OpenRaiser/Intern-Atlas huggingface dataset:
A Survey on Security Issues of 5G NR: Perspective of Artificial Dust and Artificial Rain
Venue: arXiv cs.CR 2021
Year: 2021

5G NR (New Radio) incorporates concepts of novel technologies such as spectrum sharing, D2D communication, UDN, and massive MIMO. However, providing security and identifying the security threats to these technologies occupies the prime concern. This paper intends to provide an ample survey of security issues and their countermeasures encompassed in the technologies of 5G NR. Further, security concerns of each technology are defined mathematically. Thereby defining the impact on the factors of security. Moreover, a methodology is developed in which the influence on security due to artificially generated rain and artificially generated dust on the wireless communication network is studied. By doing so, an attacking scenario is identified, where a half-duplex attack in D2D communication is attained. Half-duplex attack specifies the attack solely on the downlink to spoof the allocated resources, with reduced miss-rate. Thus, ultra-reliable and adequate advances are required to be addressed to remove the obstacles that create a hindrance in achieving the secured and authenticated communicating network
An Exploratory Study on the Repeatedly Shared External Links on Stack Overflow
Venue: arXiv cs.SE 2021
Year: 2021

On Stack Overflow, users reuse 11,926,354 external links to share the resources hosted outside the Stack Overflow website. The external links connect to the existing programming-related knowledge and extend the crowdsourced knowledge on Stack Overflow. Some of the external links, so-called as repeated external links, can be shared for multiple times. We observe that 82.5% of the link sharing activities (i.e., sharing links in any question, answer, or comment) on Stack Overflow share external resources, and 57.0% of the occurrences of the external links are sharing the repeated external links. However, it is still unclear what types of external resources are repeatedly shared. To help users manage their knowledge, we wish to investigate the characteristics of the repeated external links in knowledge sharing on Stack Overflow. In this paper, we analyze the repeated external links on Stack Overflow. We observe that external links that point to the text resources (hosted in documentation websites, tutorial websites, etc.) are repeatedly shared the most. We observe that: 1) different users repeatedly share the same knowledge in the form of repeated external links, thus increasing the maintenance effort of knowledge (e.g., update invalid links in multiple posts), 2) the same users can repeatedly share the external links for the purpose of promotion, and 3) external links can point to webpages with an overload of information that is difficult for users to retrieve relevant information. Our findings provide insights to Stack Overflow moderators and researchers. For example, we encourage Stack Overflow to centrally manage the commonly occurring knowledge in the form of repeated external links in order to better maintain the crowdsourced knowledge on Stack Overflow.
Sound Probabilistic Inference via Guide Types
Venue: arXiv cs.PL 2021
Year: 2021

Probabilistic programming languages aim to describe and automate Bayesian modeling and inference. Modern languages support programmable inference, which allows users to customize inference algorithms by incorporating guide programs to improve inference performance. For Bayesian inference to be sound, guide programs must be compatible with model programs. One pervasive but challenging condition for model-guide compatibility is absolute continuity, which requires that the model and guide programs define probability distributions with the same support. This paper presents a new probabilistic programming language that guarantees absolute continuity, and features general programming constructs, such as branching and recursion. Model and guide programs are implemented as coroutines that communicate with each other to synchronize the set of random variables they sample during their execution. Novel guide types describe and enforce communication protocols between coroutines. If the model and guide are well-typed using the same protocol, then they are guaranteed to enjoy absolute continuity. An efficient algorithm infers guide types from code so that users do not have to specify the types. The new programming language is evaluated with an implementation that includes the type-inference algorithm and a prototype compiler that targets Pyro. Experiments show that our language is capable of expressing a variety of probabilistic models with nontrivial control flow and recursion, and that the coroutine-based computation does not introduce significant overhead in actual Bayesian inference.
A Reinforcement Learning Environment For Job-Shop Scheduling
Venue: arXiv cs.LG 2021
Year: 2021

Scheduling is a fundamental task occurring in various automated systems applications, e.g., optimal schedules for machines on a job shop allow for a reduction of production costs and waste. Nevertheless, finding such schedules is often intractable and cannot be achieved by Combinatorial Optimization Problem (COP) methods within a given time limit. Recent advances of Deep Reinforcement Learning (DRL) in learning complex behavior enable new COP application possibilities. This paper presents an efficient DRL environment for Job-Shop Scheduling -- an important problem in the field. Furthermore, we design a meaningful and compact state representation as well as a novel, simple dense reward function, closely related to the sparse make-span minimization criteria used by COP methods. We demonstrate that our approach significantly outperforms existing DRL methods on classic benchmark instances, coming close to state-of-the-art COP approaches.
Detection of Message Injection Attacks onto the CAN Bus using Similarity of Successive Messages-Sequence Graphs
Venue: arXiv cs.CR 2021
Year: 2021

