nils-herrmann/clean_scirepeval huggingface dataset:
Design Procedure of Dual-Stator Spoke-Array Vernier Permanent-Magnet Machines

The dual-stator spoke-array vernier permanent-magnet (DSSA VPM) machines proposed in the previous papers have been proven to be with high torque density and high power factor. However, the design procedure on the DSSA VPM machines has not been well established, and there is little design experience to be followed, which makes the DSSA VPM machine design quite difficult. This paper presents the detailed DSSA VPM machine design procedure including decision of design parameter initial values, analytical sizing equation, geometric size relationship, and so on. In order to get reasonable design parameter initial values which can reduce the number of design iteration loop, the influence of the key parameters, such as rotor/stator pole combination, slot opening, magnet thickness, etc., on the performances is analyzed based on the finite-element algorithm (FEA) in this paper, and the analysis results can be regarded as design experience during the selection process of the initial values. After that, the analytical sizing equation and geometric relationship formulas are derived and can be used to obtain and optimize the size data of the DSSA VPM machines with little time consumption. The combination of the analytical and FEA methods makes the design procedure time-effective and reliable. Finally, the design procedure is validated by experiments on a DSSA VPM prototype with 2000 N·m.
Binary code is not easy

Binary code analysis is an enabling technique for many applications. Modern compilers and run-time libraries have introduced significant complexities to binary code, which negatively affect the capabilities of binary analysis tool kits to analyze binary code, and may cause tools to report inaccurate information about binary code. Analysts may hence be confused and applications based on these tool kits may have degrading quality. We examine the problem of constructing control flow graphs from binary code and labeling the graphs with accurate function boundary annotations. We identified several challenging code constructs that represent hard-to-analyze aspects of binary code, and show code examples for each code construct. As part of this discussion, we present new code parsing algorithms in our open source Dyninst tool kit that support these constructs, including a new model for describing jump tables that improves our ability to precisely determine the control flow targets, a new interprocedural analysis to determine when a function is non-returning, and techniques for handling tail calls. We evaluated how various tool kits fare when handling these code constructs with real software as well as test binaries patterned after each challenging code construct we found in real software.
Interaction in the segmentation of medical images: A survey

Segmentation of the object of interest is a difficult step in the analysis of digital images. Fully automatic methods sometimes fail, producing incorrect results and requiring the intervention of a human operator. This is often true in medical applications, where image segmentation is particularly difficult due to restrictions imposed by image acquisition, pathology and biological variation. In this paper we present an early review of the largely unknown territory of human-computer interaction in image segmentation. The purpose is to identify patterns in the use of interaction and to develop qualitative criteria to evaluate interactive segmentation methods. We discuss existing interactive methods with respect to the following aspects: the type of information provided by the user, how this information affects the computational part, and the purpose of interaction in the segmentation process. The discussion is based on the potential impact of each strategy on the accuracy, repeatability and interaction efficiency. Among others, these are important aspects to characterise and understand the implications of interaction to the results generated by an interactive segmentation method. This survey is focused on medical imaging, however similar patterns are expected to hold for other applications as well.
Review of Video and Image Defogging Algorithms and Related Studies on Image Restoration and Enhancement

Video and images acquired by a visual system are seriously degraded under hazy and foggy weather, which will affect the detection, tracking, and recognition of targets. Thus, restoring the true scene from such a foggy video or image is of significance. The main goal of this paper was to summarize current video and image defogging algorithms. We first presented a review of the detection and classification method of a foggy image. Then, we summarized existing image defogging algorithms, including image restoration algorithms, image contrast enhancement algorithms, and fusion-based defogging algorithms. We also presented current video defogging algorithms. We summarized objective image quality assessment methods that have been widely used for the comparison of different defogging algorithms, followed by an experimental comparison of various classical image defogging algorithms. Finally, we presented the problems of video and image defogging which need to be further studied. The code of all algorithms will be available at <;uri xlink:href="http://www.yongxu.org/lunwen.html" xlink:type="simple">http://www.yongxu.org/lunwen.html<;/uri>.
The Future of Disaster Response: Humans Working with Multiagent Teams using DEFACTO

