Reviewerly/exHarmony huggingface dataset:
Threshold Implementations in the Robust Probing Model

Threshold Implementations (TI) are provably secure algorithmic countermeasures against side-channel attacks in the form of differential power analysis. The strength of TI lies in its minimal algorithmic requirements. These requirements have been studied over more than 10 years and many efficient implementations for symmetric primitives have been proposed. Thus, over the years the practice of protecting implementations matured, however, the theory behind threshold implementations remained the same. In this work, we revise this theory by looking at the properties of correctness, non-completeness, and uniformity as a composable security model. We prove that this model provides first-order and higher-order univariate security in the glitch-robust probing model which lets us expand the theoretic framework of TI. We first provide a link between uniformity and the notion of non-interference, a known composable security notion building out the probing model. We then relax the notion of non-completeness which helps the design of secure expansion and compression functions. Lastly, we provide generalisations of the threshold notions to allow for general secret sharing schemes and provide examples of how different sharing schemes affect the security and efficiency of the countermeasure.
Deconstructing Creativity

The stereotype of creativity as an isolated, individual activity has given way to a more nuanced understanding of creativity as a social process. Our interview study of 23 contemporary music composers and choreographers focuses on the role that artifacts play in shaping creative collaborations with performers. We found that creators and performers form relationships where the creator acts as an author, a curator, a planner, or a researcher, and the performer acts as an interpreter, a creator, an improvisor, or an informant. Furthermore, we found that creators sculpt, layer, remix artifacts, moving fluidly across these different forms of interaction throughout the creative process. We conclude that the slippages that occur at the seams between roles and interactions drive creativity forward by opening up pathways into the future.
Super-resolution reconstruction algorithms based on fusion of deep learning mechanism and wavelet

In this paper, we consider the problem of super-resolution reconstruction. This is a hot topic because super-resolution reconstruction has a wide range of applications in the medical field, remote sensing monitoring, and criminal investigation. Compared with traditional algorithms, the current super-resolution reconstruction algorithm based on deep learning greatly improves the clarity of reconstructed pictures. Existing work like Super-Resolution Using a Generative Adversarial Network (SRGAN) can effectively restore the texture details of the image. However, experimentally verified that the texture details of the image recovered by the SRGAN are not robust. In order to get super-resolution reconstructed images with richer high-frequency details, we improve the network structure and propose a super-resolution reconstruction algorithm combining wavelet transform and Generative Adversarial Network. The proposed algorithm can efficiently reconstruct high-resolution images with rich global information and local texture details. We have trained our model by PyTorch framework and VOC2012 dataset, and tested it by Set5, Set14, BSD100 and Urban100 test datasets.
Fast Actively Secure Five-Party Computation with Security Beyond Abort

Secure Multi-party Computation (MPC) with small population and honest majority has drawn focus specifically due to customization in techniques and resulting efficiency that the constructions can offer. In this work, we investigate a wide range of security notions in the five-party setting, tolerating two active corruptions. Being constant-round, our protocols are best suited for real-time, high latency networks such as the Internet. In a minimal setting of pairwise-private channels, we present efficient instantiations with unanimous abort (where either all honest parties obtain the output or none of them do) and fairness (where the adversary obtains its output only if all honest parties also receive it). With the presence of an additional broadcast channel (known to be necessary), we present a construction with guaranteed output delivery (where any adversarial behaviour cannot prevent the honest parties from receiving the output). The broadcast communication is minimal and independent of circuit size. In terms of performance (communication and run time), our protocols incur minimal overhead over the best known protocol of Chandran et al. (ACM CCS 2016) that achieves the least security of selective abort. Further, our protocols for fairness and unanimous abort can be extended to n-parties with at most √n corruptions, similar to Chandran et al. Going beyond the most popular honest-majority setting of three parties with one corruption, our results demonstrate feasibility of attaining stronger security notions for more than one active corruption at an expense not too far from the least desired security of selective abort.
Poster

With the advent of the Internet, social media platforms have become an increasingly popular medium of communication for people. Platforms like Twitter and Quora allow people to express their opinions on a large scale. These platforms are, however, plagued by the problem of hate speech and toxic content. Such content is generally sexist, homophobic or racist. Automatic text classification can filter out toxic content so some extent. In this paper, we discuss the adversarial attacks on hate speech classifiers. We demonstrate that by changing the text slightly, a classifier can be fooled to misclassifying a toxic comment as acceptable. We attack hate speech classifiers with known attacks as well as introduce four new attacks. We find that our method can degrade the performance of a Random Forest classifier by 20%. We hope that our work sheds light on the vulnerabilities of text classifiers, and opens doors for further research on this topic.
Survey of Recommendation Based on Collaborative Filtering

