Reviewerly/exHarmony huggingface dataset:
Lower Bounds for Leakage-Resilient Secret Sharing

Threshold secret sharing allows a dealer to split a secret into n shares such that any authorized subset of cardinality at least t of those shares efficiently reveals the secret, while at the same time any unauthorized subset of cardinality less than t contains no information about the secret. Leakage-resilience additionally requires that the secret remains hidden even if one is given a bounded amount of additional leakage from every share. In this work, we study leakage-resilient secret sharing schemes and prove a lower bound on the share size and the required amount of randomness of any information-theoretically secure scheme. We prove that for any information-theoretically secure leakage-resilient secret sharing scheme either the amount of randomness across all shares or the share size has to be linear in n. More concretely, for a secret sharing scheme with p-bit long shares, $$\ell $$ -bit leakage per share, where $$\widehat{t}$$ shares uniquely define the remaining $$n - \widehat{t}$$ shares, it has to hold that $$ p \ge \frac{\ell (n - t)}{\widehat{t}}\ . $$ We use this lower bound to gain further insights into a question that was recently posed by Benhamouda et al. (CRYPTO'18), who ask to what extend existing regular secret sharing schemes already provide protection against leakage. The authors proved that Shamir's secret sharing is 1-bit leakage-resilient for reconstruction thresholds $$t \ge 0.85n$$ and conjectured that it is also 1-bit leakage-resilient for any other threshold that is a constant fraction of the total number of shares. We do not disprove their conjecture, but show that it is the best one could possibly hope for. Concretely, we show that for large enough n and any constant $$0< c < 1$$ it holds that Shamir's secret sharing scheme is not leakage-resilient for . In contrast to the setting with information-theoretic security, we show that our lower bound does not hold in the computational setting. That is, we show how to construct a leakage-resilient secret sharing scheme in the random oracle model that is secure against computationally bounded adversaries and violates the lower bound stated above.
A Lattice-Based Public Key Encryption with Equality Test in Standard Model

Public key encryption with equality test (PKEET) allows testing whether two ciphertexts are generated by the same message or not. PKEET is a potential candidate for many practical applications like efficient data management on encrypted databases. Potential applicability of PKEET leads to intensive research from its first instantiation by Yang et al. (CT-RSA 2010). Most of the followup constructions are secure in the random oracle model. Moreover, the security of all the concrete constructions is based on number-theoretic hardness assumptions which are vulnerable in the post-quantum era. Recently, Lee et al. (ePrint 2016) proposed a generic construction of PKEET schemes in the standard model and hence it is possible to yield the first instantiation of PKEET schemes based on lattices. Their method is to use a 2-level hierarchical identity-based encryption (HIBE) scheme together with a one-time signature scheme. In this paper, we propose, for the first time, a direct construction of a PKEET scheme based on the hardness assumption of lattices in the standard model. More specifically, the security of the proposed scheme is reduces to the hardness of the Learning With Errors problem. We have used the idea of the full identity-based encryption scheme by Agrawal et al. (EUROCRYPT 2010) to construct the proposed PKEET.
SCOR: Source Code Retrieval with Semantics and Order

Word embeddings produced by the word2vec algorithm provide us with a strong mechanism to discover relationships between the words based on the degree to which they are contextually related to one another. In and of itself, algorithms like word2vec do not give us a mechanism to impose ordering constraints on the embedded word representations. Our main goal in this paper is to exploit the semantic word vectors obtained from word2vec in such a way that allows for the ordering constraints to be invoked on them when comparing a sequence of words in a query with a sequence of words in a file for source code retrieval. These ordering constraints employ the logic of Markov Random Fields (MRF), a framework used previously to enhance the precision of the source-code retrieval engines based on the Bag-of-Words (BoW) assumption. The work we present here demonstrates that by combining word2vec with the power of MRF, it is possible to achieve improvements between 6% and 30% in retrieval accuracy over the best results that can be obtained with the more traditional applications of MRF to representations based on term and term-term frequencies. The performance improvement was 30% for the Java AspectJ repository using only the titles of the bug reports provided by iBUGS, and 6% for the case of the Eclipse repository using titles as well as descriptions of the bug reports provided by BUGLinks.
Python Coding Style Compliance on Stack Overflow

