OpenRaiser/Intern-Atlas huggingface dataset:
Misinformation detection in Luganda-English code-mixed social media text
Venue: arXiv cs.CL 2021
Year: 2021

The increasing occurrence, forms, and negative effects of misinformation on social media platforms has necessitated more misinformation detection tools. Currently, work is being done addressing COVID-19 misinformation however, there are no misinformation detection tools for any of the 40 distinct indigenous Ugandan languages. This paper addresses this gap by presenting basic language resources and a misinformation detection data set based on code-mixed Luganda-English messages sourced from the Facebook and Twitter social media platforms. Several machine learning methods are applied on the misinformation detection data set to develop classification models for detecting whether a code-mixed Luganda-English message contains misinformation or not. A 10-fold cross validation evaluation of the classification methods in an experimental misinformation detection task shows that a Discriminative Multinomial Naive Bayes (DMNB) method achieves the highest accuracy and F-measure of 78.19% and 77.90% respectively. Also, Support Vector Machine and Bagging ensemble classification models achieve comparable results. These results are promising since the machine learning models are based on n-gram features from only the misinformation detection dataset.
Drowsiness Detection Based On Driver Temporal Behavior Using a New Developed Dataset
Venue: arXiv cs.CV 2021
Year: 2021

Driver drowsiness detection has been the subject of many researches in the past few decades and various methods have been developed to detect it. In this study, as an image-based approach with adequate accuracy, along with the expedite process, we applied YOLOv3 (You Look Only Once-version3) CNN (Convolutional Neural Network) for extracting facial features automatically. Then, LSTM (Long-Short Term Memory) neural network is employed to learn driver temporal behaviors including yawning and blinking time period as well as sequence classification. To train YOLOv3, we utilized our collected dataset alongside the transfer learning method. Moreover, the dataset for the LSTM training process is produced by the mentioned CNN and is formatted as a two-dimensional sequence comprised of eye blinking and yawning time durations. The developed dataset considers both disturbances such as illumination and drivers' head posture. To have real-time experiments a multi-thread framework is developed to run both CNN and LSTM in parallel. Finally, results indicate the hybrid of CNN and LSTM ability in drowsiness detection and the effectiveness of the proposed method.
Showing Academic Performance Predictions during Term Planning: Effects on Students' Decisions, Behaviors, and Preferences
Venue: arXiv cs.HC 2021
Year: 2021

Course selection is a crucial activity for students as it directly impacts their workload and performance. It is also time-consuming, prone to subjectivity, and often carried out based on incomplete information. This task can, nevertheless, be assisted with computational tools, for instance, by predicting performance based on historical data. We investigate the effects of showing grade predictions to students through an interactive visualization tool. A qualitative study suggests that in the presence of predictions, students may focus too much on maximizing their performance, to the detriment of other factors such as the workload. A follow-up quantitative study explored whether these effects are mitigated by changing how predictions are conveyed. Our observations suggest the presence of a framing effect that induces students to put more effort into course selection when faced with more specific predictions. We discuss these and other findings and outline considerations for designing better data-driven course selection tools.
Towards Prior-Free Approximately Truthful One-Shot Auction Learning via Differential Privacy
Venue: arXiv cs.GT 2021
Year: 2021

Designing truthful, revenue maximizing auctions is a core problem of auction design. Multi-item settings have long been elusive. Recent work (arXiv:1706.03459) introduces effective deep learning techniques to find such auctions for the prior-dependent setting, in which distributions about bidder preferences are known. One remaining problem is to obtain priors in a way that excludes the possibility of manipulating the resulting auctions. Using techniques from differential privacy for the construction of approximately truthful mechanisms, we modify the RegretNet approach to be applicable to the prior-free setting. In this more general setting, no distributional information is assumed, but we trade this property for worse performance. We present preliminary empirical results and qualitative analysis for this work in progress.
Adaptive Configuration of In Situ Lossy Compression for Cosmology Simulations via Fine-Grained Rate-Quality Modeling
Venue: arXiv cs.DC 2021
Year: 2021

