OpenRaiser/Intern-Atlas huggingface dataset:
CarnegiePLUG: Prosumer-in-the-Loop simUlation Grid
Venue: arXiv cs.DC 2020
Year: 2020

This paper introduces Carnegie Mellon campus-wide CarnegiePLUG test-bed. This test-bed in a hardware-in-the-loop simulator that enables large-scale sensing, computation and actuation over a network of heterogeneous energy producers and consumers (prosuming) assets. CarnegiePLUG is a multi-agent framework that facilitates plug-and-play integration of a wide range of virtual and physical sensors and controllers through a multi-layer architecture. Virtual functionalities are accessed in software environments that interface with communication layer of our test-bed. This paper discusses CarnegiePLUG layers, provides implementation details and showcases potential applications of this test-bed through preliminary results.
An Optimize-Aware Target Tracking Method with Combine MAC layer and Active Nodes in Wireless Sensor Networks
Venue: arXiv cs.NI 2020
Year: 2020

Wireless Sensor Network WSN is consisted of nodes with different sizes and a specific goal. Tracking applications are very important in WSNs. This study proposes a method for reducing energy consumption in WSNs, considering target tracking. A prediction method is also presented with the help of MAC Medium Access Control layer in which a few number of nodes are in active mode to track the target while the rest of the nodes are switched into sleeping mode. Energy consumption was reduced by decreasing the number of nodes involved in target tracking and activating merely a limited number of necessary nodes. The conducted experiments suggest that the proposed algorithm has a much better performance compared to similar algorithms in different conditions, including energy consumption in different communication ranges, effect of the number of nodes on energy consumption, the number of nodes involved in tracking in terms of communication range, and losing the target. This means the proposed method is superior and more efficient. When the target enters the simulation area, nodes of the network identify the target node through the proposed method (which is the optimum method) and track it as long as it is inside the simulation area. Performance criteria, including energy, other nodes, and throughput were used in simulation. The results showed that the proposed method has the optimum performance and reduces energy consumption of the network during tracking. The results of the simulation showed that the proposed method reduces energy consumption to an acceptable extent, compared to previous methods, in addition to increasing the accuracy of target tracking. Keywords: Active nodes, MAC layer, Energy efficiency, Target tracking, Wireless sensor networks, Target tracking.
Understanding and Detecting Dangerous Speech in Social Media
Venue: arXiv cs.CL 2020
Year: 2020

Social media communication has become a significant part of daily activity in modern societies. For this reason, ensuring safety in social media platforms is a necessity. Use of dangerous language such as physical threats in online environments is a somewhat rare, yet remains highly important. Although several works have been performed on the related issue of detecting offensive and hateful language, dangerous speech has not previously been treated in any significant way. Motivated by these observations, we report our efforts to build a labeled dataset for dangerous speech. We also exploit our dataset to develop highly effective models to detect dangerous content. Our best model performs at 59.60% macro F1, significantly outperforming a competitive baseline.
Neural Machine Translation for South Africa's Official Languages
Venue: arXiv cs.CL 2020
Year: 2020

Recent advances in neural machine translation (NMT) have led to state-of-the-art results for many European-based translation tasks. However, despite these advances, there is has been little focus in applying these methods to African languages. In this paper, we seek to address this gap by creating an NMT benchmark BLEU score between English and the ten remaining official languages in South Africa.
ImpactCite: An XLNet-based method for Citation Impact Analysis
Venue: arXiv cs.CL 2020
Year: 2020

Citations play a vital role in understanding the impact of scientific literature. Generally, citations are analyzed quantitatively whereas qualitative analysis of citations can reveal deeper insights into the impact of a scientific artifact in the community. Therefore, citation impact analysis (which includes sentiment and intent classification) enables us to quantify the quality of the citations which can eventually assist us in the estimation of ranking and impact. The contribution of this paper is two-fold. First, we benchmark the well-known language models like BERT and ALBERT along with several popular networks for both tasks of sentiment and intent classification. Second, we provide ImpactCite, which is XLNet-based method for citation impact analysis. All evaluations are performed on a set of publicly available citation analysis datasets. Evaluation results reveal that ImpactCite achieves a new state-of-the-art performance for both citation intent and sentiment classification by outperforming the existing approaches by 3.44% and 1.33% in F1-score. Therefore, we emphasize ImpactCite (XLNet-based solution) for both tasks to better understand the impact of a citation. Additional efforts have been performed to come up with CSC-Clean corpus, which is a clean and reliable dataset for citation sentiment classification.
India nudges to contain COVID-19 pandemic: a reactive public policy analysis using machine-learning based topic modelling
Venue: arXiv cs.CY 2020
Year: 2020

