OpenRaiser/Intern-Atlas huggingface dataset:
Recoding latent sentence representations -- Dynamic gradient-based activation modification in RNNs
Venue: arXiv cs.CL 2021
Year: 2021

In Recurrent Neural Networks (RNNs), encoding information in a suboptimal or erroneous way can impact the quality of representations based on later elements in the sequence and subsequently lead to wrong predictions and a worse model performance. In humans, challenging cases like garden path sentences (an instance of this being the infamous "The horse raced past the barn fell") can lead their language understanding astray. However, they are still able to correct their representation accordingly and recover when new information is encountered. Inspired by this, I propose an augmentation to standard RNNs in form of a gradient-based correction mechanism: This way I hope to enable such models to dynamically adapt their inner representation of a sentence, adding a way to correct deviations as soon as they occur. This could therefore lead to more robust models using more flexible representations, even during inference time. I conduct different experiments in the context of language modeling, where the impact of using such a mechanism is examined in detail. To this end, I look at modifications based on different kinds of time-dependent error signals and how they influence the model performance. Furthermore, this work contains a study of the model's confidence in its predictions during training and for challenging test samples and the effect of the manipulation thereof. Lastly, I also study the difference in behavior of these novel models compared to a standard LSTM baseline and investigate error cases in detail to identify points of future research. I show that while the proposed approach comes with promising theoretical guarantees and an appealing intuition, it is only able to produce minor improvements over the baseline due to challenges in its practical application and the efficacy of the tested model variants.
Enhanced Pub/Sub Communications for Massive IoT Traffic with SARSA Reinforcement Learning
Venue: arXiv cs.AI 2021
Year: 2021

Sensors are being extensively deployed and are expected to expand at significant rates in the coming years. They typically generate a large volume of data on the internet of things (IoT) application areas like smart cities, intelligent traffic systems, smart grid, and e-health. Cloud, edge and fog computing are potential and competitive strategies for collecting, processing, and distributing IoT data. However, cloud, edge, and fog-based solutions need to tackle the distribution of a high volume of IoT data efficiently through constrained and limited resource network infrastructures. This paper addresses the issue of conveying a massive volume of IoT data through a network with limited communications resources (bandwidth) using a cognitive communications resource allocation based on Reinforcement Learning (RL) with SARSA algorithm. The proposed network infrastructure (PSIoTRL) uses a Publish/ Subscribe architecture to access massive and highly distributed IoT data. It is demonstrated that the PSIoTRL bandwidth allocation for buffer flushing based on SARSA enhances the IoT aggregator buffer occupation and network link utilization. The PSIoTRL dynamically adapts the IoT aggregator traffic flushing according to the Pub/Sub topic's priority and network constraint requirements.
Towards Network Traffic Monitoring Using Deep Transfer Learning
Venue: arXiv cs.LG 2021
Year: 2021

Network traffic is growing at an outpaced speed globally. The modern network infrastructure makes classic network intrusion detection methods inefficient to classify an inflow of vast network traffic. This paper aims to present a modern approach towards building a network intrusion detection system (NIDS) by using various deep learning methods. To further improve our proposed scheme and make it effective in real-world settings, we use deep transfer learning techniques where we transfer the knowledge learned by our model in a source domain with plentiful computational and data resources to a target domain with sparse availability of both the resources. Our proposed method achieved 98.30% classification accuracy score in the source domain and an improved 98.43% classification accuracy score in the target domain with a boost in the classification speed using UNSW-15 dataset. This study demonstrates that deep transfer learning techniques make it possible to construct large deep learning models to perform network classification, which can be deployed in the real world target domains where they can maintain their classification performance and improve their classification speed despite the limited accessibility of resources.
Learning to Optimize Under Constraints with Unsupervised Deep Neural Networks
Venue: arXiv cs.LG 2021
Year: 2021

In this paper, we propose a machine learning (ML) method to learn how to solve a generic constrained continuous optimization problem. To the best of our knowledge, the generic methods that learn to optimize, focus on unconstrained optimization problems and those dealing with constrained problems are not easy-to-generalize. This approach is quite useful in optimization tasks where the problem's parameters constantly change and require resolving the optimization task per parameter update. In such problems, the computational complexity of optimization algorithms such as gradient descent or interior point method preclude near-optimal designs in real-time applications. In this paper, we propose an unsupervised deep learning (DL) solution for solving constrained optimization problems in real-time by relegating the main computation load to offline training phase. This paper's main contribution is proposing a method for enforcing the equality and inequality constraints to the DL-generated solutions for generic optimization tasks.
Gaussian Function On Response Surface Estimation
Venue: arXiv cs.LG 2021
Year: 2021

