OpenRaiser/Intern-Atlas huggingface dataset:
SwarMS: Swarm-Computations for Mobile Scenarios
Venue: arXiv cs.NI 2021
Year: 2021

Nowadays, most network systems are based on fixed and reliable infrastructure. In this context Information Centric Networking (ICN) is a novel network approach, where data is in the focus instead of hosts. Therefore, requests for data are independent from the location where the data is actually stored on. This property is optimal for infrastructure-less and swarm networking. However, the ICN does not provide support for networks without infrastructure. In this paper we present SwarMS, an architecture for swarm on-boarding, swarm coordination and swarm computations in ICN style networks. We use append-only logs to solve the challenges of loose mobile swarm organisation and executing computations. By replicating tasks on multiple nodes we achieve more reliability and trust in results.
Tangle Centric Networking (TCN)
Venue: arXiv cs.NI 2021
Year: 2021

Today's Internet is heavily used for multimedia streaming from cloud backends, while the Internet of Things (IoT) reverses the traditional data flow, with high data volumes produced at the network edge. Information Centric Networking (ICN) advocates against a host-centric communication model which is promising for distributed edge computing environments and the execution of IoT applications in a decentralized fashion. By using naming schemes, data is tightly coupled to names instead of hosts which simplifies discovery and access to data and services. However, the tight coupling challenges network performance due to additional synchronization overhead of large data volumes and services. We present Tangle Centric Networking (TCN) -- a decentralized data structure for coordinated distributed applications and data exchange following principles of ICN. TCN can react on data and service changes and update them accordingly in network nodes, provide distributed data structures and enable cooperative work on the same data without huge overhead by using Tangles for coordination. We implemented TCN in simulations and evaluated the concept against a base line scenario.
Socio-Technological Challenges and Opportunities: Paths Forward
Venue: arXiv cs.CY 2021
Year: 2021

Advancements in digital technologies have a bootstrapping effect. The past fifty years of technological innovations from the computer architecture community have brought innovations and orders-of-magnitude efficiency improvements that engender use cases that were not previously possible -- stimulating novel application domains and increasing uses and deployments at an ever-faster pace. Consequently, computing technologies have fueled significant economic growth, creating education opportunities, enabling access to a wider and more diverse spectrum of information, and, at the same time, connecting people of differing needs in the world together. Technology must be offered that is inclusive of the world's physical, cultural, and economic diversity, and which is manufactured, used, and recycled with environmental sustainability at the forefront. For the next decades to come, we envision significant cross-disciplinary efforts to build a circular development cycle by placing pervasive connectivity, sustainability, and demographic inclusion at the design forefront in order to sustain and expand the benefits of a technologically rich society. We hope this work will inspire our computing community to take broader and more holistic approaches when developing technological solutions to serve people from different parts of the world.
Exploring Generalization Ability of Pretrained Language Models on Arithmetic and Logical Reasoning
Venue: arXiv cs.CL 2021
Year: 2021

To quantitatively and intuitively explore the generalization ability of pre-trained language models (PLMs), we have designed several tasks of arithmetic and logical reasoning. We both analyse how well PLMs generalize when the test data is in the same distribution as the train data and when it is different, for the latter analysis, we have also designed a cross-distribution test set other than the in-distribution test set. We conduct experiments on one of the most advanced and publicly released generative PLM - BART. Our research finds that the PLMs can easily generalize when the distribution is the same, however, it is still difficult for them to generalize out of the distribution.
An authentication model based on cryptography
Venue: arXiv cs.CR 2021
Year: 2021

In this paper we proposed an authentication technique based on the user cards, to improve the authentication process in systems that allows remote access for the users, and raise the security rate during an exchange of their messages. in this technique the server performs two functions, the first function, register the users, and give him user ID, PIN code, and user private card contains secrecy information, which is used to encrypt user messages by using two kinds of encryption symmetric using RC4-Pr and asymmetric using RSA encryption., the second function, distribute the user's public card if the user demand that, in which the user sends the own authentication code with their own user ID and recipient user ID to the authentication check, and then the server sends the user public card to the recipient user, thus the sender user can send the messages to recipient user without back to the server again. We attained confidentiality using RC4-Pr and RSA encryption and message authentication, user signature, and mutual secret key by using RSA encryption. in this paper we also implement the proposal in [1] RC4-pr algorithm which is modified to improve the key weakness of basic RC4.
Crowdsourcing the State of the Art(ifacts)
Venue: arXiv cs.SE 2021
Year: 2021

