nils-herrmann/clean_scirepeval huggingface dataset:
A Novel Approach for Network Attack Classification Based on Sequential Questions

With the development of incipient technologies, user devices becoming more exposed and ill-used by foes. In upcoming decades, traditional security measures will not be sufficient enough to handle this huge threat towards distributed hardware and software. Lack of standard network attack taxonomy has become an indispensable dispute on developing a clear understanding about the attacks in order to have an operative protection mechanism. Present attack categorization techniques protect a specific group of threat which has either messed the entire taxonomy structure or ambiguous when one network attacks get blended with few others attacks. Hence, this raises concerns about developing a common and general purpose taxonomy. In this study, a sequential question-answer based model of categorization is proposed. In this article, an intrusion detection framework and threat grouping schema are proposed on the basis of four sequential questions (“Who”, “Where”, “How” and “What”). We have used our method for classifying traditional network attacks in order to identify initiator, source, attack style and seriousness of an attack. Another focus of the paper is to provide a preventive list of actions for network administrator as a guideline to reduce overall attack consequence. Recommended taxonomy is designed to detect common attacks rather than any particular type of attack which can have a practical effect in real life attack classification. From the analysis of the classifications obtained from few infamous attacks, it is obvious that the proposed system holds certain benefits related to the prevailing taxonomies. Future research directions have also been well acknowledged.
Obstacle avoidance for industrial AGVs

The paper deals with the obstacle avoidance problem for Automated Guided Vehicles (AGVs). We propose an automatic algorithm to build new obstacle-free trajectories that leave the original roadmap. The new path has to also comply with the dynamic and kinematic constraints of the AGV. The new paths are generated by means of polar spline curves, lane-change maneuver curves and line segments. The proposed method is validated by means of simulations.
A packing algorithm for non-Manhattan hexagon/triangle placement design by using an adaptive O-tree representation

A non-Manhattan Hexagon/Triangle Placement (HTP for short) paradigm is proposed in the present paper. Main feature of this paradigm lies in adapting to the Y- architecture which is one of the promising non-Manhattan VLSI circuit layout architectures. Aim of the HTP is to place a set of equilateral triangles with given size onto a hexagonal chip with maximal chip area usage. Based on the O-tree representation, some adaptive packing rules are adopted to develop an effective placement algorithm for solving the HTP problem in BBL mode. Two examples with benchmark data transformed from the Manhattan BBL mode placement (ami33/49) are presented to justify the feasibility and effectiveness of our algorithms. Experiment results demonstrate that the chip area usage of 94% is achieved through simulated annealing optimization.
Developing Nomad for robotic exploration of the Atacama Desert

Recent years have seen renewed attention to planetary exploration, and robotics is recognized as essential to many upcoming missions. In this article we describe the ongoing efforts of the Nomad Project to develop robots for planetary and terrestrial exploration. The project is developing, demonstrating, and evaluating systems capable of long-distance, long-duration missions. In 1997 this work has resulted in the Atacama Desert Trek, in which a mobile robot nmaed Nomad explored the Atacama Desert of northern Chile. Nomad’s 45-day objective was to travel 200 kilometers across the rugged, barren landscape. We will describe the technologies for Nomad's transforming chassis, high-data rate communication, safeguarded teleoperation and autonomy, panoramic imaging and visualization, and remote science. We then focus on issues of long-distance, long-duration exploration and describe the events and results of the Atacama
Efficacy of botulinum toxin A in upper limb function of hemiplegic patients

