nils-herrmann/clean_scirepeval huggingface dataset:
What If You Can't Trust Your Network Card?

In the last few years, many different attacks against computing platform targeting hardware or low level firmware have been published. Such attacks are generally quite hard to detect and to defend against as they target components that are out of the scope of the operating system and may not have been taken into account in the security policy enforced on the platform. In this paper, we study the case of remote attacks against network adapters. In our case study, we assume that the target adapter is running a flawed firmware that an attacker may subvert remotely by sending packets on the network to the adapter. We study possible detection techniques and their efficiency. We show that, depending on the architecture of the adapter and the interface provided by the NIC to the host operating system, building an efficient detection framework is possible. We explain the choices we made when designing such a framework that we called NAVIS and give details on our proof of concept implementation.
A survey study of critical success factors in agile software projects

While software is so important for all facets of the modern world, software development itself is not a perfect process. Agile software engineering methods have recently emerged as a new and different way of developing software as compared to the traditional methodologies. However, their success has mostly been anecdotal, and research in this subject is still scant in the academic circles. This research study was a survey study on the critical success factors of Agile software development projects using quantitative approach. Based on existing literature, a preliminary list of potential critical success factors of Agile projects were identified and compiled. Subsequently, reliability analysis and factor analysis were conducted to consolidate this preliminary list into a final set of 12 possible critical success factors for each of the four project success categories – Quality, Scope, Time, and Cost. A survey was conducted among Agile professionals, gathering survey data from 109 Agile projects from 25 countries across the world. Multiple regression techniques were used, both at the full regression model and at the optimized regression model via the stepwise screening procedure. The results revealed that only 10 out of 48 hypotheses were supported, identifying three critical success factors for Agile software development projects: (a) Delivery Strategy, (b) Agile Software Engineering Techniques, and (c) Team Capability. Limitations of the study are discussed together with interpretations for practitioners. To ensure success of their projects, managers are urged to focus on choosing a high-caliber team, practicing Agile engineering techniques and following Agile-style delivery strategy.
E-commerce web page classification based on automatic content extraction

Currently, There are many E-commerce websites around the internet world. These E-commerce websites can be categorized into many types which one of them is C2C (Customer to Customer) websites such as eBay and Amazon. The main objective of C2C websites is an online market place that everyone can buy or sell anything at any time. Since, there are a lot of products in the E-commerce websites and each product are classified into its category by human. It is very hard to define their categories in automatic manner when the data is very large. In this paper, we propose the method for classifying E-commerce web pages based on their product types. Firstly, we apply the proposed automatic content extraction to extract the contents of E-commerce web pages. Then, we apply the automatic key word extraction to select words from these extracted contents for generating the feature vectors that represent the E-commerce web pages. Finally, we apply the machine learning technique for classifying the E-commerce web pages based on their product types. The experimental results signify that our proposed method can classify the E-commerce web pages in automatic fashion.
Wirelessly Controlled Mines Detection Robot

This paper demonstrates the problem and effects of landmines in defense fields. We are proposing a robot that has the aptitude to detect the buried mines and lets user control it wirelessly to avoid human causalities. The robot is equipped with special wheels controlled by H-Bridge module, allowing it to move in all possible directions. In this paper, we focus on the safety of humans and the robot; the robot is equipped with special range sensors that help in avoiding the obstacles in the field by specifically detecting the position of obstacles. For the fabrication of the project, a special type of prototype made of lightweight temperature resistant metal is used to carry all objects. A wireless camera is added to the robot, which captures and broadcasts the present location of the robot. Microcontroller commands the robot. This technique has the practical benefit of reducing the number of casualties, after the implementation of the technique, the robot can be controlled efficiently and it robustly determines the position of the obstacle.
Obfuscation resilient binary code reuse through trace-oriented programming

With the wide existence of binary code, it is desirable to reuse it in many security applications, such as malware analysis and software patching. While prior approaches have shown that binary code can be extracted and reused, they are often based on static analysis and face challenges when coping with obfuscated binaries. This paper introduces trace-oriented programming (TOP), a general framework for generating new software from existing binary code by elevating the low-level binary code to C code with templates and inlined assembly. Different from existing work, TOP gains benefits from dynamic analysis such as resilience against obfuscation and avoidance of points-to analysis. Thus, TOP can be used for malware analysis, especially for malware function analysis and identification. We have implemented a proof-of-concept of TOP and our evaluation results with a range of benign and malicious software indicate that TOP is able to reconstruct source code from binary execution traces in malware analysis and identification, and binary function transplanting.
Cell Detection with Star-Convex Polygons

