nils-herrmann/clean_scirepeval huggingface dataset:
Digital Signatures for Flows and Multicasts

We present chaining techniques for signing/verifying multiple packets using a single signing/verification operation. We then present flow signing and verification procedures based upon a tree chaining technique. Since a single signing/verification operation is amortized over many packets, these procedures improve signing and verification rates by one to two orders of magnitude compared to the approach of signing/verifying packets individually. Our procedures do not depend upon reliable delivery of packets, provide delay-bounded signing, and are thus suitable for delay-sensitive flows and multicast applications. To further improve our procedures, we propose several extensions to the Feige-Fiat-Shamir digital signature scheme to speed up both the signing and verification operations, as well as to allow “adjustable and incremental” verification. The extended scheme, called eFFS, is compared to four other digital signature schemes (RSA, DSA, ElGamal, Rabin). We compare their signing and verification times, as well as key and signature sizes. We observe that (i) the signing and verification operations of eFFS are highly efficient compared to the other schemes, (ii) eFFS allows a tradeoff between memory and signing/verification time, and (iii) eFFS allows adjustable and incremental verification by receivers.
A Sectioning Technique for Extraction of Impacted Third Molar by Using a Straight Handpiece and Carbide Bur : Case Report

Extraction of the lower third molar is one of the most common procedures performed in oral surgery. In general, impacted tooth extraction involves sectioning the tooth’s crown and roots. In order to divide the impacted tooth so that it can be extracted, high-speed air turbine drills are frequently used. However, complications related to air turbine drills may occur. In this report, we propose an alternative tooth sectioning method that obviates the need for air turbine drill use by using a low-speed straight handpiece and carbide bur. A 21-year-old female patient presented to the institute’s dental hospital complaining of symptoms localized to the left lower third molar tooth that were suggestive of impaction. After physical examination, tooth extraction of the impacted left lower third molar was proposed and the patient consented to the procedure. The crown was divided using a conventional straight low-speed handpiece and carbide bur. This carbide bur can easily cut through the enamel of crown. On post-operative day number five, suture was removed and the wound was extremely clear. This technique could minimise intra-operative time and reduce the morbidity associated with air turbine drill assisted lower third molar extraction.
Crop type mapping in a highly fragmented and heterogeneous agricultural landscape: A case of central Iran using multi-temporal Landsat 8 imagery

Crop type mapping and studying the dynamics of agricultural fields in arid and semi-arid environments are of high importance since these ecosystems have witnessed an unprecedented rate of area decline during the last decades. Crop type mapping using medium spatial resolution imagery data has been considered as one of the most important management tools. Remotely sensed data provide reliable, cost and time effective information for monitoring, analyzing and mapping of agricultural land areas. This research was conducted to explore the utility of Landsat 8 imagery data for crop type mapping in a highly fragmented and heterogeneous agricultural landscape in Najaf-Abad Hydrological Unit, Iran. Based on the phenological information from long-term field surveys, five Landsat 8 image scenes (from March to October) were processed to classify the main crop types. In this regard, wheat, barley, alfalfa, and fruit trees have been classified applying inventive decision tree algorithms and Support Vector Machine was used to categorize rice, potato, vegetables, and greenhouse vegetable crops. Accuracy assessment was then undertaken based on spring and summer crop maps (two confusion matrices) that resulted in Kappa coefficients of 0.89. The employed images and classification methods could form a basis for better crop type mapping in central Iran that is undergoing severe drought condition.
'Everything is everywhere: but the environment selects': ubiquitous distribution and ecological determinism in microbial biogeography.

Recent discoveries of geographical patterns in microbial distribution are undermining microbiology's exclusively ecological explanations of biogeography and their fundamental assumption that 'everything is everywhere: but the environment selects'. This statement was generally promulgated by Dutch microbiologist Martinus Wilhelm Beijerinck early in the twentieth century and specifically articulated in 1934 by his compatriot, Lourens G. M. Baas Becking. The persistence of this precept throughout twentieth-century microbiology raises a number of issues in relation to its formulation and widespread acceptance. This paper will trace the conceptual history of Beijerinck's claim that 'everything is everywhere' in relation to a more general account of its theoretical, experimental and institutional context. His principle also needs to be situated in relationship to plant and animal biogeography, which, this paper will argue, forms a continuum of thought with microbial biogeography. Finally, a brief overview of the contemporary microbiological research challenging 'everything is everywhere' reveals that philosophical issues from Beijerinck's era of microbiology still provoke intense discussion in twenty-first century investigations of microbial biogeography.
Tongue tracking in ultrasound images using eigentongue decomposition and artificial neural networks