Software developers all over the world use Stack Overflow (SO) to interact and exchange code snippets. Research also uses SO to harvest code snippets for use with recommendation systems. However, previous work has shown that code on SO may have quality issues, such as security or license problems. We analyse Python code on SO to determine its coding style compliance. From 1,962,535 code snippets tagged with 'python', we extracted 407,097 snippets of at least 6 statements of Python code. Surprisingly, 93.87% of the extracted snippets contain style violations, with an average of 0.7 violations per statement and a huge number of snippets with a considerably higher ratio. Researchers and developers should, therefore, be aware that code snippets on SO may not representative of good coding style. Furthermore, while user reputation seems to be unrelated to coding style compliance, for posts with vote scores in the range between -10 and 20, we found a strong correlation (r = -0.87, p <; 10^-7) between the vote score a post received and the average number of violations per statement for snippets in such posts.
A Chatbot for Conflict Detection and Resolution

We present Sayme, a chatbot that we are developing to address the detection and resolution of potential code conflicts that may arise in parallel software development. Sayme is designed to operate both proactively, informing developers when they engage in activities that create conflicting changes, and reactively, responding to user inquiries regarding the state of different developers' work and how it may overlap. We introduce our motivation for developing Sayme, present its design and features, and offer an outlook at our future work.
On the benefits of populations for the exploitation speed of standard steady-state genetic algorithms

It is generally accepted that populations are useful for the global exploration of multi-modal optimisation problems. Indeed, several theoretical results are available showing such advantages over single-trajectory search heuristics. In this paper we provide evidence that evolving populations via crossover and mutation may also benefit the optimisation time for hillclimbing unimodal functions. In particular, we prove bounds on the expected runtime of the standard (µ+1) GA for OneMax that are lower than its unary black box complexity and decrease in the leading constant with the population size up to [MATH HERE]. Our analysis suggests that the optimal mutation strategy is to flip two bits most of the time. To achieve the results we provide two interesting contributions to the theory of randomised search heuristics: 1) A novel application of drift analysis which compares absorption times of different Markov chains without defining an explicit potential function. 2) The inversion of fundamental matrices to calculate the absorption times of the Markov chains. The latter strategy was previously proposed in the literature but to the best of our knowledge this is the first time is has been used to show non-trivial bounds on expected runtimes.
Solving symbolic regression problems with formal constraints

In many applications of symbolic regression, domain knowledge constrains the space of admissible models by requiring them to have certain properties, like monotonicity, convexity, or symmetry. As only a handful of variants of genetic programming methods proposed to date can take such properties into account, we introduce a principled approach capable of synthesizing models that simultaneously match the provided training data (tests) and meet user-specified formal properties. To this end, we formalize the task of symbolic regression with formal constraints and present a range of formal properties that are common in practice. We also conduct a comparative experiment that confirms the feasibility of the proposed approach on a suite of realistic symbolic regression benchmarks extended with various formal properties. The study is summarized with discussion of results, properties of the method, and implications for symbolic regression.
Order-aware Embedding Neural Network for CTR Prediction

Product based models, which represent multi-field categorical data as embedding vectors of features, then model feature interactions in terms of vector product of shared embedding, have been extensively studied and have become one of the most popular techniques for CTR prediction. However, if the shared embedding is applied: (1) the angles of feature interactions of different orders may conflict with each other, (2) the gradients of feature interactions of high-orders may vanish, which result in learned feature interactions less effective. To solve these problems, we propose a novel technique named Order-aware Embedding (i.e., multi-embeddings are learned for each feature, and different embeddings are applied for feature interactions of different orders), which can be applied to various models and generates feature interactions more effectively. We further propose a novel order-aware embedding neural network (OENN) based on this embedding technique for CTR prediction. Extensive experiments on three publicly available datasets demonstrate the effectiveness of Order-aware Embedding and show that our OENN outperforms the state-of-the-art models.
Collaborating with Industrial Customers in a Capstone Project Course: The Customers' Perspective

Using capstone projects to teach software development in practice has become increasingly popular. To make the projects as realistic as possible, collaboration with industrial customers is preferable. However, we are lacking information on how this collaboration should take place in practice. Especially the customer's point of view on the collaboration is missing. In this paper, we first report how we have arranged the collaboration with the industrial customers in our yearly capstone project of around 120 students forming 7-9 person Scrum teams. Second, we performed 13 semi-structured interviews with the customer companies from the last instance of the course to collected their perspective. The main reasons for companies to participate in the course were: recruiting, getting the software developed and researching new technologies. A good project topic, according to our customer companies, has a clear vision, is suitably important for the customer company, is realistic with enough challenge, is motivating and illustrates the work at the company. Customers emphasized the importance of spending enough time with the teams and actively collaborating and communicating with them especially in the beginning, as the students might be too shy to ask enough questions. Typically, a customer representative used a few hours per week for the project and mostly collaborated with the team on the customer's premises. Overall, the companies were happy with the results: most accomplished their recruiting goals and all projects finished with a functioning product.
Metal-Bearing Nanoparticles Observed in Soils and Fault Gouges over the Shenjiayao Gold Deposit and Their Significance

