nils-herrmann/clean_scirepeval huggingface dataset:
Highly-Scalable Searchable Symmetric Encryption with Support for Boolean Queries

This work presents the design, analysis and implementation of the first searchable symmetric encryption (SSE) protocol that supports conjunctive search and general Boolean queries on outsourced symmetrically-encrypted data and that scales to very large databases and arbitrarilystructured data including free text search. To date, work in this area has focused mainly on single-keyword search. For the case of conjunctive search, prior dedivated SSE constructions (not using generic technique such as FHE or ORAM) required work linear in the total number of documents in the database and provided good privacy only for structured attribute-value data, rendering these solutions too slow and inflexible for large practical databases. In contrast, our solution provides a realistic and practical trade-off between performance and privacy by efficiently supporting very large databases at the cost of moderate and welldefined leakage to the outsourced server (leakage is in the form of data access patterns, never as direct exposure of plaintext data or searched values). Our design follows a careful process of trading security for efficiency which are both quantified via rigorous analysis. We present a detailed formal cryptographic analysis of the privacy and security of our protocols and establish precise upper bounds on the allowed leakage. To demonstrate the real-world practicality of our approach, we provide performance results of a prototype applied to several large representative data sets, including encrypted search over the whole English Wikipedia (and beyond).   stasio@ics.uci.edu. IBM Research.  csjutla@us.ibm.com IBM Research.  hugo@ee.technion.ac.il IBM Research.  marcelrosu@gmail.com IBM Research.
Optimization of sensor deployment in WSN for precision irrigation using spatial arrangement of permanent crop

Precision irrigation is the best practice for efficient water management. Wireless Sensor Networks (WSN) are widely used in environment monitoring especially in the precision irrigation. One of the fundamental issues of WSN is optimum senor deployment. Type of radio communication, number, and location of sensor deployment will have direct impact on the coverage, connectivity, cost and life of the sensors in WSN. This paper explores the problem of optimal WSN deployment by considering the unique way of spatial arrangement of the permanent crop during transplantation. Instead of conventional horizontal or linear row farming we propose the hexagonal pattern farming for the suitable permanent crops. This modified spatial arrangement will increase the coverage of the WSN without losing the specification of growing conditions. There is an improvement in total number of trees covered by 13 percentage in this method. Mathematical model is proposed, which justify the increase in coverage by using this modified spatial arrangement.
WordNet : an electronic lexical database

WordNet is perhaps the most important and widely used lexical resource for natural language processing systems up to now.  An Electronic Lexical Database, edited by Christiane Fellbaum, discusses the design of WordNet from both theoretical and historical perspectives, provides an up-to-date description of the lexical database, and presents a set of applications of WordNet. The book contains a foreword by George Miller, an introduction by Christiane Fellbaum, seven chapters from the Cognitive Sciences Laboratory of Princeton University, where WordNet was produced, and nine chapters contributed by scientists from elsewhere. Miller's foreword offers a fascinating account of the history of WordNet. He discusses the presuppositions of such a lexical database, how the top-level noun categories were determined, and the sources of the words in WordNet. He also writes about the evolution of WordNet from its original incarnation as a dictionary browser to a broad-coverage lexicon, and the involvement of different people during its various stages of development over a decade. It makes very interesting reading for casual and serious users of WordNet and anyone who is grateful for the existence of WordNet. The book is organized in three parts. Part I is about WordNet itself and consists of four chapters: "Nouns in WordNet" by George Miller, "Modifiers in WordNet" by Katherine Miller, "A semantic network of English verbs" by Christiane Fellbaum, and "Design and implementation of the WordNet lexical database and search software" by Randee Tengi. These chapters are essentially updated versions of four papers from Miller (1990). Compared with the earlier papers, the chapters in this book focus more on the underlying assumptions and rationales behind the design decisions. The description of the information contained in WordNet, however, is not as detailed as in Miller (1990). The main new additions in these chapters include an explanation of sense grouping in George Miller's chapter, a section about adverbs in Katherine Miller's chapter, observations about autohyponymy (one sense of a word being a hyponym of another sense of the same word) and autoantonymy (one sense of a word being an antonym of another sense of the same word) in Fellbaum's chapter, and Tengi's description of the Grinder, a program that converts the files the lexicographers work with to searchable lexical databases. The three papers in Part II are characterized as "extensions, enhancements and
Robust and false positive free watermarking in IWT domain using SVD and ABC

