new noise based computing where the interconnect interference between nanoscale metal lines is intentionally engineered to exhibit programmable Boolean logic behavior. The reliance on just coupling between metal lines and not on transistors for computing, and the programmability are the foundations for better scalability, and security by obscurity. Here, we show experimental evidence of a functioning Crosstalk computing chip at 65nm technology. Our demonstration of computing constructs, gate level configurability and utilization of foundry processes show feasibility. These results in conjunction with our simulation results at 7nm for various benchmarks, which show over 48%, 57%, and 10% density, power and performance respectively, gains over equivalent CMOS in the best case, show potentials. The benefits of Crosstalk circuits and inherent programmable features set it apart and make it a promising prospect for future electronics.
Enriching the Transformer with Linguistic Factors for Low-Resource Machine Translation
Venue: arXiv cs.CL 2020
Year: 2020

Introducing factors, that is to say, word features such as linguistic information referring to the source tokens, is known to improve the results of neural machine translation systems in certain settings, typically in recurrent architectures. This study proposes enhancing the current state-of-the-art neural machine translation architecture, the Transformer, so that it allows to introduce external knowledge. In particular, our proposed modification, the Factored Transformer, uses linguistic factors that insert additional knowledge into the machine translation system. Apart from using different kinds of features, we study the effect of different architectural configurations. Specifically, we analyze the performance of combining words and features at the embedding level or at the encoder level, and we experiment with two different combination strategies. With the best-found configuration, we show improvements of 0.8 BLEU over the baseline Transformer in the IWSLT German-to-English task. Moreover, we experiment with the more challenging FLoRes English-to-Nepali benchmark, which includes both extremely low-resourced and very distant languages, and obtain an improvement of 1.2 BLEU.
Neural Approaches for Data Driven Dependency Parsing in Sanskrit
Venue: arXiv cs.CL 2020
Year: 2020

Data-driven approaches for dependency parsing have been of great interest in Natural Language Processing for the past couple of decades. However, Sanskrit still lacks a robust purely data-driven dependency parser, probably with an exception to Krishna (2019). This can primarily be attributed to the lack of availability of task-specific labelled data and the morphologically rich nature of the language. In this work, we evaluate four different data-driven machine learning models, originally proposed for different languages, and compare their performances on Sanskrit data. We experiment with 2 graph based and 2 transition based parsers. We compare the performance of each of the models in a low-resource setting, with 1,500 sentences for training. Further, since our focus is on the learning power of each of the models, we do not incorporate any Sanskrit specific features explicitly into the models, and rather use the default settings in each of the paper for obtaining the feature functions. In this work, we analyse the performance of the parsers using both an in-domain and an out-of-domain test dataset. We also investigate the impact of word ordering in which the sentences are provided as input to these systems, by parsing verses and their corresponding prose order (anvaya) sentences.
Algorithmic labeling in hierarchical classifications of publications: Evaluation of bibliographic fields and term weighting approaches
Venue: arXiv cs.DL 2020
Year: 2020

Algorithmic classifications of research publications can be used to study many different aspects of the science system, such as the organization of science into fields, the growth of fields, interdisciplinarity, and emerging topics. How to label the classes in these classifications is a problem that has not been thoroughly addressed in the literature. In this study we evaluate different approaches to label the classes in algorithmically constructed classifications of research publications. We focus on two important choices: the choice of (1) different bibliographic fields and (2) different approaches to weight the relevance of terms. To evaluate the different choices, we created two baselines: one based on the Medical Subject Headings in MEDLINE and another based on the Science-Metrix journal classification. We tested to what extent different approaches yield the desired labels for the classes in the two baselines. Based on our results we recommend extracting terms from titles and keywords to label classes at high levels of granularity (e.g. topics). At low levels of granularity (e.g. disciplines) we recommend extracting terms from journal names and author addresses. We recommend the use of a new approach, term frequency to specificity ratio, to calculate the relevance of terms.
Triplet Loss for Knowledge Distillation
Venue: arXiv cs.LG 2020
Year: 2020

In recent years, deep learning has spread rapidly, and deeper, larger models have been proposed. However, the calculation cost becomes enormous as the size of the models becomes larger. Various techniques for compressing the size of the models have been proposed to improve performance while reducing computational costs. One of the methods to compress the size of the models is knowledge distillation (KD). Knowledge distillation is a technique for transferring knowledge of deep or ensemble models with many parameters (teacher model) to smaller shallow models (student model). Since the purpose of knowledge distillation is to increase the similarity between the teacher model and the student model, we propose to introduce the concept of metric learning into knowledge distillation to make the student model closer to the teacher model using pairs or triplets of the training samples. In metric learning, the researchers are developing the methods to build a model that can increase the similarity of outputs for similar samples. Metric learning aims at reducing the distance between similar and increasing the distance between dissimilar. The functionality of the metric learning to reduce the differences between similar outputs can be used for the knowledge distillation to reduce the differences between the outputs of the teacher model and the student model. Since the outputs of the teacher model for different objects are usually different, the student model needs to distinguish them. We think that metric learning can clarify the difference between the different outputs, and the performance of the student model could be improved. We have performed experiments to compare the proposed method with state-of-the-art knowledge distillation methods.
ThermoSim: Deep Learning based Framework for Modeling and Simulation of Thermal-aware Resource Management for Cloud Computing Environments
Venue: arXiv cs.DC 2020
Year: 2020

