nils-herrmann/clean_scirepeval huggingface dataset:
Dexterous Robotic Manipulation of Deformable Objects with Multi-Sensory Feedback - a Review

Designing autonomous robotic systems able to manipulate deformable objects without human intervention constitutes a challenging area of research. The complexity of interactions between a robot manipulator and a deformable object originates from a wide range of deformation characteristics that have an impact on varying degrees of freedom. Such sophisticated interaction can only take place with the assistance of intelligent multisensory systems that combine vision data with force and tactile measurements. Hence, several issues must be considered at the robotic and sensory levels to develop genuine dexterous robotic manipulators for deformable objects. This chapter presents a thorough examination of the modern concepts developed by the robotic community related to deformable objects grasping and manipulation. Since the convention widely adopted in the literature is often to extend algorithms originally proposed for rigid objects, a comprehensive coverage on the new trends on rigid objects manipulation is initially proposed. State-of-the-art techniques on robotic interaction with deformable objects are then examined and discussed. The chapter proposes a critical evaluation of the manipulation algorithms, the instrumentation systems adopted and the examination of end-effector technologies, including dexterous robotic hands. The motivation for this review is to provide an extensive appreciation of state-of-the-art solutions to help researchers and developers determine the best possible options when designing autonomous robotic systems to interact with deformable objects. Typically in a robotic setup, when robot manipulators are programmed to perform their tasks, they must have a complete knowledge about the exact structure of the manipulated object (shape, surface texture, rigidity) and about its location in the environment (pose). For some of these tasks, the manipulator becomes in contact with the object. Hence, interaction forces and moments are developed and consequently these interaction forces and moments, as well as the position of the end-effector, must be controlled, which leads to the concept of “force controlled manipulation” (Natale, 2003). There are different control strategies used in 28
Solving the dynamic Vehicle Routing Problem using genetic algorithms

The Vehicle Routing Problem (VRP) is an important problem occurring in many logistics systems. The objective of VRP is to serve a set of customers at minimum cost, such that every node is visited by exactly one vehicle only once. In this paper, we consider the Dynamic Vehicle Routing Problem (DVRP) which new customer demands are received along the day. Hence, they must be serviced at their locations by a set of vehicles in real time minimizing the total travel distance. The main goal of this research is to find a solution of DVRP using genetic algorithm. However we used some heuristics in addition during generation of the initial population and crossover for tuning the system to obtain better result. The computational experiments were applied to 22 benchmarks instances with up to 385 customers and the effectiveness of the proposed approach is validated by comparing the computational results with those previously presented in the literature.
A comparative study of text classification algorithms on user submitted bug reports

In software industries, various open source projects utilize the services of Bug Tracking Systems that let users submit software issues or bugs and allow developers to respond to and fix them. The users label the reports as bugs or any other relevant class. This classification helps to decide which team or personnel would be responsible for dealing with an issue. A major problem here is that users tend to wrongly classify the issues, because of which a middleman called a bug triager is required to resolve any misclassifications. This ensures no time is wasted at the developer end. This approach is very time consuming and therefore it has been of great interest to automate the classification process, not only to speed things up, but to lower the amount of errors as well. In the literature, several approaches including machine learning techniques have been proposed to automate text classification. However, there has not been an extensive comparison on the performance of different natural language classifiers in this field. In this paper we compare general natural language data classifying techniques using five different machine learning algorithms: Naive Bayes, kNN, Pegasos, Rocchio and Perceptron. The performance comparison of these algorithms was done on the basis of their apparent error rates. The data-set involved four different projects, Httpclient, Jackrabbit, Lucene and Tomcat5, that used two different Bug Tracking Systems - Bugzilla and Jira. An experimental comparison of pre-processing techniques was also performed.
Insect resistance to Bt crops: lessons from the first billion acres

Evolution of resistance in pests can reduce the effectiveness of insecticidal proteins from Bacillus thuringiensis (Bt) produced by transgenic crops. We analyzed results of 77 studies from five continents reporting field monitoring data for resistance to Bt crops, empirical evaluation of factors affecting resistance or both. Although most pest populations remained susceptible, reduced efficacy of Bt crops caused by field-evolved resistance has been reported now for some populations of 5 of 13 major pest species examined, compared with resistant populations of only one pest species in 2005. Field outcomes support theoretical predictions that factors delaying resistance include recessive inheritance of resistance, low initial frequency of resistance alleles, abundant refuges of non-Bt host plants and two-toxin Bt crops deployed separately from one-toxin Bt crops. The results imply that proactive evaluation of the inheritance and initial frequency of resistance are useful for predicting the risk of resistance and improving strategies to sustain the effectiveness of Bt crops.
Mixed Precision Training