Extreme-scale cosmological simulations have been widely used by today's researchers and scientists on leadership supercomputers. A new generation of error-bounded lossy compressors has been used in workflows to reduce storage requirements and minimize the impact of throughput limitations while saving large snapshots of high-fidelity data for post-hoc analysis. In this paper, we propose to adaptively provide compression configurations to compute partitions of cosmological simulations with newly designed post-analysis aware rate-quality modeling. The contribution is fourfold: (1) We propose a novel adaptive approach to select feasible error bounds for different partitions, showing the possibility and efficiency of adaptively configuring lossy compression for each partition individually. (2) We build models to estimate the overall loss of post-analysis result due to lossy compression and to estimate compression ratio, based on the property of each partition. (3) We develop an efficient optimization guideline to determine the best-fit configuration of error bounds combination in order to maximize the compression ratio under acceptable post-analysis quality loss. (4) Our approach introduces negligible overheads for feature extraction and error-bound optimization for each partition, enabling post-analysis-aware in situ lossy compression for cosmological simulations. Experiments show that our proposed models are highly accurate and reliable. Our fine-grained adaptive configuration approach improves the compression ratio of up to 73% on the tested datasets with the same post-analysis distortion with only 1% performance overhead.
Unsupervised Person Re-identification via Simultaneous Clustering and Consistency Learning
Venue: arXiv cs.CV 2021
Year: 2021

Unsupervised person re-identification (re-ID) has become an important topic due to its potential to resolve the scalability problem of supervised re-ID models. However, existing methods simply utilize pseudo labels from clustering for supervision and thus have not yet fully explored the semantic information in data itself, which limits representation capabilities of learned models. To address this problem, we design a pretext task for unsupervised re-ID by learning visual consistency from still images and temporal consistency during training process, such that the clustering network can separate the images into semantic clusters automatically. Specifically, the pretext task learns semantically meaningful representations by maximizing the agreement between two encoded views of the same image via a consistency loss in latent space. Meanwhile, we optimize the model by grouping the two encoded views into same cluster, thus enhancing the visual consistency between views. Experiments on Market-1501, DukeMTMC-reID and MSMT17 datasets demonstrate that our proposed approach outperforms the state-of-the-art methods by large margins.
The $k$-Colorable Unit Disk Cover Problem
Venue: arXiv cs.CG 2021
Year: 2021

In this article, we consider colorable variations of the Unit Disk Cover ({\it UDC}) problem as follows. {\it $k$-Colorable Discrete Unit Disk Cover ({\it $k$-CDUDC})}: Given a set $P$ of $n$ points, and a set $D$ of $m$ unit disks (of radius=1), both lying in the plane, and a parameter $k$, the objective is to compute a set $D'\subseteq D$ such that every point in $P$ is covered by at least one disk in $D'$ and there exists a function $χ:D'\rightarrow C$ that assigns colors to disks in $D'$ such that for any $d$ and $d'$ in $D'$ if $d\cap d'\neq\emptyset$, then $χ(d)\neqχ(d')$, where $C$ denotes a set containing $k$ distinct colors. For the {\it $k$-CDUDC} problem, our proposed algorithms approximate the number of colors used in the coloring if there exists a $k$-colorable cover. We first propose a 4-approximation algorithm in $O(m^{7k}n\log k)$ time for this problem and then show that the running time can be improved by a multiplicative factor of $m^k$, where a positive integer $k$ denotes the cardinality of a color-set. The previous best known result for the problem when $k=3$ is due to the recent work of Biedl et al., (2021), who proposed a 2-approximation algorithm in $O(m^{25}n)$ time. For $k=3$, our algorithm runs in $O(m^{18}n)$ time, faster than the previous best algorithm, but gives a 4-approximate result. We then generalize our approach to exhibit a $O((1+\lceil\frac{2}τ\rceil)^2)$-approximation algorithm in $O(m^{(\lfloor\frac{4π+8τ+τ^2}{\sqrt{12}}\rfloor)k}n\log k)$ time for a given $1 \leq τ\leq 2$. We also extend our algorithm to solve the {\it $k$-Colorable Line Segment Disk Cover ({\it $k$-CLSDC})} and {\it $k$-Colorable Rectangular Region Cover ({\it $k$-CRRC})} problems, in which instead of the set $P$ of $n$ points, we are given a set $S$ of $n$ line segments, and a rectangular region $\cal R$, respectively.
Evaluating Neural Word Embeddings for Sanskrit
Venue: arXiv cs.CL 2021
Year: 2021

