OpenRaiser/Intern-Atlas huggingface dataset:
Trends, Opportunities, and Challenges in Using Restricted Device Authentication in Fog Computing
Venue: arXiv cs.CR 2022
Year: 2022

The few resources available on devices restricted in Internet of Things are an important issue when we think about security. In this perspective, our work proposes a agile systematic review literature on works involving the Internet of Things, authentication, and Fog Computing. As a result, related works, opportunities, and challenges found at these areas' intersections were brought, supporting other researchers and developers who work in these areas.
Environmental and Social Sustainability of Creative-Ai
Venue: arXiv cs.HC 2022
Year: 2022

The recent developments of artificial intelligence increase its capability for the creation of arts in both largely autonomous and collaborative contexts. In both contexts, Ai aims to imitate, combine, and extend existing artistic styles, and can transform creative practices. In our ongoing research, we investigate such Creative-Ai from sustainability and ethical perspectives. The two main focus areas are understanding the environmental sustainability aspects (material, practices) in the context of artistic processes that involve Creative-Ai, and ethical issues related to who gets to be involved in the creation process (power, authorship, ownership). This paper provides an outline of our ongoing research in these two directions. We will present our interdisciplinary approach, which combines interviews, workshops, online ethnography, and energy measurements, to address our research questions: How is Creative-Ai currently used by artist communities, and which future applications do artists imagine? When Ai is applied to creating art, how might it impact the economy and environment? And, how can answers to these questions guide requirements for intellectual property regimes for Creative-Ai?
Lookahead Auctions with Pooling
Venue: arXiv cs.GT 2022
Year: 2022

A Lookahead Auction (LA), introduced by Ronen, is an auction format for the sale of a single item among multiple buyers, which is considered simpler and more fair than the optimal auction. Indeed, it anonymously selects a provisional winner by a symmetric ascending-price process, and only then uses a personalized posted price. A LA auction extracts at least 1/2 of the optimal revenue, even under a correlated value distribution. This bound is tight, even for 2 buyers with independent values. We introduce a natural extension of LA, called lookahead with pooling (LAP). A LAP auction proceeds as LA, with one difference: it allows the seller to pool together a range of values during the ascending-price stage, and treat them the same; thus, it preserves the simplicity and fairness of LA. Our main result is that this simple pooling operation improves the revenue guarantees for independent buyers from 1/2 to 4/7 of the optimal revenue. We also give a complementary negative result, showing that for arbitrary correlated priors LAP cannot do better than 1/2 approximation.
A Comprehensive Review of Trends, Applications and Challenges In Out-of-Distribution Detection
Venue: arXiv cs.LG 2022
Year: 2022

With recent advancements in artificial intelligence, its applications can be seen in every aspect of humans' daily life. From voice assistants to mobile healthcare and autonomous driving, we rely on the performance of AI methods for many critical tasks; therefore, it is essential to assert the performance of models in proper means to prevent damage. One of the shortfalls of AI models in general, and deep machine learning in particular, is a drop in performance when faced with shifts in the distribution of data. Nonetheless, these shifts are always expected in real-world applications; thus, a field of study has emerged, focusing on detecting out-of-distribution data subsets and enabling a more comprehensive generalization. Furthermore, as many deep learning based models have achieved near-perfect results on benchmark datasets, the need to evaluate these models' reliability and trustworthiness for pushing towards real-world applications is felt more strongly than ever. This has given rise to a growing number of studies in the field of out-of-distribution detection and domain generalization, which begs the need for surveys that compare these studies from various perspectives and highlight their straightens and weaknesses. This paper presents a survey that, in addition to reviewing more than 70 papers in this field, presents challenges and directions for future works and offers a unifying look into various types of data shifts and solutions for better generalization.
FaRO 2: an Open Source, Configurable Smart City Framework for Real-Time Distributed Vision and Biometric Systems
Venue: arXiv cs.CV 2022
Year: 2022

