Reviewerly/exHarmony huggingface dataset:
Evaluating multi-modal mobile behavioral biometrics using public datasets

• Testing authentication performance of fusing three modalities with two public datasets. • Comparing performance of three different feature sets on motion events. • Utilizing likelihood ratio-based fusion and one- and two-class SVMs. • Achieving best EERs of 0.9% (OCC) and 0.2% (BC) when three modalities are fused. Behavioral biometric-based continuous user authentication is promising for securing mobile phones while complementing traditional security mechanisms. However, the existing state of art perform continuous authentication to evaluate deep learning models, but lacks examining different feature sets over the data. Therefore, we evaluate the performance of user authentication based on acceleration, gyroscope (angular velocity), and swipe data from two public mobile datasets, HMOG (Hand-Movement, Orientation, and Grasp) (Sitová et al., (2015) dataset et al. (2015)) and BB-MAS (Behavioral Biometrics Multi-device and multi-Activity data from Same users) (Belman et al., (2019) dataset et al. (2019)) extracted with different feature sets to observe the variation in authentication performance. We evaluate the performances of both individual modalities and their fusion. Since the swipe data is intermittent but the motion event data continuous, we evaluate fusion of swipes with motion events that occur within the swipes versus fusion of motion events outside of swipes. Moreover, we extract Frank et al.’s (2012) Touchalytics features Frank et al. (2012) on the swipe data but three different feature sets (median, HMOG (Sitová et al. (2015)), and Shen’s (Shen et al. (2017))) on the motion event data, among which the Shen’s features were shown to perform the best. More specifically, we perform score-level fusion for a single modality utilizing binary SVMs (Support Vector Machine). Furthermore, we evaluate the fusion of multiple modalities using Nandakumar’s likelihood ratio-based score fusion (Nandakumar et al. (2007)) by utilizing both one-class and binary SVMs. The best EERs (Equal Error Rates) of fusing all three modalities when using the one-class SVMs are 8.8% and 0.9% for HMOG and BB-MAS respectively. On the other hand, the best EERs in the case of binary SVMs are 1.5% and 0.2% respectively. Observing the better performances of BB-MAS compared to HMOG in swipe-based experiments, we examine the difference of swipe trajectory between the two datasets and find that BB-MAS has longer swipes than HMOG which would explain the performance difference in the experiments.
Flakify: A Black-Box, Language Model-Based Predictor for Flaky Tests

Software testing assures that code changes do not adversely affect existing functionality. However, a test case can be flaky, i.e., passing and failing across executions, even for the same version of the source code. Flaky test cases introduce overhead to software development as they can lead to unnecessary attempts to debug production or testing code. The state-of-the-art ML-based flaky test case predictors rely on pre-defined sets of features that are either project-specific, require access to production code, which is not always available to software test engineers. Therefore, in this paper, we propose Flakify, a black-box, language model-based predictor for flaky test cases. Flakify relies exclusively on the source code of test cases, thus not requiring to (a) access to production code (black-box), (b) rerun test cases, (c) pre-define features. To this end, we employed CodeBERT, a pre-trained language model, and fine-tuned it to predict flaky test cases using the source code of test cases. We evaluated Flakify on two publicly available datasets (FlakeFlagger and IDoFT) for flaky test cases and compared our technique with the FlakeFlagger approach using two different evaluation procedures: cross-validation and per-project validation. Flakify achieved high F1-scores on both datasets using cross-validation and per-project validation, and surpassed FlakeFlagger by 10 and 18 percentage points in terms of precision and recall, respectively, when evaluated on the FlakeFlagger dataset, thus reducing the cost bound to be wasted on unnecessarily debugging test cases and production code by the same percentages. Flakify also achieved significantly higher prediction results when used to predict test cases on new projects, suggesting better generalizability over FlakeFlagger. Our results further show that a black-box version of FlakeFlagger is not a viable option for predicting flaky test cases.
Efficient Self-Supervised Learning Representations for Spoken Language Identification