Recently, the supervised learning paradigm's surprisingly remarkable performance has garnered considerable attention from Sanskrit Computational Linguists. As a result, the Sanskrit community has put laudable efforts to build task-specific labeled data for various downstream Natural Language Processing (NLP) tasks. The primary component of these approaches comes from representations of word embeddings. Word embedding helps to transfer knowledge learned from readily available unlabelled data for improving task-specific performance in low-resource setting. Last decade, there has been much excitement in the field of digitization of Sanskrit. To effectively use such readily available resources, it is very much essential to perform a systematic study on word embedding approaches for the Sanskrit language. In this work, we investigate the effectiveness of word embeddings. We classify word embeddings in broad categories to facilitate systematic experimentation and evaluate them on four intrinsic tasks. We investigate the efficacy of embeddings approaches (originally proposed for languages other than Sanskrit) for Sanskrit along with various challenges posed by language.
Arbitrary-Shaped Text Detection withAdaptive Text Region Representation
Venue: arXiv cs.CV 2021
Year: 2021

Text detection/localization, as an important task in computer vision, has witnessed substantialadvancements in methodology and performance with convolutional neural networks. However, the vastmajority of popular methods use rectangles or quadrangles to describe text regions. These representationshave inherent drawbacks, especially relating to dense adjacent text and loose regional text boundaries,which usually cause difficulty detecting arbitrarily shaped text. In this paper, we propose a novel text regionrepresentation method, with a robust pipeline, which can precisely detect dense adjacent text instances witharbitrary shapes. We consider a text instance to be composed of an adaptive central text region mask anda corresponding expanding ratio between the central text region and the full text region. More specifically,our pipeline generates adaptive central text regions and corresponding expanding ratios with a proposedtraining strategy, followed by a new proposed post-processing algorithm which expands central text regionsto the complete text instance with the corresponding expanding ratios. We demonstrated that our new textregion representation is effective, and that the pipeline can precisely detect closely adjacent text instances ofarbitrary shapes. Experimental results on common datasets demonstrate superior performance o
Training Medical Communication Skills with Virtual Patients: Literature Review and Directions for Future Research
Venue: arXiv cs.HC 2021
Year: 2021

Effective communication is a crucial skill for healthcare providers since it leads to better patient health, satisfaction and avoids malpractice claims. In standard medical education, students' communication skills are trained with role-playing and Standardized Patients (SPs), i.e., actors. However, SPs are difficult to standardize, and are very resource consuming. Virtual Patients (VPs) are interactive computer-based systems that represent a valuable alternative to SPs. VPs are capable of portraying patients in realistic clinical scenarios and engage learners in realistic conversations. Approaching medical communication skill training with VPs has been an active research area in the last ten years. As a result, the number of works in this field has grown significantly. The objective of this work is to survey the recent literature, assessing the state of the art of this technology with a specific focus on the instructional and technical design of VP simulations. After having classified and analysed the VPs selected for our research, we identified several areas that require further investigation, and we drafted practical recommendations for VP developers on design aspects that, based on our findings, are pivotal to create novel and effective VP simulations or improve existing ones.
Famous Companies Use More Letters in Logo:A Large-Scale Analysis of Text Area in Logo
Venue: arXiv cs.CV 2021
Year: 2021

