nils-herrmann/clean_scirepeval huggingface dataset:
Secured Data Storage Scheme Based on Block Chain for Agricultural Products Tracking

The Internet of Things technology has been widely used in the quality tracking of agricultural products, however, the safety of storage for tracked data is still a serious challenge. Recently, with the expansion of blockchain technology applied in cross-industry field, the unchangeable features of its stored data provide us new vision about ensuring the storage safety for tracked data. Unfortunately, when the blockchain technology is directly applied in agricultural products tracking and data storage, it is difficult to automate storage and obtain the hash data stored in the blockchain in batches base on the identity. Addressing this issue, we propose a double-chain storage structure, and design a secured data storage scheme for tracking agricultural products based on blockchain. Specifically, the chained data structure is utilized to store the blockchain transaction hash, and together with the chain of the blockchain to form a double-chain storage, which ensures the data of agricultural products will not be maliciously tampered or destructed. Finally, in the practical application system, we verify the correctness and security of the proposed storage scheme.
Syntactic parsing of chat language in contact center conversation corpus

Chat language is often referred to as Computer-mediated communication (CMC). Most of the previous studies on chat language has been dedicated to collecting ”chat room” data as it is the kind of data which is the most accessible on the WEB. This kind of data falls under the informal register whereas we are interested in this paper in understanding the mechanisms of a more formal kind of CMC: dialog chat in contact centers. The particularities of this type of dialogs and the type of language used by customers and agents is the focus of this paper towards understanding this new kind of CMC data. The challenges for processing chat data comes from the fact that Natural Language Processing tools such as syntactic parsers and part of speech taggers are typically trained on mismatched conditions, we describe in this study the impact of such a mismatch for a syntactic parsing task.
An exact algorithm for team orienteering problems

Optimising routing of vehicles constitutes a major logistic stake in many industrial contexts. We are interested here in the optimal resolution of special cases of vehicle routing problems, known as team orienteering problems. In these problems, vehicles are guided by a reward that can be collected from customers, while the length of routes is limited. The main difference with classical vehicle routing problems is that not all customers have to be visited. The solution method we propose here is based on a Branch & Price algorithm. It is, as far as we know, the first exact method proposed for such problems, except for a preliminary work from Gueguen (Methodes de résolution exacte pour problémes de tournées de véhicules. Thése de doctorat, école Centrale Paris 1999) and a work from Butt and Ryan (). It permits to solve instances with up to 100 customers.
Design of a SIW meander antenna for 60 GHz applications

This paper describes the development of a Substrate Integrated Waveguide (SIW) Meander Antenna whose main lobe is steerable by variation of the feed frequency. Generally for radar-tracking of fast moving objects a high scanning rate is needed. Due to the mass inertia mechanically steering radar systems are limited in their scanning rates. A possible solution to overcome this disadvantage is given by using electric beam steering. In most cases conventional array-feed networks are very complex, large and expensive. This fact restricts the mobile usage of such systems. The proposed design of a SIW slot array antenna allows steering of the main lobe by a simple frequency change. This concept leads to a significant reduction of the array dimensions and an improvement of the scanning range. A broadside radiation is achieved at the centre frequency of 60 GHz. Measured scan angle show the values of ± 20 degrees.
A Survey of Visual Analytic Pipelines

Visual analytics has been widely studied in the past decade. One key to make visual analytics practical for both research and industrial applications is the appropriate definition and implementation of the visual analytics pipeline which provides effective abstractions for designing and implementing visual analytics systems. In this paper we review the previous work on visual analytics pipelines and individual modules from multiple perspectives: data, visualization, model and knowledge. In each module we discuss various representations and descriptions of pipelines inside the module, and compare the commonalities and the differences among them.
Design of a resistively loaded vee dipole for ultrawide-band ground-penetrating Radar applications

A new resistively loaded vee dipole (RVD) is designed and implemented for ultrawide-band short-pulse ground-penetrating radar (GPR) applications. The new RVD is improved in terms of voltage standing wave ratio, gain, and front-to-back ratio while maintaining many advantages of the typical RVD, such as the ability to radiate a short-pulse into a small spot on the ground, a low radar cross section, applicability in an array, etc. The improvements are achieved by curving the arms and modifying the Wu-King loading profile. The curve and the loading profile are designed to decrease the reflection at the drive point of the antenna while increasing the forward gain. The new RVD is manufactured by printing the curved arms on a thin Kapton film and loading them with chip resistors, which approximate the continuous loading profile. The number of resistors is chosen such that the resonant frequency due to the resistor spacing occurs at a frequency higher than the operation bandwidth. The antenna and balun are made in a module by sandwiching them between two blocks of polystyrene foam, attaching a plastic support, and encasing the foam blocks in heat-sealable plastic. The antenna module is mechanically reliable without significant performance degradation. The use of the new RVD module in a GPR system is also demonstrated with an experiment.
Aspern smart ICT: Data analytics and privacy challenges in a smart city