Recent global growth in the interest of smart cities has led to trillions of dollars of investment toward research and development. These connected cities have the potential to create a symbiosis of technology and society and revolutionize the cost of living, safety, ecological sustainability, and quality of life of societies on a world-wide scale. Some key components of the smart city construct are connected smart grids, self-driving cars, federated learning systems, smart utilities, large-scale public transit, and proactive surveillance systems. While exciting in prospect, these technologies and their subsequent integration cannot be attempted without addressing the potential societal impacts of such a high degree of automation and data sharing. Additionally, the feasibility of coordinating so many disparate tasks will require a fast, extensible, unifying framework. To that end, we propose FaRO2, a completely reimagined successor to FaRO1, built from the ground up. FaRO2 affords all of the same functionality as its predecessor, serving as a unified biometric API harness that allows for seamless evaluation, deployment, and simple pipeline creation for heterogeneous biometric software. FaRO2 additionally provides a fully declarative capability for defining and coordinating custom machine learning and sensor pipelines, allowing the distribution of processes across otherwise incompatible hardware and networks. FaRO2 ultimately provides a way to quickly configure, hot-swap, and expand large coordinated or federated systems online without interruptions for maintenance. Because much of the data collected in a smart city contains Personally Identifying Information (PII), FaRO2 also provides built-in tools and layers to ensure secure and encrypted streaming, storage, and access of PII data across distributed systems.
A Bibliometrics Analysis on 28 years of Authentication and Threat Model Area
Venue: arXiv cs.CR 2022
Year: 2022

The large volume of publications in any research area can make it difficult for researchers to track their research areas' trends, challenges, and characteristics. Bibliometrics solves this problem by bringing statistical tools to help the analysis of selected publications from an online database. Although there are different works in security, our study aims to fill the bibliometric gap in the authentication and threat model area. As a result, a description of the dataset obtained, an overview of some selected variables, and an analysis of the ten most cited articles in this selected dataset is presented, which brings together publications from the last 28 years in these areas combined.
Preprint: Privacy-preserving IoT Data Sharing Scheme
Venue: arXiv cs.CR 2022
Year: 2022

Data sharing can be granted using different factors one of which is something in a users or an IoT devices environment which is in this paper broadcast signals. Using broadcast signals to measure Received Signal Strength Indicator values and Machine Learning models this paper implements an IoT data sharing scheme based on something that is in an IoT devices environment. The proposed scheme is experimentally tested using different ML models and shows 97.78 percent as its highest accuracy.
Gain without Pain: Recycling Reflected Energy from Wireless Powered RIS-aided Communications
Venue: arXiv cs.IT 2022
Year: 2022

In this paper, we investigate and analyze energy recycling for a reconfigurable intelligent surface (RIS)-aided wireless-powered communication network. As opposed to the existing works where the energy harvested by Internet of things (IoT) devices only come from the power station, IoT devices are also allowed to recycle energy from other IoT devices. In particular, we propose group switching- and user switching-based protocols with time-division multiple access to evaluate the impact of energy recycling on system performance. Two different optimization problems are respectively formulated for maximizing the sum throughput by jointly optimizing the energy beamforming vectors, the transmit power, the transmission time, the receive beamforming vectors, the grouping factors, and the phase-shift matrices, where the constraints of the minimum throughput, the harvested energy, the maximum transmit power, the phase shift, the grouping, and the time allocation are taken into account. In light of the intractability of the above problems, we respectively develop two alternating optimization-based iterative algorithms by combining the successive convex approximation method and the penalty-based method to obtain corresponding sub-optimal solutions. Simulation results verify that the energy recycling-based mechanism can assist in enhancing the performance of IoT devices in terms of energy harvesting and information transmission. Besides, we also verify that the group switching-based algorithm can improve more sum throughput of IoT devices, and the user switching-based algorithm can harvest more energy.
A Review: Random Walk in Graph Sampling
Venue: arXiv cs.SI 2022
Year: 2022

Graph sampling is a technique to pick a subset of vertices and/ or edges from original graph. Among various graph sampling approaches, Traversal Based Sampling (TBS) are widely used due to low cost and feasibility for many cases, in which Simple Random Walk (SRW) and its variants share a large proportion in TBS. We illustrate the foundation SRW and presents the problems of SRW. Based on the problems, we provide a taxonomy of different Random Walk (RW) based graph sampling methods and give an insight to the reason why and how they revise SRW. our summary includes classical methods and state-of-art RW-based methods. There are 3 ways to propose new algorithms based on SRW, including SRW and its combinations, modified selection mechanisms, and the graph topology modification. We explained the ideas behind those algorithms, and present detailed pseudo codes. In addition, we add the mathematics behind random walk, and the essence of random walk variants, which is not mentioned in detail in many research papers and literature reviews. Apart from RW-based methods, SRW also has related with the non-RW and non-TBS methods, we discuss the relationships between SRW and non-RW methods, and the relationships between SRW and non-TBS methods. The relations between these approaches are formally argued and a general framework to bridge theoretical analysis and practical implementation is provided.
HMM-V: Heterogeneous Memory Management for Virtualization
Venue: arXiv cs.OS 2022
Year: 2022

