nils-herrmann/clean_scirepeval huggingface dataset:
Evaluating the security of logic encryption algorithms

Contemporary integrated circuits are designed and manufactured in a globalized environment leading to concerns of piracy, overproduction and counterfeiting. One class of techniques to combat these threats is logic encryption. Logic encryption modifies an IC design such that it operates correctly only when a set of newly introduced inputs, called key inputs, are set to the correct values. In this paper, we use algorithms based on satisfiability checking (SAT) to investigate the security of logic encryption. We present a SAT-based algorithm which allows an attacker to “decrypt” an encrypted netlist using a small number of carefully-selected input patterns and their corresponding output observations. We also present a “partial-break” algorithm that can reveal some of the key inputs even when the attack is not fully successful. We conduct a thorough evaluation of our attack by examining six proposals for logic encryption from the literature. We find that all of these are vulnerable to our attack. Among the 441 encrypted circuits we examined, we were able to decrypt 418 (95%). We discuss the strengths and limitations of our attack and suggest directions that may lead to improved logic encryption algorithms.
Compact Reconfigurable UWB Slot Antenna for Cognitive Radio Applications

A very compact reconfigurable ultrawideband (UWB) stepped-slot antenna for cognitive radio (CR) applications is presented in this letter. The proposed antenna operates in two modes: UWB mode for sensing application and communicating mode for different narrow/wideband communication applications. The UWB mode of antenna is obtained by the creation of three resonances that lie in UWB spectrum. The narrow/wide communicating bands are obtained by different switching states of five p-i-n diodes. The impedance bandwidth of the proposed antenna in UWB mode is 2.8−10.7 GHz. The communicating bands (3.2−4.5, 4.3−7.8, and 7.9−11.2 GHz) cover the entire UWB spectrum. The proposed antenna has the compact size of 21 × 9 mm2. A prototype of the designed antenna is fabricated and measured. The measured results are in good agreement with the simulated results that validate the proposed approach of reconfigurable UWB antenna design for CR applications.
The Creation and Analysis of a Website Privacy Policy Corpus

Website privacy policies are often ignored by Internet users, because these documents tend to be long and difficult to understand. However, the significance of privacy policies greatly exceeds the attention paid to them: these documents are binding legal agreements between website operators and their users, and their opaqueness is a challenge not only to Internet users but also to policy regulators. One proposed alternative to the status quo is to automate or semi-automate the extraction of salient details from privacy policy text, using a combination of crowdsourcing, natural language processing, and machine learning. However, there has been a relative dearth of datasets appropriate for identifying data practices in privacy policies. To remedy this problem, we introduce a corpus of 115 privacy policies (267K words) with manual annotations for 23K fine-grained data practices. We describe the process of using skilled annotators and a purpose-built annotation tool to produce the data. We provide findings based on a census of the annotations and show results toward automating the annotation procedure. Finally, we describe challenges and opportunities for the research community to use this corpus to advance research in both privacy and language technologies.
An Isolated Bidirectional DC-DC Soft Switching Converter for Super Capacitor Based Energy Storage Systems

In recent years, power electronic energy storage systems using super capacitor bank have been widely studied and developed for the electronic vehicles. In this paper, a full-bridge/centertapped push-pull circuit with active clamp-based soft switching bidirectional DC-DC converter and its control method are presented and discussed. From the results of basic experimental demonstration, the proposed system is able to perform adequate charging and discharging operation between low- voltage high-current super capacitor side and high-voltage low-current side with drive train and main battery. In addition, RCDi snubber losses appeared in the basic circuit topology are drastically reduced by ZCS/ZVS operation with the assistance of newly added active clump circuit, as well as ZVS operation with lossless snubber capacitor in high-voltage primary side.
Learning Model Predictive Control for Iterative Tasks. A Data-Driven Control Framework

A learning model predictive controller for iterative tasks is presented. The controller is reference-free and is able to improve its performance by learning from previous iterations. A safe set and a terminal cost function are used in order to guarantee recursive feasibility and nondecreasing performance at each iteration. This paper presents the control design approach, and shows how to recursively construct terminal set and terminal cost from state and input trajectories of previous iterations. Simulation results show the effectiveness of the proposed control logic.
Classification of Rice Leaf Diseases Based on Morphological Changes

is widely cultivated economical crop in the world. During cultivation the earliest and accurate diagnosis of the rice plant diseases able to reduce the damage, resulting environment protection and better return. In the work, an automated system has been developed to classify the leaf brown spot and the leaf blast diseases of rice plant based on the morphological changes of the plants caused by the diseases. Radial distribution of the hue from the center to the boundary of the spot images has been used as features to classify the diseases by Bayes' and SVM Classifier. The system has been validated using 1000 test spot images of infected rice leaves collected from the field, gives 79.5% and 68.1% accuracies for Bayes' and SVM Classifier based system respectively.
Robust Unsupervised Feature Selection

