OpenRaiser/Intern-Atlas huggingface dataset:
GANG-MAM: GAN based enGine for Modifying Android Malware
Venue: arXiv cs.CR 2021
Year: 2021

Malware detectors based on machine learning are vulnerable to adversarial attacks. Generative Adversarial Networks (GAN) are architectures based on Neural Networks that could produce successful adversarial samples. The interest towards this technology is quickly growing. In this paper, we propose a system that produces a feature vector for making an Android malware strongly evasive and then modify the malicious program accordingly. Such a system could have a twofold contribution: it could be used to generate datasets to validate systems for detecting GAN-based malware and to enlarge the training and testing dataset for making more robust malware classifiers.
Exploring The Role of Local and Global Explanations in Recommender Systems
Venue: arXiv cs.IR 2021
Year: 2021

Explanations are well-known to improve recommender systems' transparency. These explanations may be local, explaining an individual recommendation, or global, explaining the recommender model in general. Despite their widespread use, there has been little investigation into the relative benefits of these two approaches. Do they provide the same benefits to users, or do they serve different purposes? We conducted a 30-participant exploratory study and a 30-participant controlled user study with a research-paper recommender system to analyze how providing participants local, global, or both explanations influences user understanding of system behavior. Our results provide evidence suggesting that both explanations are more helpful than either alone for explaining how to improve recommendations, yet both appeared less helpful than global alone for efficiency in identifying false positives and negatives. However, we note that the two explanation approaches may be better compared in the context of a higher-stakes or more opaque domain.
Clustering with Neighborhoods
Venue: arXiv cs.CG 2021
Year: 2021

In the standard planar $k$-center clustering problem, one is given a set $P$ of $n$ points in the plane, and the goal is to select $k$ center points, so as to minimize the maximum distance over points in $P$ to their nearest center. Here we initiate the systematic study of the clustering with neighborhoods problem, which generalizes the $k$-center problem to allow the covered objects to be a set of general disjoint convex objects $\mathscr{C}$ rather than just a point set $P$. For this problem we first show that there is a PTAS for approximating the number of centers. Specifically, if $r_{opt}$ is the optimal radius for $k$ centers, then in $n^{O(1/\varepsilon^2)}$ time we can produce a set of $(1+\varepsilon)k$ centers with radius $\leq r_{opt}$. If instead one considers the standard goal of approximating the optimal clustering radius, while keeping $k$ as a hard constraint, we show that the radius cannot be approximated within any factor in polynomial time unless $\mathsf{P=NP}$, even when $\mathscr{C}$ is a set of line segments. When $\mathscr{C}$ is a set of unit disks we show the problem is hard to approximate within a factor of $\frac{\sqrt{13}-\sqrt{3}}{2-\sqrt{3}}\approx 6.99$. This hardness result complements our main result, where we show that when the objects are disks, of possibly differing radii, there is a $(5+2\sqrt{3})\approx 8.46$ approximation algorithm. Additionally, for unit disks we give an $O(n\log k)+(k/\varepsilon)^{O(k)}$ time $(1+\varepsilon)$-approximation to the optimal radius, that is, an FPTAS for constant $k$ whose running time depends only linearly on $n$. Finally, we show that the one dimensional version of the problem, even when intersections are allowed, can be solved exactly in $O(n\log n)$ time.
How Low Can You Go? Practical cold-start performance limits in FaaS
Venue: arXiv cs.DC 2021
Year: 2021

Function-as-a-Service (FaaS) has recently emerged as a new cloud computing paradigm. It promises high utilization of data center resources through allocating resources on demand at per-function request granularity. High cold-start overheads, however, have been limiting FaaS systems' such potential. Prior work has recognized that time redundancy exists across different cold function invocations and has proposed varied snapshots that capture the instantaneous execution state so allow for jump-starts through restoration. However, it remains unclear what the cold-start performance limits are as the previous snapshots employ different techniques and aredesigned for different environments. In this paper, we summarize these snapshots from a taxonomic perspective andpresent a model that depicts the cold-start performance fromfirst principles. To approximate the performance limits, we propose a snapshot design SnapFaaS. We evaluate SnapFaaS using real-world FaaS functions. Our empirical results prove SnapFaaS' efficiency. It is 2-10x as fast as prior work for most functions and achieves near-optimal cold-start performance.
0-Gaps on 3D Digital Curves
Venue: arXiv cs.DM 2021
Year: 2021

In Digital Geometry, gaps are some basic portion of a digital object that a discrete ray can cross without intersecting any voxel of the object itself. Such a notion is quite important in combinatorial image analysis and it is strictly connected with some applications in fields as CAD and Computer graphics. In this paper we prove that the number of $0$-gaps of a $3$D digital curve can be expressed as a linear combination of the number of its $i$-cells (with $i = 0, \ldots, 3$).
Control Barrier Functions for Singularity Avoidance in Passivity-Based Manipulator Control
Venue: arXiv cs.RO 2021
Year: 2021

