OpenRaiser/Intern-Atlas huggingface dataset:
Stochastic Buffer-Aided Relay-Assisted MEC in Time-Slotted Systems
Venue: arXiv cs.NI 2021
Year: 2021

Mobile Edge Computing (MEC) has attracted significant research efforts in the recent years. However, these works consider mostly the computation resources located at the cloud centers and wireless access nodes, ignoring the possibility of utilizing server-empowered relays to improve the performance. In this paper, we study stochastic relay-assisted MEC in systems with discrete transmission time-line and block fading wireless channels. In order to clearly identify and inspect the fundamental affecting factors, we investigate the building block of this architecture, namely a hierarchical network consisting of a source, a buffer-and-server-aided relay and another higher-level computing node. We provide a framework to take into account the effects of the fading channels, the task arrival dynamics as well as the queuing delays in both the transmission and computation buffers, which facilitates the derivation of the expression for the Average Response Time (ART). Based on that and the system average power consumption in each slot, we introduce the concept of Average Response Energy (ARE) as a novel metric to capture the energy efficiency in MEC while considering the stochastic nature of the system parameters. Accordingly, we propose two offloading schemes with their respective problem formulations, namely the Minimum ART (MART) and the Minimum ARE (MARE) schemes, to optimize the transmission power and task assignment probability while keeping the system queues stable. We demonstrate the difference of the formulated problems with the relevant problem in a recent work, analyze the properties of the problems and noting them, we propose effective solution methods. Using extensive simulations, we validate the presented analysis and show the effectiveness of the proposed schemes in comparison with various baseline methods adapting existing approaches.
AI-Decision Support System Interface Using Cancer Related Data for Lung Cancer Prognosis
Venue: arXiv cs.LG 2021
Year: 2021

Until the beginning of 2021, lung cancer is known to be the most common cancer in the world. The disease is common due to factors such as occupational exposure, smoking and environmental pollution. The early diagnosis and treatment of the disease is of great importance as well as the prevention of the causes that cause the disease. The study was planned to create a web interface that works with machine learning algorithms to predict prognosis using lung cancer clinical and gene expression in the GDC data portal.
Designing AI-based Conversational Agent for Diabetes Care in a Multilingual Context
Venue: arXiv cs.AI 2021
Year: 2021

Conversational agents (CAs) represent an emerging research field in health information systems, where there are great potentials in empowering patients with timely information and natural language interfaces. Nevertheless, there have been limited attempts in establishing prescriptive knowledge on designing CAs in the healthcare domain in general, and diabetes care specifically. In this paper, we conducted a Design Science Research project and proposed three design principles for designing health-related CAs that embark on artificial intelligence (AI) to address the limitations of existing solutions. Further, we instantiated the proposed design and developed AMANDA - an AI-based multilingual CA in diabetes care with state-of-the-art technologies for natural-sounding localised accent. We employed mean opinion scores and system usability scale to evaluate AMANDA's speech quality and usability, respectively. This paper provides practitioners with a blueprint for designing CAs in diabetes care with concrete design guidelines that can be extended into other healthcare domains.
Pilot Assignment Schemes for Cell-Free Massive MIMO Systems
Venue: arXiv cs.IT 2021
Year: 2021

In this work, we propose three pilot assignment schemes to reduce the effect of pilot contamination in cell-free massive multiple-input-multiple-output (MIMO) systems. Our first algorithm, which is based on the idea of random sequential adsorption (RSA) process from the statistical physics literature, can be implemented in a distributed and scalable manner while ensuring a minimum distance among the co-pilot users. Further, leveraging the rich literature of the RSA process, we present an approximate analytical approach to accurately determine the density of the co-pilot users as well as the pilot assignment probability for the typical user in this network. We also develop two optimization-based centralized pilot allocation schemes with the primary goal of benchmarking the RSA-based scheme. The first centralized scheme is based only on the user locations (just like the RSA-based scheme) and partitions the users into sets of co-pilot users such that the minimum distance between two users in a partition is maximized. The second centralized scheme takes both user and remote radio head (RRH) locations into account and provides a near-optimal solution in terms of sum-user spectral efficiency (SE). The general idea is to first cluster the users with similar propagation conditions with respect to the RRHs using spectral graph theory and then ensure that the users in each cluster are assigned different pilots using the branch and price (BnP) algorithm. Our simulation results demonstrate that despite admitting distributed implementation, the RSA-based scheme has a competitive performance with respect to the first centralized scheme in all regimes as well as to the near-optimal second scheme when the density of RRHs is high.
Size does not matter -- in the virtual world. Comparing online social networking behaviour with business success of entrepreneurs
Venue: arXiv cs.SI 2021
Year: 2021