When addressing terrorist threats we must give special attention to both prevention and disaster response. Enabling effective interactions between agent teams and humans for disaster response is a critical area of research, with encouraging progress in the past few years. However, previous work suffers from two key limitations: (i) limited human situational awareness, reducing human effectiveness in directing agent teams and (ii) the agent team’s rigid interaction strategies that limit team performance. This paper focuses on a novel disaster response software prototype, called DEFACTO (Demonstrating Effective Flexible Agent Coordination of Teams through Omnipresence). DEFACTO is based on a software proxy architecture and 3D visualization system, which addresses the two limitations described above. First, the 3D visualization interface enables human virtual omnipresence in the environment, improving human situational awareness and ability to assist agents. Second, generalizing past work on adjustable autonomy, the agent team chooses among a variety of “team-level” interaction strategies, even excluding humans from the loop in extreme cir-
Internet Reflections on Teenagers

The Internet is a piece of today's way of life that numerous young people can't envision what the world resembled before the Internet existed. The Internet is fun, enlightening and an extraordinary wellspring of correspondence with others. It's an instructive device and clients can find out about just about anything. Sharing data through Internet is simple, shoddy and quick. Young people have admittance to billions of sites containing data as content, pictures and recordings [1-10].
An Inductive 2-D Position Detection IC With 99.8% Accuracy for Automotive EMR Gear Control System

In this paper, the analog front end (AFE) for an inductive position sensor in an automotive electromagnetic resonance gear control applications is presented. To improve the position detection accuracy, a coil driver with an automatic two-step impedance calibration is proposed which, despite the load variation, provides the desired driving capability by controlling the main driver size. Also, a time shared analog-to-digital converter (ADC) is proposed to convert eight-phase signals while reducing the current consumption and area to 1/8 of the conventional structure. A relaxation oscillator with temperature compensation is proposed to generate a constant clock frequency in vehicle temperature conditions. This chip is fabricated using a 0.18-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> CMOS process and the die area is 2 mm <inline-formula> <tex-math notation="LaTeX">$\times 1.5$ </tex-math></inline-formula> mm. The power consumption of the AFE is 23.1 mW from the supply voltage of 3.3 V to drive one transmitter (Tx) coil and eight receiver (Rx) coils. The measured position detection accuracy is greater than 99.8 %. The measurement of the Tx shows a driving capability higher than 35 mA with respect to the load change.
Variable radius pulley design methodology for pneumatic artificial muscle-based antagonistic actuation systems

There is a growing interest in utilizing pneumatic artificial muscles (PAMs) as actuators for human-friendly robots. However, several performance drawbacks prevent the widespread use of PAMs. Although many approaches have been proposed to overcome the low control bandwidth of PAMs, some limitations of PAMs such as restricted workspace and torque capacity remain to be addressed. This paper analyzes the limitations of conventional circular pulley joints and subsequently proposes a design methodology to synthesize a pair of variable radius pulleys to improve joint torque capacity over a large workspace. Experimental results show that newly synthesized variable radius pulleys significantly improve position tracking performance in the enlarged workspace.
Adaptively generating high quality fixes for atomicity violations

It is difficult to fix atomicity violations correctly. Existing gate lock algorithm (GLA) simply inserts gate locks to serialize exe-cutions, which may introduce performance bugs and deadlocks. Synthesized context-aware gate locks (by Grail) require complex source code synthesis. We propose Fixer to adaptively fix ato-micity violations. It firstly analyses the lock acquisitions of an atomicity violation. Then it either adjusts the existing lock scope or inserts a gate lock. The former addresses cases where some locks are used but fail to provide atomic accesses. For the latter, it infers the visibility (being global or a field of a class/struct) of the gate lock such that the lock only protects related accesses. For both cases, Fixer further eliminates new lock orders to avoid introducing deadlocks. Of course, Fixer can produce both kinds of fixes on atomicity violations with locks. The experi-mental results on 15 previously used atomicity violations show that: Fixer correctly fixed all 15 atomicity violations without introducing deadlocks. However, GLA and Grail both intro-duced 5 deadlocks. HFix (that only targets on fixing certain types of atomicity violations) only fixed 2 atomicity violations and introduced 4 deadlocks. Fixer also provides an alternative way to insert gate locks (by inserting gate locks with proper visibility) considering fix acceptance.
Disturbless flash memory due to high boost efficiency on BiCS structure and optimal memory film stack for ultra high density storage device