In any field, finding the "leading edge" of research is an on-going challenge. Researchers cannot appease reviewers and educators cannot teach to the leading edge of their field if no one agrees on what is the state-of-the-art. Using a novel crowdsourced "reuse graph" approach, we propose here a new method to learn this state-of-the-art. Our reuse graphs are less effort to build and verify than other community monitoring methods (e.g. artifact tracks or citation-based searches). Based on a study of 170 papers from software engineering (SE) conferences in 2020, we have found over 1,600 instances of reuse; i.e., reuse is rampant in SE research. Prior pessimism about a lack of reuse in SE research may have been a result of using the wrong methods to measure the wrong things.
A Fast Algorithm for Computing the Deficiency Number of a Mahjong Hand
Venue: arXiv cs.AI 2021
Year: 2021

The tile-based multiplayer game Mahjong is widely played in Asia and has also become increasingly popular worldwide. Face-to-face or online, each player begins with a hand of 13 tiles and players draw and discard tiles in turn until they complete a winning hand. An important notion in Mahjong is the deficiency number (a.k.a. shanten number in Japanese Mahjong) of a hand, which estimates how many tile changes are necessary to complete the hand into a winning hand. The deficiency number plays an essential role in major decision-making tasks such as selecting a tile to discard. This paper proposes a fast algorithm for computing the deficiency number of a Mahjong hand. Compared with the baseline algorithm, the new algorithm is usually 100 times faster and, more importantly, respects the agent's knowledge about available tiles. The algorithm can be used as a basic procedure in all Mahjong variants by both rule-based and machine learning-based Mahjong AI.
Do Proportionate Algorithms Exploit Sparsity?
Venue: arXiv cs.LG 2021
Year: 2021

Adaptive filters exploiting sparsity have been a very active research field, among which the algorithms that follow the "proportional-update principle", the so-called proportionate-type algorithms, are very popular. Indeed, there are hundreds of works on proportionate-type algorithms and, therefore, their advantages are widely known. This paper addresses the unexplored drawbacks and limitations of using proportional updates and their practical impacts. Our findings include the theoretical justification for the poor performance of these algorithms in several sparse scenarios, and also when dealing with non-stationary and compressible systems. Simulation results corroborating the theory are presented.
Introduction to Quantum Reinforcement Learning: Theory and PennyLane-based Implementation
Venue: arXiv cs.LG 2021
Year: 2021

The emergence of quantum computing enables for researchers to apply quantum circuit on many existing studies. Utilizing quantum circuit and quantum differential programming, many research are conducted such as \textit{Quantum Machine Learning} (QML). In particular, quantum reinforcement learning is a good field to test the possibility of quantum machine learning, and a lot of research is being done. This work will introduce the concept of quantum reinforcement learning using a variational quantum circuit, and confirm its possibility through implementation and experimentation. We will first present the background knowledge and working principle of quantum reinforcement learning, and then guide the implementation method using the PennyLane library. We will also discuss the power and possibility of quantum reinforcement learning from the experimental results obtained through this work.
Text-Aware Single Image Specular Highlight Removal
Venue: arXiv cs.CV 2021
Year: 2021

Removing undesirable specular highlight from a single input image is of crucial importance to many computer vision and graphics tasks. Existing methods typically remove specular highlight for medical images and specific-object images, however, they cannot handle the images with text. In addition, the impact of specular highlight on text recognition is rarely studied by text detection and recognition community. Therefore, in this paper, we first raise and study the text-aware single image specular highlight removal problem. The core goal is to improve the accuracy of text detection and recognition by removing the highlight from text images. To tackle this challenging problem, we first collect three high-quality datasets with fine-grained annotations, which will be appropriately released to facilitate the relevant research. Then, we design a novel two-stage network, which contains a highlight detection network and a highlight removal network. The output of highlight detection network provides additional information about highlight regions to guide the subsequent highlight removal network. Moreover, we suggest a measurement set including the end-to-end text detection and recognition evaluation and auxiliary visual quality evaluation. Extensive experiments on our collected datasets demonstrate the superior performance of the proposed method.
AutoChart: A Dataset for Chart-to-Text Generation Task
Venue: arXiv cs.CL 2021
Year: 2021

The analytical description of charts is an exciting and important research area with many applications in academia and industry. Yet, this challenging task has received limited attention from the computational linguistics research community. This paper proposes \textsf{AutoChart}, a large dataset for the analytical description of charts, which aims to encourage more research into this important area. Specifically, we offer a novel framework that generates the charts and their analytical description automatically. We conducted extensive human and machine evaluations on the generated charts and descriptions and demonstrate that the generated texts are informative, coherent, and relevant to the corresponding charts.
Aegis: A Trusted, Automatic and Accurate Verification Framework for Vertical Federated Learning
Venue: arXiv cs.CR 2021
Year: 2021

