OpenRaiser/Intern-Atlas huggingface dataset:
Multiple RISs-Aided Networks: Performance Analysis and Optimization
Venue: arXiv cs.IT 2022
Year: 2022

This paper analyzes the performance of multiple reconfigurable intelligent surfaces (RISs)-aided networks. The paper also provides some optimization results on the number of reflecting elements on RISs and the optimal placement of RISs. We first derive accurate closed-form approximations for RIS channels' distributions assuming independent non-identically distributed (i.ni.d.) Nakagami-\emph{m} fading environment. Then, the approximate expressions for outage probability (OP) and average symbol error probability are derived in closed-form. Furthermore, to get more insights into the system performance, we derive the asymptotic OP at the high signal-to-noise ratio regime and provide closed-form expressions for the system diversity order and coding gain. Finally, the accuracy of our theoretical analysis is validated through Monte-Carlo simulations. The obtained results show that the considered RIS scenario can provide a diversity order of $\frac{a}{2}K$, where $a$ is a function of the Nakagami fading parameter $m$ and the number of meta-surface elements $N$, and $K$ is the number of RISs.
Detecting Context-Aware Deviations in Process Executions
Venue: arXiv cs.AI 2022
Year: 2022

A deviation detection aims to detect deviating process instances, e.g., patients in the healthcare process and products in the manufacturing process. A business process of an organization is executed in various contextual situations, e.g., a COVID-19 pandemic in the case of hospitals and a lack of semiconductor chip shortage in the case of automobile companies. Thus, context-aware deviation detection is essential to provide relevant insights. However, existing work 1) does not provide a systematic way of incorporating various contexts, 2) is tailored to a specific approach without using an extensive pool of existing deviation detection techniques, and 3) does not distinguish positive and negative contexts that justify and refute deviation, respectively. In this work, we provide a framework to bridge the aforementioned gaps. We have implemented the proposed framework as a web service that can be extended to various contexts and deviation detection methods. We have evaluated the effectiveness of the proposed framework by conducting experiments using 255 different contextual scenarios.
The Impact Of Social Media In The Fight Against The Spread Of Coronavirus (Covid-19) Pandemic In Anambra State, Nigeria
Venue: arXiv cs.CY 2022
Year: 2022

This study examined the impact of social media in the fight against the spread of coronavirus (COVID-19) pandemic in Anambra state, Nigeria. The key objectives are to: find out if the numbers of social media users increased in Anambra state since the wake of coronavirus pandemic; find out if the social media is being utilised in the fight against the spread of coronavirus pandemic in Anambra state; find out how the social media is being utilised in the fight against the spread of coronavirus pandemic in Anambra state; and discover the impact of social media in the fight against the spread of coronavirus pandemic in Anambra State. It was anchored on Agenda Setting Theory, and the Technological Determinism Theory (TDT). The study was designed as a survey with close-ended questionnaire distributed to 400 respondents. The findings of this study revealed that usage and accessibility of social media increased in Anambra state because of coronavirus pandemic. It also revealed that the social media is being utilised by individuals, NGOs and government in the fight against the spread of coronavirus in Anambra state. The study also found that the social media is being utilised to gather and disseminate information, study, transact businesses, among other things. The finding also showed that the social media has positive impact in the fight against the spread of coronavirus in Anambra state. The study concluded that social media has much benefits than negative impact, and should be used to contain the spread of coronavirus. It, among other things, recommended training and empowerment of the citizens on effective utilisation of social media to create impact in the society.
A Two-Dimensional FFT Precoded Filter Bank Scheme
Venue: arXiv cs.IT 2022
Year: 2022

