nils-herrmann/clean_scirepeval huggingface dataset:
The limits of automatic OS fingerprint generation

Remote operating system fingerprinting relies on implementation differences between OSs to identify the specific variant executing on a remote host. Because these differences can be subtle and difficult to find, most fingerprinting tools require expert manual effort to construct discriminative fingerprints and classification models. In prior work, Caballero et al. proposed a promising technique to eliminate manual intervention: the automatic generation of fingerprints using an approach similar to fuzz testing [6]. Their work evaluated the technique in a small-scale, carefully controlled test environment. In this paper, we re-examine automatic OS fingerprinting in a more challenging large-scale scenario to better understand the viability of the technique. In contrast to the prior work, we find that automatic fingerprint generation suffers from several limitations and technical hurdles that can limit its effectiveness, particularly in more demanding, realistic environments.
 We use machine learning algorithms from the well-known Weka [11] data mining toolkit to automatically generate fingerprints over 329 different machine instances, and we compare the accuracy of our automatically generated fingerprints to Nmap. Our results suggest that overfitting to non-OS-specific behavioral differences, the indistinguishability of different OS variants, the biasing of an automatic tool to the makeup of the training data, and the lack of ability of an automatic tool to exploit protocol and software semantics significantly limit the usefulness of this technique in practice. Automatic techniques can help identify candidate signatures, but our results suggest that manual expertise will remain an integral part of fingerprint generation.
A Comparison of Nine Basic Techniques for Requirements Prioritization

Requirements prioritization is an important activity in software development. Numerous different techniques toprioritize requirements exist. In this paper, we focus on the foll owing research questions: 1) What empirically studied requirements prioritization techniques are presented in the literature? 2) How easy to use, accurate and scalable are these techniques? Tot al of nine basic requirements prioritization techniques wereidentified: 1) Numeral assignment technique, 2) Analytic hierar chy process (AHP), 3) Hierarchy AHP, 4) Minimal spanning tree, 5) Cumulative voting (CV), 6) Hierarchical cumulative voting (HCV), 7) Priority groups, 8) Binary priority list (BPL), 9) Bubble sort. The techniques are presented and analyzed based on the empirical studies from the literature. The results indicate that none of the techniques can be considered the best one. The bes t prioritization technique depends on the situation (e.g. number of requirements used or desired results scale). This study a lso reveals that there are several problems with the existing empirical studies, which make it hard to compare their results to each other. Therefore, this paper also presents recommended pra ctices for future empirical studies about requirements prioritiz ation techniques.
Construction of Cubic Structures with Quadrotor Teams

We propose and investigate a system in which teams of quadrotor helicopters assemble 2.5-D structures from simple structural nodes and members equipped with magnets. The structures, called Special Cubic Structures (SCS), are a class of 2.5-D truss-like structures free of overhangs and holes. Grippers attached to the bottom of each quadrotor enable them to pick up, transport, and assemble the structural elements. The design of the nodes and members imposes constraints on assembly which are incorporated into the design of the algorithms used for assembly. We show that any SCS can be built using only the feasible assembly modes for individual structural elements and present simulation and experimental results with a team of quadrotors performing automated assembly. The paper includes a theoretical analysis of the SCS construction algorithm, the rationale for the design of the structural nodes, members and quadrotor gripper, a description of the quadrotor control methods for part pickup, transport and assembly, and an empirical analysis of system performance.
Gesture Recognition with a 3-D Accelerometer

Gesture-based interaction, as a natural way for human-computer interaction, has a wide range of applications in ubiquitous computing environment. This paper presents an acceleration-based gesture recognition approach, called FDSVM (Frame-based Descriptor and multi-class SVM), which needs only a wearable 3-dimensional accelerometer. With FDSVM, firstly, the acceleration data of a gesture is collected and represented by a frame-based descriptor, to extract the discriminative information. Then a SVM-based multi-class gesture classifier is built for recognition in the nonlinear gesture feature space. Extensive experimental results on a data set with 3360 gesture samples of 12 gestures over weeks demonstrate that the proposed FDSVM approach significantly outperforms other four methods: DTW, Naïve Bayes, C4.5 and HMM. In the user-dependent case, FDSVM achieves the recognition rate of 99.38% for the 4 direction gestures and 95.21% for all the 12 gestures. In the user-independent case, it obtains the recognition rate of 98.93% for 4 gestures and 89.29% for 12 gestures. Compared to other accelerometer-based gesture recognition approaches reported in literature FDSVM gives the best resulrs for both user-dependent and user-independent cases.
Promoting physical activity and health literacy: study protocol for a longitudinal, mixed methods evaluation of a cross-provider workplace-related intervention in Germany (The AtRisk study)

Physical activity and health literacy are topics of utmost importance in the prevention of chronic diseases. The present article describes the study protocol for evaluating a cross-provider workplace-related intervention promoting physical activity and health literacy.