Program and erase operation on NAND-string of Bit-Cost Scalable (BiCS) flash memory has been successfully achieved. High boost efficiency of floating pillars and ONON (block oxide/charge SiN/tunnel oxide/tunnel SiN) structure as a memory film stack improve disturbance characteristics enough to realize tera-bit density of three dimensional flash memory. BiCS flash memory has become a more promising candidate for ultra high density memory.
Survey propagation: An algorithm for satisfiability

We study the satisfiability of randomly generated formulas formed by M clauses of exactly K literals over N Boolean variables. For a given value of N the problem is known to be most difficult when α = M/N is close to the experimental threshold αc separating the region where almost all formulas are SAT from the region where all formulas are UNSAT. Recent results from a statistical physics analysis suggest that the difficulty is related to the existence of a clustering phenomenon of the solutions when α is close to (but smaller than) αc. We introduce a new type of message passing algorithm which allows to find efficiently a satisfying assignment of the variables in this difficult region. This algorithm is iterative and composed of two main parts. The first is a message-passing procedure which generalizes the usual methods like Sum-Product or Belief Propagation: It passes messages that may be thought of as surveys over clusters of the ordinary messages. The second part uses the detailed probabilistic information obtained from the surveys in order to fix variables and simplify the problem. Eventually, the simplified problem that remains is solved by a conventional heuristic.
A Novel Multi-task Deep Learning Model for Skin Lesion Segmentation and Classification

In this study, a multi-task deep neural network is proposed for skin lesion analysis. The proposed multi-task learning model solves different tasks (e.g., lesion segmentation and two independent binary lesion classifications) at the same time by exploiting commonalities and differences across tasks. This results in improved learning efficiency and potential prediction accuracy for the task-specific models, when compared to training the individual models separately. The proposed multi-task deep learning model is trained and evaluated on the dermoscopic image sets from the International Skin Imaging Collaboration (ISIC) 2017 Challenge “Skin Lesion Analysis towards Melanoma Detection”, which consists of 2000 training samples and 150 evaluation samples. The experimental results show that the proposed multi-task deep learning model achieves promising performances on skin lesion segmentation and classification. The average value of Jaccard index for lesion segmentation is 0.724, while the average values of area under the receiver operating characteristic curve (AUC) on two individual lesion classifications are 0.880 and 0.972, respectively.
Hierarchical Attentive Recurrent Tracking

Class-agnostic object tracking is particularly difficult in cluttered environments as target specific discriminative models cannot be learned a priori. Inspired by how the human visual cortex employs spatial attention and separate “where” and “what” processing pathways to actively suppress irrelevant visual features, this work develops a hierarchical attentive recurrent model for single object tracking in videos. The first layer of attention discards the majority of background by selecting a region containing the object of interest, while the subsequent layers tune in on visual features particular to the tracked object. This framework is fully differentiable and can be trained in a purely data driven fashion by gradient methods. To improve training convergence, we augment the loss function with terms for a number of auxiliary tasks relevant for tracking. Evaluation of the proposed model is performed on two datasets of increasing difficulty: pedestrian tracking on the KTH activity recognition dataset and the KITTI object tracking dataset.
Multi-Document Summarization with GIST EXTER

This paperpresentsthe architectureandthe multidocument summarizationtechniquesimplementedin the GISTEXTER system.The paperpresentsanalgorithmfor producing incrementalmulti-document summariesif extractiontemplatesof goodquality areavailable. An empirical methodof generatingad-hoctemplatesthat can be populated with information extractedfrom texts by automatically acquiredextractionpatternsis alsopresented . The resultsof GISTEXTER in the DUC-2001evaluations account for the advantagesof usingthetechniquespresentedin this paper .
Bayesian Optimization of Combinatorial Structures

The optimization of expensive-to-evaluate blackbox functions over combinatorial structures is an ubiquitous task in machine learning, engineering and the natural sciences. The combinatorial explosion of the search space and costly evaluations pose challenges for current techniques in discrete optimization and machine learning, and critically require new algorithmic ideas. This article proposes, to the best of our knowledge, the first algorithm to overcome these challenges, based on an adaptive, scalable model that identifies useful combinatorial structure even when data is scarce. Our acquisition function pioneers the use of semidefinite programming to achieve efficiency and scalability. Experimental evaluations demonstrate that this algorithm consistently outperforms other methods from combinatorial and Bayesian optimization.
Transaction aggregation as a strategy for credit card fraud detection