Increasing the size of a neural network typically improves accuracy but also increases the memory and compute requirements for training the model. We introduce methodology for training deep neural networks using half-precision floating point numbers, without losing model accuracy or having to modify hyperparameters. This nearly halves memory requirements and, on recent GPUs, speeds up arithmetic. Weights, activations, and gradients are stored in IEEE halfprecision format. Since this format has a narrower range than single-precision we propose three techniques for preventing the loss of critical information. Firstly, we recommend maintaining a single-precision copy of weights that accumulates the gradients after each optimizer step (this copy is rounded to half-precision for the forwardand back-propagation). Secondly, we propose loss-scaling to preserve gradient values with small magnitudes. Thirdly, we use half-precision arithmetic that accumulates into single-precision outputs, which are converted to halfprecision before storing to memory. We demonstrate that the proposed methodology works across a wide variety of tasks and modern large scale (exceeding 100 million parameters) model architectures, trained on large datasets.
DIVE: A Mixed-Initiative System Supporting Integrated Data Exploration Workflows

Generating knowledge from data is an increasingly important activity. This process of data exploration consists of multiple tasks: data ingestion, visualization, statistical analysis, and storytelling. Though these tasks are complementary, analysts often execute them in separate tools. Moreover, these tools have steep learning curves due to their reliance on manual query specification. Here, we describe the design and implementation of DIVE, a web-based system that integrates state-of-the-art data exploration features into a single tool. DIVE contributes a mixed-initiative interaction scheme that combines recommendation with point-and-click manual specification, and a consistent visual language that unifies different stages of the data exploration workflow. In a controlled user study with 67 professional data scientists, we find that DIVE users were significantly more successful and faster than Excel users at completing predefined data visualization and analysis tasks.
Underwater Electromagnetic Sensor Networks, Part II: Localization and Network Simulations

In the first part of the paper, we modeled and characterized the underwater radio channel in shallowwaters. In the second part,we analyze the application requirements for an underwaterwireless sensor network (U-WSN) operating in the same environment and perform detailed simulations. We consider two localization applications, namely self-localization and navigation aid, and propose algorithms that work well under the specific constraints associated with U-WSN, namely low connectivity, low data rates and high packet loss probability. We propose an algorithm where the sensor nodes collaboratively estimate their unknown positions in the network using a low number of anchor nodes and distance measurements from the underwater channel. Once the network has been self-located, we consider a node estimating its position for underwater navigation communicating with neighboring nodes. We also propose a communication system and simulate the whole electromagnetic U-WSN in the Castalia simulator to evaluate the network performance, including propagation impairments (e.g., noise, interference), radio parameters (e.g., modulation scheme, bandwidth, transmit power), hardware limitations (e.g., clock drift, transmission buffer) and complete MAC and routing protocols. We also explain the changes that have to be done to Castalia in order to perform the simulations. In addition, we propose a parametric model of the communication channel that matches well with the results from the first part of this paper. Finally, we provide simulation results for some illustrative scenarios.
( DEPSCOR FY 09 ) Obfuscation and Deobfuscation of Intent of Computer Programs

This research aimed at developing a theoretical framework to predict the next obfuscation (or deobfuscation) move of the adversary, with the intent of making cyber defense proactive. More specifically, the goal was to understand the relationship between obfuscation and deobfuscation techniques employed in malware offense and defense. The strategy was to build upon previous work of Giacobazzi and Dalla Preda on modeling obfuscation and deobfuscation as abstract interpretations. It furthers that effort by developing an analytical model of the best obfuscation with respect to a deobfuscator. In addition, this research aimed at developing cost models for obfuscation and deobfuscations. The key findings of this research include: a theoretical model of computing the best obfuscation for a deobfuscator, a method for context-sensitive analysis of obfuscated code, a method for learning obfuscation transformations used by a metamorphic engine, several insights into the use of machine learning in deobfuscation, and game-theoretic models of certain scenarios of offense-defense games in software protection.
A broadband 200W GaN push-pull power amplifier enhanced second harmonic suppression with point-symmetric 2-stage baluns