Automatic detection and segmentation of cells and nuclei in microscopy images is important for many biological applications. Recent successful learning-based approaches include per-pixel cell segmentation with subsequent pixel grouping, or localization of bounding boxes with subsequent shape refinement. In situations of crowded cells, these can be prone to segmentation errors, such as falsely merging bordering cells or suppressing valid cell instances due to the poor approximation with bounding boxes. To overcome these issues, we propose to localize cell nuclei via star-convex polygons, which are a much better shape representation as compared to bounding boxes and thus do not need shape refinement. To that end, we train a convolutional neural network that predicts for every pixel a polygon for the cell instance at that position. We demonstrate the merits of our approach on two synthetic datasets and one challenging dataset of diverse fluorescence microscopy images.
Regulation of adult neurogenesis by stress, sleep disruption, exercise and inflammation: Implications for depression and antidepressant action

Adult hippocampal neurogenesis, a once unorthodox concept, has changed into one of the most rapidly growing fields in neuroscience. The present report results from the ECNP targeted expert meeting in 2007 during which cellular plasticity changes were addressed in the adult brain, focusing on neurogenesis and apoptosis in hippocampus and frontal cortex. We discuss recent studies investigating factors that regulate neurogenesis with special emphasis on effects of stress, sleep disruption, exercise and inflammation, a group of seemingly unrelated factors that share at least two unifying properties, namely that they all regulate adult hippocampal neurogenesis and have all been implicated in the pathophysiology of mood disorders. We conclude that although neurogenesis has been implicated in cognitive function and is stimulated by antidepressant drugs, its functional impact and contribution to the etiology of depression remains unclear. A lasting reduction in neurogenesis following severe or chronic stress exposure, either in adult or early life, may represent impaired hippocampal plasticity and can contribute to the cognitive symptoms of depression, but is, by itself, unlikely to produce the full mood disorder. Normalization of reductions in neurogenesis appears at least partly, implicated in antidepressant action.
Big Data Privacy: Challenges to Privacy Principles and Models

This paper explores the challenges raised by big data in privacy-preserving data management. First, we examine the conflicts raised by big data with respect to preexisting concepts of private data management, such as consent, purpose limitation, transparency and individual rights of access, rectification and erasure. Anonymization appears as the best tool to mitigate such conflicts, and it is best implemented by adhering to a privacy model with precise privacy guarantees. For this reason, we evaluate how well the two main privacy models used in anonymization (k-anonymity and $$\varepsilon $$ ε -differential privacy) meet the requirements of big data, namely composability, low computational cost and linkability.
Integrated Buck-Flyback Converter as a High-Power-Factor Off-Line Power Supply

This paper investigates the integrated buck-flyback converter (IBFC) as a good solution for implementing low-cost high-power-factor ac-dc converters with fast output regulation. It will be shown that, when both buck and flyback semistages are operated in discontinuous conduction mode, the voltage across the bulk capacitor, which is used to store energy at low frequency, is independent of the output power. This makes it possible to maintain the bulk capacitor voltage at a low value within the whole line voltage range. The off-line operating modes of the IBFC are also investigated to demonstrate that the control switch of the proposed converter handles lower root-mean-square currents than those in similar integrated converters. The off-line operation of the IBFC is analyzed to obtain the design characteristics of the bulk capacitor voltage. Finally, the design and experimental results of a universal input 48 V-output 100 W ac-dc converter operating at 100 kHz is presented. Experiments show that the IEC-61000-3-2 input current harmonic limits are well satisfied and efficiency can be as high as 82%.
Online flow size prediction for improved network routing

We describe an emerging application of data mining in the context of computer networks. This application concerns the problem of predicting the size of a flow and detecting elephant flows (very large flows). Flow size is a very important statistic that can be used to improve routing, load balancing and scheduling in computer networks. Flow size prediction is particularly challenging since flow patterns continuously change and predictions must be done in real time (milliseconds) to avoid delays. We describe how to formulate the problem as an online machine learning task to continuously adjust to changes in flow traffic. We evaluate the predictive nature of a set of features and the accuracy of three online predictors based on neural networks, Gaussian process regression and online Bayesian Moment Matching on three datasets of real traffic. We also demonstrate how to use such online predictors to improve routing (i.e., reduced flow completion time) in a network simulation.
Chip design of a 12-bit 5MS/s fully differential SAR ADC with resistor-capacitor array DAC technique for wireless application