The problem of preprocessing transaction data for supervised fraud classification is considered. It is impractical to present an entire series of transactions to a fraud detection system, partly because of the very high dimensionality of such data but also because of the heterogeneity of the transactions. Hence, a framework for transaction aggregation is considered and its effectiveness is evaluated against transaction-level detection, using a variety of classification methods and a realistic cost-based performance measure. These methods are applied in two case studies using real data. Transaction aggregation is found to be advantageous in many but not all circumstances. Also, the length of the aggregation period has a large impact upon performance. Aggregation seems particularly effective when a random forest is used for classification. Moreover, random forests were found to perform better than other classification methods, including SVMs, logistic regression and KNN. Aggregation also has the advantage of not requiring precisely labeled data and may be more robust to the effects of population drift.
On the usability of deep networks for object-based image analysis

As computer vision before, remote sensing has been radically changed by the introduction of Convolution Neural Networks. Land cover use, object detection and scene understanding in aerial images rely more and more on deep learning to achieve new state-of-theart results. Recent architectures such as Fully Convolutional Networks (Long et al., 2015) can even produce pixel level annotations for semantic mapping. In this work, we show how to use such deep networks to detect, segment and classify different varieties of wheeled vehicles in aerial images from the ISPRS Potsdam dataset. This allows us to tackle object detection and classification on a complex dataset made up of visually similar classes, and to demonstrate the relevance of such a subclass modeling approach. Especially, we want to show that deep learning is also suitable for object-oriented analysis of Earth Observation data. First, we train a FCN variant on the ISPRS Potsdam dataset and show how the learnt semantic maps can be used to extract precise segmentation of vehicles, which allow us studying the repartition of vehicles in the city. Second, we train a CNN to perform vehicle classification on the VEDAI (Razakarivony and Jurie, 2016) dataset, and transfer its knowledge to classify candidate segmented vehicles on the Potsdam dataset.
15 Years of Semantic Web: An Incomplete Survey

It has been 15 years since the first publications proposed the use of ontologies as a basis for defining information semantics on the Web starting what today is known as the Semantic Web Research Community. This work undoubtedly had a significant influence on AI as a field and in particular the knowledge representation and Reasoning Community that quickly identified new challenges and opportunities in using Description Logics in a practical setting. In this survey article, we will try to give an overview of the developments the field has gone through in these 15 years. We will look at three different aspects: the evolution of Semantic Web Language Standards, the evolution of central topics in the Semantic Web Community and the evolution of the research methodology.
Effects of Emoticons on the Acceptance of Negative Feedback in Computer-Mediated Communication

Delivering negative performance feedback is inevitable in the workplace. However, recipients may feel uncomfortable and behave defensively, and may be unwilling to accept negative feedback mainly because they fear losing face. Such unproductive responses are heightened when negative feedback is delivered through computer-mediated communication (CMC) channels in which many nonverbal cues in face-to-face communication cannot be used to alleviate the concerns of losing face. This study examines the effectiveness of emoticons, which are designed as surrogates for facial expressions in CMC environments, in conveying social and emotional signals of the feedback provider. Specifically, based on the feedback process model and the dissonance reduction theory, this study investigates the differing effects of two types of emoticons (i.e., liking and disliking ones) on the acceptance of negative feedback by considering feedback specificity as a contingent factor. Our results suggest that using liking emoticons increases perceived good intention of the feedback provider and decreases perceived feedback negativity when the feedback is specific; however, it has no significant effect for unspecific feedback. By contrast, our results suggest that using disliking emoticons decreases perceived good intention of the feedback provider and increases perceived feedback negativity when the feedback is unspecific, whereas such effects are not significant for specific feedback. In turn, both perceived good intention of the feedback provider and perceived feedback negativity affect acceptance of the negative feedback.
Non‐Advertized does not Mean Concealed: Body Odour Changes across the Human Menstrual Cycle

The visual appearance of females of various primate species changes considerably across their menstrual cycle. These changes usually take place in the anogenital region and are commonly called sexual swellings. Sexual swellings are related to ovulation (e.g. Deschner et al. 2003), correlate with female sexual proceptivity (e.g. Wallis 1992) and are found to be attractive to males (Hrdy & Whitten 1987). Traditionally, human females are different from species which possess ‘sexual swelling’, with human Correspondence Jan Havlı́ček, Department of Anthropology, Faculty of Humanities, Charles University, Husnikova 2075, 158 00 Prague 13, Czech Republic.