nils-herrmann/clean_scirepeval huggingface dataset:
CONSTRAINTS AND APPROACHES FOR DISTRIBUTED SENSOR NETWORK SECURITY

Executive Summary Confidentiality, integrity, and authentication services are critical to preventing an adversary from compromising the security of a distributed sensor network. Key management is likewise critical to establishing the keys necessary to provide this protection. However, providing key management is difficult due to the ad hoc nature, intermittent connectivity, and resource limitations of the sensor network environment. As part of the SensIT program, NAI Labs is addressing this problem by identifying and developing cryptographic protocols and mechanisms that efficiently provide key management security support services. This document describes our sensor network constraints and key management approaches research for FY 2000. As a first step, NAI Labs has researched battlefield sensor and sensor network technology and the unique communications environment in which it will be deployed. We have identified the requirements specific to our problem of providing key management for confidentiality and group-level authentication. We have also identified constraints, particularly energy consumption, that render this problem difficult. NAI Labs has developed novel key management protocols specifically designed for the distributed sensor network environment, including Identity-Based Symmetric Keying and Rich Uncle. We have analyzed both existing and NAI Labs-developed keying protocols for their suitability at satisfying identified requirements while overcoming battlefield energy constraints. Our research has focused heavily on key management energy consumption, evaluating protocols based on total system, average sensor node, and individual sensor node energy consumption. We examined a number of secret-key-based protocols, determining some to be suitable for sensor networks but all of the protocols have flexibility limitations. Secret-key-based protocols are generally energy-efficient, using encryption and hashing algorithms that consume relatively little energy. Security of secret-key-based protocols is generally determined by the granularity of established keys, which vary widely for the protocols described herein. During our examination of these protocols we noted that some of these protocols are not sufficiently flexible for use in battlefield sensor network, since they cannot efficiently handle unanticipated additions of sensor nodes to the network. Our Identity-Based Symmetric Keying protocol and the less efficient Symmetric Key Certificate Based Protocol are well suited for certain sensor networks, establishing granular keys while consuming relatively little energy. However, all of the secure secret-key-based protocols use special nodes that operate as Key Distribution Centers (or Translators). The sensor nodes communicate with these centers exchanging information as part of the key establishment process. Since these special nodes are expected to make up less than 1% of the sensor …
Adapting attributes by selecting features similar across domains

Attributes are semantic visual properties shared by objects. They have been shown to improve object recognition and to enhance content-based image search. While attributes are expected to cover multiple categories, e.g. a dalmatian and a whale can both have "smooth skin", we find that the appearance of a single attribute varies quite a bit across categories. Thus, an attribute model learned on one category may not be usable on another category. We show how to adapt attribute models towards new categories. We ensure that positive transfer can occur between a source domain of categories and a novel target domain, by learning in a feature subspace found by feature selection where the data distributions of the domains are similar. We demonstrate that when data from the novel domain is limited, regularizing attribute models for that novel domain with models trained on an auxiliary domain (via Adaptive SVM) improves the accuracy of attribute prediction.
Space-Time Codes for High Data Rate Wireless Communications

Space-time codes (STC) are a class of signaling techniques, offering coding and diversity gains along with improved spectral efficiency. These codes exploit both the spatial and the temporal diversity of the wireless link by combining the design of the error correction code, modulation scheme and array processing. STC are well suited for improving the downlink performance, which is the bottleneck in asymmetric applications such as downstream Internet. Three original contributions to the area of STC are presented in this dissertation. First, the development of analytic tools that determine the fundamental limits on the performance of STC in a variety of channel conditions. For trellis-type STC, transfer function based techniques are applied to derive performance bounds over Rayleigh, Rician and correlated fading environments. For block-type STC, an analytic framework that supports various complex orthogonal designs with arbitrary signal cardinalities and array configurations is developed. In the second part of the dissertation, the Virginia Tech Space-Time Advanced Radio (VT-STAR) is designed, introducing a multi-antenna hardware laboratory test bed, which facilitates characterization of the multiple-input multiple-output (MIMO) channel and validation of various space-time approaches. In the third part of the dissertation, two novel space-time architectures paired with iterative processing principles are proposed. The first scheme extends the suitability of STC to outdoor wireless communications by employing iterative equalization/decoding for time dispersive channels and the second scheme employs iterative interference cancellation/decoding to solve the error propagation problem of Bell-Labs Layered Space-Time Architecture (BLAST). Results show that remarkable energy and spectral efficiencies are achievable by combining concepts drawn from space-time coding, multiuser detection, array processing and iterative decoding.
A free but efficient low-voltage class-AB two-stage operational amplifier