Task-space Passivity-Based Control (PBC) for manipulation has numerous appealing properties, including robustness to modeling error and safety for human-robot interaction. Existing methods perform poorly in singular configurations, however, such as when all the robot's joints are fully extended. Additionally, standard methods for constrained task-space PBC guarantee passivity only when constraints are not active. We propose a convex-optimization-based control scheme that provides guarantees of singularity avoidance, passivity, and feasibility. This work paves the way for PBC with passivity guarantees under other types of constraints as well, including joint limits and contact/friction constraints. The proposed methods are validated in simulation experiments on a 7 degree-of-freedom manipulator.
Audio-to-Image Cross-Modal Generation
Venue: arXiv cs.MM 2021
Year: 2021

Cross-modal representation learning allows to integrate information from different modalities into one representation. At the same time, research on generative models tends to focus on the visual domain with less emphasis on other domains, such as audio or text, potentially missing the benefits of shared representations. Studies successfully linking more than one modality in the generative setting are rare. In this context, we verify the possibility to train variational autoencoders (VAEs) to reconstruct image archetypes from audio data. Specifically, we consider VAEs in an adversarial training framework in order to ensure more variability in the generated data and find that there is a trade-off between the consistency and diversity of the generated images - this trade-off can be governed by scaling the reconstruction loss up or down, respectively. Our results further suggest that even in the case when the generated images are relatively inconsistent (diverse), features that are critical for proper image classification are preserved.
Trustworthy AI and Robotics and the Implications for the AEC Industry: A Systematic Literature Review and Future Potentials
Venue: arXiv cs.HC 2021
Year: 2021

Human-technology interaction deals with trust as an inevitable requirement for user acceptance. As the applications of artificial intelligence (AI) and robotics emerge and with their ever-growing socio-economic influence in various fields of research and practice, there is an imminent need to study trust in such systems. With the opaque work mechanism of AI-based systems and the prospect of intelligent robots as workers' companions, context-specific interdisciplinary studies on trust are key in increasing their adoption. Through a thorough systematic literature review on (1) trust in AI and robotics (AIR) and (2) AIR applications in the architecture, engineering, and construction (AEC) industry, this study identifies common trust dimensions in the literature and uses them to organize the paper. Furthermore, the connections of the identified dimensions to the existing and potential AEC applications are determined and discussed. Finally, major future directions on trustworthy AI and robotics in AEC research and practice are outlined.
Weakly Supervised Keypoint Discovery
Venue: arXiv cs.CV 2021
Year: 2021

In this paper, we propose a method for keypoint discovery from a 2D image using image-level supervision. Recent works on unsupervised keypoint discovery reliably discover keypoints of aligned instances. However, when the target instances have high viewpoint or appearance variation, the discovered keypoints do not match the semantic correspondences over different images. Our work aims to discover keypoints even when the target instances have high viewpoint and appearance variation by using image-level supervision. Motivated by the weakly-supervised learning approach, our method exploits image-level supervision to identify discriminative parts and infer the viewpoint of the target instance. To discover diverse parts, we adopt a conditional image generation approach using a pair of images with structural deformation. Finally, we enforce a viewpoint-based equivariance constraint using the keypoints from the image-level supervision to resolve the spatial correlation problem that consistently appears in the images taken from various viewpoints. Our approach achieves state-of-the-art performance for the task of keypoint estimation on the limited supervision scenarios. Furthermore, the discovered keypoints are directly applicable to downstream tasks without requiring any keypoint labels.
AMA-K: Aggressive Multi-Temporal Allocation An Algorithm for Aggressive Online Portfolio Selection
Venue: arXiv cs.CE 2021
Year: 2021

Online portfolio selection is an integral componentof wealth management. The fundamental undertaking is tomaximise returns while minimising risk given investor con-straints. We aim to examine and improve modern strategiesto generate higher returns in a variety of market conditions.By integrating simple data mining, optimisation techniques andmachine learning procedures, we aim to generate aggressive andconsistent high yield portfolios. This leads to a new methodologyof Pattern-Matching that may yield further advances in dynamicand competitive portfolio construction. The resulting strategiesoutperform a variety of benchmarks, when compared using Max-imum Drawdown, Annualised Percentage Yield and AnnualisedSharpe Ratio, that make use of similar approaches. The proposedstrategy returns showcase acceptable risk with high reward thatperforms well in a variety of market conditions. We concludethat our algorithm provides an improvement in searching foroptimal portfolios compared to existing methods.
Multi-Semantic Image Recognition Model and Evaluating Index for explaining the deep learning models
Venue: arXiv cs.CV 2021
Year: 2021

