OpenRaiser/Intern-Atlas huggingface dataset:
ICDAR 2021 Competition on On-Line Signature Verification
Venue: arXiv cs.CV 2021
Year: 2021

This paper describes the experimental framework and results of the ICDAR 2021 Competition on On-Line Signature Verification (SVC 2021). The goal of SVC 2021 is to evaluate the limits of on-line signature verification systems on popular scenarios (office/mobile) and writing inputs (stylus/finger) through large-scale public databases. Three different tasks are considered in the competition, simulating realistic scenarios as both random and skilled forgeries are simultaneously considered on each task. The results obtained in SVC 2021 prove the high potential of deep learning methods. In particular, the best on-line signature verification system of SVC 2021 obtained Equal Error Rate (EER) values of 3.33% (Task 1), 7.41% (Task 2), and 6.04% (Task 3). SVC 2021 will be established as an on-going competition, where researchers can easily benchmark their systems against the state of the art in an open common platform using large-scale public databases such as DeepSignDB and SVC2021_EvalDB, and standard experimental protocols.
Covid-19 Spread Detection and Controlling with Fog-based Infection Probability Evaluation Model
Venue: arXiv cs.SI 2021
Year: 2021

COVID-19 has created a pandemic around the world, paused the path of building the future, and still ongoing without having any long-term solution shortly. The time taken in vaccine distribution is too slow compared to the spread of COVID-19. Hence, it is important to aware and takes precautions on time without delaying and waiting for long-duration after getting infected with the virus. Currently used technology is more advanced than ever before. Almost everyone has access to at least one mobile device with an Internet connection. Therefore, we propose a Fog Server (FS) based system that can be used to create awareness about the spread of COVID-19 within the surroundings of individuals utilizing the concept of Hidden Markov Models (HMM) and Bluetooth contact tracing, in polynomial computational time complexity. Moreover, we evaluate the effectiveness of the proposed model through real-world data analysis on different simulation parameter settings.
Lattices of sensors reconsidered when less information is preferred
Venue: arXiv cs.RO 2021
Year: 2021

To treat sensing limitations (with uncertainty in both conflation of information and noise) we model sensors as covers. This leads to a semilattice organization of abstract sensors that is appropriate even when additional information is problematic (e.g., for tasks involving privacy considerations).
Weighting vectors for machine learning: numerical harmonic analysis applied to boundary detection
Venue: arXiv cs.LG 2021
Year: 2021

Metric space magnitude, an active field of research in algebraic topology, is a scalar quantity that summarizes the effective number of distinct points that live in a general metric space. The {\em weighting vector} is a closely-related concept that captures, in a nontrivial way, much of the underlying geometry of the original metric space. Recent work has demonstrated that when the metric space is Euclidean, the weighting vector serves as an effective tool for boundary detection. We recast this result and show the weighting vector may be viewed as a solution to a kernelized SVM. As one consequence, we apply this new insight to the task of outlier detection, and we demonstrate performance that is competitive or exceeds performance of state-of-the-art techniques on benchmark data sets. Under mild assumptions, we show the weighting vector, which has computational cost of matrix inversion, can be efficiently approximated in linear time. We show how nearest neighbor methods can approximate solutions to the minimization problems defined by SVMs.
A Continuous Liveness Detection for Voice Authentication on Smart Devices
Venue: arXiv cs.CR 2021
Year: 2021

Voice biometrics is drawing increasing attention as it is a promising alternative to legacy passwords for user authentication. Recently, a growing body of work shows that voice biometrics is vulnerable to spoofing through replay attacks, where an adversary tries to spoof voice authentication systems by using a pre-recorded voice sample collected from a genuine user. To this end, we propose VoiceGesture, a liveness detection solution for voice authentication on smart devices such as smartphones and smart speakers. VoiceGesture detects a live user by leveraging both the unique articulatory gesture of the user when speaking a passphrase and the audio hardware advances on these smart devices. Specifically, our system re-uses a pair of built-in speaker and microphone on a smart device as a Doppler radar, which transmits a high-frequency acoustic sound from the speaker and listens to the reflections at the microphone when a user speaks a passphrase. Then we extract Doppler shifts resulted from the user's articulatory gestures for liveness detection. VoiceGesture is practical as it requires neither cumbersome operations nor additional hardware but a speaker and a microphone commonly available on smart devices that support voice input. Our experimental evaluation with 21 participants and different smart devices shows that VoiceGesture achieves over 99% and around 98% detection accuracy for text-dependent and text-independent liveness detection, respectively. Results also show that VoiceGesture is robust to different device placements, low audio sampling frequency, and supports medium range liveness detection on smart speakers in various use scenarios like smart homes and smart vehicles.
Object Sensing for Fruit Ripeness Detection Using WiFi Signals
Venue: arXiv cs.HC 2021
Year: 2021

