OpenRaiser/Intern-Atlas huggingface dataset:
Accurate outdoor ground truth based on total stations
Venue: arXiv cs.RO 2021
Year: 2021

In robotics, accurate ground-truth position fostered the development of mapping and localization algorithms through the creation of cornerstone datasets. In outdoor environments and over long distances, total stations are the most accurate and precise measurement instruments for this purpose. Most total station-based systems in the literature are limited to three Degrees Of Freedoms (DOFs), due to the use of a single-prism tracking approach. In this paper, we present preliminary work on measuring a full pose of a vehicle, bringing the referencing system to six DOFs. Three total stations are used to track in real time three prisms attached to a target platform. We describe the structure of the referencing system and the protocol for acquiring the ground truth with this system. We evaluated its precision in a variety of different outdoor environments, ranging from open-sky to forest trails, and compare this system with another popular source of reference position, the Real Time Kinematics (RTK) positioning solution. Results show that our approach is the most precise, reaching an average positional error of 10 mm and 0.6 deg. This difference in performance was particularly stark in environments where Global Navigation Satellite System (GNSS) signals can be weaker due to overreaching vegetation.
MUSE: Multi-faceted Attention for Signed Network Embedding
Venue: arXiv cs.SI 2021
Year: 2021

Signed network embedding is an approach to learn low-dimensional representations of nodes in signed networks with both positive and negative links, which facilitates downstream tasks such as link prediction with general data mining frameworks. Due to the distinct properties and significant added value of negative links, existing signed network embedding methods usually design dedicated methods based on social theories such as balance theory and status theory. However, existing signed network embedding methods ignore the characteristics of multiple facets of each node and mix them up in one single representation, which limits the ability to capture the fine-grained attentions between node pairs. In this paper, we propose MUSE, a MUlti-faceted attention-based Signed network Embedding framework to tackle this problem. Specifically, a joint intra- and inter-facet attention mechanism is introduced to aggregate fine-grained information from neighbor nodes. Moreover, balance theory is also utilized to guide information aggregation from multi-order balanced and unbalanced neighbors. Experimental results on four real-world signed network datasets demonstrate the effectiveness of our proposed framework.
Star DGT: a Robust Gabor Transform for Speech Denoising
Venue: arXiv cs.SD 2021
Year: 2021

In this paper, we address the speech denoising problem, where Gaussian, pink and blue additive noises are to be removed from a given speech signal. Our approach is based on a redundant, analysis-sparse representation of the original speech signal. We pick an eigenvector of the Zauner unitary matrix and -- under certain assumptions on the ambient dimension -- we use it as window vector to generate a spark deficient Gabor frame. The analysis operator associated with such a frame, is a (highly) redundant Gabor transform, which we use as a sparsifying transform in denoising procedure. We conduct computational experiments on real-world speech data, using as baseline three Gabor transforms generated by state-of-the-art window vectors in time-frequency analysis and compare their performance to the proposed Gabor transform. The results show that our proposed redundant Gabor transform outperforms all others, consistently for all signals.
Identifying Hubs in Undergraduate Course Networks Based on Scaled Co-Enrollments: Extended Version
Venue: arXiv cs.SI 2021
Year: 2021

Understanding course enrollment patterns is valuable to predict upcoming demands for future courses, and to provide student with realistic courses to pursue given their current backgrounds. This study uses undergraduate student enrollment data to form networks of courses where connections are based on student co-enrollments. The course networks generated in this paper are based on eight years of undergraduate course enrollment data from a large metropolitan university. The networks are analyzed to identify "hub" courses often taken with many other courses. Two notions of hubs are considered: one focused on raw popularity across all students, and one focused on proportional likelihoods of co-enrollment with other courses. A variety of network metrics are calculated to evaluate the course networks. Academic departments and high-level academic categories, such as Humanities vs STEM, are studied for their influence over course groupings. The identification of hub courses has practical applications, since it can help better predict the impact of changes in course offerings and in course popularity, and in the case of interdisciplinary hub courses, can be used to increase or decrease interest and enrollments in specific academic departments and areas.
The Big Brother NaijaTV Reality Show as Coordinate of Media Functions and Dysfunctions
Venue: arXiv cs.MM 2021
Year: 2021