The abundance of data in the context of smart cities yields huge potential for data-driven businesses but raises unprecedented challenges on data privacy and security. Some of these challenges can be addressed merely through appropriate technical measures, while other issues can only be solved through strategic organizational decisions. In this paper, we present few cases from a real smart city project. We outline some exemplary data analytics scenarios and describe the measures that we adopt for a secure handling of data. Finally, we show how the chosen solutions impact the awareness of the public and acceptability of the project.
Circadian molecular clock in lung pathophysiology.

Disrupted daily or circadian rhythms of lung function and inflammatory responses are common features of chronic airway diseases. At the molecular level these circadian rhythms depend on the activity of an autoregulatory feedback loop oscillator of clock gene transcription factors, including the BMAL1:CLOCK activator complex and the repressors PERIOD and CRYPTOCHROME. The key nuclear receptors and transcription factors REV-ERBα and RORα regulate Bmal1 expression and provide stability to the oscillator. Circadian clock dysfunction is implicated in both immune and inflammatory responses to environmental, inflammatory, and infectious agents. Molecular clock function is altered by exposomes, tobacco smoke, lipopolysaccharide, hyperoxia, allergens, bleomycin, as well as bacterial and viral infections. The deacetylase Sirtuin 1 (SIRT1) regulates the timing of the clock through acetylation of BMAL1 and PER2 and controls the clock-dependent functions, which can also be affected by environmental stressors. Environmental agents and redox modulation may alter the levels of REV-ERBα and RORα in lung tissue in association with a heightened DNA damage response, cellular senescence, and inflammation. A reciprocal relationship exists between the molecular clock and immune/inflammatory responses in the lungs. Molecular clock function in lung cells may be used as a biomarker of disease severity and exacerbations or for assessing the efficacy of chronotherapy for disease management. Here, we provide a comprehensive overview of clock-controlled cellular and molecular functions in the lungs and highlight the repercussions of clock disruption on the pathophysiology of chronic airway diseases and their exacerbations. Furthermore, we highlight the potential for the molecular clock as a novel chronopharmacological target for the management of lung pathophysiology.
Macrocephaly in infancy: benign enlargement of the subarachnoid spaces and subdural collections.

Benign enlargement of the subarachnoid spaces (BESS) is a common finding on imaging studies indicated by macrocephaly in infancy. This finding has been associated with the presence of subdural fluid collections that are sometimes construed as suggestive of abusive head injury. The prevalence of BESS among infants with macrocephaly and the prevalence of subdural collections among infants with BESS are both poorly defined. The goal of this study was to determine the relative frequencies of BESS, hydrocephalus, and subdural collections in a large consecutive series of imaging studies performed for macrocephaly and to determine the prevalence of subdural fluid collections among patients with BESS.  A text search of radiology requisitions identified studies performed for macrocephaly in patients ≤ 2 years of age. Studies of patients with hydrocephalus or acute trauma were excluded. Studies that demonstrated hydrocephalus or chronic subdural hematoma not previously recognized but responsible for macrocephaly were noted but not investigated further. The remaining studies were reviewed for the presence of incidental subdural collections and for measurement of the depth of the subarachnoid space. A 3-point scale was used to grade BESS: Grade 0, < 5 mm; Grade 1, 5-9 mm; and Grade 2, ≥ 10 mm.  After exclusions, there were 538 studies, including 7 cases of hydrocephalus (1.3%) and 1 large, bilateral chronic subdural hematoma (0.2%). There were incidental subdural collections in 21 cases (3.9%). Two hundred sixty-five studies (49.2%) exhibited Grade 1 BESS, and 46 studies (8.6%) exhibited Grade 2 BESS. The prevalence of incidental subdural collections among studies with BESS was 18 of 311 (5.8%). The presence of BESS was associated with a greater prevalence of subdural collections, and higher grades of BESS were associated with increasing prevalence of subdural collections. After controlling for imaging modality, the odds ratio of the association of BESS with subdural collections was 3.68 (95% CI 1.12-12.1, p = 0.0115). There was no association of race, sex, or insurance status with subdural collections. Patients with BESS had larger head circumference Z-scores, but there was no association of head circumference or age with subdural collections. Interrater reliability in the diagnosis and grading of BESS was only fair.  The current study confirms the association of BESS with incidental subdural collections and suggests that greater depth of the subarachnoid space is associated with increased prevalence of such collections. These observations support the theory that infants with BESS have a predisposition to subdural collections on an anatomical basis. Incidental subdural collections in the setting of BESS are not necessarily indicative of abusive head injury.
Performance evaluation of self organizing neural networks for clustering in ESM systems

