nils-herrmann/clean_scirepeval huggingface dataset:
Effective two-dimensional pattern generation for self-aligned double patterning

In nano-scale systems, design for manufacturability is essentially required. For sub 20 nm technology node, Self-Aligned Double Patterning (SADP) is an important manufacturing technique, and complex two-dimensional patterns are requested to be fabricated by SADP. However all two-dimensional patterns cannot be fabricated by SADP. In this paper, two-dimensional patterns that satisfy connection requirements as well as manufacturability by SADP are effectively derived by our proposed approach in which partially pre-colored two-color base grid is used.
Video-based 3D reconstruction, laparoscope localization and deformation recovery for abdominal minimally invasive surgery: a survey.

The intra-operative three-dimensional (3D) structure of tissue organs and laparoscope motion are the basis for many tasks in computer-assisted surgery (CAS), such as safe surgical navigation and registration of pre-operative and intra-operative data for soft tissues.



This article provides a literature review on laparoscopic video-based intra-operative techniques of 3D surface reconstruction, laparoscope localization and tissue deformation recovery for abdominal minimally invasive surgery (MIS).



This article introduces a classification scheme based on the motions of a laparoscope and the motions of tissues. In each category, comprehensive discussion is provided on the evolution of both classic and state-of-the-art methods.



Video-based approaches have many advantages, such as providing intra-operative information without introducing extra hardware to the current surgical platform. However, an extensive discussion on this important topic is still lacking. This survey paper is therefore beneficial for researchers in this field.
A Three-Step Vehicle Detection Framework for Range Estimation Using a Single Camera

This paper proposes and validates a real-time on-road vehicle detection system, which uses a single camera for the purpose of intelligent driver assistance. A three-step vehicle detection framework is presented to detect and track the target vehicle within an image. In the first step, probable vehicle locations are hypothesized using pattern recognition. The vehicle candidates are then verified in the hypothesis verification step. In this step, lane detection is used to filter vehicle candidates that are not within the lane region of interest. In the final step tracking and online learning are implemented to optimize the detection algorithm during misdetection and temporary occlusion. Good detection performance and accuracy was observed in highway driving environments with minimal shadows.
Automatically Inferring Malware Signatures for Anti-Virus Assisted Attacks

Although anti-virus software has significantly evolved over the last decade, classic signature matching based on byte patterns is still a prevalent concept for identifying security threats. Anti-virus signatures are a simple and fast detection mechanism that can complement more sophisticated analysis strategies. However, if signatures are not designed with care, they can turn from a defensive mechanism into an instrument of attack. In this paper, we present a novel method for automatically deriving signatures from anti-virus software and discuss how the extracted signatures can be used to attack sensible data with the aid of the virus scanner itself. To this end, we study the practicability of our approach using four commercial products and exemplary demonstrate anti-virus assisted attacks in three different scenarios.
Evolutionary Computation Meets Machine Learning: A Survey

Evolutionary computation (EC) is a kind of optimization methodology inspired by the mechanisms of biological evolution and behaviors of living organisms. In the literature, the terminology evolutionary algorithms is frequently treated the same as EC. This article focuses on making a survey of researches based on using ML techniques to enhance EC algorithms. In the framework of an ML-technique enhanced-EC algorithm (MLEC), the main idea is that the EC algorithm has stored ample data about the search space, problem features, and population information during the iterative search process, thus the ML technique is helpful in analyzing these data for enhancing the search performance. The paper presents a survey of five categories: ML for population initialization, ML for fitness evaluation and selection, ML for population reproduction and variation, ML for algorithm adaptation, and ML for local search.
Free Form based active contours for image segmentation and free space perception