A broadband 200W Gallium Nitride (GaN) push-pull amplifier enhanced a second harmonic (2fo) suppression is presented. A point-symmetric 2-stage baluns are applied to enhance the 2fo suppression in input and output circuits. With the presented configuration, the 2fo suppression of 10dB is improved. The broadband amplifier is fabricated with the presented configuration. Between 0.2GHz and 0.8GHz, the 2fo distortion bellow −40dBc and power added efficiency (PAE) of over 45% can be achieved at the saturated power of over 52dBm.
An overview of vulnerability assessment and penetration testing techniques

All Internet facing systems and applications carry security risks. Security professionals across the globe generally address these security risks by Vulnerability Assessment and Penetration Testing (VAPT). The VAPT is an offensive way of defending the cyber assets of an organization. It consists of two major parts, namely Vulnerability Assessment (VA) and Penetration Testing (PT). Vulnerability assessment, includes the use of various automated tools and manual testing techniques to determine the security posture of the target system. In this step all the breach points and loopholes are found. These breach points/loopholes if found by an attacker can lead to heavy data loss and fraudulent intrusion activities. In Penetration testing the tester simulates the activities of a malicious attacker who tries to exploit the vulnerabilities of the target system. In this step the identified set of vulnerabilities in VA is used as input vector. This process of VAPT helps in assessing the effectiveness of the security measures that are present on the target system. In this paper we have described the entire process of VAPT, along with all the methodologies, models and standards. A shortlisted set of efficient and popular open source/free tools which are useful in conducting VAPT and the required list of precautions is given. A case study of a VAPT test conducted on a bank system using the shortlisted tools is also discussed.
A review paper on attendance marking system based on face recognition

Providing accurate attendance marking system in real-time is challenging. It is tough to mark the attendance of a student in the large classroom when there are many students attending the class. Many attendance management systems have been implemented in the recent research. However, the attendance management system based on facial recognition still has issues. Thus many research have been conducted to improve system. This paper reviewed the previous works on attendance management system based on facial recognition. This article does not only provide the literature review on the earlier work or related work, but it also provides the deep analysis of Principal Component Analysis, discussion, suggestions for future work.
ENVIRONMENTAL ACCOUNTING AS PERSPECTIVE FOR HOTEL SUSTAINABILITY : LITERATURE REVIEW

Competitive environment and numerous stakeholders’ pressures are forcing hotels to comply their operations with the principles of sustainable development, especially in the field of environmental responsibility. Therefore, more and more of them incorporate environmental objectives in their business policies and strategies. The fulfilment of the environmental objectives requires the hotel to develop and implement environmentally sustainable business practices, as well as to implement reliable tools to assess environmental impact, of which environmental accounting and reporting are particularly emphasized. The purpose of this paper is to determine the development of hotel environmental accounting practices, based on previous research and literature review.  This paper provides an overview of current research in the field of hotel environmental accounting and reporting, based on established knowledge about hotel environmental responsibility. The research has been done according to the review of articles in academic journals. Conclusions about the requirements for achieving hotel long-term sustainability have been drawn.  Previous studies have shown that environmental accounting and reporting practice in hotel business is weaker when compared to other activities, and that most hotels still insufficiently use the abovementioned instruments of environmental management to reduce their environmental footprint and to improve their relationship with stakeholders. The paper draws conclusions about possible perspectives that environmental accounting has in ensuring hotel sustainability.  The study provides insights into the problem of environmental responsibility of hotels, from the standpoint of environmental accounting and reporting, as tools for assessing hotel impact on the environment and for improving its environmentally sustainable business practice. The ideas for improving hotel environmental efficiency are shaped based on previous findings.
Minimally invasive levator advancement: a practical approach to eyelid ptosis repair.

