nils-herrmann/clean_scirepeval huggingface dataset:
Underwater Wireless Sensor Networks: A New Challenge for Topology Control-Based Systems

Underwater wireless sensor networks (UWSNs) will pave the way for a new era of underwater monitoring and actuation applications. The envisioned landscape of UWSN applications will help us learn more about our oceans, as well as about what lies beneath them. They are expected to change the current reality where no more than 5% of the volume of the oceans has been observed by humans. However, to enable large deployments of UWSNs, networking solutions toward efficient and reliable underwater data collection need to be investigated and proposed. In this context, the use of topology control algorithms for a suitable, autonomous, and on-the-fly organization of the UWSN topology might mitigate the undesired effects of underwater wireless communications and consequently improve the performance of networking services and protocols designed for UWSNs. This article presents and discusses the intrinsic properties, potentials, and current research challenges of topology control in underwater sensor networks. We propose to classify topology control algorithms based on the principal methodology used to change the network topology. They can be categorized in three major groups: power control, wireless interface mode management, and mobility assisted–based techniques. Using the proposed classification, we survey the current state of the art and present an in-depth discussion of topology control solutions designed for UWSNs.
Controlled switching of transformers - effects of closing time scatter and residual flux uncertainty

Controlled switching taking into account the residual flux can - in theory - completely eliminate transient transformer inrush currents. Real factors such as closing time scatter of the circuit breaker and uncertainty in the residual flux measurement significantly reduce its performance in the field. To quantify the acceptable tolerances of these factors if no inrush currents have to occur, a systematic inrush current study was carried out in the laboratory. Instantaneous switches and ideal residual flux measurement in combination with a controller unit are used to emulate the scatter respectively the uncertainty by presetting deterministic deviations. The analyses were carried out for ldquorapid closing strategyrdquo and ldquodelayed closing strategyrdquo and showed that the acceptable tolerances for both strategies can be evaluated independent of the controlled switching strategy with the inrush current studies of the first phase that is energized.
Implementing a Web Browser with Phishing Detection Techniques

Phishing is the combination of social engineering and technical exploits designed to convince a victim to provide personal information, usually for the monetary gain of the attacker. Phishing has become the most popular practice among the criminals of the Web. Phishing attacks are becoming more frequent and sophisticated. The impact of phishing is drastic and significant since it can involve the risk of identity theft and financial losses. Phishing scams have become a problem for online banking and e-commerce users. In this paper we propose a novel approach to detect phishing attacks. We implemented a prototype web browser which can be used as an agent and processes each arriving email for phishing attacks. Using email data collected over a period time we demonstrate data that our approach is able to detect more phishing attacks than existing schemes. KeywordsPhishing detection; Web browser.
Erasure code-based low storage blockchain node

The concept of a decentralized ledger usually implies that each node of a blockchain network stores the entire blockchain. However, in the case of popular blockchains, which each weigh several hundreds of GB, the large amount of data to be stored can incite new or low-capacity nodes to run lightweight clients. Such nodes do not participate to the global storage effort and can result in a centralization of the blockchain by very few nodes, which is contrary to the basic concepts of a blockchain. To avoid this problem, we propose new low storage nodes that store a reduced amount of data generated from the blockchain by using erasure codes. The properties of this technique ensure that any block of the chain can be easily rebuilt from a small number of such nodes. This system should encourage low storage nodes to contribute to the storage of the blockchain and to maintain decentralization despite of a globally increasing size of the blockchain. This system paves the way to new types of blockchains which would only be managed by low capacity nodes.
Guaranteeing Safety and Liveness of Unmanned Aerial Vehicle Platoons on Air Highways

Recently, there has been immense interest in using unmanned aerial vehicles (UAVs) for civilian operations such as package delivery, aerial surveillance, and disaster response. As a result, UAV traffic management systems are needed to support potentially thousands of UAVs flying simultaneously in the air space, in order to ensure their liveness and safety requirements are met. Currently, the analysis of large multi-agent systems cannot tractably provide these guarantees if the agents’ set of maneuvers are unrestricted. In this paper, we propose to have platoons of UAVs flying on air highways in order to impose the air space structure that allows for tractable analysis and intuitive monitoring. For the air highway placement problem, we use the flexible and efficient fast marching method to solve the Eikonal equation, which produces a sequence of air highways that minimizes the cost of flying from an origin to any destination. Within the platoons that travel on the air highways, we model each vehicle as a hybrid system with modes corresponding to its role in the platoon. Using Hamilton-Jacobi reachability, we propose several liveness controllers and a safety controller that guarantee the success and safety of all mode transitions. For a single altitude range, our approach guarantees safety for one safety breach per vehicle; in the unlikely event of multiple safety breaches, safety can be guaranteed over multiple altitude ranges. We demonstrate the satisfaction of liveness and safety requirements through simulations of three common scenarios.
EXTRUSION INSTABILITIES AND WALL SLIP