This work proposes a new precoded filter bank (FB) system via a two-dimensional (2D) fast Fourier transform (2D-FFT). Its structure is similar to Orthogonal Time Frequency Space (OTFS) systems, where the OFDM transmitter is changed to a filter bank multi-carrier (FBMC) one, thus obtaining a lower out-of-band emission. The complex orthogonality of the FBMC transmission is guaranteed by using precoding based on a discrete Fourier transform, which is also used to implement the two-dimensional fast Fourier transform. Through the use of a global transmission matrix, we propose a hybrid receiver for the new system. First, a frequency domain equalization is performed, followed by an interference cancellation on the delay-Doppler domain. The simulation results show that the proposed system obtains an error performance similar to other OTFS systems, and superior performance as compared to other precoded FBMC systems.
Learning Model Preconditions for Planning with Multiple Models
Venue: arXiv cs.RO 2022
Year: 2022

Different models can provide differing levels of fidelity when a robot is planning. Analytical models are often fast to evaluate but only work in limited ranges of conditions. Meanwhile, physics simulators are effective at modeling complex interactions between objects but are typically more computationally expensive. Learning when to switch between the various models can greatly improve the speed of planning and task success reliability. In this work, we learn model deviation estimators (MDEs) to predict the error between real-world states and the states outputted by transition models. MDEs can be used to define a model precondition that describes which transitions are accurately modeled. We then propose a planner that uses the learned model preconditions to switch between various models in order to use models in conditions where they are accurate, prioritizing faster models when possible. We evaluate our method on two real-world tasks: placing a rod into a box and placing a rod into a closed drawer.
Modeling and Optimization of a Longitudinally-Distributed Global Solar Grid
Venue: arXiv cs.CY 2022
Year: 2022

Our simulation-based experiments are aimed to demonstrate a use case on the feasibility of fulfillment of global energy demand by primarily relying on solar energy through the integration of a longitudinally-distributed grid. These experiments demonstrate the availability of simulation technologies, good approximation models of grid components, and data for simulation. We also experimented with integrating different tools to create realistic simulations as we are currently developing a detailed tool-chain for experimentation. These experiments consist of a network of model houses at different locations in the world, each producing and consuming only solar energy. The model includes houses, various appliances, appliance usage schedules, regional weather information, floor area, HVAC systems, population, number of houses in the region, and other parameters to imitate a real-world scenario. Data gathered from the power system simulation is used to develop optimization models to find the optimal solar panel area required at the different locations to satisfy energy demands in different scenarios.
Determinable and interpretable network representation for link prediction
Venue: arXiv cs.SI 2022
Year: 2022

As an intuitive description of complex physical, social, or brain systems, complex networks have fascinated scientists for decades. Recently, to abstract a network's structural and dynamical attributes for utilization, network representation has been one focus, mapping a network or its substructures (like nodes) into a low-dimensional vector space. Since the current methods are mostly based on machine learning, a black box of an input-output data fitting mechanism, generally the space's dimension is indeterminable and its elements are not interpreted. Although massive efforts to cope with this issue have included, for example, automated machine learning by computer scientists and computational theory by mathematics, the root causes still remain unresolved. Given that, from a physical perspective, this article proposes two determinable and interpretable node representation methods. To evaluate their effectiveness and generalization, this article further proposes Adaptive and Interpretable ProbS (AIProbS), a network-based model that can utilize node representations for link prediction. Experimental results showed that the AIProbS can reach state-of-the-art precision beyond baseline models, and by and large it can make a good trade-off with machine learning-based models on precision, determinacy, and interpretability, indicating that physical methods could also play a large role in the study of network representation.
Can the Language of the Collation be Translated into the Language of the Stemma? Using Machine Translation for Witness Localization
Venue: arXiv cs.CL 2022
Year: 2022

