nils-herrmann/clean_scirepeval huggingface dataset:
Interactions between the Midbrain Superior Colliculus and the Basal Ganglia

An important component of the architecture of cortico-basal ganglia connections is the parallel, re-entrant looped projections that originate and return to specific regions of the cerebral cortex. However, such loops are unlikely to have been the first evolutionary example of a closed-loop architecture involving the basal ganglia. A phylogenetically older, series of subcortical loops can be shown to link the basal ganglia with many brainstem sensorimotor structures. While the characteristics of individual components of potential subcortical re-entrant loops have been documented, the full extent to which they represent functionally segregated parallel projecting channels remains to be determined. However, for one midbrain structure, the superior colliculus (SC), anatomical evidence for closed-loop connectivity with the basal ganglia is robust, and can serve as an example against which the loop hypothesis can be evaluated for other subcortical structures. Examination of ascending projections from the SC to the thalamus suggests there may be multiple functionally segregated systems. The SC also provides afferent signals to the other principal input nuclei of the basal ganglia, the dopaminergic neurones in substantia nigra and to the subthalamic nucleus. Recent electrophysiological investigations show that the afferent signals originating in the SC carry important information concerning the onset of biologically significant events to each of the basal ganglia input nuclei. Such signals are widely regarded as crucial for the proposed functions of selection and reinforcement learning with which the basal ganglia have so often been associated.
PLEdestrians: A Least-Effort Approach to Crowd Simulation

We present a new algorithm for simulating large-scale crowds at interactive rates based on the Principle of Least Effort. Our approach uses an optimization method to compute a biomechanically energy-efficient, collision-free trajectory that minimizes the amount of effort for each heterogeneous agent in a large crowd. Moreover, the algorithm can automatically generate many emergent phenomena such as lane formation, crowd compression, edge and wake effects ant others. We compare the results from our simulations to data collected from prior studies in pedestrian and crowd dynamics, and provide visual comparisons with real-world video. In practice, our approach can interactively simulate large crowds with thousands of agents on a desktop PC and naturally generates a diverse set of emergent behaviors.
STANDARDISATION AND CLASSIFICATION OF ALERTS GENERATED BY INTRUSION DETECTION SYSTEMS

Intrusion detection systems are most popular de-fence mechanisms used to provide security to IT infrastructures. Organisation need best performance, so it uses multiple IDSs from different vendors. Different vendors are using different formats and protocols. Difficulty imposed by this is the generation of several false alarms. Major part of this work concentrates on the collection of alerts from different intrusion detection systems to represent them in IDMEF(Intrusion Detection Message Exchange Format) format. Alerts were collected from intrusion detection systems like snort, ossec, suricata etc. Later classification is attempted using machine learning technique, which helps to mitigate generation of false positives.
SciPlore Xtract: Extracting Titles from Scientific PDF Documents by Analyzing Style Information (Font Size)

Extracting titles from a PDF’s full text is an important task in information retrieval to identify PDFs. Existing approaches apply complicated and expensive (in terms of calculating power) machine learning algorithms such as Support Vector Machines and Conditional Random Fields. In this paper we present a simple rule based heuristic, which considers style information (font size) to identify a PDF’s title. In a first experiment we show that this heuristic delivers better results (77.9% accuracy) than a support vector machine by CiteSeer (69.4% accuracy) in an ‘academic search engine’ scenario and better run times (8:19 minutes vs. 57:26 minutes).
Computer-generated Floral Ornament

This paper describes some of the principles of traditional floral ornamental design, and explores ways in which these designs can be created algorithmically. It introduces the idea of “adaptive clip art,” which encapsulates the rules for creating a specific ornamental pattern. Adaptive clip art can be used to generate patterns that are tailored to fit a particularly shaped region of the plane. If the region is resized or reshaped, the ornament can be automatically regenerated to fill this new area in an appropriate way. Our ornamental patterns are created in two steps: first, the geometry of the pattern is generated as a set of two-dimensional curves and filled boundaries; second, this geometry is rendered in any number of styles. We demonstrate our approach with a variety of floral ornamental designs. CR Categories: I.3.3 [Computer Graphics]: Picture/Image Generation; I.3.4 [Computer Graphics]: Graphics Utilities— Picture description languages.
Sentiment Analysis with Long Short-Term Memory networks