Vertical federated learning (VFL) leverages various privacy-preserving algorithms, e.g., homomorphic encryption or secret sharing based SecureBoost, to ensure data privacy. However, these algorithms all require a semi-honest secure definition, which raises concerns in real-world applications. In this paper, we present Aegis, a trusted, automatic, and accurate verification framework to verify the security of VFL jobs. Aegis is separated from local parties to ensure the security of the framework. Furthermore, it automatically adapts to evolving VFL algorithms by defining the VFL job as a finite state machine to uniformly verify different algorithms and reproduce the entire job to provide more accurate verification. We implement and evaluate Aegis with different threat models on financial and medical datasets. Evaluation results show that: 1) Aegis can detect 95% threat models, and 2) it provides fine-grained verification results within 84% of the total VFL job time.
Constrained Synchronization and Subset Synchronization Problems for Weakly Acyclic Automata
Venue: arXiv cs.FL 2021
Year: 2021

We investigate the constrained synchronization problem for weakly acyclic, or partially ordered, input automata. We show that, for input automata of this type, the problem is always in NP. Furthermore, we give a full classification of the realizable complexities for constraint automata with at most two states and over a ternary alphabet. We find that most constrained problems that are PSPACE-complete in general become NP-complete. However, there also exist constrained problems that are PSPACE-complete in the general setting but become polynomial time solvable when considered for weakly acyclic input automata. We also investigate two problems related to subset synchronization, namely if there exists a word mapping all states into a given target subset of states, and if there exists a word mapping one subset into another. Both problems are PSPACE-complete in general, but in our setting the former is polynomial time solvable and the latter is NP-complete.
Detecting and interpreting faults in vulnerable power grids with machine learning
Venue: arXiv cs.LG 2021
Year: 2021

Unscheduled power disturbances cause severe consequences both for customers and grid operators. To defend against such events, it is necessary to identify the causes of interruptions in the power distribution network. In this work, we focus on the power grid of a Norwegian community in the Arctic that experiences several faults whose sources are unknown. First, we construct a data set consisting of relevant meteorological data and information about the current power quality logged by power-quality meters. Then, we adopt machine-learning techniques to predict the occurrence of faults. Experimental results show that both linear and non-linear classifiers achieve good classification performance. This indicates that the considered power-quality and weather variables explain well the power disturbances. Interpreting the decision process of the classifiers provides valuable insights to understand the main causes of disturbances. Traditional features selection methods can only indicate which are the variables that, on average, mostly explain the fault occurrences in the dataset. Besides providing such a global interpretation, it is also important to identify the specific set of variables that explain each individual fault. To address this challenge, we adopt a recent technique to interpret the decision process of a deep learning model, called Integrated Gradients. The proposed approach allows to gain detailed insights on the occurrence of a specific fault, which are valuable for the distribution system operators to implement strategies to prevent and mitigate power disturbances.
Autocomplete Repetitive Stroking with Image Guidance
Venue: arXiv cs.GR 2021
Year: 2021

Image-guided drawing can compensate for the lack of skills but often requires a significant number of repetitive strokes to create textures. Existing automatic stroke synthesis methods are usually limited to predefined styles or require indirect manipulation that may break the spontaneous flow of drawing. We present a method to autocomplete repetitive short strokes during users' normal drawing process. Users can draw over a reference image as usual. At the same time, our system silently analyzes the input strokes and the reference to infer strokes that follow users' input style when certain repetition is detected. Users can accept, modify, or ignore the system predictions and continue drawing, thus maintaining the fluid control of drawing. Our key idea is to jointly analyze image regions and operation history for detecting and predicting repetitions. The proposed system can effectively reduce users' workload in drawing repetitive short strokes and facilitates users in creating results with rich patterns.
A Program Synthesis Approach for Adding Architectural Tactics to An Existing Code Base
Venue: arXiv cs.SE 2021
Year: 2021

Automatically constructing a program based on given specifications has been studied for decades. Despite the advances in the field of Program Synthesis, the current approaches still synthesize a block of code snippet and leave the task of reusing it in an existing code base to program developers. Due to its program-wide effects, synthesizing an architectural tactic and reusing it in a program is even more challenging. Architectural tactics need to be synthesized based on the context of different locations of the program, broken down to smaller pieces, and added to corresponding locations in the code. Moreover, each piece needs to establish correct data- and control-dependencies to its surrounding environment as well as to the other synthesized pieces. This is an error-prone and challenging task, especially for novice program developers. In this paper, we introduce a novel program synthesis approach that synthesizes architectural tactics and adds them to an existing code base.
Neural Predictive Monitoring under Partial Observability
Venue: arXiv cs.LG 2021
Year: 2021