Stemmatology is a subfield of philology where one approach to understand the copy-history of textual variants of a text (witnesses of a tradition) is to generate an evolutionary tree. Computational methods are partly shared between the sister discipline of phylogenetics and stemmatology. In 2022, a surveypaper in nature communications found that Deep Learning (DL), which otherwise has brought about major improvements in many fields (Krohn et al 2020) has had only minor successes in phylogenetics and that "it is difficult to conceive of an end-to-end DL model to directly estimate phylogenetic trees from raw data in the near future"(Sapoval et al. 2022, p.8). In stemmatology, there is to date no known DL approach at all. In this paper, we present a new DL approach to placement of manuscripts on a stemma and demonstrate its potential. This could be extended to phylogenetics where the universal code of DNA might be an even better prerequisite for the method using sequence to sequence based neural networks in order to retrieve tree distances.
Interest Flooding Attacks in Named Data Networking: Survey of Existing Solutions, Open Issues, Requirements and Future Directions (Extended version)
Venue: arXiv cs.NI 2022
Year: 2022

Named Data Networking (NDN) is a prominent realization of the vision of Information-Centric Networking. The NDN architecture adopts name-based routing and location-independent data retrieval. Among other important features, NDN integrates security mechanisms and focuses on protecting the content rather than the communications channels. Along with a new architecture come new threats and NDN is no exception. NDN is a potential target for new network attacks such as Interest Flooding Attacks (IFAs). Attackers take advantage of IFA to launch (D)DoS attacks in NDN. Many IFA detection and mitigation solutions have been proposed in the literature. However, there is no comprehensive review study of these solutions that has been proposed so far. Therefore, in this paper, we propose a survey of the various IFAs with a detailed comparative study of all the relevant proposed solutions as counter-measures against IFAs. We also review the requirements for a complete and efficient IFA solution and pinpoint the various issues encountered by IFA detection and mitigation mechanisms through a series of attack scenarios. Finally, in this survey, we offer an analysis of the open issues and future research directions regarding IFAs. This manuscript consists of an extended version of the paper published in ACM Computing Surveys: https://dl.acm.org/doi/10.1145/3539730.
Deep Learning Models for Automated Classification of Dog Emotional States from Facial Expressions
Venue: arXiv cs.CV 2022
Year: 2022

Similarly to humans, facial expressions in animals are closely linked with emotional states. However, in contrast to the human domain, automated recognition of emotional states from facial expressions in animals is underexplored, mainly due to difficulties in data collection and establishment of ground truth concerning emotional states of non-verbal users. We apply recent deep learning techniques to classify (positive) anticipation and (negative) frustration of dogs on a dataset collected in a controlled experimental setting. We explore the suitability of different backbones (e.g. ResNet, ViT) under different supervisions to this task, and find that features of a self-supervised pretrained ViT (DINO-ViT) are superior to the other alternatives. To the best of our knowledge, this work is the first to address the task of automatic classification of canine emotions on data acquired in a controlled experiment.
STD-NET: Search of Image Steganalytic Deep-learning Architecture via Hierarchical Tensor Decomposition
Venue: arXiv cs.CV 2022
Year: 2022

Recent studies shows that the majority of existing deep steganalysis models have a large amount of redundancy, which leads to a huge waste of storage and computing resources. The existing model compression method cannot flexibly compress the convolutional layer in residual shortcut block so that a satisfactory shrinking rate cannot be obtained. In this paper, we propose STD-NET, an unsupervised deep-learning architecture search approach via hierarchical tensor decomposition for image steganalysis. Our proposed strategy will not be restricted by various residual connections, since this strategy does not change the number of input and output channels of the convolution block. We propose a normalized distortion threshold to evaluate the sensitivity of each involved convolutional layer of the base model to guide STD-NET to compress target network in an efficient and unsupervised approach, and obtain two network structures of different shapes with low computation cost and similar performance compared with the original one. Extensive experiments have confirmed that, on one hand, our model can achieve comparable or even better detection performance in various steganalytic scenarios due to the great adaptivity of the obtained network architecture. On the other hand, the experimental results also demonstrate that our proposed strategy is more efficient and can remove more redundancy compared with previous steganalytic network compression methods.
Federated Learning on Riemannian Manifolds
Venue: arXiv cs.LG 2022
Year: 2022