This paper analyzes a large number of logo images from the LLD-logo dataset, by recent deep learning-based techniques, to understand not only design trends of logo images and but also the correlation to their owner company. Especially, we focus on three correlations between logo images and their text areas, between the text areas and the number of followers on Twitter, and between the logo images and the number of followers. Various findings include the weak positive correlation between the text area ratio and the number of followers of the company. In addition, deep regression and deep ranking methods can catch correlations between the logo images and the number of followers.
Nine Potential Pitfalls when Designing Human-AI Co-Creative Systems
Venue: arXiv cs.HC 2021
Year: 2021

This position paper examines potential pitfalls on the way towards achieving human-AI co-creation with generative models in a way that is beneficial to the users' interests. In particular, we collected a set of nine potential pitfalls, based on the literature and our own experiences as researchers working at the intersection of HCI and AI. We illustrate each pitfall with examples and suggest ideas for addressing it. Reflecting on all pitfalls, we discuss and conclude with implications for future research directions. With this collection, we hope to contribute to a critical and constructive discussion on the roles of humans and AI in co-creative interactions, with an eye on related assumptions and potential side-effects for creative practices and beyond.
Low-Resource Neural Machine Translation for Southern African Languages
Venue: arXiv cs.CL 2021
Year: 2021

Low-resource African languages have not fully benefited from the progress in neural machine translation because of a lack of data. Motivated by this challenge we compare zero-shot learning, transfer learning and multilingual learning on three Bantu languages (Shona, isiXhosa and isiZulu) and English. Our main target is English-to-isiZulu translation for which we have just 30,000 sentence pairs, 28% of the average size of our other corpora. We show the importance of language similarity on the performance of English-to-isiZulu transfer learning based on English-to-isiXhosa and English-to-Shona parent models whose BLEU scores differ by 5.2. We then demonstrate that multilingual learning surpasses both transfer learning and zero-shot learning on our dataset, with BLEU score improvements relative to the baseline English-to-isiZulu model of 9.9, 6.1 and 2.0 respectively. Our best model also improves the previous SOTA BLEU score by more than 10.
Target Transformed Regression for Accurate Tracking
Venue: arXiv cs.CV 2021
Year: 2021

Accurate tracking is still a challenging task due to appearance variations, pose and view changes, and geometric deformations of target in videos. Recent anchor-free trackers provide an efficient regression mechanism but fail to produce precise bounding box estimation. To address these issues, this paper repurposes a Transformer-alike regression branch, termed as Target Transformed Regression (TREG), for accurate anchor-free tracking. The core to our TREG is to model pair-wise relation between elements in target template and search region, and use the resulted target enhanced visual representation for accurate bounding box regression. This target contextualized representation is able to enhance the target relevant information to help precisely locate the box boundaries, and deal with the object deformation to some extent due to its local and dense matching mechanism. In addition, we devise a simple online template update mechanism to select reliable templates, increasing the robustness for appearance variations and geometric deformations of target in time. Experimental results on visual tracking benchmarks including VOT2018, VOT2019, OTB100, GOT10k, NFS, UAV123, LaSOT and TrackingNet demonstrate that TREG obtains the state-of-the-art performance, achieving a success rate of 0.640 on LaSOT, while running at around 30 FPS. The code and models will be made available at https://github.com/MCG-NJU/TREG.
Learning Rates for Multi-task Regularization Networks
Venue: arXiv cs.LG 2021
Year: 2021

Multi-task learning is an important trend of machine learning in facing the era of artificial intelligence and big data. Despite a large amount of researches on learning rate estimates of various single-task machine learning algorithms, there is little parallel work for multi-task learning. We present mathematical analysis on the learning rate estimate of multi-task learning based on the theory of vector-valued reproducing kernel Hilbert spaces and matrix-valued reproducing kernels. For the typical multi-task regularization networks, an explicit learning rate dependent both on the number of sample data and the number of tasks is obtained. It reveals that the generalization ability of multi-task learning algorithms is indeed affected as the number of tasks increases.
Edge-Assisted Congestion Control Mechanism for 5G Network Using Software-Defined Networking
Venue: arXiv cs.NI 2021
Year: 2021

