OpenRaiser/Intern-Atlas huggingface dataset:
Uplink Cooperative Interference Cancellation for Cellular-Connected UAV: A Quantize-and-Forward Approach
Venue: arXiv cs.IT 2020
Year: 2020

Aerial-ground interference is the main obstacle to achieving high spectral efficiency in cellular networks with both traditional terrestrial users and new unmanned aerial vehicle (UAV) users. Due to their strong line-of-sight (LoS) channels with the ground, UAVs could cause/suffer severe interference to/from a large number of non-associated but co-channel base stations (BSs) in their uplink/downlink communications. In this letter, we propose a new cooperative interference cancellation (CIC) scheme for the UAV's uplink communication to mitigate its strong interference to co-channel BSs, which requires only local cooperation between each co-channel BS and its adjacent helping BSs. Specifically, the helping BSs without serving any users in the UAV's communication channel quantize and forward their received signals from the UAV to their aided co-channel BS, which then processes the quantized signals jointly with its own received signal to decode its served terrestrial user's message via canceling the UAV's signal by linear/nonlinear interference cancellation techniques (ICTs). We derive the achievable rates of the proposed CIC scheme with different ICTs as a function of the UAV's transmit power and rate, and thereby unveil the conditions under which the proposed CIC scheme outperforms the existing CIC scheme based on the decode-and-forward (DF) operation of the helping BSs.
Neural Network-Aided BCJR Algorithm for Joint Symbol Detection and Channel Decoding
Venue: arXiv cs.IT 2020
Year: 2020

Recently, deep learning-assisted communication systems have achieved many eye-catching results and attracted more and more researchers in this emerging field. Instead of completely replacing the functional blocks of communication systems with neural networks, a hybrid manner of BCJRNet symbol detection is proposed to combine the advantages of the BCJR algorithm and neural networks. However, its separate block design not only degrades the system performance but also results in additional hardware complexity. In this work, we propose a BCJR receiver for joint symbol detection and channel decoding. It can simultaneously utilize the trellis diagram and channel state information for a more accurate calculation of branch probability and thus achieve global optimum with 2.3 dB gain over separate block design. Furthermore, a dedicated neural network model is proposed to replace the channel-model-based computation of the BCJR receiver, which can avoid the requirements of perfect CSI and is more robust under CSI uncertainty with 1.0 dB gain.
CS-NLP team at SemEval-2020 Task 4: Evaluation of State-of-the-art NLP Deep Learning Architectures on Commonsense Reasoning Task
Venue: arXiv cs.CL 2020
Year: 2020

In this paper, we investigate a commonsense inference task that unifies natural language understanding and commonsense reasoning. We describe our attempt at SemEval-2020 Task 4 competition: Commonsense Validation and Explanation (ComVE) challenge. We discuss several state-of-the-art deep learning architectures for this challenge. Our system uses prepared labeled textual datasets that were manually curated for three different natural language inference subtasks. The goal of the first subtask is to test whether a model can distinguish between natural language statements that make sense and those that do not make sense. We compare the performance of several language models and fine-tuned classifiers. Then, we propose a method inspired by question/answering tasks to treat a classification problem as a multiple choice question task to boost the performance of our experimental results (96.06%), which is significantly better than the baseline. For the second subtask, which is to select the reason why a statement does not make sense, we stand within the first six teams (93.7%) among 27 participants with very competitive results. Our result for last subtask of generating reason against the nonsense statement shows many potentials for future researches as we applied the most powerful generative model of language (GPT-2) with 6.1732 BLEU score among first four teams.
A Thousand Words are Worth More Than One Recording: NLP Based Speaker Change Point Detection
Venue: arXiv cs.CL 2020
Year: 2020

Speaker Diarization (SD) consists of splitting or segmenting an input audio burst according to speaker identities. In this paper, we focus on the crucial task of the SD problem which is the audio segmenting process and suggest a solution for the Change Point Detection (CPD) problem. We empirically demonstrate the negative correlation between an increase in the number of speakers and the Recall and F1-Score measurements. This negative correlation is shown to be the outcome of a massive experimental evaluation process, which accounts its superiority to recently developed voice based solutions. In order to overcome the number of speakers issue, we suggest a robust solution based on a novel Natural Language Processing (NLP) technique, as well as a metadata features extraction process, rather than a vocal based alone. To the best of our knowledge, we are the first to propose an intelligent NLP based solution that (I) tackles the CPD problem with a dataset in Hebrew, and (II) solves the CPD variant of the SD problem. We empirically show, based on two distinct datasets, that our method is abled to accurately identify the CPDs in an audio burst with 82.12% and 89.02% of success in the Recall and F1-score measurements.
Do All Good Actors Look The Same? Exploring News Veracity Detection Across The U.S. and The U.K
Venue: arXiv cs.CL 2020
Year: 2020

