nils-herrmann/clean_scirepeval huggingface dataset:
Path planning of automated guided vehicles based on improved A-Star algorithm

With the development of automated logistics systems, flexible manufacture systems (FMS) and unmanned automated factories, the application of automated guided vehicle (AGV) gradually become more important to improve production efficiency and logistics automatism for enterprises. The development of the AGV systems play an important role in reducing labor cost, improving working conditions, unifying information flow and logistics. Path planning has been a key issue in AGV control system. In this paper, two key problems, shortest time path planning and collision in multi AGV have been solved. An improved A-Star (A*) algorithm is proposed, which introduces factors of turning, and edge removal based on the improved A* algorithm is adopted to solve k shortest path problem. Meanwhile, a dynamic path planning method based on A* algorithm which searches effectively the shortest-time path and avoids collision has been presented. Finally, simulation and experiment have been conducted to prove the feasibility of the algorithm.
IoT home gateway for auto-configuration and management of MQTT devices

IoT service in home domain needs common and effective ways to manage various appliances and devices. So, the home environment needs a gateway that provides dynamical device registration and discovery. In this paper, we propose the IoT Home Gateway that supports abstracted device data to remove heterogeneity, device discovery by DPWS, Auto-configuration for constrained devices such as Arduino. Also, the IoT Home Gateway provides lightweight information delivery using MQTT protocol. In addition, we show implementation results that access and control the device according to the home energy saving scenario.
A psychoacoustic approach to combined acoustic echo cancellation and noise reduction

This paper presents and compares algorithms for combined acoustic echo cancellation and noise reduction for hands-free telephones. A structure is proposed, consisting of a conventional acoustic echo canceler and a frequency domain postfilter in the sending path of the hands-free system. The postfilter applies the spectral weighting technique and attenuates both the background noise and the residual echo which remains after imperfect echo cancellation. Two weighting rules for the postfilter are discussed. The first is a conventional one, known from noise reduction, which is extended to attenuate residual echo as well as noise. The second is a psychoacoustically motivated weighting rule. Both rules are evaluated and compared by instrumental and auditive tests. They succeed about equally well in attenuating the noise and the residual echo. In listening tests, however, the psychoacoustically motivated weighting rule is mostly preferred since it leads to more natural near end speech and to less annoying residual noise.
Flight control of tethered kites and winch control for autonomous airborne wind energy generation in pumping cycles

Energy harvesting based on tethered kites makes use of the advantage, that these airborne wind energy systems are able to exploit higher wind speeds at higher altitudes. The setup, considered in this paper, is based on the pumping cycle, which generates energy by winching out at high tether forces, driving an electrical generator while flying crosswind and winching in at a stationary neutral position, thus leaving a net amount of generated energy. The economic operation of such airborne wind energy plants demands for a reliable control system allowing for a complete autonomous operation of cycles. This task involves the flight control of the kite as well as the operation of a winch for the tether. The focus of this paper is put on the flight control, which implements an accurate direction control towards target points allowing for eight-down pattern flights. In addition, efficient winch control strategies are provided. The paper summarises a simple comprehensible model with equations of motion in order to motivate the approach of the control system design. After an extended overview on the control system, the flight controller parts are discussed in detail. Subsequently, the winch strategies based on an optimisation scheme are presented. In order to demonstrate the real world functionality of the presented algorithms, flight data from a fully automated pumping-cycle operation of a small-scale prototype setup based on a 30 m2 kite and a 50 kW electrical motor/generator is given.
Novel MHMIC millimeter wave power divider/combiner

This paper presents a novel ring power divider/combiner operating in the 60 GHz band. The circuit is integrated on a thin ceramic substrate, having a relative permittivity of 9.9 and a thickness of 127 μm. The agreement between scattering parameter measurements and simulations ensures very good RF performance and manufacturability. The measured transmission magnitudes are less than 3.5 dB, while the overall imbalance is less than 0.25 dB. In addition, the matching of all ports is less than −15 dB, and the measured isolation factor between the two output ports is at least 25 dB. The measured transmission phase imbalance is less than 2.2° over the whole 10 GHz of bandwidth.
A hybrid recommendation technique based on product category attributes

