Reviewerly/exHarmony huggingface dataset:
Ordered SGD: A New Stochastic Optimization Framework for Empirical Risk Minimization

We propose a new stochastic optimization framework for empirical risk minimization problems such as those that arise in machine learning. The traditional approaches, such as (mini-batch) stochastic gradient descent (SGD), utilize an unbiased gradient estimator of the empirical average loss. In contrast, we develop a computationally efficient method to construct a gradient estimator that is purposely biased toward those observations with higher current losses. On the theory side, we show that the proposed method minimizes a new ordered modification of the empirical average loss, and is guaranteed to converge at a sublinear rate to a global optimum for convex loss and to a critical point for weakly convex (non-convex) loss. Furthermore, we prove a new generalization bound for the proposed algorithm. On the empirical side, the numerical experiments show that our proposed method consistently improves the test errors compared with the standard mini-batch SGD in various models including SVM, logistic regression, and deep learning problems.
Artificial neural network based crossover for evolutionary algorithms

Recombination is a powerful way of generating new solutions in Evolutionary Algorithms. There are many ways to implement recombination. Traditional recombination operators do not use information about parents, evolutionary process, or models for variable interaction in order to find better ways to recombine solutions. Some modern recombination operators use information about parents and models for variable interaction, but they cannot always be efficiently applied. We propose to use an artificial neural network to compute the recombination mask, given two parents. Here, a radial basis function network (RBFN) is trained online using past successful recombination cases obtained during the optimization performed by the evolutionary algorithm. The RBFN crossover (RBFNX) is used together with other recombination operators (here, uniform crossover is employed). Applying RBFNX has O(N) time complexity, where N is the dimension of the optimization problem. Results of experiments with genetic algorithms, applied to two binary optimization problems, and evolution strategies, applied to continuous optimization test problems, indicate that RBFNX is generally able to improve the successful recombination rates.
Detection of Hazardous Road Events From Audio Streams: An Ensemble Outlier Detection Approach

Change detection in audio data streams has become an efficient way for online notification of events. An interesting application is audio surveillance, including road traffic monitoring and online car crash alarms. However, in the particular case of crash alarms, most of the collected sounds are background noises, with various sub-classes, such as talking pedestrians, engine sounds, horn blowing, etc., whereas those corresponding to the event of interest are much less abundant. Therefore, it is difficult to apply classical tools of classification or clustering to detect crash sounds as a particular class or as outliers. To tackle this problem, we propose an ensemble classifier based on one-class SVM in order to separate outliers from normal data first, and deep neural networks to classify event-related data. Finally the results of both outlier detection and classification outputs are aggregated in such a way that outliers are considered as a novel class, a priori unknown by the DNN classifier. The application of this method on an audio traffic monitoring database confirms its ability to detect, (a) non-events (background noise), nonhazardous and hazardous event, and (b) non-accidents and accidents, from a stream of audio data.
Non-malleable Secret Sharing Against Bounded Joint-Tampering Attacks in the Plain Model

Secret sharing enables a dealer to split a secret into a set of shares, in such a way that certain authorized subsets of share holders can reconstruct the secret, whereas all unauthorized subsets cannot. Non-malleable secret sharing (Goyal and Kumar, STOC 2018) additionally requires that, even if the shares have been tampered with, the reconstructed secret is either the original or a completely unrelated one. In this work, we construct non-malleable secret sharing tolerating $$p$$ -time joint-tampering attacks in the plain model (in the computational setting), where the latter means that, for any $$p>0$$ fixed a priori, the attacker can tamper with the same target secret sharing up to $$p$$ times. In particular, assuming one-to-one one-way functions, we obtain: At the heart of our result for selective partitioning lies a new technique showing that every one-time statistically non-malleable secret sharing against joint tampering is in fact leakage-resilient non-malleable (i.e., the attacker can leak jointly from the shares prior to tampering). We believe this may be of independent interest, and in fact we show it implies lower bounds on the share size and randomness complexity of statistically non-malleable secret sharing against independent tampering.
Cyber attacks on hospitality sector: stock market reaction