The memory demand of virtual machines (VMs) is increasing, while DRAM has limited capacity and high power consumption. Non-volatile memory (NVM) is an alternative to DRAM, but it has high latency and low bandwidth. We observe that the VM with heterogeneous memory may incur up to a $1.5\times$ slowdown compared to a DRAM VM, if not managed well. However, none of the state-of-the-art heterogeneous memory management designs are customized for virtualization on a real system. In this paper, we propose HMM-V, a Heterogeneous Memory Management system for Virtualization. HMM-V automatically determines page hotness and migrates pages between DRAM and NVM to achieve performance close to the DRAM system. First, HMM-V tracks memory accesses through page table manipulation, but reduces the cost by leveraging Intel page-modification logging (PML) and a multi-level queue. Second, HMM-V quantifies the ``temperature'' of page and determines the hot set with bucket-sorting. HMM-V then efficiently migrates pages with minimal access pause and handles dirty pages with the assistance of PML. Finally, HMM-V provides pooling management to balance precious DRAM across multiple VMs to maximize utilization and overall performance. HMM-V is implemented on a real system with Intel Optane DC persistent memory. The four-VM co-running results show that HMM-V outperforms NUMA balancing and hardware management (Intel Optane memory mode) by $51\%$ and $31\%$, respectively.
Spatio-Temporal Relation Learning for Video Anomaly Detection
Venue: arXiv cs.CV 2022
Year: 2022

Anomaly identification is highly dependent on the relationship between the object and the scene, as different/same object actions in same/different scenes may lead to various degrees of normality and anomaly. Therefore, object-scene relation actually plays a crucial role in anomaly detection but is inadequately explored in previous works. In this paper, we propose a Spatial-Temporal Relation Learning (STRL) framework to tackle the video anomaly detection task. First, considering dynamic characteristics of the objects as well as scene areas, we construct a Spatio-Temporal Auto-Encoder (STAE) to jointly exploit spatial and temporal evolution patterns for representation learning. For better pattern extraction, two decoding branches are designed in the STAE module, i.e. an appearance branch capturing spatial cues by directly predicting the next frame, and a motion branch focusing on modeling the dynamics via optical flow prediction. Then, to well concretize the object-scene relation, a Relation Learning (RL) module is devised to analyze and summarize the normal relations by introducing the Knowledge Graph Embedding methodology. Specifically in this process, the plausibility of object-scene relation is measured by jointly modeling object/scene features and optimizable object-scene relation maps. Extensive experiments are conducted on three public datasets, and the superior performance over the state-of-the-art methods demonstrates the effectiveness of our method.
Explainable Graph Pyramid Autoformer for Long-Term Traffic Forecasting
Venue: arXiv cs.LG 2022
Year: 2022

Accurate traffic forecasting is vital to an intelligent transportation system. Although many deep learning models have achieved state-of-art performance for short-term traffic forecasting of up to 1 hour, long-term traffic forecasting that spans multiple hours remains a major challenge. Moreover, most of the existing deep learning traffic forecasting models are black box, presenting additional challenges related to explainability and interpretability. We develop Graph Pyramid Autoformer (X-GPA), an explainable attention-based spatial-temporal graph neural network that uses a novel pyramid autocorrelation attention mechanism. It enables learning from long temporal sequences on graphs and improves long-term traffic forecasting accuracy. Our model can achieve up to 35 % better long-term traffic forecast accuracy than that of several state-of-the-art methods. The attention-based scores from the X-GPA model provide spatial and temporal explanations based on the traffic dynamics, which change for normal vs. peak-hour traffic and weekday vs. weekend traffic.
Partial and Simultaneous Transitive Orientations via Modular Decomposition
Venue: arXiv cs.DS 2022
Year: 2022

A natural generalization of the recognition problem for a geometric graph class is the problem of extending a representation of a subgraph to a representation of the whole graph. A related problem is to find representations for multiple input graphs that coincide on subgraphs shared by the input graphs. A common restriction is the sunflower case where the shared graph is the same for each pair of input graphs. These problems translate to the setting of comparability graphs where the representations correspond to transitive orientations of their edges. We use modular decompositions to improve the runtime for the orientation extension problem and the sunflower orientation problem to linear time. We apply these results to improve the runtime for the partial representation problem and the sunflower case of the simultaneous representation problem for permutation graphs to linear time. We also give the first efficient algorithms for these problems on circular permutation graphs.
Genetic Programming-Based Evolutionary Deep Learning for Data-Efficient Image Classification
Venue: arXiv cs.NE 2022
Year: 2022

