nils-herrmann/clean_scirepeval huggingface dataset:
Fucoidan, a sulfated polysaccharide from brown algae, improves cognitive impairment induced by infusion of Aβ peptide in rats.

Fucoidan is a complex sulfated polysaccharide, derived from marine brown seaweed. In the present study, we investigated the effects of fucoidan on improving learning and memory impairment in rats induced by infusion of Aβ (1-40), and its possible mechanisms. The results indicated that fucoidan could ameliorate Aβ-induced learning and memory impairment in animal behavioral tests. Furthermore, fucoidan reversed the decreased activity of choline acetyl transferase (ChAT), superoxide dismutase (SOD), glutathione peroxidase (GSH-Px) and content of acetylcholine (Ach), as well as the increased activity of acetylcholine esterase (AchE) and content of malondialdehyde (MDA) in hippocampal tissue of Aβ-injected rats. Moreover, these were accompanied by an increase of Bcl-2/Bax ratio and a decrease of caspase-3 activity. These results suggested that fucoidan could ameliorate the learning and memory abilities in Aβ-induced AD rats, and the mechanisms appeared to be due to regulating the cholinergic system, reducing oxidative stress and inhibiting the cell apoptosis.
Postural Stability of Biped Robots and the Foot-Rotation Indicator (FRI) Point

The focus of this paper is the problem of foot rotation in biped robots during the single-support phase. Foot rotation is an indication of postural instability, which should be carefully treated in a dynamically stable walk and avoided altogether in a statically stable walk. We introduce the foot-rotation indicator (FRI) point, which is a point on the foot/ground-contact surface where the net groundreaction force would have to act to keep the foot stationary. To ensure no foot rotation, the FRI point must remain within the convex hull of the foot-support area. In contrast with the ground projection of the center of mass (GCoM), which is a static criterion, the FRI point incorporates robot dynamics. As opposed to the center of pressure (CoP)—better known as the zero-moment point (ZMP) in the robotics literature—which may not leave the support area, the FRI point may leave the area. In fact, the position of the FRI point outside the footprint indicates the direction of the impending rotation and the magnitude of rotational moment acting on the foot. Owing to these important properties, the FRI point helps not only to monitor the state of postural stability of a biped robot during the entire gait cycle, but indicates the severity of instability of the gait as well. In response to a recent need, the paper also resolves the misconceptions surrounding the CoP/ZMP
The effects of different resistance training protocols on muscular strength and endurance development in children.

Previous research has shown that children can increase their muscular strength and muscular endurance as a result of regular participation in a progressive resistance training program. However, the most effective exercise prescription regarding the number of repetitions remains questionable.



To compare the effects of a low repetition-heavy load resistance training program and a high repetition-moderate load resistance training program on the development of muscular strength and muscular endurance in children.  Prospective, controlled trial.



Community-based youth fitness center.



Eleven girls and 32 boys between the ages of 5.2 and 11.8 years.



In twice-weekly sessions of resistance training for 8 weeks, children performed 1 set of 6 to 8 repetitions with a heavy load (n = 15) or 1 set of 13 to 15 repetitions with a moderate load (n = 16) on child-size exercise machines. Children in the control group (n = 12) did not resistance train. One repetition maximum (RM) strength and muscular endurance (repetitions performed posttraining with the pretraining 1-RM load) were determined on the leg extension and chest press exercises.



One RM leg extension strength significantly increased in both exercise groups compared with that in the control subjects. Increases of 31.0% and 40.9%, respectively, for the low repetition-heavy load and high repetition-moderate load groups were observed. Leg extension muscular endurance significantly increased in both exercise groups compared with that in the control subjects, although gains resulting from high repetition-moderate load training (13.1 +/- 6.2 repetitions) were significantly greater than those resulting from low repetition-heavy load training (8.7 +/- 2.9 repetitions). On the chest press exercise, only the high repetition-moderate load exercise group made gains in 1-RM strength (16.3%) and muscular endurance (5.2 +/- 3.6 repetitions) that were significantly greater than gains in the control subjects.



These findings support the concept that muscular strength and muscular endurance can be improved during the childhood years and favor the prescription of higher repetition-moderate load resistance training programs during the initial adaptation period.
What We Instagram: A First Analysis of Instagram Photo Content and User Types

Instagram is a relatively new form of communication where users can easily share their updates by taking photos and tweaking them using filters. It has seen rapid growth in the number of users as well as uploads since it was launched in October 2010. In spite of the fact that it is the most popular photo capturing and sharing application, it has attracted relatively less attention from the research community. In this paper, we present both qualitative and quantitative analysis on Instagram. We use computer vision techniques to examine the photo content. Based on that, we identify the different types of active users on Instagram using clustering. Our results reveal several insights about Instagram which were never studied before, that include: 1) Eight popular photos categories, 2) Five distinct types of Instagram users in terms of their posted photos, and 3) A user’s audience (number of followers) is independent of his/her shared photos on Instagram. To our knowledge, this is the first in-depth study of content and users on Instagram.
Contradiction Detection for Rumorous Claims