India locked down 1.3 billion people on March 25, 2020 in the wake of COVID-19 pandemic. The economic cost of it was estimated at USD 98 billion, while the social costs are still unknown. This study investigated how government formed reactive policies to fight coronavirus across its policy sectors. Primary data was collected from the Press Information Bureau (PIB) in the form press releases of government plans, policies, programme initiatives and achievements. A text corpus of 260,852 words was created from 396 documents from the PIB. An unsupervised machine-based topic modelling using Latent Dirichlet Allocation (LDA) algorithm was performed on the text corpus. It was done to extract high probability topics in the policy sectors. The interpretation of the extracted topics was made through a nudge theoretic lens to derive the critical policy heuristics of the government. Results showed that most interventions were targeted to generate endogenous nudge by using external triggers. Notably, the nudges from the Prime Minister of India was critical in creating herd effect on lockdown and social distancing norms across the nation. A similar effect was also observed around the public health (e.g., masks in public spaces; Yoga and Ayurveda for immunity), transport (e.g., old trains converted to isolation wards), micro, small and medium enterprises (e.g., rapid production of PPE and masks), science and technology sector (e.g., diagnostic kits, robots and nano-technology), home affairs (e.g., surveillance and lockdown), urban (e.g. drones, GIS-tools) and education (e.g., online learning). A conclusion was drawn on leveraging these heuristics are crucial for lockdown easement planning.
IEEE 7010: A New Standard for Assessing the Well-being Implications of Artificial Intelligence
Venue: arXiv cs.CY 2020
Year: 2020

Artificial intelligence (AI) enabled products and services are becoming a staple of everyday life. While governments and businesses are eager to enjoy the benefits of AI innovations, the mixed impact of these autonomous and intelligent systems on human well-being has become a pressing issue. This article introduces one of the first international standards focused on the social and ethical implications of AI: The Institute of Electrical and Electronics Engineering (IEEE) Standard (Std) 7010-2020 Recommended Practice for Assessing the Impact of Autonomous and Intelligent Systems on Human Well-being. Incorporating well-being factors throughout the lifecycle of AI is both challenging and urgent and IEEE 7010 provides key guidance for those who design, deploy, and procure these technologies. We begin by articulating the benefits of an approach for AI centered around well-being and the measurement of well-being data. Next, we provide an overview of IEEE 7010, including its key principles and how the standard relates to approaches and perspectives in place in the AI community. Finally, we indicate where future efforts are needed.
Smart Urban Mobility: When Mobility Systems Meet Smart Data
Venue: arXiv cs.CY 2020
Year: 2020

Cities around the world are expanding dramatically, with urban population growth reaching nearly 2.5 billion people in urban areas and road traffic growth exceeding 1.2 billion cars by 2050. The economic contribution of the transport sector represents 5% of the GDP in Europe and costs an average of US $482.05 billion in the United States. These figures indicate the rapid rise of industrial cities and the urgent need to move from traditional cities to smart cities. This article provides a survey of different approaches and technologies such as intelligent transportation systems (ITS) that leverage communication technologies to help maintain road users safe while driving, as well as support autonomous mobility through the optimization of control systems. The role of ITS is strengthened when combined with accurate artificial intelligence models that are built to optimize urban planning, analyze crowd behavior and predict traffic conditions. AI-driven ITS is becoming possible thanks to the existence of a large volume of mobility data generated by billions of users through their use of new technologies and online social media. The optimization of urban planning enhances vehicle routing capabilities and solves traffic congestion problems, as discussed in this paper. From an ecological perspective, we discuss the measures and incentives provided to foster the use of mobility systems. We also underline the role of the political will in promoting open data in the transport sector, considered as an essential ingredient for developing technological solutions necessary for cities to become healthier and more sustainable.
CrisisBERT: a Robust Transformer for Crisis Classification and Contextual Crisis Embedding
Venue: arXiv cs.CL 2020
Year: 2020

Classification of crisis events, such as natural disasters, terrorist attacks and pandemics, is a crucial task to create early signals and inform relevant parties for spontaneous actions to reduce overall damage. Despite crisis such as natural disasters can be predicted by professional institutions, certain events are first signaled by civilians, such as the recent COVID-19 pandemics. Social media platforms such as Twitter often exposes firsthand signals on such crises through high volume information exchange over half a billion tweets posted daily. Prior works proposed various crisis embeddings and classification using conventional Machine Learning and Neural Network models. However, none of the works perform crisis embedding and classification using state of the art attention-based deep neural networks models, such as Transformers and document-level contextual embeddings. This work proposes CrisisBERT, an end-to-end transformer-based model for two crisis classification tasks, namely crisis detection and crisis recognition, which shows promising results across accuracy and f1 scores. The proposed model also demonstrates superior robustness over benchmark, as it shows marginal performance compromise while extending from 6 to 36 events with only 51.4% additional data points. We also proposed Crisis2Vec, an attention-based, document-level contextual embedding architecture for crisis embedding, which achieve better performance than conventional crisis embedding methods such as Word2Vec and GloVe. To the best of our knowledge, our works are first to propose using transformer-based crisis classification and document-level contextual crisis embedding in the literature.
A Bandit Approach for Mode Selection in Ambient Backscatter-Assisted Wireless-Powered Relaying
Venue: arXiv cs.NI 2020
Year: 2020