The mass media play at least five basic functions which include news dissemination, surveillance of the environment, correlation of the components of the society, entertainment and transmission of social heritage. Sometimes, disruptions and impairments do occur in the performance of these roles and some of these basic functions become dysfunctions, which turn the media into purveyor of negative values. The present study investigates how popular the Nigerian TV reality show, Big Brother Naija BBN, is perceived by its viewers. Three hundred heavy viewers of the program were surveyed from Lagos and Ede, South-West Nigeria, and their opinions and attitudes were sought regarding, why they like or dislike the program; the gratifications that those who like the program derive and whether the BBN, as media content, is generally functional or dysfunctional to the society. Sixty six per cent 66 33.7 of respondents like the program because it entertains. Half of the respondents, 99 50.5 dislike immoral aspects of the program. The viewers affirm that the eviction part of the program was their highest form of gratification. Most respondents, despite public outcry against the program, consider the program to be functional. Findings reinforce the postulation that TV viewers are not passive consumers of media contents.
Learning in Feedforward Neural Networks Accelerated by Transfer Entropy
Venue: arXiv cs.LG 2021
Year: 2021

Current neural networks architectures are many times harder to train because of the increasing size and complexity of the used datasets. Our objective is to design more efficient training algorithms utilizing causal relationships inferred from neural networks. The transfer entropy (TE) was initially introduced as an information transfer measure used to quantify the statistical coherence between events (time series). Later, it was related to causality, even if they are not the same. There are only few papers reporting applications of causality or TE in neural networks. Our contribution is an information-theoretical method for analyzing information transfer between the nodes of feedforward neural networks. The information transfer is measured by the TE of feedback neural connections. Intuitively, TE measures the relevance of a connection in the network and the feedback amplifies this connection. We introduce a backpropagation type training algorithm that uses TE feedback connections to improve its performance.
AttendSeg: A Tiny Attention Condenser Neural Network for Semantic Segmentation on the Edge
Venue: arXiv cs.CV 2021
Year: 2021

In this study, we introduce \textbf{AttendSeg}, a low-precision, highly compact deep neural network tailored for on-device semantic segmentation. AttendSeg possesses a self-attention network architecture comprising of light-weight attention condensers for improved spatial-channel selective attention at a very low complexity. The unique macro-architecture and micro-architecture design properties of AttendSeg strike a strong balance between representational power and efficiency, achieved via a machine-driven design exploration strategy tailored specifically for the task at hand. Experimental results demonstrated that the proposed AttendSeg can achieve segmentation accuracy comparable to much larger deep neural networks with greater complexity while possessing a significantly lower architecture and computational complexity (requiring as much as >27x fewer MACs, >72x fewer parameters, and >288x lower weight memory requirements), making it well-suited for TinyML applications on the edge.
Test Smell Detection Tools: A Systematic Mapping Study
Venue: arXiv cs.SE 2021
Year: 2021

Test smells are defined as sub-optimal design choices developers make when implementing test cases. Hence, similar to code smells, the research community has produced numerous test smell detection tools to investigate the impact of test smells on the quality and maintenance of test suites. However, little is known about the characteristics, type of smells, target language, and availability of these published tools. In this paper, we provide a detailed catalog of all known, peer-reviewed, test smell detection tools. We start with performing a comprehensive search of peer-reviewed scientific publications to construct a catalog of 22 tools. Then, we perform a comparative analysis to identify the smell types detected by each tool and other salient features that include programming language, testing framework support, detection strategy, and adoption, among others. From our findings, we discover tools that detect test smells in Java, Scala, Smalltalk, and C++ test suites, with Java support favored by most tools. These tools are available as command-line and IDE plugins, among others. Our analysis also shows that most tools overlap in detecting specific smell types, such as General Fixture. Further, we encounter four types of techniques these tools utilize to detect smells. We envision our study as a one-stop source for researchers and practitioners in determining the tool appropriate for their needs. Our findings also empower the community with information to guide future tool development.
Physically Feasible Vehicle Trajectory Prediction
Venue: arXiv cs.RO 2021
Year: 2021

Predicting the future motion of actors in a traffic scene is a crucial part of any autonomous driving system. Recent research in this area has focused on trajectory prediction approaches that optimize standard trajectory error metrics. In this work, we describe three important properties -- physical realism guarantees, system maintainability, and sample efficiency -- which we believe are equally important for developing a self-driving system that can operate safely and practically in the real world. Furthermore, we introduce PTNet (PathTrackingNet), a novel approach for vehicle trajectory prediction that is a hybrid of the classical pure pursuit path tracking algorithm and modern graph-based neural networks. By combining a structured robotics technique with a flexible learning approach, we are able to produce a system that not only achieves the same level of performance as other state-of-the-art methods on traditional trajectory error metrics, but also provides strong guarantees about the physical realism of the predicted trajectories while requiring half the amount of data. We believe focusing on this new class of hybrid approaches is an useful direction for developing and maintaining a safety-critical autonomous driving system.
Cross-lingual hate speech detection based on multilingual domain-specific word embeddings
Venue: arXiv cs.CL 2021
Year: 2021