Botulinum toxin A has been reported to reduce spasticity and increase the comfort of hemiplegic patients. The aim of this study was to assess the efficacy of the treatment on disability, especially in manual activities, and to attempt to identify predictive factors of improvement. Twenty patients (mean age: 54.4 years; M: 14; right hemiplegia: 12) were included, with a delay of at least three months after unilateral hemispheric stroke. Botulinum toxin A (BOTOX®) was injected into the arm adductors (8 cases), forearm flexors (17 cases), pronators, wrist and finger flexors (20 cases), with a total dose of 200 to 300 U. Examination (day 1 and 15, month 2 and 5) consisted of spasticity assessment (modified Ashworth scale), muscle strength, passive range of motion (goniometry), and pain, followed by functional tests, especially the Rivermead Motor Assessment (RMA) and Nine-hole Peg Test (NHPT). Performance in daily living was assessed with the Functional Independence Measure (FIM), and an original analysis of hand grasp, grip and pinches used in domestic activities (9 items), and of comfort of patients and caregivers. Significant reduction in spasticity was observed on the elbow flexors, pronators, wrist and fingers flexors, especially at day 15 (mean 0.90 to 1 point), with wide variations in effect. Muscle strength was increased in wrist and fingers extensors, with concomitant increase in the opening of the thumb to index finger space. There was no effect on the NHPT requiring distal manipulation, but the RMA, which especially concerned picking up and releasing a tennis ball, showed significant improvement. Furthermore, use of the upper limb in daily living increased, particularly for internal grasping of objects, and for grasping by the top, transporting and releasing of objects. Patients and caregivers reported facilitation in dressing, and in proximal and distal care of the upper limb. The global flexor position of the limb improved. Adverse reactions were rare and mostly consisted of transitory pain during injection. The improvement in the RMA was better explained by the quality of the initial motor command on distal prehension (positive correlation with motor strength), and that in hand using in domestic activities by a lower level of spasticity on pronators and wrist flexors (negative correlations with spasticity). Conversely, the severity of the motor deficit (negative correlations with motor strength) and a high level of spasticity before injection (positive correlations with spasticity) mostly explained the improvement in comfort. In conclusion, botulinum toxin A is efficient in improving hand use in patients with relatively preserved distal motricity, and in increasing comfort in patients with severe global disorders.
A Tutorial on Reinforcement Learning Techniques

Reinforcement Learning (RL) is learning through direct experimentation. It does not assume the existence of a teacher that provides examples upon which learning of a task takes place. Instead, in RL experience is the only teacher. With historical roots on the study of conditioned reflexes, RL soon attracted the interest of Engineers and Computer Scientists because of its theoretical relevance and potential applications in fields as diverse as Operational Research and Robotics. Computationally, RL is intended to operate in a learning environment composed by two subjects: the learner and a dynamic process. At successive time steps, the learner makes an observation of the process state, selects an action and applies it back to the process. The goal of the learner is to find out an action policy that controls the behavior of this dynamic process, guided by signals (reinforcements) that indicate how well it is performing the required task. These signals are usually associated to some dramatic condition — e.g., accomplishment of a subtask (reward) or complete failure (punishment), and the learner’s goal is to optimize its behavior based on some performance measure (a function of the received reinforcements). The crucial point is that in order to do that, the learner must evaluate the conditions (associations between observed states and chosen actions) that lead to rewards or punishments. In other words, it must learn how to assign credit to past actions and states by correctly estimating costs associated to these events. Starting from basic concepts, this tutorial presents the many flavors of RL algorithms, develops the corresponding mathematical tools, assess their practical limitations and discusses alternatives that have been proposed for applying RL to realistic tasks, such as those involving large state spaces or partial observability. It relies on examples and diagrams to illustrate the main points, and provides many references to the specialized literature and to Internet sites where relevant demos and additional information can be obtained.
Fabric-based actuator modules for building soft pneumatic structures with high payload-to-weight ratio

This paper introduces a new concept of building soft pneumatic structures by assembling modular units of fabric-based rotary actuators (FRAs) and beams. Upon pressurization, the inner folds of FRA would expand, which causes the FRA module to unfold, generating angular displacement. Hence, FRAs would enable mobility function of the structure and its range of motion. The modular nature of the actuator units enables customized configuration of pneumatic structures, which can be modified and scaled by selecting the appropriate modules. FRAs are also designed to be bladder-less, that is, they are made without an additional layer of inner bladder. Thus, a simple fabrication process can be used to prepare the actuators. In this paper, we studied how the performance of the FRA modules changes with their dimensions and demonstrated how a soft gripper can be constructed using these modules. The kinematic response of the actuator segments of the gripper was analyzed and a pressure control algorithm was developed to regulate the pneumatic pressure of the actuator. The modular based soft robotic gripper alone weighs about 140g. Yet, based on the grip tests, it is able to lift heavier objects (up to 2.4kg), achieving a high payload-to-weight ratio of about 1714%, which is higher than the value reported by previously developed soft pneumatic grippers using elastomeric materials. Lastly, we also demonstrated that the gripper is capable of performing two essential grasping modes, which are power grasping and fingertip grasping, for objects of various shapes.
Can hardware performance counters be trusted?