The smart features of modern cars are enabled by a number of Electronic Control Units (ECUs) components that communicate through an in-vehicle network, known as Controller Area Network (CAN) bus. The fundamental challenge is the security of the communication link where an attacker can inject messages (e.g., increase the speed) that may impact the safety of the driver. Developing an effective defensive security solution depends on the knowledge of the identity of the ECUs, which is proprietary information. This paper proposes a message injection attack detection mechanism that is independent of the IDs of the ECUs, which is achieved by capturing the patterns in the message sequences. First, we represent the sequencing ofther messages in a given time-interval as a direct graph and compute the similarities of the successive graphs using the cosine similarity and Pearson correlation. Then, we apply threshold, change point detection, and Long Short-Term Memory (LSTM)-Recurrent NeuralNetwork (RNN) to detect and predict malicious message injections into the CAN bus. The evaluation of the methods using a dataset collected from a moving vehicle under malicious RPM and speed reading message injections show a detection accuracy of 98.45% when using LSTM-RNN and 97.32% when using a threshold method. Further, the pace of detecting the change isfast for the case of injection of RPM reading messagesbut slow for the case of injection of speed readingsmessages.
Affine-modeled video extraction from a single motion blurred image
Venue: arXiv cs.CV 2021
Year: 2021

A motion-blurred image is the temporal average of multiple sharp frames over the exposure time. Recovering these sharp video frames from a single blurred image is nontrivial, due to not only its strong ill-posedness, but also various types of complex motion in reality such as rotation and motion in depth. In this work, we report a generalized video extraction method using the affine motion modeling, enabling to tackle multiple types of complex motion and their mixing. In its workflow, the moving objects are first segemented in the alpha channel. This allows separate recovery of different objects with different motion. Then, we reduce the variable space by modeling each video clip as a series of affine transformations of a reference frame, and introduce the $l0$-norm total variation regularization to attenuate the ringing artifact. The differentiable affine operators are employed to realize gradient-descent optimization of the affine model, which follows a novel coarse-to-fine strategy to further reduce artifacts. As a result, both the affine parameters and sharp reference image are retrieved. They are finally input into stepwise affine transformation to recover the sharp video frames. The stepwise retrieval maintains the nature to bypass the frame order ambiguity. Experiments on both public datasets and real captured data validate the state-of-the-art performance of the reported technique.
Experiences with User Studies in Augmented Reality
Venue: arXiv cs.HC 2021
Year: 2021

The research field of augmented reality (AR) is of increasing popularity, as seen, among others, in several recently published surveys. To produce further advancements in AR, it is not only necessary to create new systems or applications, but also to evaluate them. One important aspect in regards to the evaluation is the general understanding of how users experience a given AR application, which can also be seen by the increased number of papers focusing on this topic that were published in the last years. With the steadily growing understanding and development of AR in general, it is only a matter of time until AR devices make the leap into the consumer market where such an in-depth user understanding is even more essential. Thus, a better understanding of factors that could influence the design and results of user experience studies can help us to make them more robust and dependable in the future. In this position paper, we describe three challenges which researchers face while designing and conducting AR users studies. We encountered these challenges in our past and current research, including papers that focus on perceptual studies of visualizations, interaction studies, and studies exploring the use of AR applications and their design spaces.
Beaming Displays
Venue: arXiv cs.HC 2021
Year: 2021

Existing near-eye display designs struggle to balance between multiple trade-offs such as form factor, weight, computational requirements, and battery life. These design trade-offs are major obstacles on the path towards an all-day usable near-eye display. In this work, we address these trade-offs by, paradoxically, \textit{removing the display} from near-eye displays. We present the beaming displays, a new type of near-eye display system that uses a projector and an all passive wearable headset. We modify an off-the-shelf projector with additional lenses. We install such a projector to the environment to beam images from a distance to a passive wearable headset. The beaming projection system tracks the current position of a wearable headset to project distortion-free images with correct perspectives. In our system, a wearable headset guides the beamed images to a user's retina, which are then perceived as an augmented scene within a user's field of view. In addition to providing the system design of the beaming display, we provide a physical prototype and show that the beaming display can provide resolutions as high as consumer-level near-eye displays. We also discuss the different aspects of the design space for our proposal.
Exploiting Natural Language for Efficient Risk-Aware Multi-robot SaR Planning
Venue: arXiv cs.RO 2021
Year: 2021