Electronic Support Measures (ESM) system is an important function of electronic warfare which provides the real time projection of radar activities. Such systems may encounter with very high density pulse sequences and it is the main task of an ESM system to deinterleave these mixed pulse trains with high accuracy and minimum computation time. These systems heavily depend on time of arrival analysis and need efficient clustering algorithms to assist deinterleaving process in modern evolving environments. On the other hand, self organizing neural networks stand very promising for this type of radar pulse clustering. In this study, performances of self organizing neural networks that meet such clustering criteria are evaluated in detail and the results are presented.
Microwave image reconstruction utilizing log-magnitude and unwrapped phase to improve high-contrast object recovery

Reconstructing images of large high-contrast objects with microwave methods has proved difficult. Successful images have generally been obtained by using a priori information to constrain the image reconstruction to recover the correct electromagnetic property distribution. In these situations, the measured electric field phases as a function of receiver position around the periphery of the imaging field-of-view vary rapidly often undergoing changes of greater than /spl pi/ radians especially when the object contrast and illumination frequency increase. Here, the authors introduce a modified form of a Maxwell equation model-based image reconstruction algorithm which directly incorporates log-magnitude and phase of the measured electric field data. By doing so, measured phase variation can be unwrapped and distributed over more than one Rieman sheet in the complex plane. Simulation studies and microwave imaging experiments demonstrate that significant image quality enhancements occur with this approach for large high-contrast objects. Simple strategies for visualizing and unwrapping phase values as a function of the transmitter and receiver positions within our microwave imaging array are described. Metrics of the degree of phase variation expressed in terms of the amount and extent of phase wrapping are defined and found to be figures-of-merit which estimate when it is critical to deploy the new image reconstruction approach. In these cases, the new algorithm recovers high-quality images without resorting to the use of a priori information on object contrast and/or size as previously required.
DroidScribe: Classifying Android Malware Based on Runtime Behavior

The Android ecosystem has witnessed a surge in malware, which not only puts mobile devices at risk but also increases the burden on malware analysts assessing and categorizing threats. In this paper, we show how to use machine learning to automatically classify Android malware samples into families with high accuracy, while observing only their runtime behavior. We focus exclusively on dynamic analysis of runtime behavior to provide a clean point of comparison that is dual to static approaches. Specific challenges in the use of dynamic analysis on Android are the limited information gained from tracking low-level events and the imperfect coverage when testing apps, e.g., due to inactive command and control servers. We observe that on Android, pure system calls do not carry enough semantic content for classification and instead rely on lightweight virtual machine introspection to also reconstruct Android-level inter-process communication. To address the sparsity of data resulting from low coverage, we introduce a novel classification method that fuses Support Vector Machines with Conformal Prediction to generate high-accuracy prediction sets where the information is insufficient to pinpoint a single family.
Language-driven synthesis of 3D scenes from scene databases

We introduce a novel framework for using natural language to generate and edit 3D indoor scenes, harnessing scene semantics and text-scene grounding knowledge learned from large annotated 3D scene databases. The advantage of natural language editing interfaces is strongest when performing semantic operations at the sub-scene level, acting on groups of objects. We learn how to manipulate these sub-scenes by analyzing existing 3D scenes. We perform edits by first parsing a natural language command from the user and transforming it into a semantic scene graph that is used to retrieve corresponding sub-scenes from the databases that match the command. We then augment this retrieved sub-scene by incorporating other objects that may be implied by the scene context. Finally, a new 3D scene is synthesized by aligning the augmented sub-scene with the user's current scene, where new objects are spliced into the environment, possibly triggering appropriate adjustments to the existing scene arrangement. A suggestive modeling interface with multiple interpretations of user commands is used to alleviate ambiguities in natural language. We conduct studies comparing our approach against both prior text-to-scene work and artist-made scenes and find that our method significantly outperforms prior work and is comparable to handmade scenes even when complex and varied natural sentences are used.
European Legal Taxonomy Syllabus : A multi-lingual , multi-level ontology framework to untangle the web of European legal terminology ∗