A new unsupervised feature selection method, i.e., Robust Unsupervised Feature Selection (RUFS), is proposed. Unlike traditional unsupervised feature selection methods, pseudo cluster labels are learned via local learning regularized robust nonnegative matrix factorization. During the label learning process, feature selection is performed simultaneously by robust joint l2,1 norms minimization. Since RUFS utilizes l2,1 norm minimization on processes of both label learning and feature learning, outliers and noise could be effectively handled and redundant or noisy features could be effectively reduced. Our method adopts the advantages of robust nonnegative matrix factorization, local learning, and robust feature learning. In order to make RUFS be scalable, we design a (projected) limited-memory BFGS based iterative algorithm to efficiently solve the optimization problem of RUFS in terms of both memory consumption and computation complexity. Experimental results on different benchmark real world datasets show the promising performance of RUFS over the state-of-the-arts.
Marketplace and technology standards for B2B e-commerce: progress, challenges, and the state of the art

We have examined standards required for successful e-commerce (EC) architectures and evaluated the strengths and limitations of current systems that have been developed to support EC. We find that there is an unfilled need for systems that can reliably locate buyers and sellers in electronic marketplaces and also facilitate automated transactions. The notion of a ubiquitous network where loosely coupled buyers and sellers can reliably find each other in real time, evaluate products, negotiate prices, and conduct transactions is not adequately supported by current systems. These findings were based on an analysis of mainline EC architectures: EDI, company Websites, B2B hubs, e-Procurement systems, and Web Services. Limitations of each architecture were identified. Particular attention was given to the strengths and weaknesses of the Web Services architecture, since it may overcome some limitations of the other approaches.
The role of information in a community of hobbyist collectors

This article marries the study of serious leisure pursuits with library and information science’s (LIS) interest in people’s everyday use, need, seeking, and sharing of information. Using a qualitative approach, the role of information as a phenomenon was examined in relation to the leisure activity of hobbyist collecting. In the process, a model and a typology for these collectors were developed. We find that the information needs and information seeking of hobbyist collectors is best represented as an interrelationship between information and object needs, information sources, and interactions between collectors and their publics. Our model of the role of information in a particular domain of hobbyist collecting moves away from the idea of one individual seeking information from formal systems and shifts towards a model that takes seriously the social milieu of a community. This collecting community represents a layer of a social system with complex interactions and specialized information needs that vary across collector types. Only the serious collectors habitually engage in information seeking and, occasionally, in information dissemination, in the traditional sense, yet information flows through the community and serves as a critical resource for sustaining individual and communal collecting activities.
Research on a Skin Color Detection Algorithm Based on Self-adaptive Skin Color Model

Skin color detection is an important subject in computer vision research. Color segmentation takes a great attention because color is an effective and robust visual cue for characterizing an object from the others. To aim at existing skin color algorithms considering the luminance information not enough, a reliable color modeling approach was proposed. It is based on the fact that color distribution of a single-colored object is not invariant with respect to luminance variations even in the Cb-Cr plane and does not ignore the influence on luminance Y component in YCbCr color space. Firstly, according to statistics of skin color pixels, we take the luminance Y by ascending order, divide the total range of Y into finite number of intervals, collect pixels whose luminance belongs to the same luminance interval, calculate the covariance and the mean value of Cb and Cr with respect to Y, and use the above data to train the BP neural network, then we get the self-adaptive skin color model and design a Gaussian model classifier. The experimental results have indicated that this algorithm can effectively fulfill the skin-color detection for images captured under different environmental condition and the performance of the skin color segmentation is significantly improved.
Quantum Computation and Decision Trees

Many interesting computational problems can be reformulated in terms of decision trees. A natural classical algorithm is to then run a random walk on the tree, starting at the root, to see if the tree contains a node n levels from the root. We devise a quantum mechanical algorithm that evolves a state, initially localized at the root, through the tree. We prove that if the classical strategy succeeds in reaching level n in time polynomial in n, then so does the quantum algorithm. Moreover, we find examples of trees for which the classical algorithm requires time exponential in n, but for which the quantum algorithm succeeds in polynomial time. The examples we have so far, however, could also be solved in polynomial time by different classical algorithms. MIT-CTP-2651, quant-ph/9706062 June 1997
A Color Vision-Based Lane Tracking System for Autonomous Driving on Unmarked Roads