Backscattering assisted wireless-powered communication combines ultralow-power backscatter transmitters with energy harvesting devices. This paper investigates the transmission mode selection problem of a hybrid relay that forwards data by switching between the active wireless-powered transmission and the passive ambient backscattering. It first presents a hybrid relay system model and derives its end-to-end success probability under theoretically optimal, but practically unrealistic, conditions. The transmission mode selection is then formulated as a stochastic two-armed bandit problem in a varying environment where the distributions of rewards are nonstationary. The proposed model selection scheme does not assume to have access to any channel states or network conditions, but merely relies on learning from past transmission records. Numerical analyses are performed to validate the proposed bandit-based mode selection approach.
A Generating-Extension-Generator for Machine Code
Venue: arXiv cs.PL 2020
Year: 2020

The problem of "debloating" programs for security and performance purposes has begun to see increased attention. Of particular interest in many environments is debloating commodity off-the-shelf (COTS) software, which is most commonly made available to end users as stripped binaries (i.e., neither source code nor symbol-table/debugging information is available). Toward this end, we created a system, called GenXGen[MC], that specializes stripped binaries. Many aspects of the debloating problem can be addressed via techniques from the literature on partial evaluation. However, applying such techniques to real-world programs, particularly stripped binaries, involves non-trivial state-management manipulations that have never been addressed in a completely satisfactory manner in previous systems. In particular, a partial evaluator needs to be able to (i) save and restore arbitrary program states, and (ii) determine whether a program state is equal to one that arose earlier. Moreover, to specialize stripped binaries, the system must also be able to handle program states consisting of memory that is undifferentiated beyond the standard coarse division into regions for the stack, the heap, and global data. This paper presents a new approach to state management in a program specializer. The technique has been incorporated into GenXGen[MC], a novel tool for producing machine-code generating extensions. Our experiments show that our solution to issue (i) significantly decreases the space required to represent program states, and our solution to issue (ii) drastically improves the time for producing a specialized program (as much as 13,000x speedup).
Gaussian 1-2-1 Networks with Imperfect Beamforming
Venue: arXiv cs.IT 2020
Year: 2020

In this work, we study bounds on the capacity of full-duplex Gaussian 1-2-1 networks with imperfect beamforming. In particular, different from the ideal 1-2-1 network model introduced in [1], in this model beamforming patterns result in side-lobe leakage that cannot be perfectly suppressed. The 1-2-1 network model captures the directivity of mmWave network communications, where nodes communicate by pointing main-lobe "beams" at each other. We characterize the gap between the approximate capacities of the imperfect and ideal 1-2-1 models for the same channel coefficients and transmit power. We show that, under some conditions, this gap only depends on the number of nodes. Moreover, we evaluate the achievable rate of schemes that treat the resulting side-lobe leakage as noise, and show that they offer suitable solutions for implementation.
Optimally Fast Soft Shadows on Curved Terrain with Dynamic Programming and Maximum Mipmaps
Venue: arXiv cs.GR 2020
Year: 2020

We present a simple, novel method of efficiently rendering ray cast soft shadows on curved terrain by using dynamic programming and maximum mipmaps to rapidly find a global minimum shadow cost in constant runtime complexity. Additionally, we apply a new method of reducing view ray computation times that pre-displaces the terrain mesh to bootstrap ray starting positions. Combining these two methods, our ray casting engine runs in real-time with more than 200% speed up over uniform ray stepping with comparable image quality and without hardware ray tracing acceleration. To add support for accurate planetary ephemerides and interactive features, we integrated the engine into celestia.Sci, a general space simulation software. We demonstrate the ability of our engine to accurately handle a large range of distance scales by using it to generate videos of lunar landing trajectories. The numerical error when compared with real lunar mission imagery is small, demonstrating the accuracy and efficiency of our approach.
Surrogate Assisted Optimisation for Travelling Thief Problems
Venue: arXiv cs.AI 2020
Year: 2020