Although deep learning models are powerful among various applications, most deep learning models are still a black box, lacking verifiability and interpretability, which means the decision-making process that human beings cannot understand. Therefore, how to evaluate deep neural networks with explanations is still an urgent task. In this paper, we first propose a multi-semantic image recognition model, which enables human beings to understand the decision-making process of the neural network. Then, we presents a new evaluation index, which can quantitatively assess the model interpretability. We also comprehensively summarize the semantic information that affects the image classification results in the judgment process of neural networks. Finally, this paper also exhibits the relevant baseline performance with current state-of-the-art deep learning models.
A Formally Verified Configuration for Hardware Security Modules in the Cloud
Venue: arXiv cs.CR 2021
Year: 2021

Hardware Security Modules (HSMs) are trusted machines that perform sensitive operations in critical ecosystems. They are usually required by law in financial and government digital services. The most important feature of an HSM is its ability to store sensitive credentials and cryptographic keys inside a tamper-resistant hardware, so that every operation is done internally through a suitable API, and such sensitive data are never exposed outside the device. HSMs are now conveniently provided in the cloud, meaning that the physical machines are remotely hosted by some provider and customers can access them through a standard API. The property of keeping sensitive data inside the device is even more important in this setting as a vulnerable application might expose the full API to an attacker. Unfortunately, in the last 20+ years a multitude of practical API-level attacks have been found and proved feasible in real devices. The latest version of PKCS#11, the most popular standard API for HSMs, does not address these issues leaving all the flaws possible. In this paper, we propose the first secure HSM configuration that does not require any restriction or modification of the PKCS#11 API and is suitable to cloud HSM solutions, where compliance to the standard API is of paramount importance. The configuration relies on a careful separation of roles among the different HSM users so that known API flaws are not exploitable by any attacker taking control of the application. We prove the correctness of the configuration by providing a formal model in the state-of-the-art Tamarin prover and we show how to implement the configuration in a real cloud HSM solution.
Investigation of Dataset Features for Just-in-Time Defect Prediction
Venue: arXiv cs.SE 2021
Year: 2021

Just-in-time (JIT) defect prediction refers to the technique of predicting whether a code change is defective. Many contributions have been made in this area through the excellent dataset by Kamei. In this paper, we revisit the dataset and highlight preprocessing difficulties with the dataset and the limitations of the dataset on unsupervised learning. Secondly, we propose certain features in the Kamei dataset that can be used for training models. Lastly, we discuss the limitations of the dataset's features.
Multilingual Counter Narrative Type Classification
Venue: arXiv cs.CL 2021
Year: 2021

The growing interest in employing counter narratives for hatred intervention brings with it a focus on dataset creation and automation strategies. In this scenario, learning to recognize counter narrative types from natural text is expected to be useful for applications such as hate speech countering, where operators from non-governmental organizations are supposed to answer to hate with several and diverse arguments that can be mined from online sources. This paper presents the first multilingual work on counter narrative type classification, evaluating SoTA pre-trained language models in monolingual, multilingual and cross-lingual settings. When considering a fine-grained annotation of counter narrative classes, we report strong baseline classification results for the majority of the counter narrative types, especially if we translate every language to English before cross-lingual prediction. This suggests that knowledge about counter narratives can be successfully transferred across languages.
Preemptive Two-stage Goal-Programming Formulation of a Strict Version of the Unbounded Knapsack Problem with Bounded Weights
Venue: arXiv cs.DS 2021
Year: 2021

The unbounded knapsack problem with bounded weights is a variant of the well-studied variant of the traditional binary knapsack problem; key changes being the relaxation of the binary constraint and allowing the unit weights of each item to fall within a range. In this paper, we formulate a variant of this problem, which we call the strict unbounded knapsack problem with bounded weights, by replacing the inequality constraint on the total weight with an equality. We show that this problem can be decomposed into a two-stage, pre-emptive goal programming problem, with the first stage being a 2-dimensional knapsack problem and the second being either a linear feasibility program (per canonical formulation) or simply a linearly-constrained program in the general case. This reformulation is shown to be equivalent to the original formulation but allows the use of well-studied, efficient algorithms for multidimensional knapsack problems. In addition, it separates the modeling effort around what to put in the knapsack from considerations around what unit weight one should assign to each item type, providing substantially more flexibility to the modeler without adding complexity to the choice of knapsack configuration. Finally, we show that for the feasibility version of the second stage, one can immediately get a feasible solution to the first stage solution.
Are iPhones Really Better for Privacy? Comparative Study of iOS and Android Apps
Venue: arXiv cs.CR 2021
Year: 2021