When creating architectural tools, it is essential to know whether the generated results make sense. Comparing a toolpsilas outputs against hardware performance counters on an actual machine is a common means of executing a quick sanity check. If the results do not match, this can indicate problems with the tool, unknown interactions with the benchmarks being investigated, or even unexpected behavior of the real hardware. To make future analyses of this type easier, we explore the behavior of the SPEC benchmarks with both dynamic binary instrumentation (DBI) tools and hardware counters. We collect retired instruction performance counter data from the full SPEC CPU 2000 and 2006 benchmark suites on nine different implementations of the times86 architecture. When run with no special preparation, hardware counters have a coefficient of variation of up to 1.07%. After analyzing results in depth, we find that minor changes to the experimental setup reduce observed errors to less than 0.002% for all benchmarks. The fact that subtle changes in how experiments are conducted can largely impact observed results is unexpected, and it is important that researchers using these counters be aware of the issues involved.
Bearish-Bullish Sentiment Analysis on Financial Microblogs

User-generated data in blogs and social networks has recently become a valuable resource for sentiment analysis in the financial domain since it has been shown to be extremely significant to marketing research companies and public opinion organizations. In this paper a fine-grained approach is proposed to predict a real-valued sentiment score. We use several feature sets consisting of lexical features, semantic features and combination of lexical and semantic features. To evaluate our approach a microblog messages dataset is used. Since our dataset includes confidence scores of real numbers within the [0-1] range, we compare the performance of two learning methods: Random Forest and SVR. We test the results of the training model boosted by semantics against classification results obtained by n-grams. Our results indicate that our approach succeeds in performing the accuracy level of more than 72% in some cases.
Design of Multimode Net-Type Resonators and Their Applications to Filters and Multiplexers

Net-type resonators with dual- and tri-mode electrical behaviors have been presented and analyzed theoretically in this paper. The proposed net-type resonator is constructed by a short-ended and several open-ended transmission-line sections, which has the advantages of small size and more flexible resonant frequency allocation and is therefore particularly suitable for applications to the design of microwave devices. To verify the usefulness of the proposed resonators, three experimental examples, including a dual-mode bandpass filter, a dual-passband filter, and a triplexer, have been designed and fabricated with microstrip technology. Each of the designed circuits occupies a very small size and has a good upper stop and performance. All measured results are in good agreement with the full-wave simulation results.
Performance Measure Analysis between Anisotropic Diffusion Filter and Bilateral Filter for Post Processing of Fractal Compressed Medical Images

Filtering is Prime important processes in Medical Image processing applications. Any post processing process aims in the removal of unwanted noise which usually corrupts the image quality and perception. This research paper focuses on searching effective De noising filters for post processing of Fractal compressed Images on Medical Images like CT of Bone , MR Images of Brain ,Mammograms, Ultrasound Images of uterus. In this work Fractal Image Compression (FIC) a lossy compression scheme based on contractive mapping theorem is employed to map the Range blocks and Domain blocks by using the property of self similarity in the images. We have used two types of filters namely anisotropic diffusion filter and bilateral filter for the removal of noise in Medical images. The Peak signal to noise ratio (PSNR) was measured after applying the two different filters and a comparative analysis of PSNR values before and after filtering was recorded. The simulated results obtained showed an increase in PSNR value for bilateral filter than with anisotropic filter and also the quality of the image was improved.
Secure cloud storage using AES encryption

In cloud computing distributed resources are shared via network in open environment. Hence user can easily access their data from anywhere. At the same time there exist privacy and security issues due to many reasons. First one is dramatic development in network technologies. Another is increased demand for computing resources, which make many organizations to outsource their data storage. So there is a need for secure cloud storage service in public cloud environment where the provider is not a trusted one. This paper addresses different data security and privacy protection issues in a cloud computing environment and proposes a method for providing different security services like authentication, authorization and confidentiality along with monitoring in delay. 128 bit Advanced Encryption Standard (AES) is used for increase data security and confidentiality. In this proposed approach data is encrypted using AES and then uploaded on a cloud. The proposed model uses Short Message Service (SMS) alert mechanism for avoiding unauthorized access to user data.
Vibration-Induced Changes in the Contact Resistance of High Power Electrical Connectors for Hybrid Vehicles

Relatively little is known about the fretting mechanism of high power connectors used in hybrid vehicles, even though the vehicles are widely being introduced to the market. This paper experimentally investigates the fretting mechanisms of silver-plated high power connectors caused by vibrations. In order to emulate operational and environmental effects, a test stand was designed that is capable of measuring electrical contact resistance (ECR), relative displacement and connector temperature. The experimental results show that the variation of ECR of connectors subject to vibration is primarily due to periodic changes of contact area caused by relative motion between the contact interfaces, rather than other fretting corrosions. This finding was reinforced by observing a good correlation between relative motion and the increase of ECR under vibration. When a vibration stops, the ECR decreased to a value that is slightly larger than the original value. A surface analysis shows no obvious corrosion until the coating is worn away. In addition, the effect of high current on the fretting mechanism is also investigated.
Interactive cloth rendering of microcylinder appearance model under environment lighting