In this paper we present a novel approach for representing and evolving deformable active contours. The method combines piecewise regular Bézier models and curve evolution defined by local Free Form Deformation. The contour deformation is locally constrained which allows contour convergence with almost linear complexity while adapting to various shape settings and handling topology changes of the active contour. We demonstrate the effectiveness of the new active contour scheme for visual free space perception and segmentation using omnidirectional images acquired by a robot exploring unknown indoor and outdoor environments. Several experiments validate the approach with comparison to state-of-the art parametric and geometric active contours and provide fast and real-time robot free space segmentation and navigation.
QR code location based reverse car-searching route recommendation model

To deal with the reverse car-searching issue in large buildings and parking lots, a Quick Response Code (QR code) based reverse car-searching route recommendation model is designed. By scanning the deployed QR codes, a Smartphone can pinpoint the host location and parking location efficiently. Based on the submitted location information, the central control system can finally return the recommended routes, which facilitates a host to reach the parking location effectively. In our model, the reverse car-searching route is divided into two parts: choosing the optimal exports (elevator) and computing the shortest walking distance route. Based on the optimal export selection algorithm and regional shortest path algorithm, our model can choose the prior exports (elevator) effectively, and then recommend the optimal walking route in the buildings and parking lots. The simulation shows that this low-cost system can effectively solve the reverse car-searching problem in large buildings and parking lots, save the driver's car-searching time and improve the utilization rate of parking facilities.
Towards a New Image-Based Spectrogram Segmentation Speech Coder Optimised for Intelligibility

Speech intelligibility is the very essence of communications. When high noise can degrade a speech signal to the threshold of intelligibility, for example in mobile and military applications, introducing further degradation by a speech coder could prove critical. This paper investigates concepts towards a new speech coder that draws upon the field of image processing in a new multimedia approach. The coder is based on a spectrogram segmentation image processing procedure. The design criterion is for minimal intelligibility loss in high noise, as opposed to the conventional quality criterion, and the bit rate must be reasonable. First phase intelligibility listening test results assessing its potential alongside six standard coders are reported. Experimental results show the robustness of the LD-CELP coder, and the potential of the new coder with particularly good results in car noise conditions below -4.0dB.
Data Governance Cloud Security Checklist at Infrastructure as a Service (IaaS)

Security checklist is an important element in measuring the level of computing security, especially in cloud computing. Vulnerability in cloud computing become major concern because it will lead to security issue. While security awareness and training can educate users on the severe impact of malware, implementation on data governance and security checklist also can help to reduce the risk of being attacked. Since security checklist is important element to measure security level in cloud computing, data governance can help to manage data right with correct procedure. Due to increasing threats and attacks, service providers and service consumers need to adhere to guidelines and/or checklists when measuring the security level of services and to be prepared for unforeseen circumstances, especially in the IaaS platform. As the IaaS platform lies at the lower level in cloud computing where data are stored, it is vital that IaaS security be given serious consideration to prevent not only data breaches but also data losses. The objective of this paper is to discuss the implementation of security checklist in IaaS layer. In this paper also, several studies related with security assessment and checklist that had been discussed and developed by previous researchers and professional bodies will be discussed. This paper will also discuss the result from interview session that had been conducted by the author with several data centers (DCs) and experts regarding the implementation of security measures in small cloud DCs. IaaS; security checklist; guidelines; threats; cloud computing
Fuzzy logic control of energy storage system in microgrid operation

Recent development in Renewable Energy Sources (RES) have led to a higher penetration in existing power systems. As the majority of RES are intermittent by nature, it presents major challenges to the grid operators. An Energy Storage System (ESS) can be connected to mitigate this intermittent sources. When multiple renewable energy sources, flexible loads and ESS are connected to the grid, complexity of the system is significantly increased. Such problems have multiple constraints and objective hence it is challenging to design an effective rule-based control strategy. A control strategy based on fuzzy logic which is similar to human reasoning tolerates uncertainties and imprecision. The proposed fuzzy inference system (FIS) aims to reduce the grid fluctuation and increase the energy storage life-cycle by deciding when and how much to charge/discharge the ESS. A real data was used to test and validate the proposed FIS. In this paper, MATLAB/Simulink is used to create and implement the microgrid test bench and FIS. The proposed methodology is tested against a rule-based control strategy. Simulation studies were carried out on the developed model and results have shown that the proposed FIS can effectively reduce the fluctuation and prolong the life cycle of the ESS.
Text Classification from Positive and Unlabeled Data using Misclassified Data Correction