The ability to develop a high-level understanding of a scene, such as perceiving danger levels, can prove valuable in planning multi-robot search and rescue (SaR) missions. In this work, we propose to uniquely leverage natural language descriptions from the mission commander in chief and image data captured by robots to estimate scene danger. Given a description and an image, a state-of-the-art deep neural network is used to assess a corresponding similarity score, which is then converted into a probabilistic distribution of danger levels. Because commonly used visio-linguistic datasets do not represent SaR missions well, we collect a large-scale image-description dataset from synthetic images taken from realistic disaster scenes and use it to train our machine learning model. A risk-aware variant of the Multi-robot Efficient Search Path Planning (MESPP) problem is then formulated to use the danger estimates in order to account for high-risk locations in the environment when planning the searchers' paths. The problem is solved via a distributed approach based on Mixed-Integer Linear Programming. Our experiments demonstrate that our framework allows to plan safer yet highly successful search missions, abiding to the two most important aspects of SaR missions: to ensure both searchers' and victim safety.
Exploring Machine Speech Chain for Domain Adaptation and Few-Shot Speaker Adaptation
Venue: arXiv cs.CL 2021
Year: 2021

Machine Speech Chain, which integrates both end-to-end (E2E) automatic speech recognition (ASR) and text-to-speech (TTS) into one circle for joint training, has been proven to be effective in data augmentation by leveraging large amounts of unpaired data. In this paper, we explore the TTS->ASR pipeline in speech chain to do domain adaptation for both neural TTS and E2E ASR models, with only text data from target domain. We conduct experiments by adapting from audiobook domain (LibriSpeech) to presentation domain (TED-LIUM), there is a relative word error rate (WER) reduction of 10% for the E2E ASR model on the TED-LIUM test set, and a relative WER reduction of 51.5% in synthetic speech generated by neural TTS in the presentation domain. Further, we apply few-shot speaker adaptation for the E2E ASR by using a few utterances from target speakers in an unsupervised way, results in additional gains.
MGN-Net: a multi-view graph normalizer for integrating heterogeneous biological network populations
Venue: arXiv cs.SI 2021
Year: 2021

With the recent technological advances, biological datasets, often represented by networks (i.e., graphs) of interacting entities, proliferate with unprecedented complexity and heterogeneity. Although modern network science opens new frontiers of analyzing connectivity patterns in such datasets, we still lack data-driven methods for extracting an integral connectional fingerprint of a multi-view graph population, let alone disentangling the typical from the atypical variations across the population samples. We present the multi-view graph normalizer network (MGN-Net; https://github.com/basiralab/MGN-Net), a graph neural network based method to normalize and integrate a set of multi-view biological networks into a single connectional template that is centered, representative, and topologically sound. We demonstrate the use of MGN-Net by discovering the connectional fingerprints of healthy and neurologically disordered brain network populations including Alzheimer's disease and Autism spectrum disorder patients. Additionally, by comparing the learned templates of healthy and disordered populations, we show that MGN-Net significantly outperforms conventional network integration methods across extensive experiments in terms of producing the most centered templates, recapitulating unique traits of populations, and preserving the complex topology of biological networks. Our evaluations showed that MGN-Net is powerfully generic and easily adaptable in design to different graph-based problems such as identification of relevant connections, normalization and integration.
A Mixed-method Study on Security and Privacy Practices in Danish Companies
Venue: arXiv cs.CR 2021
Year: 2021

Increased levels of digitalization in society expose companies to new security threats, requiring them to establish adequate security and privacy measures. Additionally, the presence of exogenous forces like new regulations, e.g., GDPR and the global COVID-19 pandemic, pose new challenges for companies that should preserve an adequate level of security while having to adapt to change. In this paper, we investigate such challenges through a two-phase study in companies located in Denmark -- a country characterized by a high level of digitalization and trust -- focusing on software development and tech-related companies. Our results show a number of issues, most notably i) a misalignment between software developers and management when it comes to the implementation of security and privacy measures, ii) difficulties in adapting company practices in light of implementing GDPR compliance, and iii) different views on the need to adapt security measures to cope with the COVID-19 pandemic.
Plug-and-Blend: A Framework for Controllable Story Generation with Blended Control Codes
Venue: arXiv cs.CL 2021
Year: 2021

Large pre-trained neural language models (LM) have very powerful text generation capabilities. However, in practice, they are hard to control for creative purposes. We describe a Plug-and-Play controllable language generation framework, Plug-and-Blend, that allows a human user to input multiple control codes (topics). In the context of automated story generation, this allows a human user loose or fine-grained control of the topics and transitions between them that will appear in the generated story, and can even allow for overlapping, blended topics. Automated evaluations show our framework, working with different generative LMs, controls the generation towards given continuous-weighted control codes while keeping the generated sentences fluent, demonstrating strong blending capability. A human participant evaluation shows that the generated stories are observably transitioning between two topics.
What are Table Cartograms Good for Anyway? An Algebraic Analysis
Venue: arXiv cs.HC 2021
Year: 2021