A 1.8-V 12-bit 5MS/s successive approximation register (SAR) analog-to-digital converter (ADC) implemented in TSMC 0.18-um CMOS process is presented. To reduce DAC switching energy and chip area, a hybrid resistor-capacitor DAC is applied. To save energy, asynchronous control logic to drive the ADC is used. A pre-amplifier based comparator circuit is built to reduce the kickback noise from the dynamic latch designs. With 1.8 V supply voltage and 5.0 MHz sampling rate, measured results achieve -0.55/0.72 LSB (Least Significant Bit) of DNL (differential nonlinearity) and -0.78/0.92 LSB of integral nonlinearity (INL) respectively, and 10.76 bits of an effective number of bits (ENOB) at 1MHz input frequency. The chip area is 0.83 mm2 including pads and the power consumption is 490μW for optical and wireless communications.
Vicious and virtuous cycles in ERP implementation: a case study of interrelations between critical success factors

A submitted manuscript is the author's version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers.
A self-learning evolutionary chess program

A central challenge of artificial intelligence is to create machines that can learn from their own experience and perform at the level of human experts. Using an evolutionary algorithm, a computer program has learned to play chess by playing games against itself. The program learned to evaluate chessboard configurations by using the positions of pieces, material and positional values, and neural networks to assess specific sections of the chessboard. During evolution, the program improved its play by almost 400 rating points. Testing under simulated tournament conditions against Pocket Fritz 2.0 indicated that the evolved program performs above the master level.
Extracting Summary Sentences Based on the Document Semantic Graph

We present a method for extracting sentences from an individual document to serve as a document summary or a pre-cursor to creating a generic document abstract. We apply syntactic analysis of the text that produces a logical form analysis for each sentence. We use subject–object–predicate (SOP) triples from individual sentences to create a semantic graph of the original document and the corresponding human extracted summary. Using the Support Vector Machines learning algorithm, we train a classifier to identify SOP triples from the document semantic graph that belong to the summary. The classifier is then used for automatic extraction of summaries from test documents. Our experiments with the DUC 2002 and CAST datasets show that including semantic properties and topological graph properties of logical triples yields statistically significant improvement of the microaverage F1 measure for both the extraction of SOP triples that correspond to the semantic structure of extracts and the extraction of summary sentences. Evaluation based on ROUGE shows similar results for the extracted summary sentences.
Low-Profile RFID Tag Antenna Using Compact AMC Substrate for Metallic Objects

A low-profile (aplambda/100) passive radio frequency identification (RFID) tag antenna incorporated with a compact artificial magnetic conductor (AMC) substrate is proposed. A novel modified rectangular patch type AMC substrate with offset vias is employed to significantly reduce the planar dimensions of a tag antenna, which is about 0.1lambda times 0.2lambda. The effect of a ground plane size and the number of AMC unit cells on the antenna performance such as return loss and gain is investigated with a view of tag installation. It is demonstrated through experiments that the proposed tag antenna shows relatively tolerant reading distances from 2.4 to 4.8 m under various platform environments such as a conductive plate or a plastic water container.
Wireless Medical Sensor Networks in Emergency Response: Implementation and Pilot Results

This project demonstrates the feasibility of using cost- effective, flexible, and scalable sensor networks to address critical bottlenecks of the emergency response process. For years, emergency medical service providers conducted patient care by manually measuring vital signs, documenting assessments on paper, and communicating over handheld radios. When disasters occurred, the large numbers of casualties quickly and easily overwhelmed the responders. Collaboration with EMS and hospitals in the Baltimore Washington Metropolitan region prompted us to develop miTag (medical information tag), a cost- effective wireless sensor platform that automatically track patients throughout each step of the disaster response process, from disaster scenes, to ambulances, to hospitals. The miTag is a highly extensible platform that supports a variety of sensor add-ons - GPS, pulse oximetry, blood pressure, temperature, ECG - and relays data over a self-organizing wireless mesh network Scalability is the distinguishing characteristic of miTag: its wireless network scales across a wide range of network densities, from sparse hospital network deployments to very densely populated mass casualty sites. The miTag system is out-of-the-box operational and includes the following key technologies: 1) cost-effective sensor hardware, 2) self-organizing wireless network and 3) scalable server software that analyzes sensor data and delivers real-time updates to handheld devices and web portals. The system has evolved through multiple iterations of development and pilot deployments to become an effective patient monitoring solution. A pilot conducted with the Department of Homeland Security indicates miTags can increase the patient care capacity of responders in the field A pilot at Washington Hospital showed miTags are capable of reliably transmitting data inside radio-interference-rich critical care settings.
Wandering minds: the default network and stimulus-independent thought.