We consider the problem of predictive monitoring (PM), i.e., predicting at runtime future violations of a system from the current state. We work under the most realistic settings where only partial and noisy observations of the state are available at runtime. Such settings directly affect the accuracy and reliability of the reachability predictions, jeopardizing the safety of the system. In this work, we present a learning-based method for PM that produces accurate and reliable reachability predictions despite partial observability (PO). We build on Neural Predictive Monitoring (NPM), a PM method that uses deep neural networks for approximating hybrid systems reachability, and extend it to the PO case. We propose and compare two solutions, an end-to-end approach, which directly operates on the rough observations, and a two-step approach, which introduces an intermediate state estimation step. Both solutions rely on conformal prediction to provide 1) probabilistic guarantees in the form of prediction regions and 2) sound estimates of predictive uncertainty. We use the latter to identify unreliable (and likely erroneous) predictions and to retrain and improve the monitors on these uncertain inputs (i.e., active learning). Our method results in highly accurate reachability predictions and error detection, as well as tight prediction regions with guaranteed coverage.
Vehicle-counting with Automatic Region-of-Interest and Driving-Trajectory detection
Venue: arXiv cs.CV 2021
Year: 2021

Vehicle counting systems can help with vehicle analysis and traffic incident detection. Unfortunately, most existing methods require some level of human input to identify the Region of interest (ROI), movements of interest, or to establish a reference point or line to count vehicles from traffic cameras. This work introduces a method to count vehicles from traffic videos that automatically identifies the ROI for the camera, as well as the driving trajectories of the vehicles. This makes the method feasible to use with Pan-Tilt-Zoom cameras, which are frequently used in developing countries. Preliminary results indicate that the proposed method achieves an average intersection over the union of 57.05% for the ROI and a mean absolute error of just 17.44% at counting vehicles of the traffic video cameras tested.
Writing R Extensions in Rust
Venue: arXiv cs.PL 2021
Year: 2021

This paper complements "Writing R Extensions," the official guide for writing R extensions, for those interested in developing R packages using Rust. It highlights idiosyncrasies of R and Rust that must be addressed by any integration and describes how to develop Rust-based packages which comply with the CRAN Repository Policy. This paper introduces the "cargo" framework, a transparent Rust-based API which wraps commonly-used parts of R's API with minimal overhead and allows a programmer to easily add additional wrappers.
Masked Face Recognition Challenge: The WebFace260M Track Report
Venue: arXiv cs.CV 2021
Year: 2021

According to WHO statistics, there are more than 204,617,027 confirmed COVID-19 cases including 4,323,247 deaths worldwide till August 12, 2021. During the coronavirus epidemic, almost everyone wears a facial mask. Traditionally, face recognition approaches process mostly non-occluded faces, which include primary facial features such as the eyes, nose, and mouth. Removing the mask for authentication in airports or laboratories will increase the risk of virus infection, posing a huge challenge to current face recognition systems. Due to the sudden outbreak of the epidemic, there are yet no publicly available real-world masked face recognition (MFR) benchmark. To cope with the above-mentioned issue, we organize the Face Bio-metrics under COVID Workshop and Masked Face Recognition Challenge in ICCV 2021. Enabled by the ultra-large-scale WebFace260M benchmark and the Face Recognition Under Inference Time conStraint (FRUITS) protocol, this challenge (WebFace260M Track) aims to push the frontiers of practical MFR. Since public evaluation sets are mostly saturated or contain noise, a new test set is gathered consisting of elaborated 2,478 celebrities and 60,926 faces. Meanwhile, we collect the world-largest real-world masked test set. In the first phase of WebFace260M Track, 69 teams (total 833 solutions) participate in the challenge and 49 teams exceed the performance of our baseline. There are second phase of the challenge till October 1, 2021 and on-going leaderboard. We will actively update this report in the future.
Proximity Perception in Human-Centered Robotics: A Survey on Sensing Systems and Applications
Venue: arXiv cs.RO 2021
Year: 2021

Proximity perception is a technology that has the potential to play an essential role in the future of robotics. It can fulfill the promise of safe, robust, and autonomous systems in industry and everyday life, alongside humans, as well as in remote locations in space and underwater. In this survey paper, we cover the developments of this field from the early days up to the present, with a focus on human-centered robotics. Here, proximity sensors are typically deployed in two scenarios: first, on the exterior of manipulator arms to support safety and interaction functionality, and second, on the inside of grippers or hands to support grasping and exploration. Starting from this observation, we propose a categorization for the approaches found in the literature. To provide a basis for understanding these approaches, we devote effort to present the technologies and different measuring principles that were developed over the years, also providing a summary in form of a table. Then, we show the diversity of applications that have been presented in the literature. Finally, we give an overview of the most important trends that will shape the future of this domain.