This work describes a color Vision-based System intended to perform stable autonomous driving on unmarked roads. Accordingly, this implies the development of an accurate road surface detection system that ensures vehicle stability. Although this topic has already been documented in the technical literature by different research groups, the vast majority of the already existing Intelligent Transportation Systems are devoted to assisted driving of vehicles on marked extra urban roads and highways. The complete system was tested on the BABIECA prototype vehicle, which was autonomously driven for hundred of kilometers accomplishing different navigation missions on a private circuit that emulates an urban quarter. During the tests, the navigation system demonstrated its robustness with regard to shadows, road texture, and weather and changing illumination conditions.
Paraphrasing Revisited with Neural Machine Translation

Recognizing and generating paraphrases is an important component in many natural language processing applications. A wellestablished technique for automatically extracting paraphrases leverages bilingual corpora to find meaning-equivalent phrases in a single language by “pivoting” over a shared translation in another language. In this paper we revisit bilingual pivoting in the context of neural machine translation and present a paraphrasing model based purely on neural networks. Our model represents paraphrases in a continuous space, estimates the degree of semantic relatedness between text segments of arbitrary length, or generates candidate paraphrases for any source input. Experimental results across tasks and datasets show that neural paraphrases outperform those obtained with conventional phrase-based pivoting approaches.
Automated Detection of Threat Objects Using Adapted Implicit Shape Model

Baggage inspection using X-ray screening is a priority task that reduces the risk of crime and terrorist attacks. Manual detection of threat items is tedious because very few bags actually contain threat items and the process requires a high degree of concentration. An automated solution would be a welcome development in this field. We propose a methodology for automatic detection of threat objects using single X-ray images. Our approach is an adaptation of a methodology originally created for recognizing objects in photographs based on implicit shape models. Our detection method uses a visual vocabulary and an occurrence structure generated from a training dataset that contains representative X-ray images of the threat object to be detected. Our method can be applied to single views of grayscale X-ray images obtained using a single energy acquisition system. We tested the effectiveness of our method for the detection of three different threat objects: 1) razor blades; 2) shuriken (ninja stars); and 3) handguns. The testing dataset for each threat object consisted of 200 X-ray images of bags. The true positive and false positive rates (TPR and FPR) are: (0.99 and 0.02) for razor blades, (0.97 and 0.06) for shuriken, and (0.89 and 0.18) for handguns. If other representative training datasets were utilized, we believe that our methodology could aid in the detection of other kinds of threat objects.
Zika virus: History, emergence, biology, and prospects for control.

Zika virus (ZIKV), a previously obscure flavivirus closely related to dengue, West Nile, Japanese encephalitis and yellow fever viruses, has emerged explosively since 2007 to cause a series of epidemics in Micronesia, the South Pacific, and most recently the Americas. After its putative evolution in sub-Saharan Africa, ZIKV spread in the distant past to Asia and has probably emerged on multiple occasions into urban transmission cycles involving Aedes (Stegomyia) spp. mosquitoes and human amplification hosts, accompanied by a relatively mild dengue-like illness. The unprecedented numbers of people infected during recent outbreaks in the South Pacific and the Americas may have resulted in enough ZIKV infections to notice relatively rare congenital microcephaly and Guillain-Barré syndromes. Another hypothesis is that phenotypic changes in Asian lineage ZIKV strains led to these disease outcomes. Here, we review potential strategies to control the ongoing outbreak through vector-centric approaches as well as the prospects for the development of vaccines and therapeutics.
Scheduling Multiprocessor Tasks with Genetic Algorithms

In this paper, an efficient method based on genetic algorithms is developed to solve the multiprocessor scheduling problem. To efficiently execute programs in parallel on multiprocessor scheduling problem must be solved to determine the assignment of tasks to the processors and the execution order of the tasks so that the execution time is minimized. Even when the target processors is fully connected and no communication delay is considered among tasks in the task graph the scheduling problem is NP-complete. Complexity of scheduling problems dependent of number of processors (P), task processing time Ti and precedence constraints. This problem has been known as strong NP-hard intractable optimisation problem when it assumes arbitrary number of processors, arbitrary task processing time and arbitrary precedence constraints. We assumed fixed number of processors and tasks are represented by a directed acyclic graph (DAG) called “task graph”.
A vision of dental education in the third millennium