While many studies have looked at privacy properties of the Android and Google Play app ecosystem, comparatively much less is known about iOS and the Apple App Store, the most widely used ecosystem in the US. At the same time, there is increasing competition around privacy between these smartphone operating system providers. In this paper, we present a study of 24k Android and iOS apps from 2020 along several dimensions relating to user privacy. We find that third-party tracking and the sharing of unique user identifiers was widespread in apps from both ecosystems, even in apps aimed at children. In the children's category, iOS apps tended to use fewer advertising-related tracking than their Android counterparts, but could more often access children's location. Across all studied apps, our study highlights widespread potential violations of US, EU and UK privacy law, including 1) the use of third-party tracking without user consent, 2) the lack of parental consent before sharing personally identifiable information (PII) with third-parties in children's apps, 3) the non-data-minimising configuration of tracking libraries, 4) the sending of personal data to countries without an adequate level of data protection, and 5) the continued absence of transparency around tracking, partly due to design decisions by Apple and Google. Overall, we find that neither platform is clearly better than the other for privacy across the dimensions we studied.
VoiceFixer: Toward General Speech Restoration with Neural Vocoder
Venue: arXiv cs.SD 2021
Year: 2021

Speech restoration aims to remove distortions in speech signals. Prior methods mainly focus on single-task speech restoration (SSR), such as speech denoising or speech declipping. However, SSR systems only focus on one task and do not address the general speech restoration problem. In addition, previous SSR systems show limited performance in some speech restoration tasks such as speech super-resolution. To overcome those limitations, we propose a general speech restoration (GSR) task that attempts to remove multiple distortions simultaneously. Furthermore, we propose VoiceFixer, a generative framework to address the GSR task. VoiceFixer consists of an analysis stage and a synthesis stage to mimic the speech analysis and comprehension of the human auditory system. We employ a ResUNet to model the analysis stage and a neural vocoder to model the synthesis stage. We evaluate VoiceFixer with additive noise, room reverberation, low-resolution, and clipping distortions. Our baseline GSR model achieves a 0.499 higher mean opinion score (MOS) than the speech enhancement SSR model. VoiceFixer further surpasses the GSR baseline model on the MOS score by 0.256. Moreover, we observe that VoiceFixer generalizes well to severely degraded real speech recordings, indicating its potential in restoring old movies and historical speeches. The source code is available at https://github.com/haoheliu/voicefixer_main.
Exploring the spatiotemporal heterogeneity in the relationship between human mobility and COVID-19 prevalence using dynamic time warping
Venue: arXiv cs.SI 2021
Year: 2021

Understanding where and when human mobility is associated with disease infection is crucial for implementing location-based health care policy and interventions. Previous studies on COVID-19 have revealed the correlation between human mobility and COVID-19 cases. However, the spatiotemporal heterogeneity of such correlation is not yet fully understood. In this study, we aim to identify the spatiotemporal heterogeneities in the relationship between human mobility flows and COVID-19 cases in U.S. counties. Using anonymous mobile device location data, we compute an aggregate measure of mobility that includes flows within and into each county. We then compare the trends in human mobility and COVID-19 cases of each county using dynamic time warping (DTW). DTW results highlight the time periods and locations (counties) where mobility may have influenced disease transmission. Also, the correlation between human mobility and infections varies substantially across geographic space and time in terms of relationship, strength, and similarity.
Data-driven insight into the puzzle-based cybersecurity training
Venue: arXiv cs.HC 2021
Year: 2021

Puzzle-based training is a common type of hands-on activity accompanying formal and informal cybersecurity education, much like programming or other IT skills. However, there is a lack of tools to help the educators with the post-training data analysis. Through a visualization design study, we designed the Training Analysis Tool that supports learning analysis of a single hands-on session. It allows an in-depth trainee comparison and enables the identification of flaws in puzzle assignments. We also performed a qualitative evaluation with cybersecurity experts and students. The participants apprised the positive influence of the tool on their workflows. Our insights and recommendations could aid the design of future tools supporting educators, even beyond cyber security.
What to Prioritize? Natural Language Processing for the Development of a Modern Bug Tracking Solution in Hardware Development
Venue: arXiv cs.LG 2021
Year: 2021

Managing large numbers of incoming bug reports and finding the most critical issues in hardware development is time consuming, but crucial in order to reduce development costs. In this paper, we present an approach to predict the time to fix, the risk and the complexity of debugging and resolution of a bug report using different supervised machine learning algorithms, namely Random Forest, Naive Bayes, SVM, MLP and XGBoost. Further, we investigate the effect of the application of active learning and we evaluate the impact of different text representation techniques, namely TF-IDF, Word2Vec, Universal Sentence Encoder and XLNet on the model's performance. The evaluation shows that a combination of text embeddings generated through the Universal Sentence Encoder and MLP as classifier outperforms all other methods, and is well suited to predict the risk and complexity of bug tickets.