This paper describes a machine learning approach for extracting automatically the tongue contour in ultrasound images. This method is developed in the context of visual articulatory biofeedback for speech therapy. The goal is to provide a speaker with an intuitive visualization of his/her tongue movement, in real-time, and with minimum human intervention. Contrary to most widely used techniques based on active contours, the proposed method aims at exploiting the information of all image pixels to infer the tongue contour. For that purpose, a compact representation of each image is extracted using a PCA-based decomposition technique (named EigenTongue). Artificial neural networks are then used to convert the extracted visual features into control parameters of a PCA-based tongue contour model. The proposed method is evaluated on 9 speakers, using data recorded with the ultrasound probe hold manually (as in the targeted application). Speaker-dependent experiments demonstrated the effectiveness of the proposed method (with an average error of ~1.3 mm when training from 80 manually annotated images), even when the tongue contour is poorly imaged. The performance was significantly lower in speaker-independent experiments (i.e. when estimating contours on an unknown speaker), likely due to anatomical differences across speakers.
Bioassay directed isolation of a novel anti-inflammatory cerebroside from the leaves of Aerva sanguinolenta

This paper attempts to evaluate the anti-inflammatory potential and the possible mechanism of action of the leaf extracts and isolated compound(s) of Aerva sanguinolenta (Amaranthaceae), traditionally used in ailments related to inflammation. The anti-inflammatory activity of ethanol extract (ASE) was evaluated by acute, subacute and chronic models of inflammation, while a new cerebroside (‘trans’, ASE-1), isolated from the bioactive ASE and characterized spectroscopically, was tested by carrageenan-induced mouse paw oedema and protein exudation model. To understand the underlying mechanism, we measured the release of pro-inflammatory mediators such as nitric oxide (NO) and prostaglandin (PG)E2, along with the cytokines like tumour necrosis factor (TNF)-α, and interleukins(IL)-1β, IL-6 and IL-12 from lipopolysaccharide (LPS)-stimulated peritoneal macrophages. The results revealed that ASE at 400 mg/kg caused significant reduction of rat paw oedema, granuloma and exudative inflammation, while the inhibition of mouse paw oedema and exudative inflammation by ASE-1 (20 mg/kg) was comparable to that of the standard drug indomethacin (10 mg/kg). Interestingly, both ASE and ASE-1 showed significant inhibition of the expressions of iNOS2 and COX-2, and the down-regulation of the expressions of IL-1β, IL-6, IL-12 and TNF-α, in LPS-stimulated macrophages, via the inhibition of COX-2-mediated PGE2 release. Thus, our results validated the traditional use of A. sanguinolenta leaves in inflammation management.
Personalizing a Dialogue System with Transfer Learning

It is difficult to train a personalized task-oriented dialogue system because the data collected from each individual is often insufficient. Personalized dialogue systems trained on a small dataset can overfit and make it difficult to adapt to different user needs. One way to solve this problem is to consider a collection of multiple users’ data as a source domain and an individual user’s data as a target domain, and to perform a transfer learning from the source to the target domain. By following this idea, we propose the “PETAL” (PErsonalized Task-oriented diALogue), a transfer learning framework based on POMDP to learn a personalized dialogue system. The system first learns common dialogue knowledge from the source domain and then adapts this knowledge to the target user. This framework can avoid the negative transfer problem by considering differences between source and target users. The policy in the personalized POMDP can learn to choose different actions appropriately for different users. Experimental results on a real-world coffee-shopping data and simulation data show that our personalized dialogue system can choose different optimal actions for different users, and thus effectively improve the dialogue quality under the personalized setting.
A comparison of H.265/HEVC implementations

This paper deals with the most recent video coding standard H.265/HEVC. HEVC introduces new coding tools compared to older standards. Such a change has to influence the encoder complexity and will have influence on the encoding speed. In this paper, we focus on the performance of different implementations of the HEVC. The encoders are compared on the basis of final video quality and encoding speed. As the results showed, the differences in coding speed may be very significant among the encoders.
Mitigation of Loopback Self-Interference in Full-Duplex MIMO Relays

Full-duplex relaying is more spectrally efficient than half-duplex relaying as only one channel use is needed per two hops. However, it is crucial to minimize relay self-interference to render full duplex feasible. For this purpose, we analyze a broad range of multiple-input multiple-output (MIMO) mitigation schemes: natural isolation, time-domain cancellation, and spatial suppression. Cancellation subtracts replicated interference signal from the relay input while suppression reserves spatial dimensions for receive and transmit filtering. Spatial suppression can be achieved by antenna subset selection, null-space projection, i.e., receiving and transmitting in orthogonal subspaces, or joint transmit and receive beam selection to support more spatial streams by choosing the minimum eigenmodes for overlapping subspaces. In addition, minimum mean square error (MMSE) filtering can be employed to maintain the desired signal quality, which is inherent for cancellation, and the combination of time- and spatial-domain processing may be better than either alone. Targeting at minimal interference power, we solve optimal filters for each scheme in the cases of joint, separate and independent design. The performance of mitigation schemes is evaluated and compared by simulations. The results confirm that self-interference can be mitigated effectively also in the presence of imperfect side information.
The familiar stranger: anxiety, comfort, and play in public places