Data-efficient image classification is a challenging task that aims to solve image classification using small training data. Neural network-based deep learning methods are effective for image classification, but they typically require large-scale training data and have major limitations such as requiring expertise to design network architectures and having poor interpretability. Evolutionary deep learning is a recent hot topic that combines evolutionary computation with deep learning. However, most evolutionary deep learning methods focus on evolving architectures of neural networks, which still suffer from limitations such as poor interpretability. To address this, this paper proposes a new genetic programming-based evolutionary deep learning approach to data-efficient image classification. The new approach can automatically evolve variable-length models using many important operators from both image and classification domains. It can learn different types of image features from colour or gray-scale images, and construct effective and diverse ensembles for image classification. A flexible multi-layer representation enables the new approach to automatically construct shallow or deep models/trees for different tasks and perform effective transformations on the input data via multiple internal nodes. The new approach is applied to solve five image classification tasks with different training set sizes. The results show that it achieves better performance in most cases than deep learning methods for data-efficient image classification. A deep analysis shows that the new approach has good convergence and evolves models with high interpretability, different lengths/sizes/shapes, and good transferability.
IdeaReader: A Machine Reading System for Understanding the Idea Flow of Scientific Publications
Venue: arXiv cs.DL 2022
Year: 2022

Understanding the origin and influence of the publication's idea is critical to conducting scientific research. However, the proliferation of scientific publications makes it difficult for researchers to sort out the evolution of all relevant literature. To this end, we present IdeaReader, a machine reading system that finds out which papers are most likely to inspire or be influenced by the target publication and summarizes the ideas of these papers in natural language. Specifically, IdeaReader first clusters the references and citations (first-order or higher-order) of the target publication, and the obtained clusters are regarded as the topics that inspire or are influenced by the target publication. It then picks out the important papers from each cluster to extract the skeleton of the idea flow. Finally, IdeaReader automatically generates a literature review of the important papers in each topic. Our system can help researchers gain insight into how scientific ideas flow from the target publication's references to citations by the automatically generated survey and the visualization of idea flow.
Improving Multilingual Neural Machine Translation System for Indic Languages
Venue: arXiv cs.CL 2022
Year: 2022

Machine Translation System (MTS) serves as an effective tool for communication by translating text or speech from one language to another language. The need of an efficient translation system becomes obvious in a large multilingual environment like India, where English and a set of Indian Languages (ILs) are officially used. In contrast with English, ILs are still entreated as low-resource languages due to unavailability of corpora. In order to address such asymmetric nature, multilingual neural machine translation (MNMT) system evolves as an ideal approach in this direction. In this paper, we propose a MNMT system to address the issues related to low-resource language translation. Our model comprises of two MNMT systems i.e. for English-Indic (one-to-many) and the other for Indic-English (many-to-one) with a shared encoder-decoder containing 15 language pairs (30 translation directions). Since most of IL pairs have scanty amount of parallel corpora, not sufficient for training any machine translation model. We explore various augmentation strategies to improve overall translation quality through the proposed model. A state-of-the-art transformer architecture is used to realize the proposed model. Trials over a good amount of data reveal its superiority over the conventional models. In addition, the paper addresses the use of language relationships (in terms of dialect, script, etc.), particularly about the role of high-resource languages of the same family in boosting the performance of low-resource languages. Moreover, the experimental results also show the advantage of backtranslation and domain adaptation for ILs to enhance the translation quality of both source and target languages. Using all these key approaches, our proposed model emerges to be more efficient than the baseline model in terms of evaluation metrics i.e BLEU (BiLingual Evaluation Understudy) score for a set of ILs.
A comparative study of attention mechanism and generative adversarial network in facade damage segmentation
Venue: arXiv cs.CV 2022
Year: 2022

Semantic segmentation profits from deep learning and has shown its possibilities in handling the graphical data from the on-site inspection. As a result, visual damage in the facade images should be detected. Attention mechanism and generative adversarial networks are two of the most popular strategies to improve the quality of semantic segmentation. With specific focuses on these two strategies, this paper adopts U-net, a representative convolutional neural network, as the primary network and presents a comparative study in two steps. First, cell images are utilized to respectively determine the most effective networks among the U-nets with attention mechanism or generative adversarial networks. Subsequently, selected networks from the first test and their combination are applied for facade damage segmentation to investigate the performances of these networks. Besides, the combined effect of the attention mechanism and the generative adversarial network is discovered and discussed.