0957-4174/$ see front matter 2009  A doi:10.1016/j.eswa.2009.03.046 * Corresponding author.  E-mail address: shahbazi_mo@yahoo.com (M. Sha Recommender systems are powerful tools that allow companies to present personalized offers to their customers and defined as a system which recommends an appropriate product or service after learning the customers’ preferences and desires. Extracting users’ preferences through their buying behavior and history of purchased products is the most important element of such systems. Due to users’ unlimited and unpredictable desires, identifying their preferences is very complicated process. In most researches, less attention has been paid to user’s preferences varieties in different product categories. This may decrease quality of recommended items. In this paper, we introduced a technique of recommendation in the context of online retail store which extracts user preferences in each product category separately and provides more personalized recommendations through employing product taxonomy, attributes of product categories, web usage mining and combination of two well-known filtering methods: collaborative and content-based filtering. Experimental results show that proposed technique improves quality, as compared to similar approaches.
Automotive power generation and control

This paper describes some new developments in the application of power electronics to automotive power generation and control. A new load-matching technique is introduced that uses a simple switched-mode rectifier to achieve dramatic increases in peak and average power output from a conventional Lundell alternator, along with substantial improvements in efficiency. Experimental results demonstrate these capability improvements. Additional performance and functionality improvements of particular value for high-voltage (e.g., 42 V) alternators are also demonstrated. Tight load-dump transient suppression can be achieved using this new architecture. It is also shown that the alternator system can be used to implement jump charging (the charging of the high-voltage system battery from a low-voltage source). Dual-output extensions of the technique (e.g., 42/14 V) are also introduced. The new technology preserves the simplicity and low cost of conventional alternator designs, and can be implemented within the existing manufacturing infrastructure.
Cyber Crime Assessment

In physical crime the concepts of crime scene assessment and offender profiling are well-accepted if occasionally controversial. Work by the FBI has put profiling into the mainstream of investigative theories. However, in the digital world no such progress has been made. Further, the notion of offender profiling addresses the psychological continuum whereas we are concerned with the criminological spectrum. The ongoing research reported in this paper will begin the process of translating physical crime scene assessment techniques into digital practice. The potential benefits of accomplishing this successfully are significant. Unfortunately, the translation between physical investigation and digital investigation is not straightforward. The addition of particular techniques pioneered by crime assessment experts in physical investigations has turned out to be far more promising than application of psychological profiling. This research-in-progress paper presents a new approach to cyber crime assessment. It views the computer as the crime scene and it applies a crime typology.
Dynamic Scene Deblurring using a Locally Adaptive Linear Blur Model

State-of-the-art video deblurring methods cannot handle blurry videos recorded in dynamic scenes, since they are built under a strong assumption that the captured scenes are static. Contrary to the existing methods, we propose a video deblurring algorithm that can deal with general blurs inherent in dynamic scenes. To handle general and locally varying blurs caused by various sources, such as moving objects, camera shake, depth variation, and defocus, we estimate pixel-wise non-uniform blur kernels. We infer bidirectional optical flows to handle motion blurs, and also estimate Gaussian blur maps to remove optical blur from defocus in our new blur model. Therefore, we propose a single energy model that jointly estimates optical flows, defocus blur maps and latent frames. We also provide a framework and efficient solvers to minimize the proposed energy model. By optimizing the energy model, we achieve significant improvements in removing general blurs, estimating optical flows, and extending depth-of-field in blurry frames. Moreover, in this work, to evaluate the performance of non-uniform deblurring methods objectively, we have constructed a new realistic dataset with ground truths. In addition, extensive experimental on publicly available challenging video data demonstrate that the proposed method produces qualitatively superior performance than the state-of-the-art methods which often fail in either deblurring or optical flow estimation.
In-process metrics for software testing

In Chapter 9 we discussed quality management models with examples of in-process metrics and reports. The models cover both the front-end design and coding activities and the back-end testing phases of development. The focus of the in-process data and reports, however, are geared toward the design review and code inspection data, although testing data is included. This chapter provides a more detailed discussion of the in-process metrics from the testing perspective. 1 These metrics have been used in the IBM Rochester software development laboratory for some years with continual evolution and improvement, so there is ample implementation experience with them. This is important because although there are numerous metrics for software testing, and new ones being proposed frequently, relatively few are supported by sufficient experiences of industry implementation to demonstrate their usefulness. For each metric, we discuss its purpose, data, interpretation , and use, and provide a graphic example based on real-life data. Then we discuss in-process quality management vis-à-vis these metrics and revisit the metrics 271 1. This chapter is a modified version of a white paper written for the IBM corporate-wide Software Test Community Leaders (STCL) group, which was published as " In-process Metrics for Software Testing, "
EMM: Energy-Aware Mobility Management for Mobile Edge Computing in Ultra Dense Networks