Watermarking is used to protect the copyrighted materials from being misused and help us to know the lawful ownership. The security of any watermarking scheme is always a prime concern for the developer. In this work, the robustness and security issue of IWT (integer wavelet transform) and SVD (singular value decomposition) based watermarking is explored. Generally, SVD based watermarking techniques suffer with an issue of false positive problem. This leads to even authenticating the wrong owner. We are proposing a novel solution to this false positive problem; that arises in SVD based approach. Firstly, IWT is employed on the host image and then SVD is performed on this transformed host. The properties of IWT and SVD help in achieving high value of robustness. Singular values are used for the watermark embedding. In order to further improve the quality of watermarking, the optimization of scaling factor (mixing ratio) is performed with the help of artificial bee colony (ABC) algorithm. A comparison with other schemes is performed to show the superiority of proposed scheme. & 2015
Structural Balance Theory-Based E-Commerce Recommendation over Big Rating Data

Recommending appropriate product items to the target user is becoming the key to ensure continuous success of E-commerce. Today, many E-commerce systems adopt various recommendation techniques, e.g., Collaborative Filtering (abbreviated as CF)-based technique, to realize product item recommendation. Overall, the present CF recommendation can perform very well, if the target user owns similar friends (user-based CF), or the product items purchased and preferred by target user own one or more similar product items (item-based CF). While due to the sparsity of big rating data in E-commerce, similar friends and similar product items may be both absent from the user-product purchase network, which lead to a big challenge to recommend appropriate product items to the target user. Considering the challenge, we put forward a Structural Balance Theory-based Recommendation (i.e., SBT-Rec) approach. In the concrete, (I) user-based recommendation: we look for target user's “enemy” (i.e., the users having opposite preference with target user); afterwards, we determine target user's “possible friends”, according to “enemy's enemy is a friend” rule of Structural Balance Theory, and recommend the product items preferred by “possible friends” of target user to the target user. (II) likewise, for the product items purchased and preferred by target user, we determine their “possibly similar product items” based on Structural Balance Theory and recommend them to the target user. At last, the feasibility of SBT-Rec is validated, through a set of experiments deployed on MovieLens-1M dataset.
Video Anomaly Detection and Localization via Gaussian Mixture Fully Convolutional Variational Autoencoder

 We present a novel end-to-end partially supervised deep learning approach for video anomaly detection and localization using only normal samples. The insight that motivates this study is that the normal samples can be associated with at least one Gaussian component of a Gaussian Mixture Model (GMM), while anomalies either do not belong to any Gaussian component. The method is based on Gaussian Mixture Variational Autoencoder, which can learn feature representations of the normal samples as a Gaussian Mixture Model trained using deep learning. A Fully Convolutional Network (FCN) that does not contain a fully-connected layer is employed for the encoder-decoder structure to preserve relative spatial coordinates between the input image and the output feature map. Based on the joint probabilities of each of the Gaussian mixture components, we introduce a sample energy based method to score the anomaly of image test patches. A two-stream network framework is employed to combine the appearance and motion anomalies, using RGB frames for the former and dynamic flow images, for the latter. We test our approach on two popular benchmarks (UCSD Dataset and Avenue Dataset). The experimental results verify the superiority of our method compared to the state of the arts.
Humanoid Cognitive Robots That Learn by Imitating: Implications for Consciousness Studies

While the concept of a conscious machine is intriguing, producing such a machine remains controversial and challenging. Here, we describe how our work on creating a humanoid cognitive robot that learns to perform tasks via imitation learning relates to this issue. Our discussion is divided into three parts. First, we summarize our previous framework for advancing the understanding of the nature of phenomenal consciousness. This framework is based on identifying computational correlates of consciousness. Second, we describe a cognitive robotic system that we recently developed that learns to perform tasks by imitating human-provided demonstrations. This humanoid robot uses cause–effect reasoning to infer a demonstrator’s intentions in performing a task, rather than just imitating the observed actions verbatim. In particular, its cognitive components center on top-down control of a working memory that retains the explanatory interpretations that the robot constructs during learning. Finally, we describe our ongoing work that is focused on converting our robot’s imitation learning cognitive system into purely neurocomputational form, including both its low-level cognitive neuromotor components, its use of working memory, and its causal reasoning mechanisms. Based on our initial results, we argue that the top-down cognitive control of working memory, and in particular its gating mechanisms, is an important potential computational correlate of consciousness in humanoid robots. We conclude that developing high-level neurocognitive control systems for cognitive robots and using them to search for computational correlates of consciousness provides an important approach to advancing our understanding of consciousness, and that it provides a credible and achievable route to ultimately developing a phenomenally conscious machine.
Mobile Device Administration for Secure and Manageable Health Data Collection in Under-Resourced Areas