Self-supervised learning has been widely exploited to learn powerful speech representations. The premise of this paper is that these learned self-supervised representations contain irrelevant information for a particular downstream task. Hence, we investigate efficient methods to compute reliable representations and discard redundant information for language identification (LID) using a pre-trained multilingual wav2vec 2.0 model. To determine an optimal basic system, we compare the performance of wav2vec features extracted from different inner layers of the context network. For this approach, the x-vector self-attention LID (XSA-LID) model forms the backbone used to discriminate between distinct languages. We then propose to employ two mechanisms to reduce irrelevant information of the representations in LID. The first is the attentive squeeze-and-excitation (SE) block for dimension-wise scaling and the second is the linear bottleneck (LBN) block that reduces irrelevant information by nonlinear dimension reduction. We incorporate these two methods in the XSA-LID model and conduct experiments on AP19-OLR data and the MLS14 data in NIST LRE 2017. By replacing the previous input features with wav2vec 2.0 features, the XSA-LID model achieves 63.79% relative improvement in terms of the average cost on AP19-OLR data, and 40.42%, 41.54% and 18.97% relative improvement on 3 s, 10 s and 30 s test speech in the MLS14 data in NIST LRE 2017, respectively. In addition, the proposed LBN-XSA model achieves 9.85% relative improvement on AP19-OLR data and over 10% overall improvement on the MLS14 data with a modest number of additional parameters compared to the XSA-LID model. Finally, in terms of average cost and accuracy, the proposed LBN-XSA model outperforms the XSA-LID model which adopts the fine-tuned features on the AP19-OLR data.
A Functional and Performance Benchmark of Lightweight Virtualization Platforms for Edge Computing

The recent rise of edge computing and FaaS triggered a revolution in the field of software virtualization, improving performance and security. This paper benchmarks various lightweight virtualization platforms, such as microVMs and containers, in the context of edge microservices. Factors taken into account include toolchain maturity, networking capabilities, boot time, resource use, microservice performance, and ARM architecture readiness. We present a functional comparison and benchmarks on both a Raspberry Pi 4 and an x86-64 platform. The results indicate standard Docker containers offer reliable performance and low memory use, while microVM-based solutions such as Firecracker are more isolated. Moreover, OSv unikernels have extremely low boot times and significantly better performance than Docker containers. Finally, while gVisor offers improved security and excellent compatibility, its performance is only 10% of default Docker performance.
A Comparative Analysis of Proof-of-Authority Consensus Algorithms: Aura vs Clique

Proof-of-authority (PoA) has become a popular consensus algorithm for permissioned blockchains, in which a group of trusted entities governs the networks. This algorithm provides fast consensus while requiring less computational resources and energy. Aura and Clique are two major implementations of PoA, which have their own set of advantages and disadvantages. In this paper, we evaluate the performance of both algorithms in terms of consistency, availability, partition tolerance, security, block time, and transaction throughput. The algorithms were implemented in Python, and experiments were conducted by connecting six consensus nodes having different computing power. The experimental results were comprehensively analyzed. It was observed that Clique provides higher availability and transaction throughput but lower security and consistency than Aura. The blockchains in the Aura network are immediately consistent while the blockchains stored by individual Clique authorities become eventually consistent. Permissioned blockchain practitioners will find this paper useful in choosing the most appropriate PoA implementation based on their requirements.
A Decomposable Causal View of Compositional Zero-Shot Learning

Composing and recognizing novel concepts that are combinations of known concepts, <i>i.e.</i>, compositional generalization, is one of the greatest power of human intelligence. With the development of artificial intelligence, it becomes increasingly appealing to build a vision system that can generalize to unknown compositions based on restricted known knowledge, which has so far remained a great challenge to our community. In fact, machines can be easily misled by superficial correlations in the data, disregarding the causal patterns that are crucial to generalization. In this paper, we rethink compositional generalization with a causal perspective, upon the context of Compositional Zero-Shot Learning (CZSL). We develop a simple yet strong approach based on our novel <u>De</u>composable <u>Ca</u>usal view (dubbed &#x201C;<small>DeCa</small>&#x201D;), by approximating the causal effect with the combination of three easy-to-learn components. Our proposed <small>DeCa</small><xref ref-type="fn" rid="fn1"><sup>1</sup></xref><fn id="fn1"><label><sup>1</sup></label> Code is available on <u><uri>https://github.com/muliyangm/DeCa</uri></u>. </fn> is evaluated on two challenging CZSL benchmarks by recognizing unknown compositions of known concepts. Despite being simple in the design, our approach achieves consistent improvements over state-of-the-art baselines, demonstrating its superiority towards the goal of compositional generalization.
Scalable Fuzzy Clustering With Anchor Graph

Fuzzy clustering algorithms have been widely used to reveal the possible hidden structure of data. However, with the increasing of data amount, large scale data has brought genuine challenges for fuzzy clustering. Most fuzzy clustering algorithms suffer from the long time-consumption problem since a large amount of distance calculations are involved to update the solution per iteration. To address this problem, we introduce the popular anchor graph technique into fuzzy clustering and propose a scalable fuzzy clustering algorithm referred to as Scalable Fuzzy Clustering with Anchor Graph (SFCAG). The main characteristic of SFCAG is that it addresses the scalability issue plaguing fuzzy clustering from two perspectives: anchor graph construction and membership matrix learning. Specifically, we select a small number of anchors and construct a sparse anchor graph, which is beneficial to reduce the computational complexity. We then formulate a trace ratio model, which is parameter-free, to learn the membership matrix of anchors to speed up the clustering procedure. In addition, the proposed method enjoys linear time complexity with the data size. Extensive experiments performed on both synthetic and real world datasets demonstrate the superiority (both effectiveness and scalability) of the proposed method over some representative large scale clustering methods.
A DNN Fingerprint for Non-Repudiable Model Ownership Identification and Piracy Detection