Merging mobile edge computing (MEC) functionality with the dense deployment of base stations (BSs) provides enormous benefits such as a real proximity, low latency access to computing resources. However, the envisioned integration creates many new challenges, among which mobility management (MM) is a critical one. Simply applying existing radio access-oriented MM schemes leads to poor performance mainly due to the co-provisioning of radio access and computing services of the MEC-enabled BSs. In this paper, we develop a novel user-centric energy-aware mobility management (EMM) scheme, in order to optimize the delay due to both radio access and computation, under the long-term energy consumption constraint of the user. Based on Lyapunov optimization and multi-armed bandit theories, EMM works in an online fashion without future system state information, and effectively handles the imperfect system state information. Theoretical analysis explicitly takes radio handover and computation migration cost into consideration and proves a bounded deviation on both the delay performance and energy consumption compared with the oracle solution with exact and complete future system information. The proposed algorithm also effectively handles the scenario in which candidate BSs randomly switch ON/OFF during the offloading process of a task. Simulations show that the proposed algorithms can achieve close-to-optimal delay performance while satisfying the user energy consumption constraint.
A Survey of Automated Assessment Approaches for Programming Assignments

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Hypergraph Based Reorderings of Outer Join Queries with Complex Predicates

Complex queries containing outer joins are, for the most part, executed by commercial DBMS products in an "as written" manner. Only a very few reorderings of the operations are considered and the benefits of considering comprehensive reordering schemes are not exploited. This is largely due to the fact there are no readily usable results for reordering such operations for relations with duplicates and/or outer join predicates that are other than "simple." Most previous approaches have ignored duplicates and complex predicates; the very few that have considered these aspects have suggested approaches that lead to a possibly exponential number of, and redundant intermediate joins. Since traditional query graph models are inadequate for modeling outer join queries with complex predicates, we present the needed hypergraph abstraction and algorithms for reordering such queries with joins and outer joins. As a result, the query optimizer can explore a significantly larger space of execution plans, and choose one with a low cost. Further, these algorithms are easily incorporated into well known and widely used enumeration methods such as dynamic programming.
Functional connectivity hubs in the human brain

Brain networks appear to have few and well localized regions with high functional connectivity density (hubs) for fast integration of neural processing, and their dysfunction could contribute to neuropsychiatric diseases. However the variability in the distribution of these brain hubs is unknown due in part to the overwhelming computational demands associated to their localization. Recently we developed a fast algorithm to map the local functional connectivity density (lFCD). Here we extend our method to map the global density (gFDC) taking advantage of parallel computing. We mapped the gFCD in the brain of 1031 subjects from the 1000 Functional Connectomes project and show that the strongest hubs are located in regions of the default mode network (DMN) and in sensory cortices, whereas subcortical regions exhibited the weakest hubs. The strongest hubs were consistently located in ventral precuneus/cingulate gyrus (previously identified by other analytical methods including lFCD) and in primary visual cortex (BA 17/18), which highlights their centrality to resting connectivity networks. In contrast and after rescaling, hubs in prefrontal regions had lower gFCD than lFCD, which suggests that their local functional connectivity (as opposed to long-range connectivity) prevails in the resting state. The power scaling of the probability distribution of gFCD hubs (as for lFCD) was consistent across research centers further corroborating the "scale-free" topology of brain networks. Within and between-subject variability for gFCD were twice than that for lFCD (20% vs. 12% and 84% vs. 34%, respectively) suggesting that gFCD is more sensitive to individual differences in functional connectivity.
How Feasible Is Baby-Led Weaning as an Approach to Infant Feeding? A Review of the Evidence