Purpose The paper aims to analyze the impact of cyber attacks on stock returns of companies operating in the hospitality sector. The fast development of information and communication technologies has been posited as both an opportunity and a challenge to the United Nations Global Sustainable Development Goals. Digital technologies are significant tools for sustainable development, but if they are not addressed appropriately, they can potentially hinder the progress toward sustainability. Among negative impacts, it is necessary to consider cyber risk, a major concern today, in particular for industries which work with sensitive data, such as tourism businesses. Hospitality businesses have to adequately manage cyber insecurity and digital privacy issues, to prevent losses and contribute to socioeconomic sustainable growth. Design/methodology/approach Using event-study methodology, the paper provides empirical evidence on the effect of announcements of 170 information security breaches on the market value of firms operating in the hospitality sector in the past five years. Findings The study focuses on identifying potential threats of cyber attacks for the economic value of listed companies. The authors find that negative market returns occur following announcements of cyber attacks suffered by hospitality companies. Adequate investments in technology for cyber security and staff training are relevant in the hospitality sector to reduce cyber risk. Originality/value The paper contributes to identifying potential threats of cyber attacks for the economic value of listed companies operating in the hospitality sector. The analysis is carried out by collecting an original sample of global cyber attacks from newspaper announcements sourced from the LexisNexis database.
Short-Term Multi-Vehicle Trajectory Planning for Collision Avoidance

A trajectory planning is a technique that is widely used to automatically generate an expected geometric path that a vehicle needs to follow within a certain future time horizon. We propose a way to generate trajectories of multiple vehicles for a short-term planning with the formal guarantee of the collision avoidance using a SAT (Satisfiability) solver. We assume that each vehicle sends the local status (e.g., speed or position) and its planned trajectory to the server (e.g., road-side units or cloud servers). The server collects the information, and determines if the group of vehicles would encounter any collision within a fixed amount of time. Then, the server calculates the modified trajectories, and sends them back to the vehicles to avoid collisions. Calculating such trajectories is generally a complex problem, which makes it challenging to find them within a short amount of time. Given a driving scenario where multiple vehicles are engaged, we propose two methods to reduce the computation time to generate the trajectories. Firstly, we introduce the grouping method which systematically identifies vehicles that are not engaged in the collision scenario, and removes the related parameters from the SAT solving process. Our case study of the ramp merging driving scenario shows the grouping method saves the computation time by 40% compared to solving the constraints with the original parameters. Secondly, we introduce the collision check point method that chooses some waypoints to maintain their previous control input to reduce the problem complexity. Our experiment shows up to 98% computation time reduction compared to the approach without this method.
Recognition-Driven Compressed Image Generation Using Semantic-Prior Information

Image compression, as one of the fundamental low-level image processing tasks, is very essential for computer vision. Current image compression methods can maintain considerable visual quality even at relatively lower bit-rate, but pay little attention to their performance in high-level vision tasks, e.g, image recognition and semantic segmentation. In this letter, we aim to generate compressed images with similar visual quality as before, but with much higher recognition accuracy. To this end, we explore the significance of semantic-prior information in image compression and design a semantic-prior attention module to adaptively enhance the semantic-ware features. Moreover, a semantic perceptual loss combining semantic-prior map is employed to concentrate on the machine perceptual quality of semantic-ware content. Experiments on benchmarks demonstrate that the proposed algorithm has the effectiveness to improve recognition accuracy and maintain visual quality. In addition, performance improvement on different recognition networks and tasks shows the generality of our algorithm.
How Does Refactoring Impact Security When Improving Quality? A Security-Aware Refactoring Approach

While state of the art of software refactoring research uses various quality attributes to identify refactoring opportunities and evaluate refactoring recommendations, the impact of refactoring on the security of software systems when improving other quality objectives is under-explored. It is critical to understand how a system is resistant to security risks after refactoring to improve quality metrics. For instance, refactoring is widely used to improve the reusability of code, however such an improvement may increase the attack surface due to the created abstractions. Increasing the spread of security-critical classes in the design to improve modularity may result in reducing the resilience of software systems to attacks. In this paper, we investigated the possible impact of improving different quality attributes (e.g., reusability, extendibility, etc.), from the QMOOD model, effectiveness on a set of 8 security metrics defined in the literature related to the data access. We also studied the impact of different refactorings on these static security metrics. Then, we proposed a multi-objective refactoring recommendation approach to find a balance between quality attributes and security based on the correlation results to guide the search. We evaluated our tool on 30 open source projects. We also collected the practitioner perceptions on the refactorings recommended by our tool in terms of the possible impact on both security and other quality attributes. Our results confirm that developers need to make trade-offs between security and other qualities when refactoring software systems due to the negative correlations between them.
An Efficient Deep Learning Method for Encrypted Traffic Classification on the Web