This paper addresses the problem of dealing with a collection of labeled training documents, especially annotating negative training documents and presents a method of text classification from positive and unlabeled data. We applied an error detection and correction technique to the results of positive and negative documents classified by the Support Vector Machines (SVM). The results using Reuters documents showed that the method was comparable to the current state-of-the-art biasedSVM method as the F-score obtained by our method was 0.627 and biased-SVM was 0.614.
3.5 A 16-to-40Gb/s quarter-rate NRZ/PAM4 dual-mode transmitter in 14nm CMOS

Emerging standards in wireline communication are defining a path to data-rates of 40Gb/s and beyond. Most previous standards for these networking applications use NRZ signaling. However, practical signal integrity constraints have led to a renewed interest in also supporting PAM4 for some applications and loss profiles. Recently, several transmitters have been reported that operate between 28 and 60Gb/s using NRZ or PAM4 modulation exclusively. However, high-speed SerDes building blocks that support both a wide frequency range and multiple forms of modulation provide more compatibility between components and avoid the development of multiple IPs. In addition, these blocks must continue to scale into the next-generation of CMOS process technologies to lower the cost by reducing area and power consumption. This paper presents a dual-mode transmitter (TX) implemented in 14nm CMOS that supports both NRZ and PAM4 modulations and operates from 16 to 40Gb/s. The TX incorporates a 4-tap NRZ FIR filter that is reconfigurable to drive PAM4 levels, quarter-rate clocking with a high-bandwidth 4:1 serializer, a duty-cycle and quadrature-error correction circuit with statistical phase error detection, and compact, multi-layer T-coils for pad capacitance (Cpad) reduction.
JSForce: A Forced Execution Engine for Malicious JavaScript Detection

The drastic increase of JavaScript exploitation attacks has led to a strong interest in developing techniques to enable malicious JavaScript analysis. Existing analysis techniques fall into two general categories: static analysis and dynamic analysis. Static analysis tends to produce inaccurate results (both false positive and false negative) and is vulnerable to a wide series of obfuscation techniques. Thus, dynamic analysis is constantly gaining popularity for exposing the typical features of malicious JavaScript. However, existing dynamic analysis techniques possess limitations such as limited code coverage and incomplete environment setup, leaving a broad attack surface for evading the detection. To overcome these limitations, we present the design and implementation of a novel JavaScript forced execution engine named JSForce which drives an arbitrary JavaScript snippet to execute along different paths without any input or environment setup. We evaluate JSForce using 220,587 HTML and 23,509 PDF realworld samples. Experimental results show that by adopting our forced execution engine, the malicious JavaScript detection rate can be substantially boosted by 206.29% using same detection policy without any noticeable false positive increase. We also make JSForce publicly available as an online service and will release the source code to the security community upon the acceptance for publication.
Automatic Transformation of Data Warehouse Schema to NoSQL Data Base: Comparative Study

Driven by the ever-growing of data from social network (SN), data warehouse (DW) approaches must be adapted. Generally the star, snowflake or constellation models are used as logical ones. All these models are inadequate when dealing with social data which need scalable and flexible systems. As an alternative, NoSQL systems begin to grow. In the absence of a clear approach which allows the implementation of data warehouse under NoSQL model, we propose in this paper, new rules for transforming a multidimensional conceptual model into two NoSQL ones: column-oriented and documentoriented models. For each model, we distinguish two types of transformation: simple and hierarchical. To validate our transformation rules, we implemented four data warehouses using Cassandra as a column-oriented NoSQL system and MongoDB as document-oriented NoSQL system. These systems were implemented using java routines in Talend Data Integration tool and evaluated in terms of ”Write Request Latency” and ”Read request Latency” using TPC-DS benchmark.
The Usable Privacy Policy Project : Combining Crowdsourcing , Machine Learning and Natural Language Processing to Semi-Automatically Answer Those Privacy Questions Users Care About

