nils-herrmann/clean_scirepeval huggingface dataset:
A 50-GHz Phase-Locked Loop in 0.13-$\mu$ m CMOS

A 50-GHz charge pump phase-locked loop (PLL) utilizing an LC-oscillator-based injection-locked frequency divider (ILFD) was fabricated in 0.13-mum logic CMOS process. The PLL can be locked from 45.9 to 50.5 GHz and output power level is around -10 dBm. The operating frequency range is increased by tracking the self-oscillation frequencies of the voltage-controlled oscillator (VCO) and the frequency divider. The PLL including buffers consumes 57 mW from 1.5/0.8-V supplies. The phase noise at 50 kHz, 1 MHz, and 10 MHz offset from the carrier is -63.5, -72, and -99 dBc/Hz, respectively. The PLL also outputs second-order harmonics at frequencies between 91.8 and 101 GHz. The output frequency of 101 GHz is the highest for signals locked by a PLL fabricated using the silicon integrated circuits technology.
Pareto Efficiency in Robust Optimization

T paper formalizes and adapts the well-known concept of Pareto efficiency in the context of the popular robust optimization (RO) methodology for linear optimization problems. We argue that the classical RO paradigm need not produce solutions that possess the associated property of Pareto optimality, and we illustrate via examples how this could lead to inefficiencies and suboptimal performance in practice. We provide a basic theoretical characterization of Pareto robustly optimal (PRO) solutions and extend the RO framework by proposing practical methods that verify Pareto optimality and generate solutions that are PRO. Critically important, our methodology involves solving optimization problems that are of the same complexity as the underlying robust problems; hence, the potential improvements from our framework come at essentially limited extra computational cost. We perform numerical experiments drawn from three different application areas (portfolio optimization, inventory management, and project management), which demonstrate that PRO solutions have a significant potential upside compared with solutions obtained using classical RO methods.
3D Point Cloud Classification and Segmentation using 3D Modified Fisher Vector Representation for Convolutional Neural Networks

The point cloud is gaining prominence as a method for representing 3D shapes, but its irregular format poses a challenge for deep learning methods. The common solution of transforming the data into a 3D voxel grid introduces its own challenges, mainly large memory size. In this paper we propose a novel 3D point cloud representation called 3D Modified Fisher Vectors (3DmFV). Our representation is hybrid as it combines the discrete structure of a grid with continuous generalization of Fisher vectors, in a compact and computationally efficient way. Using the grid enables us to design a new CNN architecture for point cloud classification and part segmentation. In a series of experiments we demonstrate competitive performance or even better than state-of-the-art on challenging benchmark datasets.
Open Information Extraction from the Web

Traditionally, Information Extraction (IE) has focused on satisfying precise, narrow, pre-specified requests from small homogeneous corpora (e.g., extract the location and time of seminars from a set of announcements). Shifting to a new domain requires the user to name the target relations and to manually create new extraction rules or hand-tag new training examples. This manual labor scales linearly with the number of target relations. This paper introduces Open IE (OIE), a new extraction paradigm where the system makes a single data-driven pass over its corpus and extracts a large set of relational tuples without requiring any human input. The paper also introduces TEXTRUNNER, a fully implemented, highly scalable OIE system where the tuples are assigned a probability and indexed to support efficient extraction and exploration via user queries. We report on experiments over a 9,000,000 Web page corpus that compare TEXTRUNNER with KNOWITALL, a state-of-the-art Web IE system. TEXTRUNNER achieves an error reduction of 33% on a comparable set of extractions. Furthermore, in the amount of time it takes KNOWITALL to perform extraction for a handful of pre-specified relations, TEXTRUNNER extracts a far broader set of facts reflecting orders of magnitude more relations, discovered on the fly. We report statistics on TEXTRUNNER’s 11,000,000 highest probability tuples, and show that they contain over 1,000,000 concrete facts and over 6,500,000more abstract assertions.
Concurrency Control and Security Issue in Distributed Database System

This paper reviews the security issues and concurrency control in distributed database system and data security aspects of client and server. As distributed database became more popular, the need for improvement in distributed database management system become even more important. The most important issue is security that may arise and possibly compromise the access control and the integrity of the system. In this paper, we propose some solution for some security aspects such as multi-level access control, confidentiality, reliability, integrity and recovery that pertain to a distributed database system. This paper review some algorithm based of concurrency control such as basic timestamp algorithm, distributed two phase locking protocol (2PL), distributed optimistic protocol, wound-wait algorithm.
Major requirements for building Smart Homes in Smart Cities based on Internet of Things technologies