Healthcare delivery in under-resourced areas is critical to the future economic health of these countries, and healthcare data collection is an essential part of managing this delivery. The Medea system has been developed to support data collection on mobile devices, while addressing issues of manageability and security in healthcare data collection and delivery. The Medea approach is based on a compiler (Aeetes) that generates new data collection apps from declarative specifications, a security architecture to protect the confidentiality of patient data in case of device compromise, and a supporting app infrastructure that supports deployment in settings without local server support. Manageability is achieved by centralizing the management of the infrastructure, using remote management of mobile devices to provide IT support to deployments in relatively isolated and under-served locations.
Distributed heterogeneous event processing: enhancing scalability and interoperability of CEP in an industrial context

Although a significant amount of research has investigated the benefits of distributed CEP in terms of scalability and extensibility, there is an ongoing reluctance in deploying distributed CEP in an industrial context. In this paper we present the DHEP system developed together with the IBM® laboratory in Böblingen. It addresses some of the key problems in increasing the acceptance of distributed CEP, for example supporting interoperability between heterogeneous event processing systems. We present the concepts behind the DHEP system and show how those concepts help to achieve scalable and extensible event processing in an industrial context. Moreover, we verify in an evaluation study that the additional cost imposed by the DHEP system is moderate and 'affordable' for the benefits provided.
Customer event history for churn prediction: How long is long enough?

The key question of this study is: How long should the length of customer event history be for customer churn prediction? While most studies in predictive churn modeling aim to improve models by data augmentation or algorithm improvement, this study focuses on a another dimension: time window optimization with respect to predictive performance. This paper first presents a formalization of the time window selection strategy, along with a literature review. Next, using logistic regression, classification trees and bagging in combination with classification trees, this study analyzes the improvement in churn-model performance by extending customer event history from 1 to 16 years. The results show that, after the 5th additional year, predictive performance is only marginally increased, meaning that the company in this study can discard 69% of its data with almost no decrease in predictive performance. The practical implication is that analysts can substantially decrease datarelated burdens, such as data storage, preparation and analysis. This is particularly valuable in times of big data where decreasing computational complexity is paramount.
Covariance matrix-based fire and flame detection method in video

This paper proposes a video-based fire detection system which uses color, spatial and temporal information. The system divides the video into spatio-temporal blocks and uses covariance-based features extracted from these blocks to detect fire. Feature vectors take advantage of both the spatial and the temporal characteristics of flame-colored regions. The extracted features are trained and tested using a support vector machine (SVM) classifier. The system does not use a background subtraction method to segment moving regions and can be used, to some extent, with non-stationary cameras. The computationally efficient method can process 320 × 240 video frames at around 20 frames per second in an ordinary PC with a dual core 2.2 GHz processor. In addition, it is shown to outperform a previous method in terms of detection performance.
Detection of concealed cars in complex cargo X-ray imagery using deep learning

Non-intrusive inspection systems based on X-ray radiography techniques are routinely used at transport hubs to ensure the conformity of cargo content with the supplied shipping manifest. As trade volumes increase and regulations become more stringent, manual inspection by trained operators is less and less viable due to low throughput. Machine vision techniques can assist operators in their task by automating parts of the inspection workflow. Since cars are routinely involved in trafficking, export fraud, and tax evasion schemes, they represent an attractive target for automated detection and flagging for subsequent inspection by operators.



Development and evaluation of a novel method for the automated detection of cars in complex X-ray cargo imagery.



X-ray cargo images from a stream-of-commerce dataset were classified using a window-based scheme. The limited number of car images was addressed by using an oversampling scheme. Different Convolutional Neural Network (CNN) architectures were compared with well-established bag of words approaches. In addition, robustness to concealment was evaluated by projection of objects into car images.



CNN approaches outperformed all other methods evaluated, achieving 100% car image classification rate for a false positive rate of 1-in-454. Cars that were partially or completely obscured by other goods, a modus operandi frequently adopted by criminals, were correctly detected.