As humans we live and interact across a wildly diverse set of physical spaces. We each formulate our own personal meaning of place using a myriad of observable cues such as public-private, large-small, daytime-nighttime, loud-quiet, and crowded-empty. Not surprisingly, it is the people with which we share such spaces that dominate our perception of place. Sometimes these people are friends, family and colleagues. More often, and particularly in public urban spaces we inhabit, the individuals who affect us are ones that we repeatedly observe and yet do not directly interact with - our Familiar Strangers. This paper explores our often ignored yet real relationships with Familiar Strangers. We describe several experiments and studies that led to designs for both a personal, body-worn, wireless device and a mobile phone based application that extend the Familiar Stranger relationship while respecting the delicate, yet important, constraints of our feelings and affinities with strangers in pubic places.
Learning a Deep Hybrid Model for Semi-Supervised Text Classification

We present a novel fine-tuning algorithm in a deep hybrid architecture for semisupervised text classification. During each increment of the online learning process, the fine-tuning algorithm serves as a top-down mechanism for pseudo-jointly modifying model parameters following a bottom-up generative learning pass. The resulting model, trained under what we call the Bottom-Up-Top-Down learning algorithm, is shown to outperform a variety of competitive models and baselines trained across a wide range of splits between supervised and unsupervised training data.
AHP_GORE_PSR: Applying analytic hierarchy process in goal oriented requirements elicitation method for the prioritization of software requirements

Goal Oriented Requirements Engineering (GORE) is concerned with the identification of goals of the software according to the need of the stakeholders. In GORE, goals are the need of the stakeholders. These goals are refined and decomposed into sub-goals until the responsibility of the last goals are assigned to some agent or some software system. In literature different methods have been developed based on GORE concepts for the identification of software goals or software requirements like fuzzy attributed goal oriented software requirements analysis (FAGOSRA) method, knowledge acquisition for automated specifications (KAOS), i∗ framework, attributed goal oriented requirements analysis (AGORA) method, etc. In AGORA, decision makers use subjective values during the selection and the prioritization of software requirements. AGORA method can be extended by computing the objective values. These objective values can be obtained by using analytic hierarchy process (AHP). In AGORA, there is no support to check whether the values provided by the decision makers are consistent or not. Therefore, in order to address this issue we proposed a method for the prioritization of software requirements by applying the AHP in goal oriented requirements elicitation method. Finally, we consider an example to explain the proposed method.
Context-Aware Recommender Systems for Learning: A Survey and Future Challenges

Recommender systems have been researched extensively by the Technology Enhanced Learning (TEL) community during the last decade. By identifying suitable resources from a potentially overwhelming variety of choices, such systems offer a promising approach to facilitate both learning and teaching tasks. As learning is taking place in extremely diverse and rich environments, the incorporation of contextual information about the user in the recommendation process has attracted major interest. Such contextualization is researched as a paradigm for building intelligent systems that can better predict and anticipate the needs of users, and act more efficiently in response to their behavior. In this paper, we try to assess the degree to which current work in TEL recommender systems has achieved this, as well as outline areas in which further work is needed. First, we present a context framework that identifies relevant context dimensions for TEL applications. Then, we present an analysis of existing TEL recommender systems along these dimensions. Finally, based on our survey results, we outline topics on which further research is needed.
Near-eye light field displays

We propose a light-field-based approach to near-eye display that allows for thin, lightweight head-mounted displays capable of depicting accurate accommodation, convergence, and binocular disparity depth cues. Our near-eye light field displays depict sharp images from out-of-focus display elements by synthesizing light fields corresponding to virtual scenes located within the viewer's natural accommodation range. While sharing similarities with existing integral imaging displays and microlens-based light field cameras, we optimize performance in the context of near-eye viewing. Near-eye light field displays support continuous accommodation of the eye throughout a finite depth of field; as a result, binocular configurations provide a means to address the accommodation-convergence conflict occurring with existing stereoscopic displays. We construct a binocular prototype and a GPU-accelerated stereoscopic light field renderer.
Design and implementation of an autonomous flight control law for a UAV helicopter

In this paper, we present the design and implementation of an autonomous flight control law for a smallscale unmanned aerial vehicle (UAV) helicopter. The approach is decentralized in nature by incorporating a newly developed nonlinear control technique, namely the composite nonlinear feedback control, together with dynamic inversion. The overall control law consists of three hierarchical layers, namely, the kernel control, command generator and flight scheduling, and is implemented and verified in flight tests on the actual UAV helicopter. The flight test results demonstrate that the UAV helicopter is capable of carrying out complicated flight missions autonomously.
A semiotic framework for evolutionary and developmental biology