The recent boom in the Internet of Things (IoT) will turn Smart Cities and Smart Homes (SH) from hype to reality. SH is the major building block for Smart Cities and have long been a dream for decades, hobbyists in the late 1970smade Home Automation (HA) possible when personal computers started invading home spaces. While SH can share most of the IoT technologies, there are unique characteristics that make SH special. From the result of a recent research survey on SH and IoT technologies, this paper defines the major requirements for building SH. Seven unique requirement recommendations are defined and classified according to the specific quality of the SH building blocks.
Using an online tool to support school-based ICT policy planning in primary education

An important step towards the successful integration of information and communication technology (ICT) in schools is to facilitate their capacity to develop a school-based ICT policy resulting in an ICT policy plan. Such a plan can be defined as a school document containing strategic and operational elements concerning the integration of ICT in education. To write such a plan in an efficient way is challenging for schools. Therefore, an online tool [Planning for ICT in Schools (pICTos)] has been developed to guide schools in this process. A multiple case study research project was conducted with three Flemish primary schools to explore the process of developing a school-based ICT policy plan and the supportive role of pICTos within this process. Data from multiple sources (i.e. interviews with school leaders and ICT coordinators, school policy documents analysis and a teacher questionnaire) were collected and analysed. The results indicate that schools shape their ICT policy based on specific school data collected and presented by the pICTos environment. School teams learned about the actual and future place of ICT in teaching and learning. Consequently, different policy decisions were made according to each school’s vision on ‘good’ education and ICT integration.
A detailed look at Steganographic Techniques and their use in an Open-Systems Environment

This paper's focus is on a relatively new field of study in Information Technology known as Steganography. This paper will take an in-depth look at this technology by introducing the reader to various concepts of Steganography, a brief history of Steganography and a look at some of the Steganographic techniques available today. The paper will close by looking at how we can use Steganography in an open-systems environment such as the Internet, as well as some of the tools and resources available to help us accomplish th...
The Identification of Complex Spatiotemporal Patterns Using Coupled Map Lattice Models

Many complex and interesting spatiotemporal patterns have been observed in a wide range of scientific areas. In this paper, two kinds of spatiotemporal patterns including spot replication and Turing systems are investigated and new identification methods are proposed to obtain Coupled Map Lattice (CML) models for this class of systems. Initially, a new correlation analysis method is introduced to determine an appropriate temporal and spatial data sampling step procedure for the identification of spatiotemporal systems. A new combined Orthogonal Forward Regression and Bayesian Learning algorithm with Laplace priors is introduced to identify sparse and robust CML models for complex spatiotemporal patterns. The final identified CML models are validated using correlation based model validation tests for spatiotemporal systems. Numerical results illustrate the identification procedure and demonstrate the validity of the identified models.
Early results for Named Entity Recognition with Conditional Random Fields, Feature Induction and Web-Enhanced Lexicons

Models for many natural language tasks benefit from the flexibility to use overlapping, non-independent features. For example, the need for labeled data can be drastically reduced by taking advantage of domain knowledge in the form of word lists, part-of-speech tags, character ngrams, and capitalization patterns. While it is difficult to capture such inter-dependent features with a generative probabilistic model, conditionally-trained models, such as conditional maximum entropy models, handle them well. There has been significant work with such models for greedy sequence modeling in NLP (Ratnaparkhi, 1996; Borthwick et al., 1998). Conditional Random Fields (CRFs) (Lafferty et al., 2001) are undirected graphical models, a special case of which correspond to conditionally-trained finite state machines. While based on the same exponential form as maximum entropy models, they have efficient procedures for complete, non-greedy finite-state inference and training. CRFs have shown empirical successes recently in POS tagging (Lafferty et al., 2001), noun phrase segmentation (Sha and Pereira, 2003) and Chinese word segmentation (McCallum and Feng, 2003). Given these models’ great flexibility to include a wide array of features, an important question that remains is what features should be used? For example, in some cases capturing a word tri-gram is important, however, there is not sufficient memory or computation to include all word tri-grams. As the number of overlapping atomic features increases, the difficulty and importance of constructing only certain feature combinations grows. This paper presents a feature induction method for CRFs. Founded on the principle of constructing only those feature conjunctions that significantly increase loglikelihood, the approach builds on that of Della Pietra et al (1997), but is altered to work with conditional rather than joint probabilities, and with a mean-field approximation and other additional modifications that improve efficiency specifically for a sequence model. In comparison with traditional approaches, automated feature induction offers both improved accuracy and significant reduction in feature count; it enables the use of richer, higherorder Markov models, and offers more freedom to liberally guess about which atomic features may be relevant to a task.
Detecting Trojan horses based on system behavior using machine learning method