This paper presents FruitSense, a novel fruit ripeness sensing system that leverages wireless signals to enable non-destructive and low-cost detection of fruit ripeness. Such a system can reuse existing WiFi devices in homes without the need for additional sensors. It uses WiFi signals to sense the physiological changes associated with fruit ripening for detecting the ripeness of fruit. FruitSense leverages the larger bandwidth at 5GHz (i.e., over 600MHz) to extract the multipath-independent signal components to characterize the physiological compounds of the fruit. It then measures the similarity between the extracted features and the ones in ripeness profiles for identifying the ripeness level. We evaluate FruitSense in different multipath environments with two types of fruits (i.e, kiwi and avocado) under four levels of ripeness. Experimental results show that FruitSense can detect the ripeness levels of fruits with an accuracy of over 90%.
Necessary and Sufficient Girth Conditions for LDPC Tanner Graphs with Denser Protographs
Venue: arXiv cs.IT 2021
Year: 2021

This paper gives necessary and sufficient conditions for the Tanner graph of a quasi-cyclic (QC) low-density parity-check (LDPC) code based on the all-one protograph to have girth 6, 8, 10, and 12, respectively, in the case of parity-check matrices with column weight 4. These results are a natural extension of the girth results of the already-studied cases of column weight 2 and 3, and it is based on the connection between the girth of a Tanner graph given by a parity-check matrix and the properties of powers of the product between the matrix and its transpose. The girth conditions can be easily incorporated into fast algorithms that construct codes of desired girth between 6 and 12; our own algorithms are presented for each girth, together with constructions obtained from them and corresponding computer simulations. More importantly, this paper emphasizes how the girth conditions of the Tanner graph corresponding to a parity-check matrix composed of circulants relate to the matrix obtained by adding (over the integers) the circulant columns of the parity-check matrix. In particular, we show that imposing girth conditions on a parity-check matrix is equivalent to imposing conditions on a square circulant submatrix of size 4 obtained from it.
Multi-User Activity Recognition and Tracking Using Commodity WiFi
Venue: arXiv cs.HC 2021
Year: 2021

This paper presents MultiTrack, a commodity WiFi-based human sensing system that can track multiple users and recognize the activities of multiple users performing them simultaneously. Such a system can enable easy and large-scale deployment for multi-user tracking and sensing without the need for additional sensors through the use of existing WiFi devices (e.g., desktops, laptops, and smart appliances). The basic idea is to identify and extract the signal reflection corresponding to each individual user with the help of multiple WiFi links and all the available WiFi channels at 5GHz. Given the extracted signal reflection of each user, MultiTrack examines the path of the reflected signals at multiple links to simultaneously track multiple users. It further reconstructs the signal profile of each user as if only a single user has performed activity in the environment to facilitate multi-user activity recognition. We evaluate MultiTrack in different multipath environments with up to 4 users for multi-user tracking and up to 3 users for activity recognition. Experimental results show that our system can achieve decimeter localization accuracy and over 92% activity recognition accuracy under multi-user scenarios.
Proposed DBMS for OTT platforms in line with new age requirements
Venue: arXiv cs.DB 2021
Year: 2021

Database management has become an enormous tool for on-demand content distribution services, proffering required information and providing custom services to the user. Also plays a major role for the platforms to manage their data in such a way that data redundancy is minimized. This paper emphasizes improving the user experience for the platform by efficiently managing data. Keeping in mind all the new age requirements, especially after COVID-19 the sudden surge in subscription has led the stakeholders to try new things to lead the OTT market. Collection of shows being the root of the tree here, this paper improvises the currently existing branches via various tables and suggests some new features on how the data collected can be utilized for introducing new and much-required query results for the consumer.
Answer Generation for Retrieval-based Question Answering Systems
Venue: arXiv cs.CL 2021
Year: 2021

Recent advancements in transformer-based models have greatly improved the ability of Question Answering (QA) systems to provide correct answers; in particular, answer sentence selection (AS2) models, core components of retrieval-based systems, have achieved impressive results. While generally effective, these models fail to provide a satisfying answer when all retrieved candidates are of poor quality, even if they contain correct information. In AS2, models are trained to select the best answer sentence among a set of candidates retrieved for a given question. In this work, we propose to generate answers from a set of AS2 top candidates. Rather than selecting the best candidate, we train a sequence to sequence transformer model to generate an answer from a candidate set. Our tests on three English AS2 datasets show improvement up to 32 absolute points in accuracy over the state of the art.
FedHealth 2: Weighted Federated Transfer Learning via Batch Normalization for Personalized Healthcare
Venue: arXiv cs.LG 2021
Year: 2021