Federated learning (FL) has found many important applications in smart-phone-APP based machine learning applications. Although many algorithms have been studied for FL, to the best of our knowledge, algorithms for FL with nonconvex constraints have not been studied. This paper studies FL over Riemannian manifolds, which finds important applications such as federated PCA and federated kPCA. We propose a Riemannian federated SVRG (RFedSVRG) method to solve federated optimization over Riemannian manifolds. We analyze its convergence rate under different scenarios. Numerical experiments are conducted to compare RFedSVRG with the Riemannian counterparts of FedAvg and FedProx. We observed from the numerical experiments that the advantages of RFedSVRG are significant.
Quality-Driven Energy-Efficient Big Data Aggregation in WBANs
Venue: arXiv cs.NI 2022
Year: 2022

In the Internet-of-Things (IoT) era, the development of Wireless Body Area Networks (WBANs) and their applications in big data infrastructure has gotten a lot of attention from the medical research community. Since sensor nodes are low-powered devices that require heterogeneous Quality-of-Service (QoS), managing large amounts of medical data is critical in WBANs. Therefore, effectively aggregating a large volume of medical data is important. In this context, we propose a quality-driven and energy-efficient big data aggregation approach for cloud-assisted WBANs. For both intra-BAN (Phase I) and inter-BAN (Phase II) communications, the aggregation approach is cost-effective. Extensive simulation results show that the proposed approach DEBA greatly improves network efficiency in terms of aggregation delay and cost as compared to existing schemes.
Arena-Bench: A Benchmarking Suite for Obstacle Avoidance Approaches in Highly Dynamic Environments
Venue: arXiv cs.RO 2022
Year: 2022

The ability to autonomously navigate safely, especially within dynamic environments, is paramount for mobile robotics. In recent years, DRL approaches have shown superior performance in dynamic obstacle avoidance. However, these learning-based approaches are often developed in specially designed simulation environments and are hard to test against conventional planning approaches. Furthermore, the integration and deployment of these approaches into real robotic platforms are not yet completely solved. In this paper, we present Arena-bench, a benchmark suite to train, test, and evaluate navigation planners on different robotic platforms within 3D environments. It provides tools to design and generate highly dynamic evaluation worlds, scenarios, and tasks for autonomous navigation and is fully integrated into the robot operating system. To demonstrate the functionalities of our suite, we trained a DRL agent on our platform and compared it against a variety of existing different model-based and learning-based navigation approaches on a variety of relevant metrics. Finally, we deployed the approaches towards real robots and demonstrated the reproducibility of the results. The code is publicly available at github.com/ignc-research/arena-bench.
Data Augmentation for Intent Classification
Venue: arXiv cs.CL 2022
Year: 2022

Training accurate intent classifiers requires labeled data, which can be costly to obtain. Data augmentation methods may ameliorate this issue, but the quality of the generated data varies significantly across techniques. We study the process of systematically producing pseudo-labeled data given a small seed set using a wide variety of data augmentation techniques, including mixing methods together. We find that while certain methods dramatically improve qualitative and quantitative performance, other methods have minimal or even negative impact. We also analyze key considerations when implementing data augmentation methods in production.
Optimal Quaternary Locally Repairable Codes Attaining the Singleton-like Bound
Venue: arXiv cs.IT 2022
Year: 2022

Recent years, several new types of codes were introduced to provide fault-tolerance and guarantee system reliability in distributed storage systems, among which locally repairable codes (LRCs for short) have played an important role. A linear code is said to have locality $r$ if each of its code symbols can be repaired by accessing at most $r$ other code symbols. For an LRC with length $n$, dimension $k$ and locality $r$, its minimum distance $d$ was proved to satisfy the Singleton-like bound $d\leq n-k-\lceil k/r\rceil+2$. Since then, many works have been done for constructing LRCs meeting the Singleton-like bound over small fields. In this paper, we study quaternary LRCs meeting Singleton-like bound through a parity-check matrix approach. Using tools from finite geometry, we provide some new necessary conditions for LRCs being optimal. From this, we prove that there are $27$ different classes of parameters for optimal quaternary LRCs. Moreover, for each class, explicit constructions of corresponding optimal quaternary LRCs are presented.
Case-Based Inverse Reinforcement Learning Using Temporal Coherence
Venue: arXiv cs.LG 2022
Year: 2022