A simple and efficient low-voltage two-stage operational amplifier with Class-AB output stage is introduced. It has a large effective output current boosting factor (/spl sim/50) and close to a factor 2 bandwidth enhancement. This is achieved at the expense of minimum increase in circuit complexity and no additional static power dissipation. Experimental verification of the characteristics of the proposed circuit is provided.
Neural Simulation of Action: A Unifying Mechanism for Motor Cognition

Paradigms drawn from cognitive psychology have provided new insight into covert stages of action. These states include not only intending actions that will eventually be executed, but also imagining actions, recognizing tools, learning by observation, or even understanding the behavior of other people. Studies using techniques for mapping brain activity, probing cortical excitability, or measuring the activity of peripheral effectors in normal human subjects and in patients all provide evidence of a subliminal activation of the motor system during these cognitive states. The hypothesis that the motor system is part of a simulation network that is activated under a variety of conditions in relation to action, either self-intended or observed from other individuals, will be developed. The function of this process of simulation would be not only to shape the motor system in anticipation to execution, but also to provide the self with information on the feasibility and the meaning of potential actions.
Design of a robotic finger using series gear chain mechanisms

This paper designs a new underactuated robotic finger using a special transmission mechanism. The transmission mechanism consists of a set of gears and a spring to produce connected motions and adaptive curling when the finger moves in free space and when it contacts with the environment, respectively. The connected motion is realized by kinematic constraints of the joints instead of the static equilibrium of joint elastic elements that are frequently adopted in underactuated mechanisms. The motor torque is effectively used to increase the gripping force due to the mechanism. In addition, a reduction of the transmission components also simplifies the assembly process and decreases the size and weight of the finger, which is useful in attaching it to the arm as a prosthesis.
Wireless Industrial Monitoring and Control Networks: The Journey So Far and the Road Ahead

While traditional wired communication technologies have played a crucial role in industrial monitoring and control networks over the past few decades, they are increasingly proving to be inadequate to meet the highly dynamic and stringent demands of today’s industrial applications, primarily due to the very rigid nature of wired infrastructures. Wireless technology, however, through its increased pervasiveness, has the potential to revolutionize the industry, not only by mitigating the problems faced by wired solutions, but also by introducing a completely new class of applications. While present day wireless technologies made some preliminary inroads in the monitoring domain, they still have severe limitations especially when real-time, reliable distributed control operations are concerned. This article provides the reader with an overview of existing wireless technologies commonly used in the monitoring and control industry. It highlights the pros and cons of each technology and assesses the degree to which each technology is able to meet the stringent demands of industrial monitoring and control networks. Additionally, it summarizes mechanisms proposed by academia, especially serving critical applications by addressing the real-time and reliability requirements of industrial process automation. The article also describes certain key research problems from the physical layer communication for sensor networks and the wireless networking perspective that have yet to be addressed to allow the successful use of wireless technologies in industrial monitoring and control networks.
A Practical Approach to the Automatic Classification of Security-Relevant Commits

The lack of reliable sources of detailed information on the vulnerabilities of open-source software (OSS) components is a major obstacle to maintaining a secure software supply chain and an effective vulnerability management process. Standard sources of advisories and vulnerability data, such as the National Vulnerability Database (NVD), are known to suffer from poor coverage and inconsistent quality. To reduce our dependency on these sources, we propose an approach that uses machine-learning to analyze source code repositories and to automatically identify commits that are security-relevant (i.e., that are likely to fix a vulnerability). We treat the source code changes introduced by commits as documents written in natural language, classifying them using standard document classification methods. Combining independent classifiers that use information from different facets of commits, our method can yield high precision (80%) while ensuring acceptable recall (43%). In particular, the use of information extracted from the source code changes yields a substantial improvement over the best known approach in state of the art, while requiring a significantly smaller amount of training data and employing a simpler architecture.
System-level power estimation and optimization

Most work to date on power reduction has focused at the component level, not at the system level. In this paper, we propose a framework for describing the power behavior of system-level designs. The model consists of a set of resources, an environmental workload specification, and a power management policy, which serves as the heart of the system model. We map this model to a simulation-based framework to obtain an estimate of the system's power dissipation. Accompanying this, we propose an algorithm to optimize power management policies. The optimization algorithm can be used in a tight loop with the estimation engine to derive new power-management policy algorithms for a given system-level description. We tested our approach by applying it to a real-life low-power portable design, achieving a power estimation accuracy of ∼10%, and a 23% reduction in power after policy optimization.
Location Tracking in a Wireless Sensor Network by Mobile Agents and Its Data Fusion Strategies