Natural language privacy policies have become a de facto standard to address expectations of “notice and choice” on the Web. However, users generally do not read these policies and those who do read them struggle to understand their content. Initiatives aimed at addressing this problem through the development of machine-readable standards have run into obstacles, with many website operators showing reluctance to commit to anything more than what they currently do. This project builds on recent advances in natural language processing, privacy preference modeling, crowdsourcing, formal methods, and privacy interface design to develop a practical framework based on websites’ existing natural language privacy policy that empowers users to more meaningfully control their privacy, without requiring additional cooperation from website operators. Our approach combines fundamental research with the development of scalable technologies to (1) semi-automatically extract key privacy policy features from natural language privacy policies, and (2) present these features to users in an easy-to-digest format that enables them to make more informed privacy decisions as they interact with different websites. This work will also involve the systematic collection and analysis of website privacy policies, looking for trends and deficiencies both in the wording and content of these policies across different sectors and using this analysis to inform public policy. This report outlines the project’s research agenda and overall approach.
Adaptability and Adaptivity in Learning Systems

A good learning system may need to provide a protected learning environment (by restrictions or by warnings) to facilitate efficient learning to the students. From the human-computer interaction point of view a careful examination is necessary of how to adapt the learning environment to the learner’s goal and capability in such protected situations. This paper discusses the applicability of adaptability and adaptivity features to fulfil this purpose. The paper also discusses the adaptation needs of learning systems, with particular attention on Intelligent Learning Systems (ILS) by their comparative study with office application systems, which have been an important area of research in the field of adaptation facilitation.
A joint model for action localization and classification in untrimmed video with visual attention

In this paper, we introduce a joint model that learns to directly localize the temporal bounds of actions in untrimmed videos as well as precisely classify what actions occur. Most existing approaches tend to scan the whole video to generate action instances, which are really inefficient. Instead, inspired by human perception, our model is formulated based on a recurrent neural network to observe different locations within a video over time. And, it is capable of producing temporal localizations by only observing a fixed number of fragments, and the amount of computation it performs is independent of input video size. The decision policy for determining where to look next is learned by REINFORCE which is powerful in non-differentiable settings. In addition, different from relevant ways, our model runs localization and classification serially, and possesses a strategy for extracting appropriate features to classify. We evaluate our model on ActivityNet dataset, and it greatly outperforms the baseline. Moreover, compared with a recent approach, we show that our serial design can bring about 9% increase in detection performance.
Preserving Relation Privacy in Online Social Network Data

Online social networks routinely publish data of interest to third parties, but in so doing often reveal relationships, such as a friendship or contractual association, that an attacker can exploit. This systematic look at existing privacy-preservation techniques highlights the vulnerabilities of users even in networks that completely anonymize identities. Through a taxonomy that categorizes techniques according to the degree of user identity exposure, the authors examine the ways that existing approaches compromise relation privacy and offer more secure alternatives.
Solving POMDPs with Levin Search and EIRA

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Characterization of soft errors caused by single event upsets in CMOS processes

Radiation-induced single event upsets (SEUs) pose a major challenge for the design of memories and logic circuits in high-performance microprocessors in technologies beyond 90nm. Historically, we have considered power-performance-area trade offs. There is a need to include the soft error rate (SER) as another design parameter. In this paper, we present radiation particle interactions with silicon, charge collection effects, soft errors, and their effect on VLSI circuits. We also discuss the impact of SEUs on system reliability. We describe an accelerated measurement of SERs using a high-intensity neutron beam, the characterization of SERs in sequential logic cells, and technology scaling trends. Finally, some directions for future research are given.