The travelling thief problem (TTP) is a multi-component optimisation problem involving two interdependent NP-hard components: the travelling salesman problem (TSP) and the knapsack problem (KP). Recent state-of-the-art TTP solvers modify the underlying TSP and KP solutions in an iterative and interleaved fashion. The TSP solution (cyclic tour) is typically changed in a deterministic way, while changes to the KP solution typically involve a random search, effectively resulting in a quasi-meandering exploration of the TTP solution space. Once a plateau is reached, the iterative search of the TTP solution space is restarted by using a new initial TSP tour. We propose to make the search more efficient through an adaptive surrogate model (based on a customised form of Support Vector Regression) that learns the characteristics of initial TSP tours that lead to good TTP solutions. The model is used to filter out non-promising initial TSP tours, in effect reducing the amount of time spent to find a good TTP solution. Experiments on a broad range of benchmark TTP instances indicate that the proposed approach filters out a considerable number of non-promising initial tours, at the cost of omitting only a small number of the best TTP solutions.
Noise Homogenization via Multi-Channel Wavelet Filtering for High-Fidelity Sample Generation in GANs
Venue: arXiv cs.LG 2020
Year: 2020

In the generator of typical Generative Adversarial Networks (GANs), a noise is inputted to generate fake samples via a series of convolutional operations. However, current noise generation models merely relies on the information from the pixel space, which increases the difficulty to approach the target distribution. Fortunately, the long proven wavelet transformation is able to decompose multiple spectral information from the images. In this work, we propose a novel multi-channel wavelet-based filtering method for GANs, to cope with this problem. When embedding a wavelet deconvolution layer in the generator, the resultant GAN, called WaveletGAN, takes advantage of the wavelet deconvolution to learn a filtering with multiple channels, which can efficiently homogenize the generated noise via an averaging operation, so as to generate high-fidelity samples. We conducted benchmark experiments on the Fashion-MNIST, KMNIST and SVHN datasets through an open GAN benchmark tool. The results show that WaveletGAN has excellent performance in generating high-fidelity samples, thanks to the smallest FIDs obtained on these datasets.
Thompson Sampling for Combinatorial Semi-bandits with Sleeping Arms and Long-Term Fairness Constraints
Venue: arXiv cs.LG 2020
Year: 2020

We study the combinatorial sleeping multi-armed semi-bandit problem with long-term fairness constraints~(CSMAB-F). To address the problem, we adopt Thompson Sampling~(TS) to maximize the total rewards and use virtual queue techniques to handle the fairness constraints, and design an algorithm called \emph{TS with beta priors and Bernoulli likelihoods for CSMAB-F~(TSCSF-B)}. Further, we prove TSCSF-B can satisfy the fairness constraints, and the time-averaged regret is upper bounded by $\frac{N}{2η} + O\left(\frac{\sqrt{mNT\ln T}}{T}\right)$, where $N$ is the total number of arms, $m$ is the maximum number of arms that can be pulled simultaneously in each round~(the cardinality constraint) and $η$ is the parameter trading off fairness for rewards. By relaxing the fairness constraints (i.e., let $η\rightarrow \infty$), the bound boils down to the first problem-independent bound of TS algorithms for combinatorial sleeping multi-armed semi-bandit problems. Finally, we perform numerical experiments and use a high-rating movie recommendation application to show the effectiveness and efficiency of the proposed algorithm.
The Interval function, Ptolemaic, distance hereditary, bridged graphs and axiomatic characterizations
Venue: arXiv cs.DM 2020
Year: 2020

In this paper we consider certain types of betweenness axioms on the interval function $I_G$ of a connected graph $G$. We characterize the class of graphs for which $I_G$ satisfy these axioms. The class of graphs that we characterize include the important class of Ptolemaic graphs and some proper superclasses of Ptolemaic graphs: the distance hereditary graphs and the bridged graphs. We also provide axiomatic characterizations of the interval function of these classes of graphs using an arbitrary function known as \emph{transit function}.
Large Scale Font Independent Urdu Text Recognition System
Venue: arXiv cs.CV 2020
Year: 2020

OCR algorithms have received a significant improvement in performance recently, mainly due to the increase in the capabilities of artificial intelligence algorithms. However, this advancement is not evenly distributed over all languages. Urdu is among the languages which did not receive much attention, especially in the font independent perspective. There exists no automated system that can reliably recognize printed Urdu text in images and videos across different fonts. To help bridge this gap, we have developed Qaida, a large scale data set with 256 fonts, and a complete Urdu lexicon. We have also developed a Convolutional Neural Network (CNN) based classification model which can recognize Urdu ligatures with 84.2% accuracy. Moreover, we demonstrate that our recognition network can not only recognize the text in the fonts it is trained on but can also reliably recognize text in unseen (new) fonts. To this end, this paper makes following contributions: (i) we introduce a large scale, multiple fonts based data set for printed Urdu text recognition;(ii) we have designed, trained and evaluated a CNN based model for Urdu text recognition; (iii) we experiment with incremental learning methods to produce state-of-the-art results for Urdu text recognition. All the experiment choices were thoroughly validated via detailed empirical analysis. We believe that this study can serve as the basis for further improvement in the performance of font independent Urdu OCR systems.