Mineral deposits concealed by thick cover sequences present special problems for geochemical exploration. A variety of penetrating geochemical methods have been developed in the last few decades to explore for buried deposits. The theoretical basis of the mechanism by which metals migrate upward from buried deposits through the cover to the surface is still not fully understood. One hypothesis is that metal particles or metal elements could be carried onto bubbles or micro-flow of geogas and migrate upward to the surface. After years of study, nano-scale metal-bearing particles have been widely observed in geogas samples from different kinds of concealed deposits. However, the occurrence of these metal-bearing particles carried by geogases in near-surface media, such as soil, has not been studied in detail. In this study, metal-bearing nanoparticles were observed in samples from soils and fault gouges over the Shenjiayao gold deposit. The results indicate that (1) the ore-forming elements in soils can only come from deep-seated ore bodies and they occur in nanoparticles in the study area; (2) there is an obvious relationship between metal nanoparticles in fault gouges and soils; (3) the metallic nanoparticles in fault gouges represent a transitional phase along the whole vertical migration process. In addition, the observation results show that the metal-bearing nanoparticles tend to be adsorbed on the surface of clay minerals, which provide theoretical support for using fine fraction soils as sampling media to carry out geochemical exploration in sediment-covered terrains. Based on the results and discussion, a simple migration model was built in this paper.
Lightweight multi-language syntax transformation with parser parser combinators

Automatically transforming programs is hard, yet critical for automated program refactoring, rewriting, and repair. Multi-language syntax transformation is especially hard due to heterogeneous representations in syntax, parse trees, and abstract syntax trees (ASTs). Our insight is that the problem can be decomposed such that (1) a common grammar expresses the central context-free language (CFL) properties shared by many contemporary languages and (2) open extension points in the grammar allow customizing syntax (e.g., for balanced delimiters) and hooks in smaller parsers to handle language-specific syntax (e.g., for comments). Our key contribution operationalizes this decomposition using a Parser Parser combinator (PPC), a mechanism that generates parsers for matching syntactic fragments in source code by parsing declarative user-supplied templates. This allows our approach to detach from translating input programs to any particular abstract syntax tree representation, and lifts syntax rewriting to a modularly-defined parsing problem. A notable effect is that we skirt the complexity and burden of defining additional translation layers between concrete user input templates and an underlying abstract syntax representation. We demonstrate that these ideas admit efficient and declarative rewrite templates across 12 languages, and validate effectiveness of our approach by producing correct and desirable lightweight transformations on popular real-world projects (over 50 syntactic changes produced by our approach have been merged into 40+). Our declarative rewrite patterns require an order of magnitude less code compared to analog implementations in existing, language-specific tools.
Recommendation of Indian Cuisine Recipes Based on Ingredients

there are lots of varieties of Indian cuisine available with same ingredients. In India, Traditional cuisines consist of wide varieties due to locally available spices, herbs, vegetables, and fruits. In this paper, we purposed a method that recommends recipes of Indian cuisine on the basis of available ingredients and liked cuisine. For this work, we did web scraping to make a collection of recipes' varieties and after that apply the content-based approach of machine learning to recommend the recipes. This system gives the recommendation of Indian Cuisines based on ingredients.
Video Analytics for Visual Surveillance and Applications: An Overview and Survey