This paper proposes an interactive rendering method of cloth fabrics under environment lighting. The outgoing radiance from cloth fabrics in the microcylinder model is calculated by integrating the product of the distant environment lighting, the visibility function, the weighting function that includes shadowing/masking effects of threads, and the light scattering function of threads. The radiance calculation at each shading point of the cloth fabrics is simplified to a linear combination of triple product integrals of two circular Gaussians and the visibility function, multiplied by precomputed spherical Gaussian convolutions of the weighting function. We propose an efficient calculation method of the triple product of two circular Gaussians and the visibility function by using the gradient of signed distance function to the visibility boundary where the binary visibility changes in the angular domain of the hemisphere. Our GPU implementation enables interactive rendering of static cloth fabrics with dynamic viewpoints and lighting. In addition, interactive editing of parameters for the scattering function (e.g. thread’s albedo) that controls the visual appearances of cloth fabrics can be achieved.
Syntax-Directed Variational Autoencoder for Structured Data

Deep generative models have been enjoying success in modeling continuous data. However it remains challenging to capture the representations for discrete structures with formal grammars and semantics, e.g., computer programs and molecular structures. How to generate both syntactically and semantically correct data still remains largely an open problem. Inspired by the theory of compiler where the syntax and semantics check is done via syntax-directed translation (SDT), we propose a novel syntax-directed variational autoencoder (SD-VAE) by introducing stochastic lazy attributes. This approach converts the offline SDT check into on-the-fly generated guidance for constraining the decoder. Comparing to the state-of-the-art methods, our approach enforces constraints on the output space so that the output will be not only syntactically valid, but also semantically reasonable. We evaluate the proposed model with applications in programming language and molecules, including reconstruction and program/molecule optimization. The results demonstrate the effectiveness in incorporating syntactic and semantic constraints in discrete generative models, which is significantly better than current state-of-the-art approaches.
Ubiquitous Data Accessing Method in IoT-Based Information System for Emergency Medical Services

The rapid development of Internet of things (IoT) technology makes it possible for connecting various smart objects together through the Internet and providing more data interoperability methods for application purpose. Recent research shows more potential applications of IoT in information intensive industrial sectors such as healthcare services. However, the diversity of the objects in IoT causes the heterogeneity problem of the data format in IoT platform. Meanwhile, the use of IoT technology in applications has spurred the increase of real-time data, which makes the information storage and accessing more difficult and challenging. In this research, first a semantic data model is proposed to store and interpret IoT data. Then a resource-based data accessing method (UDA-IoT) is designed to acquire and process IoT data ubiquitously to improve the accessibility to IoT data resources. Finally, we present an IoT-based system for emergency medical services to demonstrate how to collect, integrate, and interoperate IoT data flexibly in order to provide support to emergency medical services. The result shows that the resource-based IoT data accessing method is effective in a distributed heterogeneous data environment for supporting data accessing timely and ubiquitously in a cloud and mobile computing platform.
A Compositional Interpretation of Biomedical Event Factuality

We propose a compositional method to assess the factuality of biomedical events extracted from the literature. The composition procedure relies on the notion of semantic embedding and a fine-grained classification of extrapropositional phenomena, including modality and valence shifting, and a dictionary based on this classification. The event factuality is computed as a product of the extra-propositional operators that have scope over the event. We evaluate our approach on the GENIA event corpus enriched with certainty level and polarity annotations. The results indicate that our approach is effective in identifying the certainty level component of factuality and is less successful in recognizing the other element, negative polarity.
Parallel-plate-waveguide luneburg lens through a holey plate metasurface

This paper presents a parallel plate waveguide (PPW) Luneburg lens designed using an array of subwavelength circular holes etched on a metallic plate printed on a dielectric substrate. Variation of the hole radius modulates the effective permittivity. The lens operates in the Ka band of frequencies in a quasi-TEM mode. Easy to implement and low cost, the lens could be a good candidate as a lens-like beamformer for low loss multiple beam applications.
Attention training and attention state training

The ability to attend and to exercise cognitive control are vital aspects of human adaptability. Several studies indicate that attention training using computer based exercises can lead to improved attention in children and adults. Randomized control studies of exposure to nature, mindfulness and integrative body-mind training (IBMT) yield improved attention and self-regulation. Here, we ask how attention training and attention state training might be similar and different in their training methods, neural mechanisms and behavioral outcomes. Together these various methods lead to practical ways of improving attention and self-regulation.