The RE-AIM Framework will be the conceptual framework of the AtRisk study. A controlled natural experiment and a qualitative study will be conducted. The cross-provider intervention is based on the cooperation of the German Pension Fund Rhineland and cooperating German Statutory Health Insurances. It combines two components: a behavior-oriented lifestyle intervention and the assignment of a health coach. The single-provider intervention only includes the behavior-oriented lifestyle intervention. The quantitative study (natural experiment) encompasses three measuring points (T0 = start of the behavior-oriented lifestyle intervention (baseline); T1 = end of the behavior-oriented lifestyle intervention (16 weeks); T2 = 6 month follow-up) and will compare the effectiveness of the cross-provider workplace-related intervention compared with the single provider intervention. Participants are employees with health related risk factors. ANCOVA will be used to evaluate the effect of the intervention on the outcome variables leisure time physical (primary outcome) activity and health literacy (secondary outcome). The qualitative study comprises semi-structured interviews, systematic field notes of stakeholder meetings and document analyses.



The AtRisk study will contribute towards the claim for cross-provider interventions and workplace-related approaches described in the new Preventive Health Care Act. The results of this study will inform providers, payers and policy makers about the effectiveness of a cross-provider workplace-related lifestyle intervention compared to a single-provider intervention. Beyond, the study will identify challenges for implementing cross-provider preventive interventions. With respect to the sustainability of preventive interventions the AtRisk study will give insight in the expectations and needs on health coaching from the perspective of different stakeholders.
On Multi-Sensor Radar Configurations for Vehicle Tracking in Autonomous Driving Environments

Multi-sensor radar tracking systems can provide better system performance, visibility and complementary information than single sensor tracking systems. One way to utilize multiple sensors for target tracking is to generate individual sensor detections and fuse these detections to produce optimized system tracks. All or a subset of detections from the sensors are fed into a tracker algorithm, allowing the filter to choose the best possible measurement association. This paper discusses the implementation of a multi-sensor target tracking system using three different sensor configurations for providing vehicle detections. The three sensor configurations compare the accuracy of one vs two radar sensors, operating in either a stepped frequency waveform or a hybrid stepped frequency and continuous waveform. The purpose of this paper is to provide insight into the possibilities of multi-sensor radar tracking for the advancement of autonomous driving.
Optimizing FPGA-based Accelerator Design for Deep Convolutional Neural Networks

Convolutional neural network (CNN) has been widely employed for image recognition because it can achieve high accuracy by emulating behavior of optic nerves in living creatures. Recently, rapid growth of modern applications based on deep learning algorithms has further improved research and implementations. Especially, various accelerators for deep CNN have been proposed based on FPGA platform because it has advantages of high performance, reconfigurability, and fast development round, etc. Although current FPGA accelerators have demonstrated better performance over generic processors, the accelerator design space has not been well exploited. One critical problem is that the computation throughput may not well match the memory bandwidth provided an FPGA platform. Consequently, existing approaches cannot achieve best performance due to under-utilization of either logic resource or memory bandwidth. At the same time, the increasing complexity and scalability of deep learning applications aggravate this problem. In order to overcome this problem, we propose an analytical design scheme using the roofline model. For any solution of a CNN design, we quantitatively analyze its computing throughput and required memory bandwidth using various optimization techniques, such as loop tiling and transformation. Then, with the help of rooine model, we can identify the solution with best performance and lowest FPGA resource requirement. As a case study, we implement a CNN accelerator on a VC707 FPGA board and compare it to previous approaches. Our implementation achieves a peak performance of 61.62 GFLOPS under 100MHz working frequency, which outperform previous approaches significantly.
Sentence Segmentation in Narrative Transcripts from Neuropsycological Tests using Recurrent Convolutional Neural Networks