Current cloud computing frameworks host millions of physical servers that utilize cloud computing resources in the form of different virtual machines (VM). Cloud Data Center (CDC) infrastructures require significant amounts of energy to deliver large scale computational services. Computing nodes generate large volumes of heat, requiring cooling units in turn to eliminate the effect of this heat. Thus, the overall energy consumption of the CDC increases tremendously for servers as well as for cooling units. However, current workload allocation policies do not take into account the effect on temperature and it is challenging to simulate the thermal behavior of CDCs. There is a need for a thermal-aware framework to simulate and model the behavior of nodes and measure the important performance parameters which can be affected by its temperature. In this paper, we propose a lightweight framework, ThermoSim, for modeling and simulation of thermal-aware resource management for cloud computing environments. This work presents a Recurrent Neural Network based deep learning temperature predictor for CDCs which is utilized by ThermoSim for lightweight resource management in constrained cloud environments. ThermoSim extends the CloudSim toolkit helping to analyze the performance of various key parameters such as energy consumption, SLA violation rate, number of VM migrations and temperature during the management of cloud resources for execution of workloads. Further, different energy-aware and thermal-aware resource management techniques are tested using the proposed ThermoSim framework in order to validate it against the existing framework. The experimental results demonstrate the proposed framework is capable of modeling and simulating the thermal behavior of a CDC and the ThermoSim framework is better than Thas in terms of energy consumption, cost, time, memory usage & prediction accuracy.
Space-Time Domain Tensor Neural Networks: An Application on Human Pose Classification
Venue: arXiv cs.LG 2020
Year: 2020

Recent advances in sensing technologies require the design and development of pattern recognition models capable of processing spatiotemporal data efficiently. In this study, we propose a spatially and temporally aware tensor-based neural network for human pose classification using three-dimensional skeleton data. Our model employs three novel components. First, an input layer capable of constructing highly discriminative spatiotemporal features. Second, a tensor fusion operation that produces compact yet rich representations of the data, and third, a tensor-based neural network that processes data representations in their original tensor form. Our model is end-to-end trainable and characterized by a small number of trainable parameters making it suitable for problems where the annotated data is limited. Experimental evaluation of the proposed model indicates that it can achieve state-of-the-art performance.
Deep Echo State Networks for Short-Term Traffic Forecasting: Performance Comparison and Statistical Assessment
Venue: arXiv cs.NE 2020
Year: 2020

In short-term traffic forecasting, the goal is to accurately predict future values of a traffic parameter of interest occurring shortly after the prediction is queried. The activity reported in this long-standing research field has been lately dominated by different Deep Learning approaches, yielding overly complex forecasting models that in general achieve accuracy gains of questionable practical utility. In this work we elaborate on the performance of Deep Echo State Networks for this particular task. The efficient learning algorithm and simpler parametric configuration of these alternative modeling approaches make them emerge as a competitive traffic forecasting method for real ITS applications deployed in devices and systems with stringently limited computational resources. An extensive comparison benchmark is designed with real traffic data captured over the city of Madrid (Spain), amounting to more than 130 automatic Traffic Readers (ATRs) and several shallow learning, ensembles and Deep Learning models. Results from this comparison benchmark and the analysis of the statistical significance of the reported performance gaps are decisive: Deep Echo State Networks achieve more accurate traffic forecasts than the rest of considered modeling counterparts.
MOPT: Multi-Object Panoptic Tracking
Venue: arXiv cs.CV 2020
Year: 2020

Comprehensive understanding of dynamic scenes is a critical prerequisite for intelligent robots to autonomously operate in their environment. Research in this domain, which encompasses diverse perception problems, has primarily been focused on addressing specific tasks individually rather than modeling the ability to understand dynamic scenes holistically. In this paper, we introduce a novel perception task denoted as multi-object panoptic tracking (MOPT), which unifies the conventionally disjoint tasks of semantic segmentation, instance segmentation, and multi-object tracking. MOPT allows for exploiting pixel-level semantic information of 'thing' and 'stuff' classes, temporal coherence, and pixel-level associations over time, for the mutual benefit of each of the individual sub-problems. To facilitate quantitative evaluations of MOPT in a unified manner, we propose the soft panoptic tracking quality (sPTQ) metric. As a first step towards addressing this task, we propose the novel PanopticTrackNet architecture that builds upon the state-of-the-art top-down panoptic segmentation network EfficientPS by adding a new tracking head to simultaneously learn all sub-tasks in an end-to-end manner. Additionally, we present several strong baselines that combine predictions from state-of-the-art panoptic segmentation and multi-object tracking models for comparison. We present extensive quantitative and qualitative evaluations of both vision-based and LiDAR-based MOPT that demonstrate encouraging results.
Large-scale analysis of grooming in modern social networks
Venue: arXiv cs.SI 2020
Year: 2020