A major concern with text-based news veracity detection methods is that they may not generalize across countries and cultures. In this short paper, we explicitly test news veracity models across news data from the United States and the United Kingdom, demonstrating there is reason for concern of generalizabilty. Through a series of testing scenarios, we show that text-based classifiers perform poorly when trained on one country's news data and tested on another. Furthermore, these same models have trouble classifying unseen, unreliable news sources. In conclusion, we discuss implications of these results and avenues for future work.
Crowd simulation for crisis management: the outcomes of the last decade
Venue: arXiv cs.MA 2020
Year: 2020

The last few decades, crowd simulation for crisis management is highlighted as an important topic of interest for many scientific fields. As the continues evolution of computational resources increases, along with the capabilities of Artificial Intelligence, the demand for better and more realistic simulation has become more attractive and popular to scientists. Along those years, there have been published hundreds of research articles and have been created numerous different systems that aim to simulate crowd behaviors, crisis cases and emergency evacuation scenarios. For better outcomes, recent research has focused on the separation of the problem of crisis management, to multiple research sub-fields (categories), such as the navigation of the simulated pedestrians, their psychology, the group dynamics etc. There have been extended research works suggesting new methods and techniques for those categories of problems. In this paper, we propose three main research categories, each one consist of several sub-categories, relying on crowd simulation for crisis management aspects and we present the outcomes of the last decade, focusing mostly on works exploiting multi-agent technologies. We analyze a number of technologies, methodologies, techniques, tools and systems introduced throughout the last years. A comparative review and discussion of the proposed categories is presented towards the identification of the most efficient aspects of the proposed categories. A general framework, towards the future crowd simulation for crisis management is presented based on the most efficient to yield the most realistic outcomes of the last decades. The paper is concluded with some highlights and open questions for future directions.
SOT-MRAM based Sigmoidal Neuron for Neuromorphic Architectures
Venue: arXiv cs.ET 2020
Year: 2020

In this paper, the intrinsic physical characteristics of spin-orbit torque (SOT) magnetoresistive random-access memory (MRAM) devices are leveraged to realize sigmoidal neurons in neuromorphic architectures. Performance comparisons with the previous power- and area-efficient sigmoidal neuron circuits exhibit 74x and 12x reduction in power-area-product values for the proposed SOT-MRAM based neuron. To verify the functionally of the proposed neuron within larger scale designs, we have implemented a circuit realization of a 784x16x10 SOT-MRAM based multiplayer perceptron (MLP) for MNIST pattern recognition application using SPICE circuit simulation tool. The results obtained exhibit that the proposed SOT-MRAM based MLP can achieve accuracies comparable to an ideal binarized MLP architecture implemented on GPU, while realizing orders of magnitude increase in processing speed.
Two-hand Global 3D Pose Estimation Using Monocular RGB
Venue: arXiv cs.CV 2020
Year: 2020

We tackle the challenging task of estimating global 3D joint locations for both hands via only monocular RGB input images. We propose a novel multi-stage convolutional neural network based pipeline that accurately segments and locates the hands despite occlusion between two hands and complex background noise and estimates the 2D and 3D canonical joint locations without any depth information. Global joint locations with respect to the camera origin are computed using the hand pose estimations and the actual length of the key bone with a novel projection algorithm. To train the CNNs for this new task, we introduce a large-scale synthetic 3D hand pose dataset. We demonstrate that our system outperforms previous works on 3D canonical hand pose estimation benchmark datasets with RGB-only information. Additionally, we present the first work that achieves accurate global 3D hand tracking on both hands using RGB-only inputs and provide extensive quantitative and qualitative evaluation.
Saber Pro success prediction model using decision tree based learning
Venue: arXiv cs.CY 2020
Year: 2020

The primary objective of this report is to determine what influences the success rates of students who have studied in Colombia, analyzing the Saber 11, the test done at the last school year, some socioeconomic aspects and comparing the Saber Pro results with the national average. The problem this faces is to find what influences success, but it also provides an insight in the countries education dynamics and predicts one's opportunities to be prosperous. The opposite situation to the one presented in this paper could be the desertion levels, in the sense that by detecting what makes someone outstanding, these factors can say what makes one unsuccessful. The solution proposed to solve this problem was to implement a CART decision tree algorithm that helps to predict the probability that a student has of scoring higher than the mean value, based on different socioeconomic and academic factors, such as the profession of the parents of the subject parents and the results obtained on Saber 11. It was discovered that one of the most influential factors is the score in the Saber 11, on the topic of Social Studies, and that the gender of the subject is not as influential as it is usually portrayed as. The algorithm designed provided significant insight into which factors most affect the probability of success of any given person and if further pursued could be used in many given situations such as deciding which subject in school should be given more intensity to and academic curriculum in general.
Vyasa: A High-Performance Vectorizing Compiler for Tensor Convolutions on the Xilinx AI Engine
Venue: arXiv cs.DC 2020
Year: 2020