Many fabrication processes for polymeric objects include melt extrusion, in which the molten polymer is conveyed by a ram or a screw and the melt is then forced through a shaping die in continuous processing or into a mold for the manufacture of discrete molded parts. The properties of the fabricated solid object, including morphology developed during cooling and solidification, depend in part on the stresses and orientation induced during the melt shaping. Most polymers used for commercial processing are of sufficiently high molecular weight that the polymer chains are highly entangled in the melt, resulting in flow behavior that differs qualitatively from that of low-molecular-weight liquids. Obvious manifestations of the differences from classical Newtonian fluids are a strongly shear-dependent viscosity and finite stresses normal to the direction of shear in rectilinear flow, transients of the order of seconds for the buildup or relaxation of stresses following a change in shear rate, a finite phase angle between stress and shear rate in oscillatory shear, ratios of extensional to shear viscosities that are considerably greater than 3, and substantial extrudate swell on extrusion from a capillary or slit. These rheological characteristics of molten polymers have been reviewed in textbooks (e.g. Larson 1999, Macosko 1994); the recent research emphasis in rheology has been to establish meaningful constitutive models that incorporate chain behavior at a molecular level. All polymer melts and concentrated solutions exhibit instabilities during extrusion when the stresses to which they are subjected become sufficiently high. The first manifestation of extrusion instability is usually the appearance of distortions on the extrudate surface, sometimes accompanied by oscillating flow. Gross distortion of the extrudate usually follows. The sequence of extrudate distortions
Children’s imitation of causal action sequences is influenced by statistical and pedagogical evidence

Children are ubiquitous imitators, but how do they decide which actions to imitate? One possibility is that children rationally combine multiple sources of information about which actions are necessary to cause a particular outcome. For instance, children might learn from contingencies between action sequences and outcomes across repeated demonstrations, and they might also use information about the actor's knowledge state and pedagogical intentions. We define a Bayesian model that predicts children will decide whether to imitate part or all of an action sequence based on both the pattern of statistical evidence and the demonstrator's pedagogical stance. To test this prediction, we conducted an experiment in which preschool children watched an experimenter repeatedly perform sequences of varying actions followed by an outcome. Children's imitation of sequences that produced the outcome increased, in some cases resulting in production of shorter sequences of actions that the children had never seen performed in isolation. A second experiment established that children interpret the same statistical evidence differently when it comes from a knowledgeable teacher versus a naïve demonstrator. In particular, in the pedagogical case children are more likely to "overimitate" by reproducing the entire demonstrated sequence. This behavior is consistent with our model's predictions, and suggests that children attend to both statistical and pedagogical evidence in deciding which actions to imitate, rather than obligately imitating successful action sequences.
Is Post-Traumatic Stress Disorder Associated with Premature Senescence? A Review of the Literature.

Post-traumatic stress disorder (PTSD) has major public health significance. Evidence that PTSD may be associated with premature senescence (early or accelerated aging) would have major implications for quality of life and healthcare policy. We conducted a comprehensive review of published empirical studies relevant to early aging in PTSD.



Our search included the PubMed, PsycINFO, and PILOTS databases for empirical reports published since the year 2000 relevant to early senescence and PTSD, including: 1) biomarkers of senescence (leukocyte telomere length [LTL] and pro-inflammatory markers), 2) prevalence of senescence-associated medical conditions, and 3) mortality rates.



All six studies examining LTL indicated reduced LTL in PTSD (pooled Cohen's d = 0.76). We also found consistent evidence of increased pro-inflammatory markers in PTSD (mean Cohen's ds), including C-reactive protein = 0.18, Interleukin-1 beta = 0.44, Interleukin-6 = 0.78, and tumor necrosis factor alpha = 0.81. The majority of reviewed studies also indicated increased medical comorbidity among several targeted conditions known to be associated with normal aging, including cardiovascular disease, type 2 diabetes mellitus, gastrointestinal ulcer disease, and dementia. We also found seven of 10 studies indicated PTSD to be associated with earlier mortality (average hazard ratio: 1.29).