Abstract This paper introduces the domestic and international research of collaborative filtering, and discusses the main problems of collaborative filtering algorithm, including data sparsity, cold start and accuracy of similarity measure.Then, future research and development trends of integrating deep learning to recommender systems are pointed out. In order to solve the data sparsity and cold start problems in the personalized recommendation system, a hybrid collaborative filtering recommendation algorithm is proposed, which combines the KNN model and XGBoost model. When deep learning is applied to recommendation system by integrating massive multi-sources heterogeneous data,it could improve the performance of the recommendation system.
A partial evaluation framework for order-sorted equational programs modulo axioms

Partial evaluation is a powerful and general program optimization technique with many successful applications. Existing PE schemes do not apply to expressive rule-based languages like Maude, CafeOBJ, OBJ, ASF+SDF, and ELAN, which support: 1) rich type structures with sorts, subsorts, and overloading; and 2) equational rewriting modulo various combinations of axioms such as associativity, commutativity, and identity. In this paper, we develop the new foundations needed and illustrate the key concepts by showing how they apply to partial evaluation of expressive programs written in Maude. Our partial evaluation scheme is based on an automatic unfolding algorithm that computes term variants and relies on high-performance order-sorted equational least general generalization and order-sorted equational homeomorphic embedding algorithms for ensuring termination. We show that our partial evaluation technique is sound and complete for convergent rewrite theories that may contain various combinations of associativity, commutativity, and/or identity axioms for different binary operators. We demonstrate the effectiveness of Maude's automatic partial evaluator, Victoria, on several examples where it shows significant speed-ups.
TagSniff

Although big data processing has become dramatically easier over the last decade, there has not been matching progress over big data debugging. It is estimated that users spend more than 50% of their time debugging their big data applications, wasting machine resources and taking longer to reach valuable insights. One cannot simply transplant traditional debugging techniques to big data. In this paper, we propose the TagSniff model, which can dramatically simplify data debugging for dataflows (the de-facto programming model for big data). It is based on two primitives -- tag and sniff -- that are flexible and expressive enough to model all common big data debugging scenarios. We then present Snoopy -- a general purpose monitoring and debugging system based on the TagSniff model. It supports both online and post-hoc debugging modes. Our experimental evaluation shows that Snoopy incurs a very low overhead on the main dataflow, 6% on average, as well as it is highly responsive to system events and users instructions.
Fast Dimensional Analysis for Root Cause Investigation in a Large-Scale Service Environment

Root cause analysis in a large-scale production environment is challenging due to the complexity of the services running across global data centers. Due to the distributed nature of a large-scale system, the various hardware, software, and tooling logs are often maintained separately, making it difficult to review the logs jointly for understanding production issues. Another challenge in reviewing the logs for identifying issues is the scale - there could easily be millions of entities, each described by hundreds of features. In this paper we present a fast dimensional analysis framework that automates the root cause analysis on structured logs with improved scalability. We first explore item-sets, i.e. combinations of feature values, that could identify groups of samples with sufficient support for the target failures using the Apriori algorithm and a subsequent improvement, FP-Growth. These algorithms were designed for frequent item-set mining and association rule learning over transactional databases. After applying them on structured logs, we select the item-sets that are most unique to the target failures based on lift. We propose pre-processing steps with the use of a large-scale real-time database and post-processing techniques and parallelism to further speed up the analysis and improve interpretability, and demonstrate that such optimization is necessary for handling large-scale production datasets. We have successfully rolled out this approach for root cause investigation purposes within Facebook's infrastructure. We also present the setup and results from multiple production use cases in this paper.
A Statistical Model to Determine the Behavior Adoption in Different Timestamps on Online Social Network

In this article, a statistical model has been proposed to determine the behavior adoption among the users in different timestamps on online social networks by using vector space models and term frequency – inverse document frequency techniques. The concepts of herd behavior and collective behavior have been used successfully in the proposed model. The result has been generated after analyzing the collected dataset. The result analysis shows the diffusion of information among the participants from an initial timestamp to later timestamps.
Improved fuzzy Load-Balancing Algorithm for Cloud Computing System

Cloud Computing is a modern paradigm that provides its users with any kind of service. A large number of users is attracted to this technology seeking more satisfaction. Load Balancing is a very important area of research for efficient use of IT resources. Many techniques have been proposed to improve the overall performance of the cloud, and to provide the user with more satisfactory and efficient services.
Initial Coin Offering (ICO) as a Fundraising Strategy: A Multiple Case Study on Success Factors