The Research of detection malware using machine learning method attracts much attention recent years. However, most of research focused on code analysis which is signature-based or analysis of system call sequence in Linux environment. Obviously, all methods have their strengths and weaknesses. In this paper, we concentrate on detection Trojan horse by operation system information in Windows environment using data mining technology. Our main content and contribution contains as follows: First, we collect Trojan horse samples in true network environment and classify them by scanner. Secondly, we collect operation system behavior features under infected and clean circumstances separately by WMI manager tools. And then, several classic classification algorithms are applied and a performance comparison is given. Feature selection methods are applied to those features and we get a feature order list which reflects the relevance order of Trojan horse activities and the system feature. We believe the instructive meaning of the list is significant. Finally, a feature combination method is applied and features belongs different groups are combined according their characteristic for high classification performance. Results of experiments demonstrate the feasibility of our assumption that detecting Trojan horses by system behavior information is feasible and affective.
Valuable Research for Visualization and Digital Humanities : A Balancing Act

The value of a visualization evolved in a digital humanities project is per se not evenly high for both involved research fields. When an approach is too complex – which counts as a strong argument for a publication in a visualization realm – it might get invaluable for humanities scholars due to problems of comprehension. On the other hand, if a clean, easily comprehensible visualization is valuable for a humanities scholar, the missing novelty most likely impedes a computer science publication. My own digital humanities background has shown that it is indeed a balancing act to generate beneficial research results for both the visualization and the digital humanities fields. To find out how visualizations are used as means to communicate humanities matters and to assess the impact of the visualization community to the digital humanities field, I surveyed the long papers of the last four annual digital humanities conferences, discovering that visualization scholars are rarely involved in collaborations that produce valuable digital humanities results, in other words, it seems hard to walk the tightrope of generating valuable research for both fields. Derived from my own digital humanities experiences, I suggest a methodology how to design a digital humanities project to overcome this issue.
A DNA-Based Archival Storage System

Demand for data storage is growing exponentially, but the capacity of existing storage media is not keeping up. Using DNA to archive data is an attractive possibility because it is extremely dense, with a raw limit of 1 exabyte/mm3 (109 GB/mm3), and long-lasting, with observed half-life of over 500 years. This paper presents an architecture for a DNA-based archival storage system. It is structured as a key-value store, and leverages common biochemical techniques to provide random access. We also propose a new encoding scheme that offers controllable redundancy, trading off reliability for density. We demonstrate feasibility, random access, and robustness of the proposed encoding with wet lab experiments involving 151 kB of synthesized DNA and a 42 kB random-access subset, and simulation experiments of larger sets calibrated to the wet lab experiments. Finally, we highlight trends in biotechnology that indicate the impending practicality of DNA storage for much larger datasets.
MatConvNet: Convolutional Neural Networks for MATLAB

MatConvNet is an open source implementation of Convolutional Neural Networks (CNNs) with a deep integration in the MATLAB environment. The toolbox is designed with an emphasis on simplicity and flexibility. It exposes the building blocks of CNNs as easy-to-use MATLAB functions, providing routines for computing convolutions with filter banks, feature pooling, normalisation, and much more. MatConvNet can be easily extended, often using only MATLAB code, allowing fast prototyping of new CNN architectures. At the same time, it supports efficient computation on CPU and GPU, allowing to train complex models on large datasets such as ImageNet ILSVRC containing millions of training examples
A New Solution for N-Queens Problem using Blind Approaches: DFS and BFS Algorithms

The N×N queen&apos;s puzzle is the problem of placing N chess queen on an N×N chess board so that no two queens attack each other. This approach is a classical problem in the artificial intelligence area. A solution requires that no two queens share the same row, column or diagonal. These problems for computer scientists present practical solution to many useful applications and have become an important issue. In this paper we proposed new resolution for solving n-Queens used combination of depth firs search (DFS) and breathe first search (BFS) techniques. The proposed algorithm act based on placing queens on chess board directly. This is possible by regular pattern on the basis of the work law of minister. The results show that performance and run time in this approach better then back tracking methods and hill climbing modes.
Spotless Sandboxes: Evading Malware Analysis Systems Using Wear-and-Tear Artifacts