Owing to the massive amount of video data being generated as a result of high proliferation of surveillance cameras, the manpower to monitor such system is relatively expensive. Passively monitoring surveillance video however, incapacitates the usefulness of surveillance camera. Therefore, a drive to monitor events as they happen is expedient to fully harness the massive data generated by surveillance cameras. This is the main goal of video analytics. In this chapter, we extend the notion of surveillance. Surveillance refers not only to monitoring for security or safety purposes but encapsulates all aspects of monitoring to capture the dynamics of different application domains including retail, transportation, service industries and healthcare. This chapter presents a detailed survey of video analytics as well as its application. We present advances in video analytics research and emerging trends from subdomains such as behavior analysis, moving object classification, video summarization, object detection, object tracking, congestion analysis, abnormality detection and information fusion from multiple cameras. We also summarize recent development in video analytics and intelligent video systems (IVS). We evaluated the state-of-the-art approach to video analytics including deep learning approach and outlined research direction with emphasis on algorithm-based analytics and applications. Hardware-related issues are excluded from this chapter.
Analysis and modeling of wireless channel characteristics for Internet of Things scene based on geometric features

The power equipment storage scene is a typical short and limited scenario in communication. The geometric features and electric conductivity of the power equipment storage scene affect the radio frequency propagation characteristics is discussed. During our experiment, a dynamic wireless channel is proposed for power equipment storage scene based on geometric propagation and height loss factor model. Wireless communication system of power equipment storage scene which affected by the direction, refraction, diffraction, scattering is discussed. According to the different equipment storage situation, we discuss the path losses for scene 1 and scene 2 respectively. Moreover, a semi-actual simulation scene and a consistent test solution for different equipment are developed. At last, the operating frequency, antennas height, location of antennas, and length of vehicle engine hood affect on path loss is analyze. The path loss simulation of this paper fit well with the results of semi-actual and actual tests. The standard deviation between the actual measured value and the proposed model showed that the proposed model was more in line with the actual measurement results. Experimental and simulation results provide accurate theory and practice support for the power asset management, which make inventory process more efficient.
Event-Triggered Distributed Multitarget Tracking

This paper focuses on reducing communication bandwidth and, consequently, energy consumption in the context of distributed multitarget tracking over a peer-to-peer sensor network. A consensus cardinalized probability hypothesis density (CCPHD) filter with event-triggered communication is developed by enforcing each node to broadcast its local information to the neighbors only when it is worth to, i.e., the node has gained a sufficient amount of information with respect to its latest broadcasting. To this end, each sensor node separately evaluates the discrepancies of the cardinality probability mass function (PMF) and of the spatial probability density function (PDF) between the current local posterior and the one recoverable from neighbors after the latest transmission. Then, each sensor node selectively sends the specific information on the multitarget distribution (i.e., the cardinality PMF or the spatial PDF or both) that is considered to be worth transmitting (i.e., such that the respective discrepancy exceeds a preset threshold). Two types of discrepancy measures, i.e., the Kullback-Leibler divergence and the Cauchy-Schwarz divergence, are investigated. The performance of the proposed event-triggered CCPHD filter is evaluated through simulation experiments.
Galerkin method with new quadratic spline wavelets for integral and integro-differential equations

The paper is concerned with the wavelet-Galerkin method for the numerical solution of Fredholm linear integral equations and second-order integro-differential equations. We propose a construction of a quadratic spline-wavelet basis on the unit interval, such that the wavelets have three vanishing moments and the shortest support among such wavelets. We prove that this basis is a Riesz basis in the space L20,1. We adapt the basis to homogeneous Dirichlet boundary conditions, and using a tensor product we construct a wavelet basis on the hyperrectangle. We use the wavelet-Galerkin method with the constructed bases for solving integral and integro-differential equations, and we show that the matrices arising from discretization have uniformly bounded condition numbers and that they can be approximated by sparse matrices. We present numerical examples and compare the results with the Galerkin method using other quadratic spline wavelet bases and other methods.
Model-Based Resource Analysis and Synthesis of Service-Oriented Automotive Software Architectures

Automotive software architectures describe distributed functionality through an interplay of software components. One drawback of today's architectures is their strong integration into the onboard communication network based on predefined dependencies at design-time. To foster independence, theideaofservice-orientedarchitecture(SOA) providesasuitable prospect as network communication is established dynamically at run-time. Aim: We target to provide a model-based design methodology for analysing and synthesising hardware resources of automotive service-oriented architectures. Approach: For the approach, we apply the concepts of design space exploration and simulation to analyse and synthesise deployment configurations at an early stage of development. Result: We present an architecture candidate for an example function from the domain of automated driving. Based on corresponding simulation results, we gained insights about the feasibility to implement this candidate within our currently considered next E/E architecture generation. Conclusion:Theintroductionofservice-orientedarchitecturesstrictly requires early run-time assessments. In order to get there, the usage of models and model transformations depict reasonable ways by additionally accounting quality and development speed.