The wireless sensor network is an emerging technology that may greatly aid humans by providing ubiquitous sensing, computing and communication capabilities, through which people can more closely interact with the environment wherever they go. To be context-aware, one of the central issues in sensor networks is location tracking, whose goal is to monitor the roaming path of a moving object. While similar to the location-update problem in PCS networks, this problem is more challenging in two senses: (1) there is neither a central control mechanism nor a backbone network in such an environment and (2) the wireless communication bandwidth is very limited. In this paper, we propose a novel protocol based on the mobile agent paradigm. Once a new object is detected, a mobile agent will be initiated to track the roaming path of the object. The agent is mobile since it will choose the sensor closest to the object to stay. The agent may invite some nearby slave sensors to cooperatively position the object and inhibit other irrelevant (i.e. farther) sensors from tracking the object. As a result, the communication and sensing overheads are greatly reduced. Our prototyping of the location-tracking mobile agent based on IEEE 802.11b NICs and our experimental experiences are also reported.
Exploiting the Reactive Power in Deep Neural Models for Non-Intrusive Load Monitoring

Non-intrusive load monitoring (NILM) is defined as the task of retrieving the active power consumption of two or more appliances from information gathered at a single metering point. In this work, the use of the reactive aggregate power as an additional feature to the commonly used active power for deep neural models is proposed. The NILM problem is formulated as a denoising problem, and denoising autoencoder (dAE) neural architectures are used to estimate the appliances individual active power consumption. The proposed approach is evaluated on two public datasets: the Almanac of Minutely Power dataset (AMPds) and the UK Domestic Appliance-Level Electricity (UK-DALE) dataset. In order to better evaluate the generalization capabilities of the algorithm, different testing conditions are considered for the UK-DALE dataset, namely a seen and an unseen scenario. The results show that introducing the reactive power can indeed bring and overall performance increase in all scenarios, ranging from +4.9% to +8.4% of the energy-based F1 score.
Driving style classification using long-term accelerometer information

Driving style can be characteristically divided into normal and aggressive. Related researches show that useful information about driving style can be extracted using vehicle's inertial measurement signals with the help of GPS. However, for public transportation the GPS sensor isn't necessary because of repetition of the route. This assumption helps to create low-cost intelligent public transport monitoring system that is capable to classify aggressive and normal driver. In this paper, we propose pattern recognition approach to classify driving style into aggressive or normal automatically without expert evaluation and knowledge using accelerometer data when driving the same route in different driving styles. 3-axis accelerometer signal statistical features were used as classifier inputs. The results show that aggressive and normal driving style classification of 100% precision is achieved using collected data when driving the same route.
The Structure of Design Problem Spaces

It is proposed that there are important generalizations about problem solving in design activity that reach across specific disciplines. A framework for the study of design is presented that (a) characterizes design as a radial category and fleshes out the task environment of the prototypical cases: (b) takes the task environment seriously: (c) shows that this task environment occurs in design tasks, but does not occur in every nondesign task: (d) explicates the impact of this task environment on the design problem space: and (e) demonstrates that, given the structure of the information-processing system, the features noted in the problem spaces of design tasks will not all occur in problem spaces where the task environment is vastly different. This analysis leads to the claim that there are a set of invariant features in the problem spaces of design situations that collectively constitute a design problem space. Protocol studies are reported in which the problem spaces of three design tasks in architecture, mechanical engineering, and instructional design are explored ond compared with several protocols from nondesign problem-solving tasks.
Rethinking Centrality: The Role of Dynamical Processes in Social Network Analysis

Many popular measures used in social network analysis, including centrality, are based on the random walk. The random walk is a model of a stochastic process where a node interacts with one other node at a time. However, the random walk may not be appropriate for modeling social phenomena, including epidemics and information diffusion, in which one node may interact with many others at the same time, for example, by broadcasting the virus or information to its neighbors. To produce meaningful results, social network analysis algorithms have to take into account the nature of interactions between the nodes. In this paper we classify dynamical processes as conservative and non-conservative and relate them to well-known measures of centrality used in network analysis: PageRank and Alpha-Centrality. We demonstrate, by ranking users in online social networks used for broadcasting information, that non-conservative Alpha-Centrality generally leads to a better agreement with an empirical ranking scheme than the conservative PageRank.
Social ties and mental health