Unfamiliar or esoteric visual forms arise in many areas of visualization. While such forms can be intriguing, it can be unclear how to make effective use of them without long periods of practice or costly user studies. In this work we analyze the table cartogram-a graphic which visualizes tabular data by bringing the areas of a grid of quadrilaterals into correspondence with the input data, like a heat map that has been "area-ed" rather than colored. Despite having existed for several years, little is known about its appropriate usage. We mend this gap by using Algebraic Visualization Design to show that they are best suited to relatively small tables with ordinal axes for some comparison and outlier identification tasks. In doing so we demonstrate a discount theory-based analysis that can be used to cheaply determine best practices for unknown visualizations.
Flavored Tacotron: Conditional Learning for Prosodic-linguistic Features
Venue: arXiv cs.SD 2021
Year: 2021

Neural sequence-to-sequence text-to-speech synthesis (TTS), such as Tacotron-2, transforms text into high-quality speech. However, generating speech with natural prosody still remains a challenge. Yasuda et. al. show that unlike natural speech, Tacotron-2's encoder doesn't fully represent prosodic features (e.g. syllable stress in English) from characters, and result in flat fundamental frequency variations. In this work, we propose a novel carefully designed strategy for conditioning Tacotron-2 on two fundamental prosodic features in English -- stress syllable and pitch accent, that help achieve more natural prosody. To this end, we use of a classifier to learn these features in an end-to-end fashion, and apply feature conditioning at three parts of Tacotron-2's Text-To-Mel Spectrogram: pre-encoder, post-encoder, and intra-decoder. Further, we show that jointly conditioned features at pre-encoder and intra-decoder stages result in prosodically natural synthesized speech (vs. Tacotron-2), and allows the model to produce speech with more accurate pitch accent and stress patterns. Quantitative evaluations show that our formulation achieves higher fundamental frequency contour correlation, and lower Mel Cepstral Distortion measure between synthesized and natural speech. And subjective evaluation shows that the proposed method's Mean Opinion Score of 4.14 fairs higher than baseline Tacotron-2, 3.91, when compared against natural speech (LJSpeech corpus), 4.28.
A Data Science Approach to Analyze the Association of Socioeconomic and Environmental Conditions With Disparities in Pediatric Surgery
Venue: arXiv cs.CY 2021
Year: 2021

Scientific evidence confirm that significant racial disparities exist in healthcare, including surgery outcomes. However, the causal pathway underlying disparities at preoperative physical condition of children is not well-understood. This research aims to uncover the role of socioeconomic and environmental factors in racial disparities at the preoperative physical condition of children through multidimensional integration of several data sources at the patient and population level. After the data integration process an unsupervised k-means algorithm on neighborhood quality metrics was developed to split 29 zip-codes from Memphis, TN into good and poor-quality neighborhoods. An unadjusted comparison of African Americans and white children showed that the prevalence of poor preoperative condition is significantly higher among African Americans compared to whites. No statistically significant difference in surgery outcome was present when adjusted by surgical severity and neighborhood quality. The socioenvironmental factors affect the preoperative clinical condition of children and their surgical outcomes.
An artificial intelligence and Internet of things based automated irrigation system
Venue: arXiv cs.CY 2021
Year: 2021

It is not hard to see that the need for clean water is growing by considering the decrease of the water sources day by day in the world. Potable fresh water is also used for irrigation, so it should be planned to decrease freshwater wastage. With the development of technology and the availability of cheaper and more effective solutions, the efficiency of irrigation increased and the water loss can be reduced. In particular, Internet of things (IoT) devices has begun to be used in all areas. We can easily and precisely collect temperature, humidity and mineral values from the irrigation field with the IoT devices and sensors. Most of the operations and decisions about irrigation are carried out by people. For people, it is hard to have all the real-time data such as temperature, moisture and mineral levels in the decision-making process and make decisions by considering them. People usually make decisions with their experience. In this study, a wide range of information from the irrigation field was obtained by using IoT devices and sensors. Data collected from IoT devices and sensors sent via communication channels and stored on MongoDB. With the help of Weka software, the data was normalized and the normalized data was used as a learning set. As a result of the examinations, a decision tree (J48) algorithm with the highest accuracy was chosen and an artificial intelligence model was created. Decisions are used to manage operations such as starting, maintaining and stopping the irrigation. The accuracy of the decisions was evaluated and the irrigation system was tested with the results. There are options to manage, view the system remotely and manually and also see the system s decisions with the created mobile application.