We explore what benefits network position in online business social networks like LinkedIn might confer to an aspiring entrepreneur. We compare two network attributes, size and embeddedness, and two actor attributes, location and diversity, between virtual and real-world networks. The promise of social networks like LinkedIn is that network friends enable easier access to critical resources such as legal and financial services, customers, and business partners. Our setting consists of one million public member profiles of the German business networking site XING (a German version of LinkedIn) from which we extracted the network structure of 15,000 start-up entrepreneurs from 12 large German universities. We find no positive effect of virtual network size and embeddedness, and small positive effects of location and diversity.
Constacyclic Codes over Commutative Finite Principal Ideal Rings
Venue: arXiv cs.IT 2021
Year: 2021

For any constacyclic code over a finite commutative chain ring of length coprime to the characteristic of the ring, we construct explicitly generator polynomials and check polynomials, and exhibit a BCH bound for such constacyclic codes. As a consequence, such constacyclic codes are principal. Further, we get a necessary and sufficient condition that the cyclic codes over a finite commutative principal ideal ring are all principal. This condition is still sufficient for constacyclic codes over such rings being principal.
The impact of virtual mirroring on customer satisfaction
Venue: arXiv cs.SI 2021
Year: 2021

We investigate the impact of a novel method called "virtual mirroring" to promote employee self-reflection and impact customer satisfaction. The method is based on measuring communication patterns, through social network and semantic analysis, and mirroring them back to the individual. Our goal is to demonstrate that self-reflection can trigger a change in communication behaviors, which lead to increased customer satisfaction. We illustrate and test our approach analyzing e-mails of a large global services company by comparing changes in customer satisfaction associated with team leaders exposed to virtual mirroring (the experimental group). We find an increase in customer satisfaction in the experimental group and a decrease in the control group (team leaders not involved in the virtual mirroring process). With regard to the individual communication indicators, we find that customer satisfaction is higher when employees are more responsive, use a simpler language, are embedded in less centralized communication networks, and show more stable leadership patterns.
Training Software Engineers for Qualitative Evaluation of Software Architecture
Venue: arXiv cs.SE 2021
Year: 2021

A software architect uses quality requirements to design the architecture of a system. However, it is essential to ensure that the system's final architectural design achieves the standard quality requirements. The existing architectural evaluation frameworks require basic skills and experience for practical usage, which novice software architects lack. We propose a framework that enables novice software architects to infer the system's quality requirements and tactics using the software architectural block-line diagram. The framework takes an image as input, extracts various components and connections, and maps them to viable architectural patterns, followed by identifying the system's corresponding quality attributes (QAs) and tactics. The framework includes a specifically trained machine learning model based on image processing and semantic similarity methods to assist software architects in evaluating a given design by a) evaluating an input architectural design based on the architectural patterns present in it, b) lists out the strengths and weaknesses of the design in terms of QAs, c) recommends the necessary architectural tactics that can be embedded in the design to achieve the lacking QAs. To train our framework, we developed a dataset of 2,035 architectural images from fourteen architectural patterns such as Client-Server, Microservices, and Model View Controller, available at https://www.doi.org/10.6084/m9.figshare.14156408. The framework achieves a Correct Recognition Rate of 98.71% in identifying the architectural patterns. We evaluated the proposed framework's effectiveness and usefulness by using controlled and experimental groups, in which the experimental group performed approximately 150% better than the controlled group. The experiments were performed as a part of the Masters of Computer Science course in an Engineering Institution.
Enriching Query Semantics for Code Search with Reinforcement Learning
Venue: arXiv cs.SE 2021
Year: 2021

Code search is a common practice for developers during software implementation. The challenges of accurate code search mainly lie in the knowledge gap between source code and natural language (i.e., queries). Due to the limited code-query pairs and large code-description pairs available, the prior studies based on deep learning techniques focus on learning the semantic matching relation between source code and corresponding description texts for the task, and hypothesize that the semantic gap between descriptions and user queries is marginal. In this work, we found that the code search models trained on code-description pairs may not perform well on user queries, which indicates the semantic distance between queries and code descriptions. To mitigate the semantic distance for more effective code search, we propose QueCos, a Query-enriched Code search model. QueCos learns to generate semantic enriched queries to capture the key semantics of given queries with reinforcement learning (RL). With RL, the code search performance is considered as a reward for producing accurate semantic enriched queries. The enriched queries are finally employed for code search. Experiments on the benchmark datasets show that QueCos can significantly outperform the state-of-the-art code search models.
Content-adaptive Representation Learning for Fast Image Super-resolution
Venue: arXiv cs.CV 2021
Year: 2021