We propose a new framework for 2-D interpreting (features and samples) black-box machine learning models via a metamodeling technique, by which we study the output and input relationships of the underlying machine learning model. The metamodel can be estimated from data generated via a trained complex model by running the computer experiment on samples of data in the region of interest. We utilize a Gaussian process as a surrogate to capture the response surface of a complex model, in which we incorporate two parts in the process: interpolated values that are modeled by a stationary Gaussian process Z governed by a prior covariance function, and a mean function mu that captures the known trends in the underlying model. The optimization procedure for the variable importance parameter theta is to maximize the likelihood function. This theta corresponds to the correlation of individual variables with the target response. There is no need for any pre-assumed models since it depends on empirical observations. Experiments demonstrate the potential of the interpretable model through quantitative assessment of the predicted samples.
Retrieving and Reading: A Comprehensive Survey on Open-domain Question Answering
Venue: arXiv cs.AI 2021
Year: 2021

Open-domain Question Answering (OpenQA) is an important task in Natural Language Processing (NLP), which aims to answer a question in the form of natural language based on large-scale unstructured documents. Recently, there has been a surge in the amount of research literature on OpenQA, particularly on techniques that integrate with neural Machine Reading Comprehension (MRC). While these research works have advanced performance to new heights on benchmark datasets, they have been rarely covered in existing surveys on QA systems. In this work, we review the latest research trends in OpenQA, with particular attention to systems that incorporate neural MRC techniques. Specifically, we begin with revisiting the origin and development of OpenQA systems. We then introduce modern OpenQA architecture named "Retriever-Reader" and analyze the various systems that follow this architecture as well as the specific techniques adopted in each of the components. We then discuss key challenges to developing OpenQA systems and offer an analysis of benchmarks that are commonly used. We hope our work would enable researchers to be informed of the recent advancement and also the open challenges in OpenQA research, so as to stimulate further progress in this field.
Resource Trading in Edge Computing-enabled IoV: An Efficient Futures-based Approach
Venue: arXiv cs.DC 2021
Year: 2021

Mobile edge computing (MEC) has become a promising solution to utilize distributed computing resources for supporting computation-intensive vehicular applications in dynamic driving environments. To facilitate this paradigm, the onsite resource trading serves as a critical enabler. However, dynamic communications and resource conditions could lead unpredictable trading latency, trading failure, and unfair pricing to the conventional resource trading process. To overcome these challenges, we introduce a novel futures-based resource trading approach in edge computing-enabled internet of vehicles (IoV), where a forward contract is used to facilitate resource trading related negotiations between an MEC server (seller) and a vehicle (buyer) in a given future term. Through estimating the historical statistics of future resource supply and network condition, we formulate the futures-based resource trading as the optimization problem aiming to maximize the seller's and the buyer's expected utility, while applying risk evaluations to relieve possible losses incurred by the uncertainties in the system. To tackle this problem, we propose an efficient bilateral negotiation approach which facilitates the participants reaching a consensus. Extensive simulations demonstrate that the proposed futures-based resource trading brings considerable utilities to both participants, while significantly outperforming the baseline methods on critical factors, e.g., trading failures and fairness, negotiation latency and cost.
Crypto-Hotwire: Illegal Blockchain Mining at Zero Cost Using Public Infrastructures
Venue: arXiv cs.CR 2021
Year: 2021

Blockchains and cryptocurrencies disrupted the conversion of energy into a medium of exchange. Numerous applications for blockchains and cryptocurrencies are now envisioned for purposes ranging from inventory control to banking applications. Naturally, in order to mine in an economically viable way, regions where energy is plentiful and cheap, e.g., close to hydroelectric plants, are sought. The possibility of converting energy into cash, however, also opens up opportunities for a new kind of cyber attack aimed at illegally mining cryptocurrencies by stealing energy. In this work, we indicate, using data from January and February of 2018 from our university, that such a threat is real, and present a projection of the gains derived from these attacks.
Low Light Image Enhancement via Global and Local Context Modeling
Venue: arXiv cs.CV 2021
Year: 2021

Images captured under low-light conditions manifest poor visibility, lack contrast and color vividness. Compared to conventional approaches, deep convolutional neural networks (CNNs) perform well in enhancing images. However, being solely reliant on confined fixed primitives to model dependencies, existing data-driven deep models do not exploit the contexts at various spatial scales to address low-light image enhancement. These contexts can be crucial towards inferring several image enhancement tasks, e.g., local and global contrast, brightness and color corrections; which requires cues from both local and global spatial extent. To this end, we introduce a context-aware deep network for low-light image enhancement. First, it features a global context module that models spatial correlations to find complementary cues over full spatial domain. Second, it introduces a dense residual block that captures local context with a relatively large receptive field. We evaluate the proposed approach using three challenging datasets: MIT-Adobe FiveK, LoL, and SID. On all these datasets, our method performs favorably against the state-of-the-arts in terms of standard image fidelity metrics. In particular, compared to the best performing method on the MIT-Adobe FiveK dataset, our algorithm improves PSNR from 23.04 dB to 24.45 dB.
Human machine interaction systems encounters convergence
Venue: arXiv cs.RO 2021
Year: 2021