We believe that this level of performance suggests that the method is suitable for deployment in the field. It is expected that the generic object detection workflow described can be extended to other object classes given the availability of suitable training data.
Towards SVC-based Adaptive Streaming in information centric networks

HTTP Adaptive Streaming (HAS) is becoming the de-facto standard for video streaming services. In HAS, each video is segmented and stored in different qualities. The client can dynamically select the most appropriate quality level to download, allowing it to adapt to varying network conditions. As the Internet was not designed to deliver such applications, optimal support for multimedia delivery is still missing. Information Centric Networking (ICN) is a recently proposed disruptive architecture that could solve this issue, where the focus is given to the content rather than to end-to-end connectivity. Due to the bandwidth unpredictability typical of ICN, standard AVC-based HAS performs quality selection sub-optimally, thus leading to a poor Quality of Experience (QoE). In this article, we propose to overcome this inefficiency by using Scalable Video Coding (SVC) instead. We individuate the main advantages of SVC-based HAS over ICN and outline, both theoretically and via simulation, the research challenges to be addressed to optimize the delivered QoE.
Taming Google-Scale Continuous Testing

Growth in Google's code size and feature churn rate has seen increased reliance on continuous integration (CI) and testing to maintain quality. Even with enormous resources dedicated to testing, we are unable to regression test each code change individually, resulting in increased lag time between code check-ins and test result feedback to developers. We report results of a project that aims to reduce this time by: (1) controlling test workload without compromising quality, and (2) distilling test results data to inform developers, while they write code, of the impact of their latest changes on quality. We model, empirically understand, and leverage the correlations that exist between our code, test cases, developers, programming languages, and code-change and test-execution frequencies, to improve our CI and development processes. Our findings show: very few of our tests ever fail, but those that do are generally "closer" to the code they test, certain frequently modified code and certain users/tools cause more breakages, and code recently modified by multiple developers (more than 3) breaks more often.
Automatic navigation and landing of an indoor AR. drone quadrotor using ArUco marker and inertial sensors

Within the next few years, unmanned quadrotors are likely to become an important vehicle in humans' daily life. However, their automatic navigation and landing in indoor environments are among the commonly discussed topics in this regard. In fact, the quadrotor should be able to automatically find the landing point from the nearby position, navigate toward it, and finally, land on it accurately and smoothly. In this paper, we proposed a low-cost and thorough solution to this problem by using both bottom-facing and front-facing cameras of the drone. In addition, in the case that vision data were unavailable, inertial measurements alongside a Kalman filter were used to navigate the drone to achieve the promising continuity and reliability. An AR.Drone 2.0 quadrotor, as well as an ArUco marker, were employed in order to test the proposed method experimentally. The results indicated that the drone successfully landed on the predefined position with an acceptable time and accuracy.
Database System Implementation

CS245 course then covers implementation of a DBMS, notably storage structures , query processing, and transaction management. We're on a quarter system at Stanford, so the principal course using this book — CS245 — is only ten weeks long. In the Winter of 1999, Hector Garcia-Molina used a "beta" version of this book, and covered the following parts: Sections 2. The balance of Chapters 6 and 7 (query optimization) is covered in an advanced course, CS346, where students implement their own DBMS. Other portions of the book that are not covered in CS245 may appear in another advanced course, CS347, which talks about distributed databases and advanced transaction processing. Schools that are on the semester system have the opportunity to combine the use of this book with its predecessor: A First Course in Database Systems. We recommend using that book in the first semester, coupled with a database-application programming project. The second semester could cover most or all of the content of this book. An advantage to splitting the study of databases into two courses is that students not planning to specialize in DBMS construction can take only the first course and be able to use databases in whatever branch of Computer Science they enter. Prerequisites The course on which the book is based is rarely taken before the senior year, so we expect the reader to have a fairly broad background in the traditional areas iii iv of Computer Science. We assume that the reader has learned something about database programming, especially SQL. It is helpful to know about relational algebra and to have some familiarity with basic data structures. Likewise, some knowledge of file systems and operating systems is useful. Exercises The book contains extensive exercises, with some for almost every section. We indicate harder exercises or parts of exercises with an exclamation point. The hardest exercises have a double exclamation point. Some of the exercises or parts are marked with a star. For these exercises, we shall endeavor to maintain solutions accessible through the book's Web page. These solutions are publicly available and should be used for self-testing. Note that in a few cases, one exercise B asks for modification or adaptation of your solution to another exercise A. If certain parts of A have Web-published solutions, then you should expect the corresponding parts of B to have solutions as well. Here you will find solutions to …