Automated discourse analysis tools based on Natural Language Processing (NLP) aiming at the diagnosis of languageimpairing dementias generally extract several textual metrics of narrative transcripts. However, the absence of sentence boundary segmentation in the transcripts prevents the direct application of NLP methods which rely on these marks to function properly, such as taggers and parsers. We present the first steps taken towards automatic neuropsychological evaluation based on narrative discourse analysis, presenting a new automatic sentence segmentation method for impaired speech. Our model uses recurrent convolutional neural networks with prosodic, Part of Speech (PoS) features, and word embeddings. It was evaluated intrinsically on impaired, spontaneous speech, as well as, normal, prepared speech, and presents better results for healthy elderly (CTL) (F1 = 0.74) and Mild Cognitive Impairment (MCI) patients (F1 = 0.70) than the Conditional Random Fields method (F1 = 0.55 and 0.53, respectively) used in the same context of our study. The results suggest that our model is robust for impaired speech and can be used in automated discourse analysis tools to differentiate narratives produced by MCI and CTL.
The learning and use of traversability affordance using range images on a mobile robot

We are interested in how the concept of affordances can affect our view to autonomous robot control, and how the results obtained from autonomous robotics can be reflected back upon the discussion and studies on the concept of affordances. In this paper, we studied how a mobile robot, equipped with a 3D laser scanner, can learn to perceive the traversability affordance and use it to wander in a room tilled with spheres, cylinders and boxes. The results showed that after learning, the robot can wander around avoiding contact with non-traversable objects (i.e. boxes, upright cylinders, or lying cylinders in certain orientation), but moving over traversable objects (such as spheres, and lying cylinders in a rollable orientation with respect to the robot) rolling them out of its way. We have shown that for each action approximately 1% of the perceptual features were relevant to determine whether it is afforded or not and that these relevant features are positioned in certain regions of the range image. The experiments are conducted both using a physics-based simulator and on a real robot.
Product Matching through Ontology Mapping in Comparison Shopping

Research on ontology merging and mapping is one of the most important issues in the Semantic Web because ontologies are developed and used by various sites and organizations respectively. Electronic commerce is the area that require ontology mapping on product comparison over different product classification taxonomies of various shopping malls. But, strict mapping strategy may lead a customer’s configuration to search failure. Therefore we suggest a mapping algorithm for product matching that can provide more products by increasing sensitivity with reasonable decrease of specificity. We compared our algorithm with PROMPT based on experiments with 6 sets of source ontology and target ontology.
An Overview on Mobile Data Mining

In early days the mobile phones are considered to perform only telecommunication operation. This scenario of mobile phones as communication devices changed with the emergence of a new class of mobile devices called the smart phones. These smart phones in addition to being used as a communication device are capable of doing things that a computer does. In recent times the smart phone are becoming more and more powerful in both computing and storage aspects. The data generated by the smart phone provide a means to get new knowledge about various aspects like usage, movement of the user etc. This paper provides an introduction to Mobile Data Mining and its types.
3M Syndrome:  A Report of Four Cases in Two Families

3M syndrome is a rare entity characterized by severe growth retardation, dysmorphic features and skeletal changes as its major components. It is differentiated from other types of dwarfism by its clinical features and by the typical slender long bones and foreshortened vertebral bodies that can be visualized radiographically. 3M syndrome has an autosomal recessive mode of inheritance. An early diagnosis is important for genetic counseling. In this report, we present four children (3 males, 1 female) from two families who were aged between 4(11/12) and 10(11/12) years and had clinical findings of 3M syndrome. One of these patients had received growth hormone (GH) treatment which was discontinued due to an inadequate height gain. Physicians should be aware of this entity in the differential diagnosis of children with severe short stature and mild skeletal changes.
To offload or not to offload: An efficient code partition algorithm for mobile cloud computing

A new class of cognition augmenting applications such as face recognition or natural language processing is emerging for mobile devices. This kind of applications is computation and power intensive and a cloud infrastructure would provide a great potential to facilitate the code execution. Since these applications usually consist of many composable components, finding the optimal execution layout is difficult in real time. In this paper, we propose an efficient code partition algorithm for mobile code offloading. Our algorithm is based on the observation that when a method is offloaded, the subsequent invocations will be offloaded with a high chance. Unlike the current approach which makes an individual decision for each component, our algorithm finds the offloading and integrating points on a sequence of calls by depth-first search and a linear time searching scheme. Experimental results show that, compared with the 0-1 Integer Linear Programming solver, our algorithm runs 2 orders of magnitude faster with more than 90% partition accuracy.
Toward human-like motion planning in urban environments