The utilization of social media material in journalistic workflows is increasing, demanding automated methods for the identification of misand disinformation. Since textual contradiction across social media posts can be a signal of rumorousness, we seek to model how claims in Twitter posts are being textually contradicted. We identify two different contexts in which contradiction emerges: its broader form can be observed across independently posted tweets and its more specific form in threaded conversations. We define how the two scenarios differ in terms of central elements of argumentation: claims and conversation structure. We design and evaluate models for the two scenarios uniformly as 3-way Recognizing Textual Entailment tasks in order to represent claims and conversation structure implicitly in a generic inference model, while previous studies used explicit or no representation of these properties. To address noisy text, our classifiers use simple similarity features derived from the string and part-of-speech level. Corpus statistics reveal distribution differences for these features in contradictory as opposed to non-contradictory tweet relations, and the classifiers yield state of the art performance.
Recognition-by-components: a theory of human image understanding.

The perceptual recognition of objects is conceptualized to be a process in which the image of the input is segmented at regions of deep concavity into an arrangement of simple geometric components, such as blocks, cylinders, wedges, and cones. The fundamental assumption of the proposed theory, recognition-by-components (RBC), is that a modest set of generalized-cone components, called geons (N £ 36), can be derived from contrasts of five readily detectable properties of edges in a two-dimensional image: curvature, collinearity, symmetry, parallelism, and cotermination. The detection of these properties is generally invariant over viewing position an$ image quality and consequently allows robust object perception when the image is projected from a novel viewpoint or is degraded. RBC thus provides a principled account of the heretofore undecided relation between the classic principles of perceptual organization and pattern recognition: The constraints toward regularization (Pragnanz) characterize not the complete object but the object's components. Representational power derives from an allowance of free combinations of the geons. A Principle of Componential Recovery can account for the major phenomena of object recognition: If an arrangement of two or three geons can be recovered from the input, objects can be quickly recognized even when they are occluded, novel, rotated in depth, or extensively degraded. The results from experiments on the perception of briefly presented pictures by human observers provide empirical support for the theory.
Cluster Analysis

1 2 3 4 5 6 Can we organize sampling entities into discrete classes, such that within-group similarity is maximized and among-group similarity is minimized according to some objective criterion? 2 Important Characteristics of Cluster Analysis Techniques P Family of techniques with similar goals. P Operate on data sets for which pre-specified, well-defined groups do "not" exist; characteristics of the data are used to assign entities into artificial groups. P Summarize data redundancy by reducing the information on the whole set of say N entities to information about say g groups of nearly similar entities (where hopefully g is very much smaller than N).
Agent-based modeling and simulation: ABMS examples

Agent-based modeling and simulation (ABMS) is a new approach to modeling systems comprised of autonomous, interacting agents. ABMS promises to have far-reaching effects on the way that businesses use computers to support decision-making and researchers use electronic laboratories to support their research. Some have gone so far as to contend that ABMS "is a third way of doing science," in addition to traditional deductive and inductive reasoning (Axelrod 1997). Computational advances have made possible a growing number of agent-based models across a variety of application domains. Applications range from modeling agent behavior in the stock market, supply chains, and consumer markets, to predicting the spread of epidemics, the threat of bio-warfare, and the factors responsible for the fall of ancient civilizations. This tutorial describes the theoretical and practical foundations of ABMS, identifies toolkits and methods for developing agent models, and illustrates the development of a simple agent-based model.
Implementing Pushback: Router-Based Defense Against DDoS Attacks

Pushback is a mechanism for defending against distributed denial-of-service (DDoS) attacks. DDoS attacks are treated as a congestion-control problem, but because most such congestion is caused by malicious hosts not obeying traditional end-to-end congestion control, the proble m must be handled by the routers. Functionality is added to each router to detect and preferentially drop packets that probably belong to an attack. Upstream routers are also notified to drop such packets (hence the term Pushback ) in order that the router’s resources be used to route legitimat e traffic. In this paper we present an architecture for Pushback, its implementation under FreeBSD, and suggestions for how such a system can be implemented in core routers.
Measuring emotional intelligence with the Mayer-Salovery-Caruso Emotional Intelligence Test (MSCEIT).