Traffic classification plays an important role in network management and cyber-security. With the development of the Internet, online applications and in the following encrypted techniques, encrypted traffic has changed to a major challenge for traffic classification. In fact, unbalanced data, in which the unbalanced distribution of samples across classes lead to the classification performance reduction, is considered as one of the prominent challenges in encrypted traffic classification. Although previous studies tried to deal with the class imbalance problem in the pre-processing step using machine learning and particularly deep learning models, they are still confronting with some limitations. In this regard, a new classification method is proposed in this paper that tries to deal with the problem of unbalanced data during the training process. The proposed method employs a cost-sensitive convolution neural network and considers a cost for each classification according to the distribution of classes. These costs are then applied to the network along the training process to enhance the overall accuracy. Based on the empirical results, the proposed model obtained higher classification performance (about 2% on average) compared to the Deep Packet method.
Sketch2Domino: Interactive Chain Reaction Design and Guidance

It is a challenging task for common users to design and arrange the chain reaction with domino blocks. In this paper, we propose, Sketch2Domino, an interactive chain reaction design and guidance system with the input of the user freeform sketches. The proposed design system converts the captured user sketch into an arrangement graph of domino blocks. Then, the system formats the graph into a valid chain reaction and provides visual guidance with interactive projection mapping. The guidance system notices the user of the differences among the current blocks and the calculated results. This system can support multi-users to complete the chain-reaction design in group collaboration. It is verified that Sketch2Domino is convenient and helpful from our case studies.
Background Subtraction Based on GAN and Domain Adaptation for VHR Optical Remote Sensing Videos

The application of deep learning techniques in background subtraction for VHR optical remote sensing videos holds the potential to facilitate multiple intelligent remote sensing processing tasks. However, existing methods on background subtraction for VHR optical remote sensing videos are still facing technical challenges. First, conventional CNN and other networks are limited by performance constraints. Second, existing background subtraction methods are mostly trained by natural videos due to the lack of VHR optical remote sensing video datasets. Third, VHR optical remote sensing videos have large scene sizes. In our article, we design a novel deep learning network via fully utilizing GAN and domain adaptation, which has the ability to measure and minimize the discrepancy between feature distributions of natural videos and VHR optical remote sensing videos so that the background subtraction performance for VHR optical remote sensing videos is improved significantly. Numerous experiments are conducted on the CDnet 2014 dataset and VHR optical remote sensing video dataset. Tremendous experiments demonstrate that our proposed method achieves an average FM of 0.8533, which reveals excellent performance on background subtraction.
Preprocessing alternatives for compositional data related to water, sanitation and hygiene

The Sustainable Development Goals (SDGs) 6.1 and 6.2 measure the progress of urban and rural populations in their access to different levels of water, sanitation and hygiene (WASH) services, based on multiple sources of information. Service levels add up to 100%; therefore, they are compositional data (CoDa). Despite evidence of zero value, missing data and outliers in the sources of information, the treatment of these irregularities with different statistical techniques has not yet been analyzed for CoDa in the WASH sector. Thus, the results may present biased estimates, and the decisions based on these results will not necessarily be appropriate. In this article, we therefore: i) evaluate methodological imputation alternatives that address the problem of having either zero values or missing values, or both simultaneously; and ii) propose the need to complement the point-to-point identification of the WHO/UNICEF Joint Monitoring Program (JMP) with other robust alternatives, to deal with outliers depending on the number of data points. These suggestions have been considered here using statistics for CoDa with isometric log-ratio (ilr) transformation. A selection of illustrative cases is presented to compare performance of different alternatives.
Build It, Break It, Fix It

Typical security contests focus on breaking or mitigating the impact of buggy systems. We present the Build-it, Break-it, Fix-it (BIBIFI) contest, which aims to assess the ability to securely build software, not just break it. In BIBIFI, teams build specified software with the goal of maximizing correctness, performance, and security. The latter is tested when teams attempt to break other teams’ submissions. Winners are chosen from among the best builders and the best breakers. BIBIFI was designed to be open-ended—teams can use any language, tool, process, and so on, that they like. As such, contest outcomes shed light on factors that correlate with successfully building secure software and breaking insecure software. We ran three contests involving a total of 156 teams and three different programming problems. Quantitative analysis from these contests found that the most efficient build-it submissions used C/C++, but submissions coded in a statically type safe language were 11× less likely to have a security flaw than C/C++ submissions. Break-it teams that were also successful build-it teams were significantly better at finding security bugs.
Multi-agent path finding with mutex propagation