Despite evidence pointing to a ubiquitous tendency of human minds to wander, little is known about the neural operations that support this core component of human cognition. Using both thought sampling and brain imaging, the current investigation demonstrated that mind-wandering is associated with activity in a default network of cortical regions that are active when the brain is "at rest." In addition, individuals' reports of the tendency of their minds to wander were correlated with activity in this network.
Single-pole double-throw CMOS switches for 900-MHz and 2.4-GHz applications on p/sup -/silicon substrates

A 900-MHz single-pole double-throw (SPDT) switch with an insertion loss of 0.5 dB and a 2.4-GHz SPDT switch with an insertion loss of 0.8 dB were implemented using 3.3-V 0.35-/spl mu/m NMOS transistors in a 0.18-/spl mu/m bulk CMOS process utilizing 20-/spl Omega//spl middot/cm p/sup -/ substrates. Impedance transformation was used to reduce the source and load impedances seen by the switch to increase the power handling capability. SPDT switches with 30-/spl Omega/ impedance transformation networks exhibit 0.97-dB insertion loss and 24.3-dBm output P/sub 1dB/ when tuned for 900-MHz operation, and 1.10-dB insertion loss and 20.6-dBm output P/sub 1dB/ when tuned for 2.4-GHz operation. The 2.4-GHz switch is the first bulk CMOS switch which can be used for 802.11b wireless local area network applications.
NB-IoT Technology Overview and Experience from Cloud-RAN Implementation

The 3GPP has introduced a new narrowband radio technology called narrowband Internet of Things (NB-IoT) in Release 13. NB-IoT was designed to support very low power consumption and low-cost devices in extreme coverage conditions. NB-IoT operates in very small bandwidth and will provide connectivity to a large number of low-data-rate devices. This article highlights some of the key features introduced in NB-IoT and presents performance results from real-life experiments. The experiments were carried out using an early-standard-compliant prototype based on a software defined radio partial implementation of NB-IoT that runs on a desktop computer connected to the network. It is found that a cloud radio access network is a good candidate for NB-IoT implementation.
Mining Contextual Preference Rules for Building User Profiles

The emerging of ubiquitous computing technologies in recent years has given rise to a new field of research consisting in incorporating context-aware preference querying facilities in database systems. One important step in this setting is the Preference Elicitation task which consists in providing the user ways to inform his/her choice on pairs of objects with a minimal effort. In this paper we propose an automatic preference elicitation method based on mining techniques. The method consists in extracting a user profile from a set of user preference samples. In our setting, a profile is specified by a set of contextual preference rules verifying some important properties (soundness and conciseness). We evaluate the efficacy of the proposed method in a series of experiments executed on a real-world database of user preferences about movies.
Market basket analysis in a multiple store environment

Market basket analysis (also known as association-rule mining) is a useful method of discovering customer purchasing patterns by extracting associations or co-occurrences from stores’ transactional databases. Because the information obtained from the analysis can be used in forming marketing, sales, service, and operation strategies, it has drawn increased research interest. The existing methods, however, may fail to discover important purchasing patterns in a multi-store environment, because of an implicit assumption that products under consideration are on shelf all the time across all stores. In this paper, we propose a new method to overcome this weakness. Our empirical evaluation shows that the proposed method is computationally efficient, and that it has advantage over the traditional method when stores are diverse in size, product mix changes rapidly over time, and larger numbers of stores and periods are considered. D 2004 Elsevier B.V. All rights reserved.
Design of the TUAT/Karlsruhe humanoid hand

The increasing demand for robotic applications in dynamic unstructured environments is motivating the need for dextrous end-effectors which can cope with the wide variety of tasks and objects encountered in these environments. The human hand is a very complex grasping tool that can handle objects of different sizes and shapes. Many research activities have been carried out to develop artificial robot hands with capabilities similar to the human hand. In this paper the mechanism and design of a new humanoid-type hand (called TUAT/Karlsruhe Humanoid Hand) with human-like manipulation abilities is discussed. The new hand is designed for the humanoid robot ARMAR which has to work autonomously or interactively in cooperation with humans and for an artificial lightweight arm for handicapped persons. The arm is developed as close as possible to the human arm and is driven by spherical ultrasonic motors. The ideal end-effector for such an artificial arm or a humanoid would be able to use the tools and objects that a person uses when working in the same environment. Therefore a new hand is designed for anatomical consistency with the human hand. This includes the number of fingers and the placement and motion of the thumb, the proportions of the link lengths and the shape of the palm. It can also perform most part of human grasping types. The TUAT/Karlsruhe Humanoid Hand possesses 20 DOF and is driven by one actuator which can be placed into or around the hand.