Xilinx's AI Engine is a recent industry example of energy-efficient vector processing that includes novel support for 2D SIMD datapaths and shuffle interconnection network. The current approach to programming the AI Engine relies on a C/C++ API for vector intrinsics. While an advance over assembly-level programming, it requires the programmer to specify a number of low-level operations based on detailed knowledge of the hardware. To address these challenges, we introduce Vyasa, a new programming system that extends the Halide DSL compiler to automatically generate code for the AI Engine. We evaluated Vyasa on 36 CONV2D and 6 CONV3D workloads, and achieved geometric means of 7.6 and 23.3 MACs/cycle for 32-bit and 16-bit operands (which represent 95.9% and 72.8% of the peak performance respectively). For 4 of these workloads for which expert-written codes were available to us, Vyasa demonstrated a geometric mean performance improvement of 1.10x with 50x smaller code relative to the expert-written codes.
A Survey of Neural Networks and Formal Languages
Venue: arXiv cs.CL 2020
Year: 2020

This report is a survey of the relationships between various state-of-the-art neural network architectures and formal languages as, for example, structured by the Chomsky Language Hierarchy. Of particular interest are the abilities of a neural architecture to represent, recognize and generate words from a specific language by learning from samples of the language.
Embeddings of Label Components for Sequence Labeling: A Case Study of Fine-grained Named Entity Recognition
Venue: arXiv cs.CL 2020
Year: 2020

In general, the labels used in sequence labeling consist of different types of elements. For example, IOB-format entity labels, such as B-Person and I-Person, can be decomposed into span (B and I) and type information (Person). However, while most sequence labeling models do not consider such label components, the shared components across labels, such as Person, can be beneficial for label prediction. In this work, we propose to integrate label component information as embeddings into models. Through experiments on English and Japanese fine-grained named entity recognition, we demonstrate that the proposed method improves performance, especially for instances with low-frequency labels.
Rate-Splitting Multiple Access: A New Frontier for the PHY Layer of 6G
Venue: arXiv cs.IT 2020
Year: 2020

In order to efficiently cope with the high throughput, reliability, heterogeneity of Quality-of-Service (QoS), and massive connectivity requirements of future 6G multi-antenna wireless networks, multiple access and multiuser communication system design need to depart from conventional interference management strategies, namely fully treat interference as noise (as commonly used in 4G/5G, MU-MIMO, CoMP, Massive MIMO, millimetre wave MIMO) and fully decode interference (as in Non-Orthogonal Multiple Access, NOMA). This paper is dedicated to the theory and applications of a more general and powerful transmission framework based on Rate-Splitting Multiple Access (RSMA) that splits messages into common and private parts and enables to partially decode interference and treat remaining part of the interference as noise. This enables RSMA to softly bridge and therefore reconcile the two extreme strategies of fully decode interference and treat interference as noise and provide room for spectral efficiency, energy efficiency and QoS enhancements, robustness to imperfect Channel State Information at the Transmitter (CSIT), and complexity reduction. We give an overview of RSMA and its potential to address the requirements of 6G. This paper provides an overview of RSMA and its potential to address the requirements of 6G.
Less is More: Robust and Novel Features for Malicious Domain Detection
Venue: arXiv cs.CR 2020
Year: 2020

Malicious domains are increasingly common and pose a severe cybersecurity threat. Specifically, many types of current cyber attacks use URLs for attack communications (e.g., C\&C, phishing, and spear-phishing). Despite the continuous progress in detecting these attacks, many alarming problems remain open, such as the weak spots of the defense mechanisms. Since machine learning has become one of the most prominent methods of malware detection, A robust feature selection mechanism is proposed that results in malicious domain detection models that are resistant to evasion attacks. This mechanism exhibits high performance based on empirical data. This paper makes two main contributions: First, it provides an analysis of robust feature selection based on widely used features in the literature. Note that even though the feature set dimensional space is reduced by half (from nine to four features), the performance of the classifier is still improved (an increase in the model's F1-score from 92.92\% to 95.81\%). Second, it introduces novel features that are robust to the adversary's manipulation. Based on an extensive evaluation of the different feature sets and commonly used classification models, this paper shows that models that are based on robust features are resistant to malicious perturbations, and at the same time useful for classifying non-manipulated data.
Extending the Multiple Traveling Salesman Problem for Scheduling a Fleet of Drones Performing Monitoring Missions
Venue: arXiv cs.AI 2020
Year: 2020