Malware sandboxes, widely used by antivirus companies, mobile application marketplaces, threat detection appliances, and security researchers, face the challenge of environment-aware malware that alters its behavior once it detects that it is being executed on an analysis environment. Recent efforts attempt to deal with this problem mostly by ensuring that well-known properties of analysis environments are replaced with realistic values, and that any instrumentation artifacts remain hidden. For sandboxes implemented using virtual machines, this can be achieved by scrubbing vendor-specific drivers, processes, BIOS versions, and other VM-revealing indicators, while more sophisticated sandboxes move away from emulation-based and virtualization-based systems towards bare-metal hosts. We observe that as the fidelity and transparency of dynamic malware analysis systems improves, malware authors can resort to other system characteristics that are indicative of artificial environments. We present a novel class of sandbox evasion techniques that exploit the "wear and tear" that inevitably occurs on real systems as a result of normal use. By moving beyond how realistic a system looks like, to how realistic its past use looks like, malware can effectively evade even sandboxes that do not expose any instrumentation indicators, including bare-metal systems. We investigate the feasibility of this evasion strategy by conducting a large-scale study of wear-and-tear artifacts collected from real user devices and publicly available malware analysis services. The results of our evaluation are alarming: using simple decision trees derived from the analyzed data, malware can determine that a system is an artificial environment and not a real user device with an accuracy of 92.86%. As a step towards defending against wear-and-tear malware evasion, we develop statistical models that capture a system's age and degree of use, which can be used to aid sandbox operators in creating system images that exhibit a realistic wear-and-tear state.
Configurable 3D Scene Synthesis and 2D Image Rendering with Per-pixel Ground Truth Using Stochastic Grammars

We propose a systematic learning-based approach to the generation of massive quantities of synthetic 3D scenes and arbitrary numbers of photorealistic 2D images thereof, with associated ground truth information, for the purposes of training, benchmarking, and diagnosing learning-based computer vision and robotics algorithms. In particular, we devise a learning-based pipeline of algorithms capable of automatically generating and rendering a potentially infinite variety of indoor scenes by using a stochastic grammar, represented as an attributed Spatial And-Or Graph, in conjunction with state-of-the-art physics-based rendering. Our pipeline is capable of synthesizing scene layouts with high diversity, and it is configurable inasmuch as it enables the precise customization and control of important attributes of the generated scenes. It renders photorealistic RGB images of the generated scenes while automatically synthesizing detailed, per-pixel ground truth data, including visible surface depth and normal, object identity, and material information (detailed to object parts), as well as environments (e.g., illuminations and camera viewpoints). We demonstrate the value of our synthesized dataset, by improving performance in certain machine-learning-based scene understanding tasks—depth and surface normal prediction, semantic segmentation, reconstruction, etc.—and by providing benchmarks for and diagnostics of trained models by modifying object attributes and scene properties in a controllable manner.
Multimodal Grounding for Language Processing

This survey discusses how recent developments in multimodal processing facilitate conceptual grounding of language. We categorize the information flow in multimodal processing with respect to cognitive models of human information processing and analyze different methods for combining multimodal representations. Based on this methodological inventory, we discuss the benefit of multimodal grounding for a variety of language processing tasks and the challenges that arise. We particularly focus on multimodal grounding of verbs which play a crucial role for the compositional power of language. Title and Abstract in German Multimodale konzeptuelle Verankerung für die automatische Sprachverarbeitung Dieser Überblick erörtert, wie aktuelle Entwicklungen in der automatischen Verarbeitung multimodaler Inhalte die konzeptuelle Verankerung sprachlicher Inhalte erleichtern können. Die automatischen Methoden zur Verarbeitung multimodaler Inhalte werden zunächst hinsichtlich der zugrundeliegenden kognitiven Modelle menschlicher Informationsverarbeitung kategorisiert. Daraus ergeben sich verschiedene Methoden um Repräsentationen unterschiedlicher Modalitäten miteinander zu kombinieren. Ausgehend von diesen methodischen Grundlagen wird diskutiert, wie verschiedene Forschungsprobleme in der automatischen Sprachverarbeitung von multimodaler Verankerung profitieren können und welche Herausforderungen sich dabei ergeben. Ein besonderer Schwerpunkt wird dabei auf die multimodale konzeptuelle Verankerung von Verben gelegt, da diese eine wichtige kompositorische Funktion erfüllen.
Specifying Software Requirements for Complex Systems: New Techniques and Their Application

This paper concerns new techniques for making requirements specifications precise, concise, unambiguous, and easy to check for completeness and consistency. The techniques are well-suited for complex real-time software systems; they were developed to document the requirements of existing flight software for the Navy's A-7 aircraft. The paper outlines the information that belongs in a requirements document and discusses the objectives behind the techniques. Each technique is described and illustrated with examples from the A-7 document. The purpose of the paper is to introduce the A-7 document as a model of a disciplined approach to requirements specification; the document is available to anyone who wishes to see a fully worked-out example of the approach.