A high-performance Deep Neural Network (DNN) model is a valuable intellectual property (IP) since designing and training such a model from scratch is very costly. Model transfer learning, compression and retraining are commonly used by pirates to evade detection or even redeploy the pirated models for new applications without compromising performance. This paper presents a novel non-intrusive DNN IP fingerprinting method that can detect pirated models and provide a non-repudiable and irrevocable ownership proof simultaneously. The fingerprint is derived from projecting a subset of front-layer weights onto a model owner identity defined random space to enable a distinguisher to differentiate pirated models that are used in the same application or retrained for a different task from originally designed DNN models. The proposed method generates compact and irrevocable fingerprints against model IP misappropriation and ownership fraud. It requires no retraining and makes no modification to the original model. The proposed fingerprinting method is evaluated on nine original DNN models trained on CIFAR-10, CIFAR-100, and ImageNet-10. It is demonstrated to have the highest discriminative power among existing fingerprinting methods in detecting pirated models deployed for the same and different applications, and fraudulent model IP ownership claims.
Drugs on the Web, Crime in the Streets. The Impact of Shutdowns of Dark Net Marketplaces on Street Crime

The Dark Net has changed the way drugs are traded globally by shifting trade away from the streets and onto the web. In this paper, I study whether the shutdown of Dark Net marketplaces has an impact on the amount of drugs traded in the streets and on crimes that are normally associated to street drug dealing. To identify a causal effect, I use daily data from the US and exploit unexpected shutdowns of large online drug trading platforms. In a regression discontinuity design, I compare crime levels in days after the shutdowns to those immediately preceding them. I find that shutting down Dark Net marketplaces leads to a significant increase in drug trade in the streets. However, the effect is short-lived. In the days immediately following shutdowns, marijuana-related crimes increase by around five percent but revert to pre-shutdown levels within eighteen days. I find no impact of shutdowns of Dark Net marketplaces on thefts, assaults, homicides and prostitution.
Design-while-verify

In the current control design of safety-critical cyber-physical systems, formal verification techniques are typically applied after the controller is designed to evaluate whether the required properties (e.g., safety) are satisfied. However, due to the increasing system complexity and the fundamental hardness of designing a controller with formal guarantees, such an open-loop process of design-then-verify often results in many iterations and fails to provide the necessary guarantees. In this paper, we propose a correct-by-construction control learning framework that integrates the verification into the control design process in a closed-loop manner, i.e., design-while-verify. Specifically, we leverage the verification results (computed reachable set of the system state) to construct feedback metrics for control learning, which measure how likely the current design of control parameters can meet the required reach-avoid property for safety and goal-reaching. We formulate an optimization problem based on such metrics for tuning the controller parameters, and develop an approximated gradient descent algorithm with a difference method to solve the optimization problem and learn the controller. The learned controller is formally guaranteed to meet the required reach-avoid property. By treating verifiability as a first-class objective and effectively leveraging the verification results during the control learning process, our approach can significantly improve the chance of finding a control design with formal property guarantees, demonstrated in a set of experiments that use model-based or neural network based controllers.
Ethical Considerations in the Application of Artificial Intelligence to Monitor Social Media for COVID-19 Data

The COVID-19 pandemic and its related policies (e.g., stay at home and social distancing orders) have increased people's use of digital technology, such as social media. Researchers have, in turn, utilized artificial intelligence to analyze social media data for public health surveillance. For example, through machine learning and natural language processing, they have monitored social media data to examine public knowledge and behavior. This paper explores the ethical considerations of using artificial intelligence to monitor social media to understand the public's perspectives and behaviors surrounding COVID-19, including potential risks and benefits of an AI-driven approach. Importantly, investigators and ethics committees have a role in ensuring that researchers adhere to ethical principles of respect for persons, beneficence, and justice in a way that moves science forward while ensuring public safety and confidence in the process.
Named Entity Recognition for Drone Forensic Using BERT and DistilBERT