Sentiment classification is a task where text documents are classified according to their sentiment. Recurrent Neural Networks and Long Short-Term networks are both models that are often used for sentiment analysis. The goal of this research is to find out why these models work well for sentiment analysis and how these models work. Recurrent Neural Networks and Long Short-Term networks introduce a memory into the model. Having a memory in a network is useful because, when dealing with sequenced data such as text, the meaning of a word depends on the context of the previous text. A shortcoming of the Recurrent Neural network is that it is only capable of dealing with short-term dependencies. Long Short-Term networks address this problem by introducing a long-term memory into the network. A Long Short-Term model is build to classify the sentiment of a movie review dataset. The model correctly classified 86.74% of the reviews in the validation set. The model is very sensitive to overfitting but gives quite a good result even without parameter tuning.
Verification of Periodically Controlled Hybrid Systems: Application to an Autonomous Vehicle

This article introduces Periodically Controlled Hybrid Automata (PCHA) for modular specification of embedded control systems. In a PCHA, control actions that change the control input to the plant occur roughly periodically, while other actions that update the state of the controller may occur in the interim. Such actions could model, for example, sensor updates and information received from higher-level planning modules that change the set point of the controller. Based on periodicity and subtangential conditions, a new sufficient condition for verifying invariant properties of PCHAs is presented. For PCHAs with polynomial continuous vector fields, it is possible to check these conditions automatically using, for example, quantifier elimination or sum of squares decomposition. We examine the feasibility of this automatic approach on a small example. The proposed technique is also used to manually verify safety and progress properties of a fairly complex planner-controller subsystem of an autonomous ground vehicle. Geometric properties of planner-generated paths are derived which guarantee that such paths can be safely followed by the controller.
Treatment of keloids and hypertrophic scars.

Clinicians always find it difficult to treat hypertrophic scars and keloids. Various treatment modalities are available. Intralesional corticosteroids, topical applications, cryotherapy, surgery, laser therapy, and silicone sheeting are the widely used options. Radiation therapy can also help in cases of recalcitrant keloids. Most recently, pulsed-dye laser has been successfully used to treat keloids and hypertrophic scars. There are no set guidelines for the treatment of keloids. Treatment has to be individualized depending upon the distribution, size, thickness, and consistency of the lesions and association of inflammation. A combination approach to therapy seems to be the best option.
Automatic on-the-fly extrinsic camera calibration of onboard vehicular cameras

This paper presents an on-the-fly procedure for obtaining extrinsic camera parameters of onboard vehicular cameras, as well as augmented reality applications for driver assistance systems and road inspection. The proposed approach employs a lane detection algorithm to extract the lane boundaries of the road, and estimates the distance between adjacent lane markings based on the speed of the vehicle. Then, a rectangular portion of the road is used to obtain the desired extrinsic camera parameters. With the complete set of camera parameters, virtual objects can be coherently inserted into the video sequence captured by the camera, so that synthetic traffic signs may be added to increase safety. Experimental results illustrate the performance of the proposed camera calibration systems, as well as possible applications involving augmented reality.
Rapid Shape Acquisition Using Color Structured Light and Multi-pass Dynamic Programming

This paper presents a color structured light technique for recovering object shape from one or more images. The technique works by projecting a pattern of stripes of alternating colors and matching the projected color transitions with observed edges in the image. The correspondence problem is solved using a novel, multi-pass dynamic programming algorithm that eliminates global smoothness assumptions and strict ordering constraints present in previous formulations. The resulting approach is suitable for generating both highspeed scans of moving objects when projecting a single stripe pattern and high-resolution scans of static scenes using a short sequence of time-shifted stripe patterns. In the latter case, spacetime analysis is used at each sensor pixel to obtain inter-frame depth localization. Results are demonstrated for a variety of complex scenes.
Universal One-Way Hash Functions and their Cryptographic Applications

We define a Universal One-Way Hash Function family, a new primitive which enables the compression of elements in the function domain. The main property of this primitive is that given an element x. We prove constructively that universal one-way hash functions exist if any 1-1 one-way functions exist.
Among the various applications of the primitive is a One-Way based Secure Digital Signature Scheme, a system which is based on the existence of any 1-1 One-Way Functions and is secure against the most general attack known. Previously, all provably secure signature schemes were based on the stronger mathematical assumption that trapdoor one-way functions exist.
Embodied Conversational Interface Agents+