Mutex propagation is a form of efficient constraint propagation popularly used in AI planning to tightly approximate the reachable states from a given state. We utilize this idea in the context of Multi-Agent Path Finding (MAPF). When adapted to MAPF, mutex propagation provides stronger constraints for conflict resolution in CBS, a popular optimal search-based MAPF algorithm, as well as in MDD-SAT, an optimal satisfiability-based MAPF algorithm. Mutex propagation provides CBS with the ability to break symmetries in MAPF and provides MDD-SAT with the ability to make stronger inferences than unit propagation. While existing work identifies a limited form of symmetries and requires the manual design of symmetry-breaking constraints, mutex propagation is more general and allows for the automated design of symmetry-breaking constraints. Our experimental results show that CBS with mutex propagation is capable of outperforming CBSH-RCT, a state-of-the-art variant of CBS, with respect to the success rate. We also show that MDD-SAT with mutex propagation often performs better than MDD-SAT with respect to the success rate.
Security Fictions

This paper begins with an observation: that threat identification is an intrinsically speculative practice. It requires imagining possible futures. Drawing on methods from speculative design, this paper presents an improvisational role-playing game designed to help software developers identify security threats. It deploys this game with seven software developers, who used the game to successfully identify diverse threats in their software. The insights from this deployment motivate future work on both the game itself and on organizational accounts of security. I call on the design research community to continue to apply its methods and perspectives to computer security, locating threat identification itself, like all speculation, as a site of social and political power.
PLANS AND PLANNING IN MATHEMATICAL PROOFS

Abstract In practice, mathematical proofs are most often the result of careful planning by the agents who produced them. As a consequence, each mathematical proof inherits a plan in virtue of the way it is produced, a plan which underlies its “architecture” or “unity.” This paper provides an account of plans and planning in the context of mathematical proofs. The approach adopted here consists in looking for these notions not in mathematical proofs themselves, but in the agents who produced them. The starting point is to recognize that to each mathematical proof corresponds a proof activity which consists of a sequence of deductive inferences —i.e., a sequence of epistemic actions —and that any written mathematical proof is only a report of its corresponding proof activity. The main idea to be developed is that the plan of a mathematical proof is to be conceived and analyzed as the plan of the agent(s) who carried out the corresponding proof activity. The core of the paper is thus devoted to the development of an account of plans and planning in the context of proof activities. The account is based on the theory of planning agency developed by Michael Bratman in the philosophy of action. It is fleshed out by providing an analysis of the notions of intention —the elementary components of plans—and practical reasoning —the process by which plans are constructed—in the context of proof activities. These two notions are then used to offer a precise characterization of the desired notion of plan for proof activities. A fruitful connection can then be established between the resulting framework and the recent theme of modularity in mathematics introduced by Jeremy Avigad. This connection is exploited to yield the concept of modular presentations of mathematical proofs which has direct implications for how to write and present mathematical proofs so as to deliver various epistemic benefits. The account is finally compared to the technique of proof planning developed by Alan Bundy and colleagues in the field of automated theorem proving. The paper concludes with some remarks on how the framework can be used to provide an analysis of understanding and explanation in the context of mathematical proofs.
An attribute-based keyword search for m-Health networks

m-Health stands for mobile health, where mobile devices are used for collecting and distributing health-related data. As the information related to personal health is sensitive in nature, so it should be encrypted first before storing it at the potentially untrusted servers. However, encryption severely affects basic sharing and search operations. To address these issues, a combination of attribute-based encryption and searchable encryption is devised, which enables secure fine-grained search over encrypted data. But the resultant technique incurs high computational complexity, which makes it unsuitable for resource-constrained mobile devices. So, in order to deploy it to mobile devices, searchable encryption should generate constant size secret keys and ciphertexts. However, in existing attribute-based keyword search (ABKS) schemes, the size of the secret key and the ciphertext is proportional to the number of attributes, thus resulting in linear complexity. In this paper, we have proposed a novel ABKS scheme with constant-size secret keys and ciphertexts, thus further reducing the computational cost. Our scheme uses a ciphertext-policy (CP) design framework and supports an AND gate access structure. Further, the proposed CP-ABKS scheme can be proved secure in the selective security model under augmented multi-sequence of exponents decisional Diffie-Hellman assumption.