Deep convolutional networks have attracted great attention in image restoration and enhancement. Generally, restoration quality has been improved by building more and more convolutional block. However, these methods mostly learn a specific model to handle all images and ignore difficulty diversity. In other words, an area in the image with high frequency tend to lose more information during compressing while an area with low frequency tends to lose less. In this article, we adrress the efficiency issue in image SR by incorporating a patch-wise rolling network(PRN) to content-adaptively recover images according to difficulty levels. In contrast to existing studies that ignore difficulty diversity, we adopt different stage of a neural network to perform image restoration. In addition, we propose a rolling strategy that utilizes the parameters of each stage more flexible. Extensive experiments demonstrate that our model not only shows a significant acceleration but also maintain state-of-the-art performance.
LAST at SemEval-2021 Task 1: Improving Multi-Word Complexity Prediction Using Bigram Association Measures
Venue: arXiv cs.CL 2021
Year: 2021

This paper describes the system developed by the Laboratoire d'analyse statistique des textes (LAST) for the Lexical Complexity Prediction shared task at SemEval-2021. The proposed system is made up of a LightGBM model fed with features obtained from many word frequency lists, published lexical norms and psychometric data. For tackling the specificity of the multi-word task, it uses bigram association measures. Despite that the only contextual feature used was sentence length, the system achieved an honorable performance in the multi-word task, but poorer in the single word task. The bigram association measures were found useful, but to a limited extent.
Newsalyze: Enabling News Consumers to Understand Media Bias
Venue: arXiv cs.CY 2021
Year: 2021

News is a central source of information for individuals to inform themselves on current topics. Knowing a news article's slant and authenticity is of crucial importance in times of "fake news," news bots, and centralization of media ownership. We introduce Newsalyze, a bias-aware news reader focusing on a subtle, yet powerful form of media bias, named bias by word choice and labeling (WCL). WCL bias can alter the assessment of entities reported in the news, e.g., "freedom fighters" vs. "terrorists." At the core of the analysis is a neural model that uses a news-adapted BERT language model to determine target-dependent sentiment, a high-level effect of WCL bias. While the analysis currently focuses on only this form of bias, the visualizations already reveal patterns of bias when contrasting articles (overview) and in-text instances of bias (article view).
Secure, Anonymity-Preserving and Lightweight Mutual Authentication and Key Agreement Protocol for Home Automation IoT Networks
Venue: arXiv cs.CR 2021
Year: 2021

Home automation Internet of Things (IoT) systems have recently become a target for several types of attacks. In this paper, we present an authentication and key agreement protocol for a home automation network based on the ZigBee standard, which connects together a central controller and several end devices. Our scheme performs mutual authentication between end devices and the controller, which is followed by device-to-device communication. The scheme achieves confidentiality, message integrity, anonymity, unlinkability, forward and backward secrecy, and availability. Our scheme uses only simple hash and XOR computations and symmetric key encryption, and hence is resource-efficient. We show using a detailed security analysis and numerical results that our proposed scheme provides better security and anonymity, and is more efficient in terms of computation time, communication cost, and storage cost than schemes proposed in prior works.
A Terabit Hybrid FPGA-ASIC Platform for Switch Virtualization
Venue: arXiv cs.AR 2021
Year: 2021

The roll-out of technologies like 5G and the need for multi-terabit bandwidth in backbone networks requires networking companies to make significant investments to keep up with growing service demands. For lower capital expenditure and faster time-to-market, companies can resort to anything-as-a-service providers to lease virtual resources. Nevertheless, existing virtualization technologies are still lagging behind next-generation networks' requirements. This paper breaks the terabit barrier by introducing a hybrid FPGA-ASIC architecture to virtualize programmable forwarding planes. In contrast to existing solutions, our architecture involves an ASIC that multiplexes network flows between programmable virtual switches running in an FPGA capable of full and partial reconfiguration, enabling virtual switch hot-swapping. Our evaluation shows the feasibility of a switch virtualization architecture capable of achieving a combined throughput of 3.2 Tbps by having up to 26 virtual switch instances in parallel with low resource occupation overhead.
Characterizing Abhorrent, Misinformative, and Mistargeted Content on YouTube
Venue: arXiv cs.CY 2021
Year: 2021