More than another friendly face, Rea knows how to have a conversation with living, breathing human users with a wink, a nod, and a sidelong glance. A nimals and humans all manifest social qualities and skills. Dogs recognize dominance and submission , stand corrected by their superiors, demonstrate consistent personalities, and so forth. On the other hand, only humans communicate through language and carry on conversations with one another. The skills involved in human conversation have developed in such a way as to exploit all the special characteristics of the human body. We make complex repre-sentational gestures with our prehensile hands, gaze away and toward one another out of the corners of our centrally set eyes, and use the pitch and melody of our flexible voices to emphasize and clarify what we are saying. Perhaps because conversation is so defining of humanness and human interaction, the metaphor of face-to-face conversation has been applied to human-computer interface design for quite some time. One of the early arguments for the utility of this metaphor pointed to the application of the features of face-to-face conversation in human-computer interaction, including mixed initiative, nonverbal communication , sense of presence, and the rules involved in transferring control [9]. However, although these features have gained widespread recognition, human-computer conversation has only recently become more than a metaphor. That is, only recently have human-computer interface designers taken the metaphor seriously enough to attempt to design a computer that could hold up its end of the conversation with a human user. Here, I describe some of the features of human-human conversation being implemented in this new genre of embodied conversational agent, exploring a notable embodied conversational agent—named Rea—based on these features. Because conversation is such a primary skill for humans and learned so early in life (practiced, in fact, between infants and their mothers taking turns cooing and burbling EMBODIED CONVERSATIONAL INTERFACE AGENTS
Educational game design for online education

The use of educational games in learning environments is an increasingly relevant trend. The motivational and immersive traits of game-based learning have been deeply studied in the literature, but the systematic design and implementation of educational games remain an elusive topic. In this study some relevant requirements for the design of educational games in online education are analyzed, and a general game design method that includes adaptation and assessment features is proposed. Finally, a particular implementation of that design is described in light of its applicability to other implementations and environments.
Mutations in EZH2 cause Weaver syndrome.

We used trio-based whole-exome sequencing to analyze two families affected by Weaver syndrome, including one of the original families reported in 1974. Filtering of rare variants in the affected probands against the parental variants identified two different de novo mutations in the enhancer of zeste homolog 2 (EZH2). Sanger sequencing of EZH2 in a third classically-affected proband identified a third de novo mutation in this gene. These data show that mutations in EZH2 cause Weaver syndrome.
A technique to build a secret key in integrated circuits for identification and authentication applications

This paper describes a technique that exploits the statistical delay variations of wires and transistors across ICs to build a secret key unique to each IC. To explore its feasibility, we fabricated a candidate circuit to generate a response based on its delay characteristics. We show that there exists enough delay variation across ICs implementing, the proposed circuit to identify individual ICs. Further. the circuit, functions reliably over a practical range of environmental variation such as temperature and voltage.
Research of 3D face recognition algorithm based on deep learning stacked denoising autoencoder theory

This electronic Due to the fact that the 3D face depth data have more information, the 3D face recognition is attracting more and more attention in the machine learning area. Firstly, this paper selects 30 feature points from the 113 feature points of Candide-3 face model to characterize face, which improves the efficiency of recognition algorithm obviously without affecting the recognition accuracy. With the significant advantage of the characterization of essential features by learning a deep nonlinear network, this paper presents a stacked denoising autoencoder algorithm model based on deep learning which improves neural networks model. This algorithm conducts the unsupervised preliminary training of face depth data and the supervised training to fine-tuning the network which is better than neural network's random initialization. The experiment indicates that compared with real face data, the reconstruction face model has a small matching error by using SDAE algorithm and it achieves an excellent face recognition effect.
Holistic DDoS mitigation using NFV

Distributed Denial of Service (DDoS) is a sophisticated cyber-attack due to its variety of types and techniques. The traditional mitigation method of this attack is to deploy dedicated security appliances such as firewall, load balancer, etc. However, due to the limited capacity of the hardware and the potential high volume of DDoS traffic, it may not be able to defend all the attacks. Therefore, cloud-based DDoS protection services were introduced to allow the organizations to redirect their traffic to the scrubbing centers in the cloud for filtering. This solution has some drawbacks such as privacy violation and latency. More recently, Network Functions Virtualization (NFV) and edge computing have been proposed as new networking service models. In this paper, we design a framework that leverages NFV and edge computing for DDoS mitigation through two-stage processes.
Solving Stochastic Path Problem: Particle Swarm Optimization Approach

An stochastic version of the classical shortest path problem whereby for each node of a graph, a probability distribution over the set of successor nodes must be chosen so as to reach a certain destination node with minimum expected cost. In this paper, we propose a new algorithm based on Particle Swarm Optimization (PSO) for solving Stochastic Shortest Path Problem (SSPP). The comparison of our algorithm with other algorithms indicates that its performance is suitable even by the less number of iterations.
Self-Efficacy in the Prediction of Academic Performance and Perceived Career Options

This study explored the relation of self-efficacy beliefs to educational/vocational choice and performance, assessing the extent to which efficacy beliefs, in concert with other relevant variables, predict academic grades, persistence, and perceived career options in students considering science and engineering fields. Subjects were 105 undergraduates who participated in a career planning course on science and engineering fields. Hierarchical regression analyses indicated that self-efficacy contributed significant unique variance to the prediction of grades, persistence, and range of perceived career options in technical/scientific fields. The two self-efficacy scales used were moderately intercorrelated but differentially related to previous academic performance; neither scale was significantly related to general selfesteem or career indecision. Implications for further career self-efficacy research, and for career and academic counseling, are discussed.
Resistance to Change : The Moderating Effects of Leader-Member Exchange and Role Breadth Self-Efficacy