In this paper we schedule the travel path of a set of drones across a graph where the nodes need to be visited multiple times at pre-defined points in time. This is an extension of the well-known multiple traveling salesman problem. The proposed formulation can be applied in several domains such as the monitoring of traffic flows in a transportation network, or the monitoring of remote locations to assist search and rescue missions. Aiming to find the optimal schedule, the problem is formulated as an Integer Linear Program (ILP). Given that the problem is highly combinatorial, the optimal solution scales only for small sized problems. Thus, a greedy algorithm is also proposed that uses a one-step look ahead heuristic search mechanism. In a detailed evaluation, it is observed that the greedy algorithm has near-optimal performance as it is on average at 92.06% of the optimal, while it can potentially scale up to settings with hundreds of drones and locations.
Information Freshness and Packet Drop Rate Interplay in a Two-User Multi-Access Channel
Venue: arXiv cs.NI 2020
Year: 2020

In this work, we combine the two notions of timely delivery of information in order to study their interplay; namely, deadline-constrained packet delivery due to latency constraints and freshness of information at the destination. More specifically, we consider a two-user multiple access setup with random access, in which user 1 is a wireless device with a queue and has external bursty traffic which is deadline-constrained, while user 2 monitors a sensor and transmits status updates to the destination. For this simple, yet meaningful setup, we provide analytical expressions for the throughput and drop probability of user 1, and an analytical expression for the average Age of Information (AoI) of user 2 monitoring the sensor. The relations reveal that there is a trade-off between the average AoI of user 2 and the drop rate of user 1: the lower the average AoI, the higher the drop rate, and vice versa. Simulations corroborate the validity of our theoretical results.
Question Answering on Scholarly Knowledge Graphs
Venue: arXiv cs.IR 2020
Year: 2020

Answering questions on scholarly knowledge comprising text and other artifacts is a vital part of any research life cycle. Querying scholarly knowledge and retrieving suitable answers is currently hardly possible due to the following primary reason: machine inactionable, ambiguous and unstructured content in publications. We present JarvisQA, a BERT based system to answer questions on tabular views of scholarly knowledge graphs. Such tables can be found in a variety of shapes in the scholarly literature (e.g., surveys, comparisons or results). Our system can retrieve direct answers to a variety of different questions asked on tabular data in articles. Furthermore, we present a preliminary dataset of related tables and a corresponding set of natural language questions. This dataset is used as a benchmark for our system and can be reused by others. Additionally, JarvisQA is evaluated on two datasets against other baselines and shows an improvement of two to three folds in performance compared to related methods.
Effectful Programming in Declarative Languages with an Emphasis on Non-Determinism: Applications and Formal Reasoning
Venue: arXiv cs.PL 2020
Year: 2020

This thesis investigates effectful declarative programming with an emphasis on non-determinism as an effect. On the one hand, we are interested in developing applications using non-determinism as underlying implementation idea. We discuss two applications using the functional logic programming language Curry. The key idea of these implementations is to exploit the interplay of non-determinism and non-strictness that Curry employs. The first application investigates sorting algorithms parametrised over a comparison function. By applying a non-deterministic predicate to these sorting functions, we gain a permutation enumeration function. We compare the implementation in Curry with an implementation in Haskell that uses a monadic interface to model non-determinism. The other application that we discuss in this work is a library for probabilistic programming. Instead of modelling distributions as list of event and probability pairs, we model distributions using Curry's built-in non-determinism. In both cases we observe that the combination of non-determinism and non-strictness has advantages over an implementation using lists to model non-determinism. On the other hand, we present an idea to apply formal reasoning on effectful declarative programming languages. In order to start with simple effects, we focus on modelling a functional subset first. That is, the effects of interest are totality and partiality. We then observe that the general scheme to model these two effects can be generalised to capture a wide range of effects. Obviously, the next step is to apply the idea to model non-determinism. More precisely, we implement a model for the non-determinism of Curry: non-strict non-determinism with call-time choice. Therefore, we finally discuss why the current representation models call-by-name rather than Curry's call-by-need semantics and give an outlook on ideas to tackle this problem.
Truthful Computation Offloading Mechanisms for Edge Computing
Venue: arXiv cs.NI 2020
Year: 2020

Edge computing (EC) is a promising paradigm providing a distributed computing solution for users at the edge of the network. Preserving satisfactory quality of experience (QoE) for users when offloading their computation to EC is a non-trivial problem. Computation offloading in EC requires jointly optimizing access points (APs) allocation and edge service placement for users, which is computationally intractable due to its combinatorial nature. Moreover, users are self-interested, and they can misreport their preferences leading to inefficient resource allocation and network congestion. In this paper, we tackle this problem and design a novel mechanism based on algorithmic mechanism design to implement a system equilibrium. Our mechanism assigns a proper pair of AP and edge server along with a service price for each new joining user maximizing the instant social surplus while satisfying all users' preferences in the EC system. Declaring true preferences is a weakly dominant strategy for the users. The experimental results show that our mechanism outperforms user equilibrium and random selection strategies in terms of the experienced end-to-end latency.