Baby-Led Weaning (BLW) is an alternative method for introducing complementary foods to infants in which the infant feeds themselves hand-held foods instead of being spoon-fed by an adult. The BLW infant also shares family food and mealtimes and is offered milk (ideally breast milk) on demand until they self-wean. Anecdotal evidence suggests that many parents are choosing this method instead of conventional spoon-feeding of purées. Observational studies suggest that BLW may encourage improved eating patterns and lead to a healthier body weight, although it is not yet clear whether these associations are causal. This review evaluates the literature with respect to the prerequisites for BLW, which we have defined as beginning complementary foods at six months (for safety reasons), and exclusive breastfeeding to six months (to align with WHO infant feeding guidelines); the gross and oral motor skills required for successful and safe self-feeding of whole foods from six months; and the practicalities of family meals and continued breastfeeding on demand. Baby-Led Weaning will not suit all infants and families, but it is probably achievable for most. However, ultimately, the feasibility of BLW as an approach to infant feeding can only be determined in a randomized controlled trial. Given the popularity of BLW amongst parents, such a study is urgently needed.
Finer Grained Entity Typing with TypeNet

We consider the challenging problem of entity typing over an extremely fine grained set of types, wherein a single mention or entity can have many simultaneous and often hierarchically-structured types. Despite the importance of the problem, there is a relative lack of resources in the form of fine-grained, deep type hierarchies aligned to existing knowledge bases. In response, we introduce TypeNet, a dataset of entity types consisting of over 1941 types organized in a hierarchy, obtained by manually annotating a mapping from 1081 Freebase types to WordNet. We also experiment with several models comparable to state-of-the-art systems and explore techniques to incorporate a structure loss on the hierarchy with the standard mention typing loss, as a first step towards future research on this dataset.
A critical review of the psychophysiology of driver fatigue

Driver fatigue is a major cause of road accidents and has implications for road safety. This review discusses the concepts of fatigue and provides a summary on psychophysiological associations with driver fatigue. A variety of psychophysiological parameters have been used in previous research as indicators of fatigue, with electroencephalography perhaps being the most promising. Most research found changes in theta and delta activity to be strongly linked to transition to fatigue. Therefore, monitoring electroencephalography during driver fatigue may be a promising variable for use in fatigue countermeasure devices. The review also identified anxiety and mood states as factors that may possibly affect driver fatigue. Furthermore, personality and temperament may also influence fatigue. Given the above, understanding the psychology of fatigue may lead to better fatigue management. The findings from this review are discussed in the light of directions for future studies and for the development of fatigue countermeasures.
Learning Causal Graphs with Small Interventions

We consider the problem of learning causal networks with int erventions, when each intervention is limited in size under Pearl’s Structural Equation Model with independent e rrors (SEM-IE). The objective is to minimize the number of experiments to discover the causal directions of all the e dges in a causal graph. Previous work has focused on the use of separating systems for complete graphs for this task. We prove that any deterministic adaptive algorithm needs to be a separating system in order to learn complete graphs in t e worst case. In addition, we present a novel separating system construction, whose size is close to optimal and is ar guably simpler than previous work in combinatorics. We also develop a novel information theoretic lower bound on th e number of interventions that applies in full generality, including for randomized adaptive learning algorithms. For general chordal graphs, we derive worst case lower bound s o the number of interventions. Building on observations about induced trees, we give a new determinist ic adaptive algorithm to learn directions on any chordal skeleton completely. In the worst case, our achievable sche me is anα-approximation algorithm where α is the independence number of the graph. We also show that there exi st graph classes for which the sufficient number of experiments is close to the lower bound. In the other extreme , there are graph classes for which the required number of experiments is multiplicativelyα away from our lower bound. In simulations, our algorithm almost always performs very c lose to the lower bound, while the approach based on separating systems for complete graphs is significantly wor se for random chordal graphs.
Internet Addiction Symptoms, Disordered Eating, and Body Image Avoidance

Internet addiction is an increasing concern among young adults. Self-presentational theory posits that the Internet offers a context in which individuals are able to control their image. Little is known about body image and eating concerns among pathological Internet users. The aim of this study was to explore the association between Internet addiction symptoms, body image esteem, body image avoidance, and disordered eating. A sample of 392 French young adults (68 percent women) completed an online questionnaire assessing time spent online, Internet addiction symptoms, disordered eating, and body image avoidance. Fourteen men (11 percent) and 26 women (9.7 percent) reported Internet addiction. Body image avoidance was associated with Internet addiction symptoms among both genders. Controlling for body-mass index, Internet addiction symptoms, and body image avoidance were both significant predictors of disordered eating among women. These findings support the self-presentational theory of Internet addiction and suggest that body image avoidance is an important factor.