This paper is the last in a series of 16 which have explored current uses of information communications technology (ICT) in all areas of dentistry in general, and in dental education in particular. In this paper the authors explore current developments, referring back to the previous 15 papers, and speculate on how ICT should increasingly contribute to dental education in the future. After describing a vision of dental education in the next 50 years, the paper considers how ICT can help to fulfil the vision. It then takes a brief look at three aspects of the use of ICT in the world in general and speculates how dentistry can learn from other areas of human endeavour. Barriers to the use of ICT in dental education are then discussed. The final section of the paper outlines new developments in haptics, immersive environments, the semantic web, the IVIDENT project, nanotechnology and ergonometrics. The paper concludes that ICT will offer great opportunities to dental education but questions whether or not human limitations will allow it to be used to maximum effect.
A solar-powered hand-launchable UAV for low-altitude multi-day continuous flight

This paper presents the conceptual design, detailed development and flight testing of AtlantikSolar, a 5.6m-wingspan solar-powered Low-Altitude Long-Endurance (LALE) Unmanned Aerial Vehicle (UAV) designed and built at ETH Zurich. The UAV is required to provide perpetual endurance at a geographic latitude of 45°N in a 4-month window centered around June 21st. An improved conceptual design method is presented and applied to maximize the perpetual flight robustness with respect to local meteorological disturbances such as clouds or winds. Airframe, avionics hardware, state estimation and control method development for autonomous flight operations are described. Flight test results include a 12-hour flight relying solely on batteries to replicate night-flight conditions. In addition, we present flight results from Search-And-Rescue field trials where a camera and processing pod were mounted on the aircraft to create high-fidelity 3D-maps of a simulated disaster area.
Ontology extraction from relational database: Concept hierarchy as background knowledge

0950-7051/$ see front matter 2010 Elsevier B.V. A doi:10.1016/j.knosys.2010.11.003 ⇑ Corresponding author. . E-mail address: herezadi@yahoo.com (H.A. Santos Relational Database (RDB) has been widely used as the back-end database of information system. Contains a wealth of high-quality information, RDB provides conceptual model and metadata needed in the ontology construction. However, most of the existing ontology building approaches convert RDB schema without considering the knowledge resided in the database. This paper proposed the approach for ontology extraction on top of RDB by incorporating concept hierarchy as background knowledge. Incorporating the background knowledge in the building process of Web Ontology Language (OWL) ontology gives two main advantages: (1) accelerate the building process, thereby minimizing the conversion cost; (2) background knowledge guides the extraction of knowledge resided in database. The experimental simulation using a gold standard shows that the Taxonomic F-measure (TF) evaluation reaches 90% while Relation Overlap (RO) is 83.33%. In term of processing time, this approach is more efficient than the current approaches. In addition, our approach can be applied in any of the fields such as eGoverment, eCommerce and so on.
SAMR: A Self-adaptive MapReduce Scheduling Algorithm in Heterogeneous Environment

Hadoop is seriously limited by its MapReduce scheduler which does not scale well in heterogeneous environment. Heterogenous environment is characterized by various devices which vary greatly with respect to the capacities of computation and communication, architectures, memorizes and power. As an important extension of Hadoop, LATE MapReduce scheduling algorithm takes heterogeneous environment into consideration. However, it falls short of solving the crucial problem – poor performance due to the static manner in which it computes progress of tasks. Consequently, neither Hadoop nor LATE schedulers are desirable in heterogeneous environment. To this end, we propose SAMR: a Self-Adaptive MapReduce scheduling algorithm, which calculates progress of tasks dynamically and adapts to the continuously varying environment automatically. When a job is committed, SAMR splits the job into lots of fine-grained map and reduce tasks, then assigns them to a series of nodes. Meanwhile, it reads historical information which stored on every node and updated after every execution. Then, SAMR adjusts time weight of each stage of map and reduce tasks according to the historical information respectively. Thus, it gets the progress of each task accurately and finds which tasks need backup tasks. What’s more, it identifies slow nodes and classifies them into the sets of slow nodes dynamically. According to the information of these slow nodes, SAMR will not launch backup tasks on them, ensuring the backup tasks will not be slow tasks any more. It gets the final results of the fine-grained tasks when either slow tasks or backup tasks finish first. The proposed algorithm is evaluated by extensive experiments over various heterogeneous environment. Experimental results show that SAMR significantly decreases the time of execution up to 25% compared with Hadoop’s scheduler and up to 14% compared with LATE scheduler.