The final publication is available at IOS Press through http://dx.doi.org/10.3233/AO-170174. This paper describes a new concept of legal ontology together with an ontology development tool, called European Legal Taxonomy Syllabus (ELTS). The tool is used to model the legal terminology created by the Uniform Terminology project on EU consumer protection law as an ontology. ELTS is not a formal ontology in the standard sense, i.e., an axiomatic ontology formalized, for instance, in description logic. Rather, it is a lightweight ontology, i.e. a knowledge base storing low-level legal concepts, connected via low-level semantic relations, and related to linguistic patterns that denote legal concepts in several languages spoken in the European Union (EU). In other words, ELTS is a multi-lingual and multi-jurisdictional terminological vocabulary enriched with concepts denoted by vocabulary entries, with semantic relations between different concepts. The choice of such an architecture is based on past studies in comparative law and is motivated by the need to reveal the differences between national systems within the EU. Past literature in comparative law highlights that axiomatic ontologies freeze legal knowledge in an unreal steadiness, i.e., they render it disconnected from legal practice. Much more flexibility is needed to make the knowledge base acceptable to legal practitioners. ELTS was developed together with legal practitioners on the basis of the comparative view of European law. The ontology framework is designed to help professionals study the meaning of national and European legal terms and how they inter-relate in the transposition of European Directives into national laws. The structure and user interface of ELTS is suitable for building multi-lingual, multi-jurisdictional legal ontologies in a bottom-up and collaborative manner, starting from the description of legal terms by legal experts. It also takes into account the interpretation of norms, the dynamic character of norms and the contextual character of legal concepts in that they are linked to their legal sources (legislation, case law and doctrine).
A High-Speed Fully-Integrated POF Receiver With Large-Area Photo Detectors in 65 nm CMOS

This paper describes the design of a multi-gigabit fiber-optic receiver with integrated large-area photo detectors for plastic optical fiber applications. An integrated 250 μm diameter non-SML NW/P-sub photo detector is adopted to allow efficient light coupling. The theory of applying a fully-differential pre-amplifier with a single-ended photo current is also examined and a super-Gm transimpedance amplifier has been proposed to drive a C PD of 14 pF to multi-gigahertz frequency. Both differential and common-mode operations of the proposed super-Gm transimpedance amplifier have been analyzed and a differential noise analysis is performed. A digitally-controlled linear equalizer is proposed to produce a slow-rising-slope frequency response to compensate for the photo detector up to 3 GHz. The proposed POF receiver consists of an illuminated signal photo detector, a shielded dummy photo detector, a super-Gm transimpedance amplifier, a variable-gain amplifier, a linear equalizer, a post amplifier, and an output driver. A test chip is fabricated in TSMC's 65 nm low-power CMOS process, and it consumes 50 mW of DC power (excluding the output driver) from a single 1.2 V supply. A bit-error rate of less than 10-12 has been measured at a data rate of 3.125 Gbps with a 670 nm VCSEL-based electro-optical transmitter.
Gravikinesis in Stylonychia mytilus is based on membrane potential changes.

The graviperception of the hypotrichous ciliate Stylonychia mytilus was investigated using electrophysiological methods and behavioural analysis. It is shown that Stylonychia can sense gravity and thereby compensates sedimentation rate by a negative gravikinesis. The graviresponse consists of a velocity-regulating physiological component (negative gravikinesis) and an additional orientational component. The latter is largely based on a physical mechanism but might, in addition, be affected by the frequency of ciliary reversals, which is under physiological control. We show that the external stimulus of gravity is transformed to a physiological signal, activating mechanosensitive calcium and potassium channels. Earlier electrophysiological experiments revealed that these ion channels are distributed in the manner of two opposing gradients over the surface membrane. Here, we show, for the first time, records of gravireceptor potentials in Stylonychia that are presumably based on this two-gradient system of ion channels. The gravireceptor potentials had maximum amplitudes of approximately 4 mV and slow activation characteristics (0.03 mV s(-1)). The presumptive number of involved graviperceptive ion channels was calculated and correlates with the analysis of the locomotive behaviour.
Automatic Recognition of Construction Worker Activities Using Dense Trajectories

Wide spread monitoring cameras on construction sites provide large amount of information for construction management. The emerging of computer vision and machine learning technologies enables automatic recognition of construction activities from videos. As the executors of construction, the activities of construction workers have strong impact on productivity and progress. Compared to machine work, manual work is more subjective and may differ largely in operation flow and productivity from one worker to another. Hence only a handful of work study on vision based activity recognition of construction workers. Lacking of publicly available datasets is one of the main reasons that currently hinder advancement. The paper studies manual work of construction workers comprehensively, selects 11 common types of activities and establishes a new real world video dataset with 1176 instances. For activity recognition, a cutting-edge video description method, dense trajectories, has been applied. Support vector machines are integrated with a bag-of-features pipeline for activity learning and classification. Performance on multiple types of descriptors (Histograms of Oriented Gradients HOG, Histograms of Optical Flow HOF, Motion Boundary Histogram MBH) and their combination has been evaluated. Experimental results show that the proposed system has achieved a state-of-art performance on the new dataset.