Social networks are evolving to engage their users more by providing them with more functionalities. One of the most attracting ones is streaming. Users may broadcast part of their daily lives to thousands of others world-wide and interact with them in real-time. Unfortunately, this feature is reportedly exploited for grooming. In this work, we provide the first in-depth analysis of this problem for social live streaming services. More precisely, using a dataset that we collected, we identify predatory behaviours and grooming on chats that bypassed the moderation mechanisms of the LiveMe, the service under investigation. Beyond the traditional text approaches, we also investigate the relevance of emojis in this context, as well as the user interactions through the gift mechanisms of LiveMe. Finally, our analysis indicates the possibility of grooming towards minors, showing the extent of the problem in such platforms.
Vehicle Position Estimation with Aerial Imagery from Unmanned Aerial Vehicles
Venue: arXiv cs.CV 2020
Year: 2020

The availability of real-world data is a key element for novel developments in the fields of automotive and traffic research. Aerial imagery has the major advantage of recording multiple objects simultaneously and overcomes limitations such as occlusions. However, there are only few data sets available. This work describes a process to estimate a precise vehicle position from aerial imagery. A robust object detection is crucial for reliable results, hence the state-of-the-art deep neural network Mask-RCNN is applied for that purpose. Two training data sets are employed: The first one is optimized for detecting the test vehicle, while the second one consists of randomly selected images recorded on public roads. To reduce errors, several aspects are accounted for, such as the drone movement and the perspective projection from a photograph. The estimated position is comapared with a reference system installed in the test vehicle. It is shown, that a mean accuracy of 20 cm can be achieved with flight altitudes up to 100 m, Full-HD resolution and a frame-by-frame detection. A reliable position estimation is the basis for further data processing, such as obtaining additional vehicle state variables. The source code, training weights, labeled data and example videos are made publicly available. This supports researchers to create new traffic data sets with specific local conditions.
A Class of Models with the Potential to Represent Fundamental Physics
Venue: arXiv cs.DM 2020
Year: 2020

A class of models intended to be as minimal and structureless as possible is introduced. Even in cases with simple rules, rich and complex behavior is found to emerge, and striking correspondences to some important core known features of fundamental physics are seen, suggesting the possibility that the models may provide a new approach to finding a fundamental theory of physics.
Stateful Premise Selection by Recurrent Neural Networks
Venue: arXiv cs.LO 2020
Year: 2020

In this work, we develop a new learning-based method for selecting facts (premises) when proving new goals over large formal libraries. Unlike previous methods that choose sets of facts independently of each other by their rank, the new method uses the notion of \emph{state} that is updated each time a choice of a fact is made. Our stateful architecture is based on recurrent neural networks which have been recently very successful in stateful tasks such as language translation. The new method is combined with data augmentation techniques, evaluated in several ways on a standard large-theory benchmark, and compared to state-of-the-art premise approach based on gradient boosted trees. It is shown to perform significantly better and to solve many new problems.
Translating Workflow Nets to Process Trees: An Algorithmic Approach
Venue: arXiv cs.LO 2020
Year: 2020

Since their recent introduction, process trees have been frequently used as a process modeling formalism in many process mining algorithms. A process tree is a tree-based model of a process, in which internal vertices represent behavioral control-flow relations and leaves represent process activities. A process tree is easily translated into a sound Workflow net (WF-net), however, the reverse is not the case. Yet, an algorithm that translates a WF-net into a process tree is of great interest, e.g., the explicit knowledge of the control-flow hierarchy in a WF-net allows one to more easily reason on its behavior. Hence, in this paper, we present such an algorithm, i.e., it detects whether a WF-net corresponds to a process tree, and, if so, constructs it. We prove that, if a process tree is discovered, the language of the process tree equals the language of the original WF-net. Conducted experiments show, that the algorithm's corresponding implementation has a quadratic time-complexity in the size of the WF-net. Furthermore, the experiments show strong evidence of process tree rediscoverability.
A Survey of Approximate Quantile Computation on Large-scale Data (Technical Report)
Venue: arXiv cs.DS 2020
Year: 2020

As data volume grows extensively, data profiling helps to extract metadata of large-scale data. However, one kind of metadata, order statistics, is difficult to be computed because they are not mergeable or incremental. Thus, the limitation of time and memory space does not support their computation on large-scale data. In this paper, we focus on an order statistic, quantiles, and present a comprehensive analysis of studies on approximate quantile computation. Both deterministic algorithms and randomized algorithms that compute approximate quantiles over streaming models or distributed models are covered. Then, multiple techniques for improving the efficiency and performance of approximate quantile algorithms in various scenarios, such as skewed data and high-speed data streams, are presented. Finally, we conclude with coverage of existing packages in different languages and with a brief discussion of the future direction in this area.