YouTube has revolutionized the way people discover and consume video. Although YouTube facilitates easy access to hundreds of well-produced and trustworthy videos, abhorrent, misinformative, and mistargeted content is also common. The platform is plagued by various types of problematic content: 1) disturbing videos targeting young children; 2) hateful and misogynistic content; and 3) pseudoscientific misinformation. While YouTube's recommendation algorithm plays a vital role in increasing user engagement and YouTube's monetization, its role in unwittingly promoting problematic content is not entirely understood. In this thesis, we shed some light on the degree of problematic content on YouTube and the role of the recommendation algorithm in the dissemination of such content. Following a data-driven quantitative approach, we analyze thousands of videos on YouTube, to shed light on: 1) the risks of YouTube media consumption by young children; 2) the role of the recommendation algorithm in the dissemination of misogynistic content, by focusing on the Involuntary Celibates (Incels) community; and 3) user exposure to pseudoscientific content on various parts of the platform and how this exposure changes based on the user's watch history. Our analysis reveals that young children are likely to encounter disturbing content when they randomly browse the platform. By analyzing the Incel community on YouTube, we find that Incel activity is increasing over time and that platforms may play an active role in steering users towards extreme content. Finally, when studying pseudoscientific misinformation, we find that YouTube suggests more pseudoscientific content regarding traditional pseudoscientific topics (e.g., flat earth) than for emerging ones (like COVID-19) and that these recommendations are more common on the search results page than on a user's homepage or the video recommendations section.
DeepAVO: Efficient Pose Refining with Feature Distilling for Deep Visual Odometry
Venue: arXiv cs.CV 2021
Year: 2021

The technology for Visual Odometry (VO) that estimates the position and orientation of the moving object through analyzing the image sequences captured by on-board cameras, has been well investigated with the rising interest in autonomous driving. This paper studies monocular VO from the perspective of Deep Learning (DL). Unlike most current learning-based methods, our approach, called DeepAVO, is established on the intuition that features contribute discriminately to different motion patterns. Specifically, we present a novel four-branch network to learn the rotation and translation by leveraging Convolutional Neural Networks (CNNs) to focus on different quadrants of optical flow input. To enhance the ability of feature selection, we further introduce an effective channel-spatial attention mechanism to force each branch to explicitly distill related information for specific Frame to Frame (F2F) motion estimation. Experiments on various datasets involving outdoor driving and indoor walking scenarios show that the proposed DeepAVO outperforms the state-of-the-art monocular methods by a large margin, demonstrating competitive performance to the stereo VO algorithm and verifying promising potential for generalization.
Multi-Perspective Anomaly Detection
Venue: arXiv cs.CV 2021
Year: 2021

Anomaly detection is a critical problem in the manufacturing industry. In many applications, images of objects to be analyzed are captured from multiple perspectives which can be exploited to improve the robustness of anomaly detection. In this work, we build upon the deep support vector data description algorithm and address multi-perspective anomaly detection using three different fusion techniques, i.e., early fusion, late fusion, and late fusion with multiple decoders. We employ different augmentation techniques with a denoising process to deal with scarce one-class data, which further improves the performance (ROC AUC $= 80\%$). Furthermore, we introduce the dices dataset, which consists of over 2000 grayscale images of falling dices from multiple perspectives, with 5\% of the images containing rare anomalies (e.g., drill holes, sawing, or scratches). We evaluate our approach on the new dices dataset using images from two different perspectives and also benchmark on the standard MNIST dataset. Extensive experiments demonstrate that our proposed {multi-perspective} approach exceeds the state-of-the-art {single-perspective anomaly detection on both the MNIST and dices datasets}. To the best of our knowledge, this is the first work that focuses on addressing multi-perspective anomaly detection in images by jointly using different perspectives together with one single objective function for anomaly detection.
XGBoost energy consumption prediction based on multi-system data HVAC
Venue: arXiv cs.LG 2021
Year: 2021

The energy consumption of the HVAC system accounts for a significant portion of the energy consumption of the public building system, and using an efficient energy consumption prediction model can assist it in carrying out effective energy-saving transformation. Unlike the traditional energy consumption prediction model, this paper extracts features from large data sets using XGBoost, trains them separately to obtain multiple models, then fuses them with LightGBM's independent prediction results using MAE, infers energy consumption related variables, and successfully applies this model to the self-developed Internet of Things platform.
VLM: Task-agnostic Video-Language Model Pre-training for Video Understanding
Venue: arXiv cs.CV 2021
Year: 2021

We present a simplified, task-agnostic multi-modal pre-training approach that can accept either video or text input, or both for a variety of end tasks. Existing pre-training are task-specific by adopting either a single cross-modal encoder that requires both modalities, limiting their use for retrieval-style end tasks or more complex multitask learning with two unimodal encoders, limiting early cross-modal fusion. We instead introduce new pretraining masking schemes that better mix across modalities (e.g. by forcing masks for text to predict the closest video embeddings) while also maintaining separability (e.g. unimodal predictions are sometimes required, without using all the input). Experimental results show strong performance across a wider range of tasks than any previous methods, often outperforming task-specific pre-training. Code is made available at https://github.com/pytorch/fairseq/tree/main/examples/MMPT.