The success of machine learning applications often needs a large quantity of data. Recently, federated learning (FL) is attracting increasing attention due to the demand for data privacy and security, especially in the medical field. However, the performance of existing FL approaches often deteriorates when there exist domain shifts among clients, and few previous works focus on personalization in healthcare. In this article, we propose FedHealth 2, an extension of FedHealth \cite{chen2020fedhealth} to tackle domain shifts and get personalized models for local clients. FedHealth 2 obtains the client similarities via a pretrained model, and then it averages all weighted models with preserving local batch normalization. Wearable activity recognition and COVID-19 auxiliary diagnosis experiments have evaluated that FedHealth 2 can achieve better accuracy (10%+ improvement for activity recognition) and personalized healthcare without compromising privacy and security.
Simple Economies are Almost Optimal
Venue: arXiv cs.GT 2021
Year: 2021

Consider a seller that intends to auction some item. The seller can invest money and effort in advertising in different market segments in order to recruit $n$ bidders to the auction. Alternatively, the seller can have a much cheaper and focused marketing operation and recruit the same number of bidders from a single market segment. Which marketing operation should the seller choose? More formally, let $D=\{\mathcal D_1,\ldots, \mathcal D_n\}$ be a set of distributions. Our main result shows that there is always $\mathcal D_i\in D$ such that the revenue that can be extracted from $n$ bidders, where the value of each is independently drawn from $\mathcal D_i$, is at least $\frac 1 2 \cdot (1-\frac 1 e)$ of the revenue that can be obtained by any possible mix of bidders, where the value of each bidder is drawn from some (possibly different) distribution that belongs to $D$. We next consider situations in which the auctioneer cannot use the optimal auction and is required to use a second price auction. We show that there is always $\mathcal D_i\in D$ such that if the value of all bidders is independently drawn from $\mathcal D_i$ then running a second price auction guarantees a constant fraction of the revenue that can be obtained by a second-price auction by any possible mix of bidders. Finally, we show that for any $\varepsilon>0$ there exists a function $f$ that depends only on $\varepsilon$ (in particular, the function does not depend on $n$ or on the set $D$), such that recruiting $n$ bidders which have at most $f(\varepsilon)$ different distributions, all from $D$, guarantees $(1-\varepsilon)$-fraction of the revenue that can be obtained by a second-price auction by any possible mix of bidders.
A Novel Edge Detection Operator for Identifying Buildings in Augmented Reality Applications
Venue: arXiv cs.CV 2021
Year: 2021

Augmented Reality is an environment-enhancing technology, widely applied in many domains, such as tourism and culture. One of the major challenges in this field is precise detection and extraction of building information through Computer Vision techniques. Edge detection is one of the building blocks operations for many feature extraction solutions in Computer Vision. AR systems use edge detection for building extraction or for extraction of facade details from buildings. In this paper, we propose a novel filter operator for edge detection that aims to extract building contours or facade features better. The proposed filter gives more weight for finding vertical and horizontal edges that is an important feature for our aim.
Efficient Deterministic Leader Election for Programmable Matter
Venue: arXiv cs.DC 2021
Year: 2021

It was suggested that a programmable matter system (composed of multiple computationally weak mobile particles) should remain connected at all times since otherwise, reconnection is difficult and may be impossible. At the same time, it was not clear that allowing the system to disconnect carried a significant advantage in terms of time complexity. We demonstrate for a fundamental task, that of leader election, an algorithm where the system disconnects and then reconnects automatically in a non-trivial way (particles can move far away from their former neighbors and later reconnect to others). Moreover, the runtime of the temporarily disconnecting deterministic leader election algorithm is linear in the diameter. Hence, the disconnecting -- reconnecting algorithm is as fast as previous randomized algorithms. When comparing to previous deterministic algorithms, we note that some of the previous work assumed weaker schedulers. Still, the runtime of all the previous deterministic algorithms that did not assume special shapes of the particle system (shapes with no holes) was at least quadratic in $n$, where $n$ is the number of particles in the system. (Moreover, the new algorithm is even faster in some parameters than the deterministic algorithms that did assume special initial shapes.) Since leader election is an important module in algorithms for various other tasks, the presented algorithm can be useful for speeding up other algorithms under the assumption of a strong scheduler. This leaves open the question: "can a deterministic algorithm be as fast as the randomized ones also under weaker schedulers?"
Improvement over Pinball Loss Support Vector Machine
Venue: arXiv cs.LG 2021
Year: 2021