This work aims at constructing a semiotic framework for an expanded evolutionary synthesis grounded on Peirce's universal categories and the six space/time/function relations [Taborsky, E., 2004. The nature of the sign as a WFF--a well-formed formula, SEED J. (Semiosis Evol. Energy Dev.) 4 (4), 5-14] that integrate the Lamarckian (internal/external) and Darwinian (individual/population) cuts. According to these guide lines, it is proposed an attempt to formalize developmental systems theory by using the notion of evolving developing agents (EDA) that provides an internalist model of a general transformative tendency driven by organism's need to cope with environmental uncertainty. Development and evolution are conceived as non-programmed open-ended processes of information increase where EDA reach a functional compromise between: (a) increments of phenotype's uniqueness (stability and specificity) and (b) anticipation to environmental changes. Accordingly, changes in mutual information content between the phenotype/environment drag subsequent changes in mutual information content between genotype/phenotype and genotype/environment at two interwoven scales: individual life cycle (ontogeny) and species time (phylogeny), respectively. Developmental terminal additions along with increment minimization of developmental steps must be positively selected.
Power Factor Correction in Bridgeless-Luo Converter-Fed BLDC Motor Drive

This paper presents a power factor correction (PFC)-based bridgeless Luo (BL-Luo) converter-fed brushless dc (BLDC) motor drive. A single voltage sensor is used for the speed control of the BLDC motor and PFC at ac mains. The voltage follower control is used for a BL-Luo converter operating in discontinuous inductor current mode. The speed of the BLDC motor is controlled by an approach of variable dc-link voltage, which allows a low-frequency switching of the voltage source inverter for the electronic commutation of the BLDC motor, thus offering reduced switching losses. The proposed BLDC motor drive is designed to operate over a wide range of speed control with an improved power quality at ac mains. The power quality indices thus obtained are under the recommended limits of IEC 61000-3-2. The performance of the proposed drive is validated with test results obtained on a developed prototype of the drive.
Autonomic Semantic-Based Context-Aware Platform for Mobile Applications in Pervasive Environments

Currently, the field of smart-* (home, city, health, tourism, etc.) is naturally heterogeneous and multimedia oriented. In such a domain, there is an increasing usage of heterogeneous mobile devices, as well as captors transmitting data (IoT). They are highly connected and can be used for many different services, such as to monitor, to analyze and to display information to users. In this context, data management and adaptation in real time are becoming a challenging task. More precisely, at one time, it is necessary to handle in a dynamic, intelligent and transparent framework various data provided by multiple devices with several modalities. This paper presents a Kali-Smart platform, which is an autonomic semantic-based context-aware platform. It is based on semantic web technologies and a middleware providing autonomy and reasoning facilities. Moreover, Kali-Smart is generic and, as a consequence, offers to users a flexible infrastructure where they can easily control various interaction modalities of their own situations. An experimental study has been made to evaluate the performance and feasibility of the proposed platform.
Novelty or Surprise?

Novelty and surprise play significant roles in animal behavior and in attempts to understand the neural mechanisms underlying it. They also play important roles in technology, where detecting observations that are novel or surprising is central to many applications, such as medical diagnosis, text processing, surveillance, and security. Theories of motivation, particularly of intrinsic motivation, place novelty and surprise among the primary factors that arouse interest, motivate exploratory or avoidance behavior, and drive learning. In many of these studies, novelty and surprise are not distinguished from one another: the words are used more-or-less interchangeably. However, while undeniably closely related, novelty and surprise are very different. The purpose of this article is first to highlight the differences between novelty and surprise and to discuss how they are related by presenting an extensive review of mathematical and computational proposals related to them, and then to explore the implications of this for understanding behavioral and neuroscience data. We argue that opportunities for improved understanding of behavior and its neural basis are likely being missed by failing to distinguish between novelty and surprise.
Beyond sliding windows: Object localization by efficient subwindow search

Most successful object recognition systems rely on binary classification, deciding only if an object is present or not, but not providing information on the actual object location. To perform localization, one can take a sliding window approach, but this strongly increases the computational cost, because the classifier function has to be evaluated over a large set of candidate subwindows. In this paper, we propose a simple yet powerful branch-and-bound scheme that allows efficient maximization of a large class of classifier functions over all possible subimages. It converges to a globally optimal solution typically in sublinear time. We show how our method is applicable to different object detection and retrieval scenarios. The achieved speedup allows the use of classifiers for localization that formerly were considered too slow for this task, such as SVMs with a spatial pyramid kernel or nearest neighbor classifiers based on the chi2-distance. We demonstrate state-of-the-art performance of the resulting systems on the UIUC Cars dataset, the PASCAL VOC 2006 dataset and in the PASCAL VOC 2007 competition.