In short, evidence from multiple lines of investigation suggests that PTSD may be associated with a phenotype of accelerated senescence. Further research is critical to understand the nature of this association. There may be a need to re-conceptualize PTSD beyond the boundaries of mental illness, and instead as a full systemic disorder.
Towards execution time estimation in abstract machine-based languages

provide a certain separation between platform-dependent and platform-independent concerns in compilation. Many of the differences between architectures are encapsulated in the specific abstract machine implementation and the bytecode is left largely architecture independent. Taking advantage of this fact, we present a framework for estimating upper and lower bounds on the execution times of logic programs running on a bytecode-based abstract machine. Our approach includes a one-time, program-independent profiling stage which calculates constants or functions bounding the execution time of each abstract machine instruction. Then, a compile-time cost estimation phase, using the instruction timing information, infers expressions giving platform-dependent upper and lower bounds on actual execution time as functions of input data sizes for each program. Working at the abstract machine level makes it possible to take into account low-level issues in new architectures and platforms by just reexecuting the calibration stage instead of having to tailor the analysis for each architecture and platform. Applications of such predicted execution times include debugging/verification of time properties, certification of time properties in mobile code, granularity control in parallel/distributed computing, and resource-oriented specialization
Enhancing Utility and Privacy using t-closeness For Multiple Sensitive Attributes

Organizations publish the individual’s information in order to utilize the data for the research purpose. But the confidential information about the individual is revealed by the adversary by combining the various releases of the several organizations. This is called as linkage attacks. This attack can be avoided by the SLOMS method which vertically partitions the single quasi table and multiple sensitive tables. The SLOMS method uses MSB-KACA algorithm to generalize the quasi identifier table in order to implement k-Anonymity and bucketizes the sensitive attribute table to implement l-diversity. But there is a chance of probabilistic inference attack due to bucketization. So, the method called t-closeness can be applied over MSB-KACA algorithm which compute the value using Earth Mover Distance(EMD) and set the minimum value as threshold in order to equally distribute the attributes in the table based on the threshold ’t’. Such that the probabilistic inference attack can be avoided. The performance of t-closeness gets improved and evaluated by Disclosure rate which becomes minimal while comparing with MSB-KACA algorithm.
Potassium Starvation Limits Soybean Growth More than the Photosynthetic Processes across CO2 Levels

Elevated carbon dioxide (eCO2) often enhances plant photosynthesis, growth, and productivity. However, under nutrient-limited conditions the beneficial effects of high CO2 are often diminished. To evaluate the combined effects of potassium (K) deficiency and eCO2 on soybean photosynthesis, growth, biomass partitioning, and yields, plants were grown under controlled environment conditions with an adequate (control, 5.0 mM) and two deficient (0.50 and 0.02 mM) levels of K under ambient CO2 (aCO2; 400 μmol mol-1) and eCO2 (800 μmol mol-1). Results showed that K deficiency limited soybean growth traits more than photosynthetic processes. An ~54% reduction in leaf K concentration under 0.5 mM K vs. the control caused about 45% less leaf area, biomass, and yield without decreasing photosynthetic rate (Pnet). In fact, the steady photochemical quenching, efficiency, and quantum yield of photosystem II, chlorophyll concentration (TChl), and stomatal conductance under 0.5 mM K supported the stable Pnet. Biomass decline was primarily attributed to the reduced plant size and leaf area, and decreased pod numbers and seed yield in K-deficient plants. Under severe K deficiency (0.02 mM K), photosynthetic processes declined concomitantly with growth and productivity. Increased specific leaf weight, biomass partitioning to the leaves, decreased photochemical quenching and TChl, and smaller plant size to reduce the nutrient demands appeared to be the means by which plants adjusted to the severe K starvation. Increased K utilization efficiency indicated the ability of K-deficient plants to better utilize the tissue-available K for biomass accumulation, except under severe K starvation. The enhancement of soybean growth by eCO2 was dependent on the levels of K, leading to a K × CO2 interaction for traits such as leaf area, biomass, and yield. A lack of eCO2-mediated growth and photosynthesis stimulation under severe K deficiency underscored the importance of optimum K fertilization for maximum crop productivity under eCO2. Thus, eCO2 compensated, at least partially, for the reduced soybean growth and seed yield under 0.5 mM K supply, but severe K deficiency completely suppressed the eCO2-enhanced seed yield.
Data warehouse design approaches from social media: review and comparison

With the rise of social media in our life, several decision makers have worked on these networks to make better decisions. In order to benefit from the data issued from these media, many researchers focused on helping companies understand how to perform a social media competitive analysis and transform these data into knowledge for decision makers. A high number of users interact at any time on different ways in social media such as by expressing their opinions about products, services or transaction related to the organization which can prove very helpful for making better projections. In this paper, we provide a literature review on data warehouse design approaches from social media. More precisely, we start by introducing the main concepts of data warehouse and social media. We also propose two classes of data warehouse design approaches from social media (behavior analysis and integration of sentiment analysis in data warehouse schema) and expose for each one the most representative existing works. Afterward, we propose a comparative study of the existing works.
Identifying the Top Threats in Cloud Computing and Its Suggested Solutions : A Survey