It is generally agreed that social ties play a beneficial role in the maintenance of psychological well-being. In this targeted review, we highlight four sets of insights that emerge from the literature on social ties and mental health outcomes (defined as stress reactions, psychological well-being, and psychological distress, including depressive symptoms and anxiety). First, the pathways by which social networks and social supports influence mental health can be described by two alternative (although not mutually exclusive) causal models—the main effect model and the stress-buffering model. Second, the protective effects of social ties on mental health are not uniform across groups in society. Gender differences in support derived from social network participation may partly account for the higher prevalence of psychological distress among women compared to men. Social connections may paradoxically increase levels of mental illness symptoms among women with low resources, especially if such connections entail role strain associated with obligations to provide social support to others. Third, egocentric networks are nested within a broader structure of social relationships. The notion of social capital embraces the embeddedness of individual social ties within the broader social structure. Fourth, despite some successes reported in social support interventions to enhance mental health, further work is needed to deepen our understanding of the design, timing, and dose of interventions that work, as well as the characteristics of individuals who benefit the most.
Neural networks for advanced control of robot manipulators

Presents an approach and a systematic design methodology to adaptive motion control based on neural networks (NNs) for high-performance robot manipulators, for which stability conditions and performance evaluation are given. The neurocontroller includes a linear combination of a set of off-line trained NNs, and an update law of the linear combination coefficients to adjust robot dynamics and payload uncertain parameters. A procedure is presented to select the learning conditions for each NN in the bank. The proposed scheme, based on fixed NNs, is computationally more efficient than the case of using the learning capabilities of the neural network to be adapted, as that used in feedback architectures that need to propagate back control errors through the model to adjust the neurocontroller. A practical stability result for the neurocontrol system is given. That is, we prove that the control error converges asymptotically to a neighborhood of zero, whose size is evaluated and depends on the approximation error of the NN bank and the design parameters of the controller. In addition, a robust adaptive controller to NN learning errors is proposed, using a sign or saturation switching function in the control law, which leads to global asymptotic stability and zero convergence of control errors. Simulation results showing the practical feasibility and performance of the proposed approach to robotics are given.
Power analysis attack: an approach based on machine learning

In cryptography, a side-channel attack is any attack based on the analysis of measurements related to the physical implementation of a cryptosystem. Nowadays, the possibility of collecting a large amount of observations paves the way to the adoption of machine learning techniques, i.e., techniques able to extract information and patterns from large datasets. The use of statistical techniques for side-channel attacks is not new. Techniques like the template attack have shown their effectiveness in recent years. However, these techniques rely on parametric assumptions and are often limited to small dimensionality settings, which limit their range of application. This paper explores the use of machine learning techniques to relax such assumption and to deal with high dimensional feature vectors.
Compact Tapered-Shape Slot Antenna for UWB Applications

A compact microstrip line-fed ultrawideband (UWB) tapered-shape slot antenna is presented. The proposed antenna comprises a tapered-shape slot and rectangular tuning stub. The antenna is fabricated onto an inexpensive FR4 substrate with an overall dimension of 22 × 24 mm2. The experiment shows that the proposed antenna achieves good impedance matching constant gain, stable radiation patterns over an operating bandwidth of 3-11.2 GHz (115.5%) that covers the entire UWB. The nearly stable radiation pattern with a maximum gain of 5.4 dBi makes the proposed antenna suitable for being used in UWB communication applications.
Exploring simultaneous keyword and key sentence extraction: improve graph-based ranking using wikipedia

Summarization and Keyword Selection are two important tasks in NLP community. Although both aim to summarize the source articles, they are usually treated separately by using sentences or words. In this paper, we propose a two-level graph based ranking algorithm to generate summarization and extract keywords at the same time. Previous works have reached a consensus that important sentence is composed by important keywords. In this paper, we further study the mutual impact between them through context analysis. We use Wikipedia to build a two-level concept-based graph, instead of traditional term-based graph, to express their homogenous relationship and heterogeneous relationship. We run PageRank and HITS rank on the graph to adjust both homogenous and heterogeneous relationships. A more reasonable relatedness value will be got for key sentence selection and keyword selection. We evaluate our algorithm on TAC 2011 data set. Traditional term-based approach achieves a score of 0.255 in ROUGE-1 and a score of 0.037 and ROUGE-2 and our approach can improve them to 0.323 and 0.048 separately.