Human machine interaction systems are those of much needed in the emerging technology to make the user aware of what is happening around. It is huge domain in which the smart material enables the factor of convergence. One such is the piezoelectric crystals, is a class of smart material and this has an incredible property of self-sensing actuation (SSA). This property of SSA has added an indescribable advantage to the robotic field by having the advantages of exhibiting both the functionality of sensing and actuating characteristics with reduced devices, space and power. This paper focuses on integrating the SSA to drive an unmanned ground vehicle with wireless radio control system which will be of great use in all the automation field. The piezo electric plate will be used as an input device to send the signal to move the UGV in certain direction and then, the same piezo-electric plate will be used as an actuator for haptic feedback with the help of drive circuit if obstacles or danger is experienced by UGV.
How Does a Student-Centered Course on Communication and Professional Skills Impact Students in the Long Run?
Venue: arXiv cs.CY 2021
Year: 2021

This Full Paper in the Research-To-Practice Category presents a long-term study about the effects of a student-centered course on communication and professional skills on students' thoughts, attitudes, and behavior. The course is offered at a European university as part of a computer science master's program. This paper shares the design and challenges of a longitudinal study that reaches ten years behind and employs a mixed-methods approach. Besides presenting and interpreting the findings, we shed light on which features tend to stay on students' minds and impact their way of being and acting in society. Moreover, we suggest implications for the design and practice in comparable courses to maximize constructive, sustainable effects, such as improved active listening, presentation skills, and openness to other perspectives. These are essential (not only) for computer science professionals. Our findings suggest that the course provided significant learning for the vast majority of respondents, including aspects such as presenting while keeping the other side in mind, managing one's stress, and becoming less shy to speak up. All in all, we aim to contribute an evidence-based source of motivation for instructors in technically focused curricula who hold a student-centered stance.
Donut visualizations for network-level and regional-level overview of Spatial Social Networks
Venue: arXiv cs.SI 2021
Year: 2021

Spatial Social Networks (SSN) build on the node and edge structure used in Social Network Analysis (SNA) by incorporating spatial information. Thus, SSNs include both topological and spatial data. The geographic embedding of the nodes makes it impossible to move the nodes freely, rendering standard topological algorithms (e.g. force layout algorithms) used in SNA ineffective to visualize SSN sociograms. We propose a new visualization technique for SSNs that utilize the spatial and social information to provide information about the orientation and scale of connections. The donut visualization can be used to summarize the entire network or can be used on a part of the network. We demonstrate the effectiveness of the donut visualization on two standard SSNs used in literature.
CRSLab: An Open-Source Toolkit for Building Conversational Recommender System
Venue: arXiv cs.CL 2021
Year: 2021

In recent years, conversational recommender system (CRS) has received much attention in the research community. However, existing studies on CRS vary in scenarios, goals and techniques, lacking unified, standardized implementation or comparison. To tackle this challenge, we propose an open-source CRS toolkit CRSLab, which provides a unified and extensible framework with highly-decoupled modules to develop CRSs. Based on this framework, we collect 6 commonly-used human-annotated CRS datasets and implement 18 models that include recent techniques such as graph neural network and pre-training models. Besides, our toolkit provides a series of automatic evaluation protocols and a human-machine interaction interface to test and compare different CRS methods. The project and documents are released at https://github.com/RUCAIBox/CRSLab.
A Two Sub-problem Decomposition for the Optimal Design of Filterless Optical Networks
Venue: arXiv cs.NI 2021
Year: 2021

Filterless optical transport networks relies on passive optical interconnections between nodes, i.e., on splitters/couplers and amplifiers. While different studies have investigated their design, none of them offer a solution for an optimal design. We propose a one step solution scheme, which combines network provisioning, i.e., routing and wavelength assignment within a single mathematical model. Decomposition into two different types sub-problems is then used in order to conceive an exact solution scheme. The first type of subproblem relies on the generation of filterless subnetworks while the second one takes care of their wavelength assignment. Numerical experiments demonstrate the improved performance of the proposed optimization model and algorithm over the state of the art, with the improvement of the solution for several open source data sets.
VIS30K: A Collection of Figures and Tables from IEEE Visualization Conference Publications
Venue: arXiv cs.CV 2021
Year: 2021