The increase in UAV usage and popularity in many fields opens new opportunities and challenges. Many business sectors are benefiting from the UAV device employment. The wide range of drone implementation is varied, from business purposes to crime. Hence, further mechanisms are needed to deal with drone crime and attacks both administratively and technically. From a technical view, the security protocol is needed to keep the drone safe from various logical or physical attacks. In case a drone experiences incidents, a forensic protocol is needed to perform analysis and investigation to uncover the incident, understand the attack behavior, and mitigate the incident risk. Among the existing drone forensic research efforts, there is limited attempt to utilize specific drone artifacts to perform forensic analysis. Therefore, this paper investigates the potential of NER (Named Entity Recognition) as an initial step to perform information extraction from drone flight logs data. We use Transformers-based techniques to perform NER and assist the forensic investigation. BERT and DistilBERT pre-trained models are fine-tuned using the annotated data and get the F1 scores of 98.63% and of 95.9%, respectively.
AI-Bazaar: A Cloud-Edge Computing Power Trading Framework for Ubiquitous AI Services

Driven by the burgeoning growth of the Internet of Everything and the substantial breakthroughs in deep learning (DL) algorithms, a booming of artificial intelligence (AI) applications keep emerging. Meanwhile, the advance in existing computing paradigms, i.e., cloud computing and edge computing, provide assorted computing solutions to satisfy the increasingly high requirements for ubiquitous AI services. Nevertheless, there are some non-trivial issues in the computing frameworks, including the underutilization of computing power, the self-interest of computing-power trading mechanism, and the inefficiency of AI services management. To tackle the above issues, we propose a computing-power trading framework based on blockchain, also named AI-Bazaar. In AI-Bazaar, the AI consumers play multiple roles and feel free to contribute the computing power rented from the computing-power provider (CPP) for blockchain mining and AI services. Accordingly, we formulate the computing trading problem as a Stackelberg game. Based on the win or learn fast principle (WoLF), we design a profit-balanced multi-agent reinforcement learning (PB-MARL) algorithm to search the AI-Bazaar equilibrium, while finding the balanced profits for AI consumers and CPP. Numerical simulations are carried out to demonstrate the satisfactory performance and effectiveness of the proposed framework.
Domain Adaptive Graph Infomax via Conditional Adversarial Networks

The emerging graph neural networks (GNNs) have demonstrated impressive performance on the node classification problem in complex networks.However, existing GNNs are mainly devised to classify nodes in a (partially) labeled graph.To classify nodes in a newly-collected unlabeled graph, it is desirable to transfer label information from an existing labeled graph.To address this cross-graph node classification problem, we propose a graph infomax method that is domain adaptive.Node representations are computed through neighborhood aggregation.Mutual information is maximized between node representations and global summaries, encouraging node representations to encode the global structural information.Conditional adversarial networks are employed to reduce the domain discrepancy by aligning the multimodal distributions of node representations.Experimental results in real-world datasets validate the performance of our method in comparison with the state-ofthe-art baselines.
Comment-Guided Semantics-Aware Image Aesthetics Assessment

Existing image aesthetics assessment methods mainly rely on the visual features of images but ignore their rich semantics. Nowadays, with the widespread application of social media, the comments corresponding to images in the form of texts can be easily accessed and provide rich semantic information, which can be utilized to effectively complement image features. This paper proposes a comment-guided semantics-aware image aesthetics assessment method, which is built upon a multi-task learning framework for image aesthetics prediction and comment-guided semantics classification. To assist image aesthetics assessment, we first model the semantics of an image as the topic features of its corresponding comments using Latent Dirichlet Allocation. We then propose a two-stream multitask learning framework for both topic feature prediction and aesthetic score distribution prediction. Topic feature prediction task enables to infer the semantics from images, since the comments are usually unavailable during inference and comment-guided semantics can only serve as supervision during training. We further propose to deeply fuse aesthetics and semantic features using a layerwise feature fusion method. Experimental results demonstrate that the proposed method outperforms state-of-the-art image aesthetics assessment methods.
CLF-CBF Based Quadratic Programs for Safe Motion Control of Nonholonomic Mobile Robots in Presence of Moving Obstacles

A control Lyapunov function and control barrier function-based quadratic program (CLF-CBF-QP) is proposed to safely navigate a nonholonomic mobile robot (NMR) facing a moving obstacle. The conventional CLF-CBF-QP realizes safety and stability conditions using distance-based CLF and CBF without considering the practical limitations of the steering angle. Hence, the NMR may execute undesirable avoidance maneuvers such as u-turns. This work introduces a safety condition on the steering angle to maintain efficient, smooth avoidance maneuvers. The safe steering condition keeps the NMR steering angle within a predefined envelope of the reference steering angle. Moreover, one can improve the response time of the control system by enforcing exponential decay rates on the CLF and CBF. However, fixed decay rates lead to aggressive maneuvers around moving obstacles, where the CLF and CBF may produce conflicting conditions. Hence, this work proposes to optimize the decay rates of the CBF and CLF in real-time to improve the NMR performance during obstacle avoidance maneuvers. Lastly, the effectiveness of the proposed control method is verified by simulating two case studies where an NMR i) overtakes and ii) avoids head-on collision with a moving obstacle.