Cryptocurrencies and Initial Coin Offerings (ICO) are some of the more prominent examples of currently used blockchain technology applications. Especially software startups have leveraged ICOs to gain funding early on in their lifecycles, going on to develop and create new blockchain based applications. Recently, larger companies such as Facebook have also begun to show interest in cryptocurrency, although thus far not for funding purposes in the form of ICOs. In this paper, we investigate factors that positively affect the abilities of companies to meet their fundraising goals via ICOs. We first identify a set of factors from extant literature and then seek to further confirm the effect of these factors while uncovering new ones by means of a multiple case study of eight firms that have carried out an ICO with varying success. Based on the data, we highlight success factors for ICOs in funding use.
Neural Review Summarization Leveraging User and Product Information

Product review summarization is a special form of text summarization, which gives a brief summary of an online product review. It is useful for both sellers to get feedback and consumers to make purchase decisions. Compared to traditional well-studied text summarization, product review summarization is highly personalized and targeted. Users have their own styles to write reviews and summaries, and products have different aspects to focus on. In this paper, we explore different ways to leverage the user and product information to help review summarization. Experiments show that our approaches are very effective and our models outperform the strong summarization baselines with a large margin.
Fast User-Guided Single Image Reflection Removal via Edge-Aware Cascaded Networks

Taking photos through a glass window leads to glare or reflection, which might distract the viewer from the scene behind the window. In this paper, we involve user interaction to tackle the ill-posedness of the reflection removal problem. Users are allowed to draw strokes or lassos to indicate the background and reflection layers. Instead of designing hand-crafted features, we propose the edge-aware cascaded networks for reflection removal. The proposed network is a two-stage pipeline. The first stage takes the edge hints converted from user guidance and the image with reflection as input, and then separates the input image into the background and reflection layers. The second stage involves a refinement network to recover the missing details of the background layers. We simulate different types of user guidance, and the networks are trained on simulated data. The cascaded networks are end-to-end and perform with a single feed-forward pass, enabling fast editing. Extensive experimental evaluations demonstrate that the proposed used-guided reflection removal network yields better performance than the state-of-the-art methods on real-world scenarios. Furthermore, we show that novice users can easily generate reflection-free images, and large improvements in reflection removal quality can be obtained in just one minute.
Breaking Mobile Firmware Encryption through Near-Field Side-Channel Analysis

Physical attacks constitute a significant threat for any cryptosystem. Among them, Side-Channel Analysis (SCA) is a common practice to stress the security of embedded devices like smartcards or secure controllers. Nowadays, it has become more than relevant on mobile and connected devices requiring a high security level. Yet, their applicability to smartphones is not obvious, as the architecture of modern System-on-Chips (SoC) is becoming ever more complex.
Gaussian prior based adaptive synthetic sampling with non-linear sample space for imbalanced learning

Abstract In the presence of skewed category distribution, most learning algorithms fail to provide favorable performance on the representation about data characteristics. Thus learning from imbalanced data is a crucial challenge in the field of data engineering and knowledge discovery. In this work, we proposed an imbalanced learning method to generate minority samples for the compensation of class distribution skews. Different from existing synthetic over-sampling techniques, the data generation is conducted within the hyperplane rather than on the hyperline, thus the proposed method breaks down the ties imposed by the linear interpolation. In addition, this proposed method minimizes the sampling uncertain and risk by integrating a prior knowledge about the minority class instances. Moreover, a multi-objective optimization combined with error bound model develops this proposed method into an adaptive imbalanced learning. Extensive experiments have been performed on imbalanced issues, and the experimental results demonstrate that this method can improve the performance of different classification algorithms.
First-Order Masking with Only Two Random Bits

Masking is the best-researched countermeasure against side-channel analysis attacks. Even though masking was introduced almost 20 years ago, its efficient implementation continues to be an active research topic. Many of the existing works focus on the reduction of randomness requirements since the production of fresh random bits with high entropy is very costly in practice. Most of these works rely on the assumption that only so-called online randomness results in additional costs. In practice, however, it shows that the distinction between randomness costs to produce the initial masking and the randomness to maintain security during computation (online) is not meaningful. In this work, we thus study the question of minimum randomness requirements for first-order Boolean masking when taking the costs for initial randomness into account. We demonstrate that first-order masking can in theory always be performed by just using two fresh random bits and without requiring online randomness. We first show that two random bits are enough to mask linear transformations and then discuss prerequisites under which nonlinear transformations are securely performed likewise. Subsequently, we introduce a new masked AND gate that fulfills these requirements and which forms the basis for our synthesis tool that automatically transforms an unmasked implementation into a first-order secure masked implementation. We demonstrate the feasibility of this approach by implementing AES in software with only two bits of randomness, including the initial masking. Finally, we use these results to discuss the gap between theory and practice and the need for more accurate adversary models.
Inertia‐based Fast Vectorization of Line Drawings