We present the VIS30K dataset, a collection of 29,689 images that represents 30 years of figures and tables from each track of the IEEE Visualization conference series (Vis, SciVis, InfoVis, VAST). VIS30K's comprehensive coverage of the scientific literature in visualization not only reflects the progress of the field but also enables researchers to study the evolution of the state-of-the-art and to find relevant work based on graphical content. We describe the dataset and our semi-automatic collection process, which couples convolutional neural networks (CNN) with curation. Extracting figures and tables semi-automatically allows us to verify that no images are overlooked or extracted erroneously. To improve quality further, we engaged in a peer-search process for high-quality figures from early IEEE Visualization papers. With the resulting data, we also contribute VISImageNavigator (VIN, visimagenavigator.github.io), a web-based tool that facilitates searching and exploring VIS30K by author names, paper keywords, title and abstract, and years.
Anomaly Recognition from surveillance videos using 3D Convolutional Neural Networks
Venue: arXiv cs.CV 2021
Year: 2021

Anomalous activity recognition deals with identifying the patterns and events that vary from the normal stream. In a surveillance paradigm, these events range from abuse to fighting and road accidents to snatching, etc. Due to the sparse occurrence of anomalous events, anomalous activity recognition from surveillance videos is a challenging research task. The approaches reported can be generally categorized as handcrafted and deep learning-based. Most of the reported studies address binary classification i.e. anomaly detection from surveillance videos. But these reported approaches did not address other anomalous events e.g. abuse, fight, road accidents, shooting, stealing, vandalism, and robbery, etc. from surveillance videos. Therefore, this paper aims to provide an effective framework for the recognition of different real-world anomalies from videos. This study provides a simple, yet effective approach for learning spatiotemporal features using deep 3-dimensional convolutional networks (3D ConvNets) trained on the University of Central Florida (UCF) Crime video dataset. Firstly, the frame-level labels of the UCF Crime dataset are provided, and then to extract anomalous spatiotemporal features more efficiently a fine-tuned 3D ConvNets is proposed. Findings of the proposed study are twofold 1)There exist specific, detectable, and quantifiable features in UCF Crime video feed that associate with each other 2) Multiclass learning can improve generalizing competencies of the 3D ConvNets by effectively learning frame-level information of dataset and can be leveraged in terms of better results by applying spatial augmentation.
HyperDegrade: From GHz to MHz Effective CPU Frequencies
Venue: arXiv cs.CR 2021
Year: 2021

Performance degradation techniques are an important complement to side-channel attacks. In this work, we propose HyperDegrade -- a combination of previous approaches and the use of simultaneous multithreading (SMT) architectures. In addition to the new technique, we investigate the root causes of performance degradation using cache eviction, discovering a previously unknown slowdown origin. The slowdown produced is significantly higher than previous approaches, which translates into an increased time granularity for Flush+Reload attacks. We evaluate HyperDegrade on different Intel microarchitectures, yielding significant slowdowns that achieve, in select microbenchmark cases, three orders of magnitude improvement over state-of-the-art. To evaluate the efficacy of performance degradation in side-channel amplification, we propose and evaluate leakage assessment metrics. The results evidence that HyperDegrade increases time granularity without a meaningful impact on trace quality. Additionally, we designed a fair experiment that compares three performance degradation strategies when coupled with Flush+Reload from an attacker perspective. We developed an attack on an unexploited vulnerability in OpenSSL in which HyperDegrade excels -- reducing by three times the number of required Flush+Reload traces to succeed. Regarding cryptography contributions, we revisit the recently proposed Raccoon attack on TLS-DH key exchanges, demonstrating its application to other protocols. Using HyperDegrade, we developed an end-to-end attack that shows how a Raccoon-like attack can succeed with real data, filling a missing gap from previous research.
Presenting a Dataset for Collaborator Recommending Systems in Academic Social Network: a Case Study on ReseachGate
Venue: arXiv cs.IR 2021
Year: 2021

Collaborator finding systems are a special type of expert finding models. There is a long-lasting challenge for research in the collaborator recommending research area, which is the lack of the structured dataset to be used by the researchers. We introduce two datasets to fill this gap. The first dataset is prepared for designing a consistent, collaborator finding system. The next one, called a co-author finding model, models an academic social network as a table that contains different relations between the pair of users. Both of them provide an opportunity for introducing potential collaborators to each other. These two models have been extracted from ResearchGate (RG) data set and are available publicly. RG dataset has been collected from Jan. 2019 to April 2019 and includes raw data of 3980 RG users. The dataset consists of almost complete information about users. In the preprocessing phase, the well-known Elmo was used for analyzing textual data. We call this as ResearchGate dataset for Recommending Systems (RGRS). For assessing the validity of data, we analyze each layer of data separately, and the results are reported. After preparing data and evaluating the collaborator finding models, we have done some assessments on RGRS. Some of these assessments are co-author, following-follower, and question answering relations. The outcomes indicate that it is the best relation in propagating knowledge in the network. To the best of our knowledge, there is no processed and analyzed dataset of this size.