There are many established plastic surgical techniques to effectively address blepharoptosis. Minimally invasive levator advancement (MILA) causes limited disruption to the anatomy while maintaining good height, contour, lid folds, function, and long-term stability. This procedure has been performed in more than 1000 patients since 1993 by the author with consistent, durable results and is a reliable method to correct blepharoptosis. It is not indicated in cases with absent to very poor levator function, where frontalis suspension is the preferred procedure. The MILA technique will be described and illustrated.
Deep Recurrent and Convolutional Networks for Accelerated Fault Tolerant Adaptive Flight Control Under Severe Failures

Design of fault tolerant systems is a popular subject in flight control system design. In particular, adaptive control approach has been successful in recovering aircraft in a wide variety of different actuator/sensor failure scenarios. However, if the aircraft goes under a severe actuator failure, control system might not be able to adapt fast enough to changes in the dynamics, which would result in performance degradation or even loss of the aircraft. Inspired by the recent success of deep learning applications, this work builds a hybrid recurren-t/convolutional neural network model to estimate adaptation parameters for aircraft dynamics under actuator/engine faults. The model is trained offline from a database of different failure scenarios. In case of an actuator/engine failure, the model identifies adaptation parameters and feeds this information to the adaptive control system, which results in significantly faster convergence of the controller coefficients. Developed control system is implemented on a nonlinear 6-DOF F-16 aircraft, and the results show that the proposed architecture is especially beneficial in severe failure scenarios.
Efficient embedded software development using QEMU

Software based processor emulators have been around for a long time and predominantly used by developers of Operating Systems. With virtualization technology becoming more common and gaining in-processor support, these emulators have expanded to support several processor architectures and a richer feature set. Consequently, popular operating systems such as Linux have been adapted to run within virtual environments seamlessly. As part of the software development process, this virtualization technology has huge potential to increase product quality and developer efficiency. This paper looks at employing virtualization for in-house real-time application software development through QEMU, a popular open source emulator. It also details the process of establishing such an environment and looks at some of the advantages of such a development model for embedded systems.
A Novel Approach to Trust Management in Unattended Wireless Sensor Networks

Unattended Wireless Sensor Networks (UWSNs) are characterized by long periods of disconnected operation and fixed or irregular intervals between sink visits. The absence of an online trusted third party implies that existing WSN trust management schemes are not applicable to UWSNs. In this paper, we propose a trust management scheme for UWSNs to provide efficient and robust trust data storage and trust generation. For trust data storage, we employ a geographic hash table to identify storage nodes and to significantly decrease storage cost. We use subjective logic based consensus techniques to mitigate trust fluctuations caused by environmental factors. We exploit a set of trust similarity functions to detect trust outliers and to sustain trust pollution attacks. We demonstrate, through extensive analyses and simulations, that the proposed scheme is efficient, robust and scalable.
Analog man-in-the-middle attack against link-based packet source identification

A novel attack model is proposed against the existing wireless link-based source identification, which classifies packet sources according to the physical-layer link signatures. A link signature is believed to be a more reliable indicator than an IP or MAC address for identifying packet source, as it is generally harder to modify/forge. It is therefore expected to be a future authentication against impersonation and DoS attacks. However, if an attacker is equipped with the same capability/hardware as the authenticator to process physical-layer signals, a link signature can be easily manipulated by any nearby wireless device during the training phase. Based on this finding, we propose an attack model, called the analog man-in-the-middle (AMITM) attack, which utilizes the latest full-duplex relay technology to inject semi-controlled link signatures into authorized packets and reproduce the injected signature in the fabricated packets. Our experimental evaluation shows that with a proper parameter setting, 90% of fabricated packets are classified as those sent from an authorized transmitter. A countermeasure against this new attack is also proposed for the authenticator to inject link-signature noise by the same attack methodology.
A Lightweight Causal Message Logging Protocol to Lower Fault Tolerance Overhead

Rollback recovery is a trustworthy and key approach to fault tolerance in high performance computing and to parallel program debugging. In various rollback recovery protocols, causal message logging shows some desirable characteristics, but its high piggybacking overhead obstructs its applications, especially in large-scale distributed systems. Its high overhead arises from its conservation in the assumption on program execution model. This paper identifies the influence of non-deterministic message delivery on the correct outcome of a process, and then gives a scheme to relax the constraints from the piecewise deterministic execution model. Subsequently, a lightweight implementation of causal message logging is proposed to decrease the overhead of piggybacking and rolling forward. The experimental results of 3 NAS NPB2.3 benchmarks show that the proposed scheme achieves a significant improvement in the overhead reduction.