Abstract Image vectorisation is a fundamental method in graphic design and is one of the tools allowing to transfer artist work into computer graphics. The existing methods are based mainly on segmentation, or they analyse every image pixel; thus, they are relatively slow. We introduce a novel method for fast line drawing image vectorisation, based on a multi‐scale second derivative detector accelerated by the summed‐area table and an auxiliary grid. Image is scanned initially along the grid lines, and nodes are added to improve accuracy. Applying inertia in the line tracing allows for better junction mapping in a single pass. Our method is dedicated to grey‐scale sketches and line drawings. It works efficiently regardless of the thickness of the line or its shading. Experiments show it is more than two orders of magnitude faster than the existing methods, not sacrificing accuracy.
A Comparative Analysis of the Application of Hashing Encryption Algorithms for MD5, SHA-1, and SHA-512

Abstract This study compares different hashing encryption algorithms and provides several suggestions about how to choose different hashing encryption algorithms for particular cases. This paper presents three different kinds of hashing encryption algorithms. The author introduces the fundamental conceptions of these algorithms and their application. The author also computes some significant factors in computer science and compares these algorithms by mathematical quantity computing data. This article discusses the advantages and disadvantages of hashing encryption algorithms and proposes its potential value in the future.
A distributed solver for multi-agent path finding problems

Multi-Agent Path Finding (MAPF) problems are traditionally solved in a centralized manner. There are works focusing on completeness, optimality, performance, or a tradeoff between them. However, there are only a few works based on spatial distribution. In this paper, we introduce ros-dmapf, a distributed MAPF solver. It consists of multiple MAPF sub-solvers, which---besides solving their assigned sub-problems---interact with each other to solve a given MAPF problem. In the current implementation, the sub-solvers are answer set planning systems for multiple agents, and are created based on spatial distribution of the problem. Interactions between components of ros-dmapf are facilitated by the Robot Operating System (ROS). The highlights of ros-dmapf are its scalability and a high degree of parallelism. We empirically evaluate ros-dmapf using the move-only domain of the asprilo system and results suggest that ros-dmapf scales up well. For instance, ros-dmapf gives a solution of length around 600 for a MAPF problem with 2000 robots in randomly generated 100×100 obstacle-free maps---a problem beyond the capability of a single sub-solver---within 7 minutes on a consumer laptop. We also evaluate ros-dmapf against some other MAPF solvers and results show that the system performs well. We also discuss possible improvements for future work.
Designing spaces to support collaborative creativity in shared virtual environments

Shared virtual environments (SVEs) have been researched extensively within the fields of education, entertainment, work, and training, yet there has been limited research on the creative and collaborative aspects of interactivity in SVEs. The important role that creativity and collaboration play in human society raises the question of the way that virtual working spaces might be designed to support collaborative creativity in SVEs. In this paper, we outline an SVE named LeMo, which allows two people to collaboratively create a short loop of music together. Then we present a study of LeMo, in which 52 users composed music in pairs using four different virtual working space configurations. Key findings indicated by results include: (i) Providing personal space is an effective way to support collaborative creativity in SVEs, (ii) personal spaces with a fluid light-weight boundary could provide enough support, worked better and was preferable to ones with rigid boundaries and (iii) a configuration that provides a movable personal space was preferred to one that provided no mobility. Following these findings, five corresponding design implications for shared virtual environments focusing on supporting collaborative creativity are given and conclusions are made.
Uncivil User Comments Increase Users’ Intention to Engage in Corrective Actions and Their Support for Authoritative Restrictive Actions

In an experimental study ( n = 213), we vary the incivility of a user comment below a mock news post on the Facebook page of a German news outlet. We investigate determinants of the users’ intention to engage in corrective actions and their support for restrictions by Facebook and law enforcement. We test an integrated model and find that incivility of a comment affects users’ support for restrictive and intention for corrective actions, whereas the presumed influence of the comment does not have an effect. The users’ commitment to free speech decreases and paternalism increases their support for authoritative restrictions.