The prevalence of resistance during change initiatives is well recognized in the change management literature. The implementation of the lean production system is no exception. It often requires substantial changes to processes and the way people work. As such, understanding how to manage this resistance is important. One view argues that the extent of resistance during change depends on the characteristics of the change process. This view posits that resistance can be reduced if organizations manage information flow, create room for participation and develop trust in management. In addition, this paper also proposes that is Leader-Member Exchange (LMX) and Role Breadth Self-Efficacy (RBSE) moderate the effect on the employees’ resistance to change. 
Current Evidence on the Socket-Shield Technique: A Systematic Review.

The recently popularized socket-shield technique involves intentional retention of a section of the remnant root at the time of immediate implant placement, thereby preserving the buccal/proximal bone from resorption. The objective of this systematic review was to assess the literature available on the socket-shield technique and weigh its biological plausibility and long-term clinical prognosis. A systematic search was performed on PubMed-Medline, Embase, Web of Knowledge, Google Scholar, and Cochrane Central for clinical/animal studies from January 1970 to April 2017. Twenty-three studies were assessed: 1 clinical case-control study, 4 animal histological reports, 1 clinical abstract, and 17+2* case reports. Eighteen out of the 23 studies had a duration of ≤12 months. A quality assessment of 5 studies (4 animal histologic and 1 clinical case-control) performed using the modified Animal Research: Reporting of In Vivo Experiments guidelines revealed that 4/5 studies had low scores. Fifty-eight out of 70 (82.86%) implants from 4 animal histological studies had complications; buccal/crestal bone loss (54.55%) and failure of osseointegration (27.27%) were the most common. Thirty-three out of 136 (24.26%) implants from 19+2 (2 studies had both histologic and clinical components, which are assessed separately) clinical studies had complications; buccal/crestal bone loss (78.78%) and shield exposure/failure (12.12%) were the most common. Other complications recorded were periodontal ligament and cementum formation on implant surfaces, pocket formation, inflammation, mucositis, and peri-implantitis. However, some clinical reports indicated stable results at 12 months. It would be difficult to predict the long-term success of this technique until high-quality evidence becomes available. A video abstract is available for viewing at https://youtu.be/lNMeUxj2XPA?list=PLvRxNhB9EJqbqjcYMbwKbwi8Xpbb0YuHI .
The Human Hippocampus and Spatial and Episodic Memory

Finding one's way around an environment and remembering the events that occur within it are crucial cognitive abilities that have been linked to the hippocampus and medial temporal lobes. Our review of neuropsychological, behavioral, and neuroimaging studies of human hippocampal involvement in spatial memory concentrates on three important concepts in this field: spatial frameworks, dimensionality, and orientation and self-motion. We also compare variation in hippocampal structure and function across and within species. We discuss how its spatial role relates to its accepted role in episodic memory. Five related studies use virtual reality to examine these two types of memory in ecologically valid situations. While processing of spatial scenes involves the parahippocampus, the right hippocampus appears particularly involved in memory for locations within an environment, with the left hippocampus more involved in context-dependent episodic or autobiographical memory.
A Universal Crease Pattern for Folding Orthogonal Shapes

We present a universal crease pattern—known in geometry as the tetrakis tiling and in origami as box pleating—that can fold into any object made up of unit cubes joined face-to-face (polycubes). More precisely, there is one universal finite crease pattern for each number n of unit cubes that need to be folded. This result contrasts previous universality results for origami, which require a different crease pattern for each target object, and confirms intuition in the origami community that box pleating is a powerful design technique.
A new Design Criteria for Hash-Functions

The most common way of constructing a hash function (e.g., SHA-1) is to iterate a compression function on the input message. The compression function is usually designed from scratch or made out of a block-cipher. In this paper, we introduce a new security notion for hash-functions, stronger than collision-resistance. Under this notion, the arbitrary length hash function H must behave as a random oracle when the fixed-length building block is viewed as an ideal primitive. This enables to eliminate all possible generic attacks against iterative hash-functions. In this paper, we show that the current design principle behind hash functions such as SHA-1 and MD5 — the (strengthened) Merkle-Damg̊ard transformation — does not satisfy this security notion. We provide several constructions that provably satisfy this notion; those new constructions introduce minimal changes to the plain Merkle-Damg̊ard construction and are easily implementable in practice. This paper is a modified version of a paper to appear at Crypto 2005.