Automatic hate speech detection in online social networks is an important open problem in Natural Language Processing (NLP). Hate speech is a multidimensional issue, strongly dependant on language and cultural factors. Despite its relevance, research on this topic has been almost exclusively devoted to English. Most supervised learning resources, such as labeled datasets and NLP tools, have been created for this same language. Considering that a large portion of users worldwide speak in languages other than English, there is an important need for creating efficient approaches for multilingual hate speech detection. In this work we propose to address the problem of multilingual hate speech detection from the perspective of transfer learning. Our goal is to determine if knowledge from one particular language can be used to classify other language, and to determine effective ways to achieve this. We propose a hate specific data representation and evaluate its effectiveness against general-purpose universal representations most of which, unlike our proposed model, have been trained on massive amounts of data. We focus on a cross-lingual setting, in which one needs to classify hate speech in one language without having access to any labeled data for that language. We show that the use of our simple yet specific multilingual hate representations improves classification results. We explain this with a qualitative analysis showing that our specific representation is able to capture some common patterns in how hate speech presents itself in different languages. Our proposal constitutes, to the best of our knowledge, the first attempt for constructing multilingual specific-task representations. Despite its simplicity, our model outperformed the previous approaches for most of the experimental setups. Our findings can orient future solutions toward the use of domain-specific representations.
Human strategic decision making in parametrized games
Venue: arXiv cs.GT 2021
Year: 2021

Many real-world games contain parameters which can affect payoffs, action spaces, and information states. For fixed values of the parameters, the game can be solved using standard algorithms. However, in many settings agents must act without knowing the values of the parameters that will be encountered in advance. Often the decisions must be made by a human under time and resource constraints, and it is unrealistic to assume that a human can solve the game in real time. We present a new framework that enables human decision makers to make fast decisions without the aid of real-time solvers. We demonstrate applicability to a variety of situations including settings with multiple players and imperfect information.
CoCon: Cooperative-Contrastive Learning
Venue: arXiv cs.CV 2021
Year: 2021

Labeling videos at scale is impractical. Consequently, self-supervised visual representation learning is key for efficient video analysis. Recent success in learning image representations suggests contrastive learning is a promising framework to tackle this challenge. However, when applied to real-world videos, contrastive learning may unknowingly lead to the separation of instances that contain semantically similar events. In our work, we introduce a cooperative variant of contrastive learning to utilize complementary information across views and address this issue. We use data-driven sampling to leverage implicit relationships between multiple input video views, whether observed (e.g. RGB) or inferred (e.g. flow, segmentation masks, poses). We are one of the firsts to explore exploiting inter-instance relationships to drive learning. We experimentally evaluate our representations on the downstream task of action recognition. Our method achieves competitive performance on standard benchmarks (UCF101, HMDB51, Kinetics400). Furthermore, qualitative experiments illustrate that our models can capture higher-order class relationships.
Cleaning Label Noise with Clusters for Minimally Supervised Anomaly Detection
Venue: arXiv cs.CV 2021
Year: 2021

Learning to detect real-world anomalous events using video-level annotations is a difficult task mainly because of the noise present in labels. An anomalous labelled video may actually contain anomaly only in a short duration while the rest of the video can be normal. In the current work, we formulate a weakly supervised anomaly detection method that is trained using only video-level labels. To this end, we propose to utilize binary clustering which helps in mitigating the noise present in the labels of anomalous videos. Our formulation encourages both the main network and the clustering to complement each other in achieving the goal of weakly supervised training. The proposed method yields 78.27% and 84.16% frame-level AUC on UCF-crime and ShanghaiTech datasets respectively, demonstrating its superiority over existing state-of-the-art algorithms.
Event-driven timeseries analysis and the comparison of public reactions on COVID-19
Venue: arXiv cs.CL 2021
Year: 2021

The rapid spread of COVID-19 has already affected human lives throughout the globe. Governments of different countries have taken various measures, but how they affected people lives is not clear. In this study, a rule-based and a machine-learning based models are applied to answer the above question using public tweets from Japan, USA, UK, and Australia. Two polarity timeseries (meanPol and pnRatio) and two events, namely "lockdown or emergency (LED)" and "the economic support package (ESP)", are considered in this study. Statistical testing on the sub-series around LED and ESP events showed their positive impacts to the people of (UK and Australia) and (USA and UK), respectively unlike Japanese people that showed opposite effects. Manual validation with the relevant tweets showed an agreement with the statistical results. A case study with Japanese tweets using supervised logistic regression classifies tweets into heath-worry, economy-worry and other classes with 83.11% accuracy. Predicted tweets around events re-confirm the statistical outcomes.
Massive-MIMO Iterative Channel Estimation and Decoding (MICED) in the Uplink
Venue: arXiv cs.IT 2021
Year: 2021