Prior autonomous navigation systems focused on the demonstration of the technological feasibility. But as the technology evolves, improving user experience through learning expert's or individual's driving pattern emerges as a promising research direction. As a first step toward this goal, we investigate methods to learn from human demonstrations in urban scenarios without any environmental disturbances (traffic-free). We propose a path model that generates a reference path with smooth and peak-value-reduced curvature, and a parameterized speed model to be fitted by human driving data. Model parameters are then learned through regression methods, and certain statistical human driving patterns are revealed. The learned model is then evaluated by comparing the generated plan with the collected data by the same human driver.
Quasar: resource-efficient and QoS-aware cluster management

Cloud computing promises flexibility and high performance for users and high cost-efficiency for operators. Nevertheless, most cloud facilities operate at very low utilization, hurting both cost effectiveness and future scalability.
 We present Quasar, a cluster management system that increases resource utilization while providing consistently high application performance. Quasar employs three techniques. First, it does not rely on resource reservations, which lead to underutilization as users do not necessarily understand workload dynamics and physical resource requirements of complex codebases. Instead, users express performance constraints for each workload, letting Quasar determine the right amount of resources to meet these constraints at any point. Second, Quasar uses classification techniques to quickly and accurately determine the impact of the amount of resources (scale-out and scale-up), type of resources, and interference on performance for each workload and dataset. Third, it uses the classification results to jointly perform resource allocation and assignment, quickly exploring the large space of options for an efficient way to pack workloads on available resources. Quasar monitors workload performance and adjusts resource allocation and assignment when needed. We evaluate Quasar over a wide range of workload scenarios, including combinations of distributed analytics frameworks and low-latency, stateful services, both on a local cluster and a cluster of dedicated EC2 servers. At steady state, Quasar improves resource utilization by 47% in the 200-server EC2 cluster, while meeting performance constraints for workloads of all types.
kAFL: Hardware-Assisted Feedback Fuzzing for OS Kernels

Many kinds of memory safety vulnerabilities have been endangering software systems for decades. Amongst other approaches, fuzzing is a promising technique to unveil various software faults. Recently, feedback-guided fuzzing demonstrated its power, producing a steady stream of security-critical software bugs. Most fuzzing efforts—especially feedback fuzzing—are limited to user space components of an operating system (OS), although bugs in kernel components are more severe, because they allow an attacker to gain access to a system with full privileges. Unfortunately, kernel components are difficult to fuzz as feedback mechanisms (i.e., guided code coverage) cannot be easily applied. Additionally, non-determinism due to interrupts, kernel threads, statefulness, and similar mechanisms poses problems. Furthermore, if a process fuzzes its own kernel, a kernel crash highly impacts the performance of the fuzzer as the OS needs to reboot. In this paper, we approach the problem of coverageguided kernel fuzzing in an OS-independent and hardware-assisted way: We utilize a hypervisor and Intel’s Processor Trace (PT) technology. This allows us to remain independent of the target OS as we just require a small user space component that interacts with the targeted OS. As a result, our approach introduces almost no performance overhead, even in cases where the OS crashes, and performs up to 17,000 executions per second on an off-the-shelf laptop. We developed a framework called kernel-AFL (kAFL) to assess the security of Linux, macOS, and Windows kernel components. Among many crashes, we uncovered several flaws in the ext4 driver for Linux, the HFS and APFS file system of macOS, and the NTFS driver of Windows.
Reconstruction of Partially Occluded Face by Fast Recursive PCA

This paper proposes a fast recursive PCA (principal component analysis) algorithm to remove face occlusions. In training phase, all faces are normalized by two eye centers and two mouth corners, and eigenvectors (eigenfaces) were obtained by PCA analysis. In test phase, face occlusion is removed by iteratively perform two steps of analysis and synthesis. New damaged face is first normalized by clicking four feature points, and PCA coefficients are obtained in analysis step. In synthesis step, reconstructed face is obtained by linear combining eigenfaces, and coefficients error between two successive analyses is used for fast PCA compensation. Experimental results on training and test faces show that the proposed algorithm convergences faster than classical PCA compensation and reconstructed faces are natural.
Reinforcement Learning and Approximate Dynamic Programming for Feedback Control

reinforcement learning and approximate dynamic programming for feedback control are a good way to achieve details about operating certainproducts. Many products that you buy can be obtained using instruction manuals. These user guides are clearlybuilt to give step-by-step information about how you ought to go ahead in operating certain equipments. Ahandbook is really a user's guide to operating the equipments. Should you loose your best guide or even the productwould not provide an instructions, you can easily obtain one on the net. You can search for the manual of yourchoice online. Here, it is possible to work with google to browse through the available user guide and find the mainone you'll need. On the net, you'll be able to discover the manual that you might want with great ease andsimplicity