Recently, there have been several papers that discuss the extension of the Pinball loss Support Vector Machine (Pin-SVM) model, originally proposed by Huang et al.,[1][2]. Pin-SVM classifier deals with the pinball loss function, which has been defined in terms of the parameter $τ$. The parameter $τ$ can take values in $[ -1,1]$. The existing Pin-SVM model requires to solve the same optimization problem for all values of $τ$ in $[ -1,1]$. In this paper, we improve the existing Pin-SVM model for the binary classification task. At first, we note that there is major difficulty in Pin-SVM model (Huang et al. [1]) for $ -1 \leq τ< 0$. Specifically, we show that the Pin-SVM model requires the solution of different optimization problem for $ -1 \leq τ< 0$. We further propose a unified model termed as Unified Pin-SVM which results in a QPP valid for all $-1\leq τ\leq 1$ and hence more convenient to use. The proposed Unified Pin-SVM model can obtain a significant improvement in accuracy over the existing Pin-SVM model which has also been empirically justified by extensive numerical experiments with real-world datasets.
End-to-End NLP Knowledge Graph Construction
Venue: arXiv cs.CL 2021
Year: 2021

This paper studies the end-to-end construction of an NLP Knowledge Graph (KG) from scientific papers. We focus on extracting four types of relations: evaluatedOn between tasks and datasets, evaluatedBy between tasks and evaluation metrics, as well as coreferent and related relations between the same type of entities. For instance, F1-score is coreferent with F-measure. We introduce novel methods for each of these relation types and apply our final framework (SciNLP-KG) to 30,000 NLP papers from ACL Anthology to build a large-scale KG, which can facilitate automatically constructing scientific leaderboards for the NLP community. The results of our experiments indicate that the resulting KG contains high-quality information.
Figurative Language in Recognizing Textual Entailment
Venue: arXiv cs.CL 2021
Year: 2021

We introduce a collection of recognizing textual entailment (RTE) datasets focused on figurative language. We leverage five existing datasets annotated for a variety of figurative language -- simile, metaphor, and irony -- and frame them into over 12,500 RTE examples.We evaluate how well state-of-the-art models trained on popular RTE datasets capture different aspects of figurative language. Our results and analyses indicate that these models might not sufficiently capture figurative language, struggling to perform pragmatic inference and reasoning about world knowledge. Ultimately, our datasets provide a challenging testbed for evaluating RTE models.
A weighted unified informetrics based on Scopus and WoS
Venue: arXiv cs.DL 2021
Year: 2021

Numerous indexing databases keep track of the number of publications, citations, etc. in order to maintain the progress of science and individual. However, the choice of journals and articles varies among these indexing databases, hence the number of citations and h-index varies. There is no common platform exists that can provide a single count for the number of publications, citations, h-index, etc. To overcome this limitation, we have proposed a weighted unified informetrics, named "conflate". The proposed system takes into account the input from multiple indexing databases and generates a single output. Here, we have used the data from Scopus and WoS to generate a conflate dataset. Further, a comparative analysis of conflate has been performed with Scopus and WoS at three levels: author, organization, and journal. Finally, a mapping is proposed between research publications and distributed ledger technology in order to provide a transparent and distributed view to its stakeholders.
Phoenix: A Formally Verified Regenerating Vault
Venue: arXiv cs.CR 2021
Year: 2021

An attacker that gains access to a cryptocurrency user's private keys can perform any operation in her stead. Due to the decentralized nature of most cryptocurrencies, no entity can revert those operations. This is a central challenge for decentralized systems, illustrated by numerous high-profile heists. Vault contracts reduce this risk by introducing artificial delay on operations, allowing abortion by the contract owner during the delay. However, the theft of a key still renders the vault unusable and puts funds at risk. We introduce Phoenix, a novel contract architecture that allows the user to restore its security properties after key loss. Phoenix takes advantage of users' ability to store keys in easily-available but less secure storage (tier-two) as well as more secure storage that is harder to access (tier-one). Unlike previous solutions, the user can restore Phoenix security after the theft of tier-two keys and does not lose funds despite losing keys in either tier. Phoenix also introduces a mechanism to reduce the damage an attacker can cause in case of a tier-one compromise. We formally specify Phoenix's required behavior and provide a prototype implementation of Phoenix as an Ethereum contract. Since such an implementation is highly sensitive and vulnerable to subtle bugs, we apply a formal verification tool to prove specific code properties and identify faults. We highlight a bug identified by the tool that could be exploited by an attacker to compromise Phoenix. After fixing the bug, the tool proved the low-level executable code's correctness.
Introducing "Neuromorphic Computing and Engineering"
Venue: arXiv cs.DC 2021
Year: 2021

The standard nature of computing is currently being challenged by a range of problems that start to hinder technological progress. One of the strategies being proposed to address some of these problems is to develop novel brain-inspired processing methods and technologies, and apply them to a wide range of application scenarios. This is an extremely challenging endeavor that requires researchers in multiple disciplines to combine their efforts and co-design at the same time the processing methods, the supporting computing architectures, and their underlying technologies. The journal ``Neuromorphic Computing and Engineering'' (NCE) has been launched to support this new community in this effort and provide a forum and repository for presenting and discussing its latest advances. Through close collaboration with our colleagues on the editorial team, the scope and characteristics of NCE have been designed to ensure it serves a growing transdisciplinary and dynamic community across academia and industry.