In order to cope with the explosive growth of data traffic which is associated with a wide plethora of emerging applications and services that are expected to be used by both ordinary users and vertical industries, the congestion control mechanism is considered to be vital. In this paper, we proposed a congestion control mechanism that could function within the framework of Multi-Access Edge Computing (MEC). The proposed mechanism is aiming to make real-time decisions for selectively buffering traffic while taking network condition and Quality of Service (QoS) into consideration. In order to support a MEC-assisted scheme, the MEC server is expected to locally store delay-tolerant data traffics until the delay conditions expire. This enables the network to have better control over the radio resource provisioning of higher priority data. To achieve this, we introduced a dedicated function known as Congestion Control Engine (CCE), which can capture Radio Access Network (RAN) condition through Radio Network Information Service (RNIS) function, and use this knowledge to make the real-time decision for selectively offloading traffic so that it can perform more intelligently. Analytical evaluation results of our proposed mechanism confirm that it can alleviate network congestion more efficiently.
Blockchain and BIM (Building Information Modeling): Progress in Academia and Industry
Venue: arXiv cs.CR 2021
Year: 2021

In construction, BIM (Building Information Modeling) promises to increase quality of data and to provide a shared, uniform view to all parties. While BIM tools and exchange formats exist, the distribution and safeguarding of data is an ongoing challenge. Distributed Ledger Technology and Blockchains offer a possible solution to this task, and they promise quality attributes such as tamper resistance, traceability/auditability and safe digitalization of assets and intellectual property. However, the practical application and adoption of Distributed Ledger Technology in the built environment requires a good understanding of tool maturity, performance and standardization. Also, user-oriented integration of BIM tools with the blockchain backend needs attention. The contribution of this paper is an overview over both industrial and academic progress at the intersection of BIM and blockchains/DLT.
LED2-Net: Monocular 360 Layout Estimation via Differentiable Depth Rendering
Venue: arXiv cs.CV 2021
Year: 2021

Although significant progress has been made in room layout estimation, most methods aim to reduce the loss in the 2D pixel coordinate rather than exploiting the room structure in the 3D space. Towards reconstructing the room layout in 3D, we formulate the task of 360 layout estimation as a problem of predicting depth on the horizon line of a panorama. Specifically, we propose the Differentiable Depth Rendering procedure to make the conversion from layout to depth prediction differentiable, thus making our proposed model end-to-end trainable while leveraging the 3D geometric information, without the need of providing the ground truth depth. Our method achieves state-of-the-art performance on numerous 360 layout benchmark datasets. Moreover, our formulation enables a pre-training step on the depth dataset, which further improves the generalizability of our layout estimation model.
Design and development of an Aerial Surveillance Security System
Venue: arXiv cs.CR 2021
Year: 2021

Aerial security means performing security-aimed monitoring and surveillance operations with the help of airborne vehicles. This kind of activities suggest that human officers (security organizations, law enforcement, police etc.) would be able to remotely monitor and view video and data acquired from Drones while planning and evaluating their operations. The spectrum of applications where drones are used for security purposes is vast: scouting and reporting emergencies, monitoring accidents and crimes, surveillance of a certain landscape area, operating in highly busy and pedestrians as well as their tracking from up in the sky, and so on. The project will serve as a bridge to connect actual happening in areas that cannot be navigated easily by security personnel of corporate institution as the Drone will be used to hover and record the actual happening as it transmit to a ground station which records and analyses the events as they streams in, also due its capability of flying over different altitudes the drone can generally be used on areas with rugged terrains or over water bodies for a time dependent on its power capacity.