The interest in cloud computing has increased rapidly in the last two decades. This increased interest is attributed to the important role played by cloud computing in the various aspects of our life. Cloud computing is recently emerged as a new paradigm for hosting and delivering services over the Internet. It is attractive to business owners as well as to researchers as it eliminates the requirement for users to plan ahead for provisioning, and allows enterprises to start from the small and increase resources only when there is a rise in service demand. As cloud computing is done through the Internet, it faces several kinds of threats due to its nature, where it depends on the network and its users who are distributed around the world. These threats differ in type, its side effect, its reasons, and its main purposes. This survey presents the most critical threats to cloud computing with its impacts, its reasons, and some suggested solutions. In addition, this survey determines what the main aspects of the cloud and the security attributes that are affected by each one of these threats. As a result of this survey, we order the most critical threats according to the level of its impact.
An Approach to Face Detection and Feature Extraction using Canny Method

This paper presents a hybrid approach to face detection and feature extraction. The remarkable advancement in technology has enhanced the use of more accurate and precise methods to detect faces. This paper presents a combination of three well known algorithms ViolaJones face detection framework, Neural Networks and Canny edge detection method to detect face in static images. The proposed work emphasizes on the face detection and identification using Viola-Jones algorithm which is a real time face detection system. Neural Networks will be used as a classifier between faces and non-faces. Canny edge detection method is an efficient method for detecting boundaries on a face in this proposed work. The Canny edge detector is primarily useful to locate sharp intensity changes and to find object boundaries in an image.
Communication between plants: induced resistance in wild tobacco plants following clipping of neighboring sagebrush

The possibility of communication between plants was proposed nearly 20 years ago, although previous demonstrations have suffered from methodological problems and have not been widely accepted. Here we report the first rigorous, experimental evidence demonstrating that undamaged plants respond to cues released by neighbors to induce higher levels of resistance against herbivores in nature. Sagebrush plants that were clipped in the field released a pulse of an epimer of methyl jasmonate that has been shown to be a volatile signal capable of inducing resistance in wild tobacco. Wild tobacco plants with clipped sagebrush neighbors had increased levels of the putative defensive oxidative enzyme, polyphenol oxidase, relative to control tobacco plants with unclipped sagebrush neighbors. Tobacco plants near clipped sagebrush experienced greatly reduced levels of leaf damage by grasshoppers and cutworms during three field seasons compared to unclipped controls. This result was not caused by an altered light regime experienced by tobacco near clipped neighbors. Barriers to soil contact between tobacco and sagebrush did not reduce the difference in leaf damage although barriers that blocked air contact negated the effect.
Structure Learning for Deep Neural Networks Based on Multiobjective Optimization

This paper focuses on the connecting structure of deep neural networks and proposes a layerwise structure learning method based on multiobjective optimization. A model with better generalization can be obtained by reducing the connecting parameters in deep networks. The aim is to find the optimal structure with high representation ability and better generalization for each layer. Then, the visible data are modeled with respect to structure based on the products of experts. In order to mitigate the difficulty of estimating the denominator in PoE, the denominator is simplified and taken as another objective, i.e., the connecting sparsity. Moreover, for the consideration of the contradictory nature between the representation ability and the network connecting sparsity, the multiobjective model is established. An improved multiobjective evolutionary algorithm is used to solve this model. Two tricks are designed to decrease the computational cost according to the properties of input data. The experiments on single-layer level, hierarchical level, and application level demonstrate the effectiveness of the proposed algorithm, and the learned structures can improve the performance of deep neural networks.
Executive Function and the Frontal Lobes: A Meta-Analytic Review

Currently, there is debate among scholars regarding how to operationalize and measure executive functions. These functions generally are referred to as “supervisory” cognitive processes because they involve higher level organization and execution of complex thoughts and behavior. Although conceptualizations vary regarding what mental processes actually constitute the “executive function” construct, there has been a historical linkage of these “higher-level” processes with the frontal lobes. In fact, many investigators have used the term “frontal functions” synonymously with “executive functions” despite evidence that contradicts this synonymous usage. The current review provides a critical analysis of lesion and neuroimaging studies using three popular executive function measures (Wisconsin Card Sorting Test, Phonemic Verbal Fluency, and Stroop Color Word Interference Test) in order to examine the validity of the executive function construct in terms of its relation to activation and damage to the frontal lobes. Empirical lesion data are examined via meta-analysis procedures along with formula derivatives. Results reveal mixed evidence that does not support a one-to-one relationship between executive functions and frontal lobe activity. The paper concludes with a discussion of the implications of construing the validity of these neuropsychological tests in anatomical, rather than cognitive and behavioral, terms.