Massive MIMO uses a large number of antennas to increase the spectral efficiency (SE) through spatial multiplexing of users, which requires accurate channel state information. It is often assumed that regular pilots (RP), where a fraction of the time-frequency resources is reserved for pilots, suffices to provide high SE. However, the SE is limited by the pilot overhead and pilot contamination. An alternative is superimposed pilots (SP) where all resources are used for pilots and data. This removes the pilot overhead and reduces pilot contamination by using longer pilots. However, SP suffers from data interference that reduces the SE gains. This paper proposes the Massive-MIMO Iterative Channel Estimation and Decoding (MICED) algorithm where partially decoded data is used as side-information to improve the channel estimation and increase SE. We show that users with precise data estimates can help users with poor data estimates to decode. Numerical results with QPSK modulation and LDPC codes show that the MICED algorithm increases the SE and reduces the block-error-rate with RP and SP compared to conventional methods. The MICED algorithm with SP delivers the highest SE and it is especially effective in scenarios with short coherence blocks like high mobility or high frequencies.
Creative Action at a Distance: A Conceptual Framework for Embodied Performance With Robotic Actors
Venue: arXiv cs.RO 2021
Year: 2021

Acting, stand-up and dancing are creative, embodied performances that nonetheless follow a script. Unless experimental or improvised, the performers draw their movements from much the same stock of embodied schemas. A slavish following of the script leaves no room for creativity, but active interpretation of the script does. It is the choices one makes, of words and actions, that make a performance creative. In this theory and hypothesis article, we present a framework for performance and interpretation within robotic storytelling. The performance framework is built upon movement theory, and defines a taxonomy of basic schematic movements and the most important gesture types. For the interpretation framework, we hypothesise that emotionally-grounded choices can inform acts of metaphor and blending, to elevate a scripted performance into a creative one. Theory and hypothesis are each grounded in empirical research, and aim to provide resources for other robotic studies of the creative use of movement and gestures.
Number and quality of diagrams in scholarly publications is associated with number of citations
Venue: arXiv cs.DL 2021
Year: 2021

Diagrams are often used in scholarly communication. We analyse a corpus of diagrams found in scholarly computational linguistics conference proceedings (ACL 2017), and find inclusion of a system diagram to be correlated with higher numbers of citations after 3 years. Inclusion of over three diagrams in this 8-page limit conference was found to correlate with a lower citation count. Focusing on neural network system diagrams, we find a correlation between highly cited papers and "good diagramming practice" quantified by level of compliance with a set of diagramming guidelines. Two diagram classification types (one visually based, one mental model based) were not found to correlate with number of citations, but enabled quantification of heterogeneity in those dimensions. Exploring scholarly paper-writing guides, we find diagrams to be a neglected media. This study suggests that diagrams may be a useful source of quality data for predicting citations, and that "graphicacy" is a key skill for scholars with insufficient support at present.
Capacity Self-Planning in Small Cell Multi-Tenant 5G Networks
Venue: arXiv cs.NI 2021
Year: 2021

Multi-tenancy allows diverse agents sharing the infrastructure in the 5 th generation of mobile networks. Such a feature calls for more automated and faster planning procedures in order to adapt the network capacity to the varying traffic demand. To achieve these goals, Small Cells offer network providers more flexible, scalable, and cost-effective solutions compared to macrocell deployments. This paper proposes a novel framework for cell planning in multi-tenant Small Cell networks. In this framework, the tenant's contracted capacity is translated to a set of detailed planning specifications over time and space domains in order to efficiently update the network infrastructure and configuration. Based on this, an algorithm is proposed that considers different actions such as adding/removing channels and adding or relocating small cells. The proposed approach is evaluated considering the deployment of a new tenant, where different sets of planning specifications are tested.
NTIRE 2021 Challenge on Image Deblurring
Venue: arXiv cs.CV 2021
Year: 2021

Motion blur is a common photography artifact in dynamic environments that typically comes jointly with the other types of degradation. This paper reviews the NTIRE 2021 Challenge on Image Deblurring. In this challenge report, we describe the challenge specifics and the evaluation results from the 2 competition tracks with the proposed solutions. While both the tracks aim to recover a high-quality clean image from a blurry image, different artifacts are jointly involved. In track 1, the blurry images are in a low resolution while track 2 images are compressed in JPEG format. In each competition, there were 338 and 238 registered participants and in the final testing phase, 18 and 17 teams competed. The winning methods demonstrate the state-of-the-art performance on the image deblurring task with the jointly combined artifacts.