Providing expert trajectories in the context of Imitation Learning is often expensive and time-consuming. The goal must therefore be to create algorithms which require as little expert data as possible. In this paper we present an algorithm that imitates the higher-level strategy of the expert rather than just imitating the expert on action level, which we hypothesize requires less expert data and makes training more stable. As a prior, we assume that the higher-level strategy is to reach an unknown target state area, which we hypothesize is a valid prior for many domains in Reinforcement Learning. The target state area is unknown, but since the expert has demonstrated how to reach it, the agent tries to reach states similar to the expert. Building on the idea of Temporal Coherence, our algorithm trains a neural network to predict whether two states are similar, in the sense that they may occur close in time. During inference, the agent compares its current state with expert states from a Case Base for similarity. The results show that our approach can still learn a near-optimal policy in settings with very little expert data, where algorithms that try to imitate the expert at the action level can no longer do so.
Efficiency Comparison of AI classification algorithms for Image Detection and Recognition in Real-time
Venue: arXiv cs.CV 2022
Year: 2022

Face detection and identification is the most difficult and often used task in Artificial Intelligence systems. The goal of this study is to present and compare the results of several face detection and recognition algorithms used in the system. This system begins with a training image of a human, then continues on to the test image, identifying the face, comparing it to the trained face, and finally classifying it using OpenCV classifiers. This research will discuss the most effective and successful tactics used in the system, which are implemented using Python, OpenCV, and Matplotlib. It may also be used in locations with CCTV, such as public spaces, shopping malls, and ATM booths.
A Graph Transformation Strategy for Optimizing SpTRSV
Venue: arXiv cs.DC 2022
Year: 2022

Sparse triangular solve (SpTRSV) is an extensively studied computational kernel. An important obstacle in parallel SpTRSV implementations is that in some parts of a sparse matrix the computation is serial. By transforming the dependency graph, it is possible to increase the parallelism of the parts that lack it. In this work, we present an approach to increase the parallelism degree of a sparse matrix, discuss its limitations and possible improvements, and we compare it to a previous manual approach. The results provide several hints on how to craft a collection of strategies to transform a dependency graph.
Improve Ranking Correlation of Super-net through Training Scheme from One-shot NAS to Few-shot NAS
Venue: arXiv cs.CV 2022
Year: 2022

The algorithms of one-shot neural architecture search(NAS) have been widely used to reduce computation consumption. However, because of the interference among the subnets in which weights are shared, the subnets inherited from these super-net trained by those algorithms have poor consistency in precision ranking. To address this problem, we propose a step-by-step training super-net scheme from one-shot NAS to few-shot NAS. In the training scheme, we firstly train super-net in a one-shot way, and then we disentangle the weights of super-net by splitting them into multi-subnets and training them gradually. Finally, our method ranks 4th place in the CVPR2022 3rd Lightweight NAS Challenge Track1. Our code is available at https://github.com/liujiawei2333/CVPR2022-NAS-competition-Track-1-4th-solution.
Signed interval graphs and bigraphs: A generalization of interval graphs and bigraphs
Venue: arXiv cs.DM 2022
Year: 2022

In this paper, we define and characterize signed interval graphs and bigraphs introducing the concept of negative interval. Also we have shown that these classes of graphs are respectively a generalization of well known classes of interval graphs and interval bigraphs. In this context we have observed that signed interval graphs coincide with the complement of Threshold tolerance graphs(co-TT graphs) introduced by Monma, Reed and Trotter \cite{22}. Finally, we have solved the open problem of forbidden induced subgraph characterization of co-TT graphs posed by them in the same paper.