This manuscript examines the measurement instrument developed from the ability model of EI (Mayer and Salovey, 1997), the Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT; Mayer, Salovey and Caruso, 2002). The four subtests, scoring methods, psychometric properties, reliability, and factor structure of the MSCEIT are discussed, with a special focus on the discriminant, convergent, predictive, and incremental validity of the test. The authors review associations between MSCEIT scores and important outcomes such as academic performance, cognitive processes, psychological well-being, depression, anxiety, prosocial and maladaptive behavior, and leadership and organizational behavior. Findings regarding the low correlations between MSCEIT scores and self-report measures of EI also are presented. In the conclusion the authors' provide potential directions for future research on emotional intelligence.
Recent Advances in Natural Language Generation: A Survey and Classification of the Empirical Literature

Natural Language Generation (NLG) is defined as the systematic approach for producing human understandable natural language text based on nontextual data or from meaning representations. This is a significant area which empowers human-computer interaction. It has also given rise to a variety of theoretical as well as empirical approaches. This paper intends to provide a detailed overview and a classification of the state-of-the-art approaches in Natural Language Generation. The paper explores NLG architectures and tasks classed under document planning, micro-planning and surface realization modules. Additionally, this paper also identifies the gaps existing in the NLG research which require further work in order to make NLG a widely usable technology.
Bilingualism delays age at onset of dementia, independent of education and immigration status.

The purpose of the study was to determine the association between bilingualism and age at onset of dementia and its subtypes, taking into account potential confounding factors.



Case records of 648 patients with dementia (391 of them bilingual) diagnosed in a specialist clinic were reviewed. The age at onset of first symptoms was compared between monolingual and bilingual groups. The influence of number of languages spoken, education, occupation, and other potentially interacting variables was examined.



Overall, bilingual patients developed dementia 4.5 years later than the monolingual ones. A significant difference in age at onset was found across Alzheimer disease dementia as well as frontotemporal dementia and vascular dementia, and was also observed in illiterate patients. There was no additional benefit to speaking more than 2 languages. The bilingual effect on age at dementia onset was shown independently of other potential confounding factors such as education, sex, occupation, and urban vs rural dwelling of subjects.



This is the largest study so far documenting a delayed onset of dementia in bilingual patients and the first one to show it separately in different dementia subtypes. It is the first study reporting a bilingual advantage in those who are illiterate, suggesting that education is not a sufficient explanation for the observed difference. The findings are interpreted in the context of the bilingual advantages in attention and executive functions.
Meta-Learning a Dynamical Language Model

Language modeling is a prototypical unsupervised task of natural language processing (NLP). It has triggered the developments of essential bricks of models used in speech recognition, translation or summarization. More recently, language modeling has been shown to give a sensible loss function for learning high-quality unsupervised representations in tasks like text classification (Howard & Ruder, 2018), sentiment detection (Radford et al., 2017) or word vector learning (Peters et al., 2018) and there is thus a revived interest in developing better language models. More generally, improvement in sequential prediction models are believed to be beneficial for a wide range of applications like model-based planning or reinforcement learning whose models have to encode some form of memory.
Zero-Shot Learning and Clustering for Semantic Utterance Classification

We propose two novel zero-shot learning methods for semantic utterance classification (SUC) using deep learning. Both approaches rely on learning deep semantic embeddings from a large amount of Query Click Log data obtained from a search engine. Traditional semantic utterance classification systems require large amounts of labelled data, whereas our proposed methods make use of the structure of the task to allow classification without labeled data. We also develop a zero-shot semantic clustering algorithm for extracting discriminative features for supervised semantic utterance classification systems. We demonstrate the effectiveness of the zero-shot semantic learning algorithm on the SUC dataset collected by . Furthermore, we show that extracting features using zero-shot semantic clustering for a linear SVM reaches state-of-the-art result on that dataset.
Automatic Reverse Engineering of Malware Emulators

Malware authors have recently begun using emulation technology to obfuscate their code. They convert native malware binaries into bytecode programs written in a randomly generated instruction set and paired with a native binary emulator that interprets the bytecode. No existing malware analysis can reliably reverse this obfuscation technique. In this paper, we present the first work in automatic reverse engineering of malware emulators. Our algorithms are based on dynamic analysis. We execute the emulated malware in a protected environment and record the entire x86 instruction trace generated by the emulator. We then use dynamic data-flow and taint analysis over the trace to identify data buffers containing the bytecode program and extract the syntactic and semantic information about the bytecode instruction set. With these analysis outputs, we are able to generate data structures, such as control-flow graphs, that provide the foundation for subsequent malware analysis. We implemented a proof-of-concept system called Rotalume and evaluated it using both legitimate programs and malware emulated by VMProtect and Code Virtualizer. The results show that Rotalume accurately reveals the syntax and semantics of emulated instruction sets and reconstructs execution paths of original programs from their bytecode representations.
Apollonian Circle Packings: Geometry and Group Theory I. The Apollonian Group

Apollonian circle packings arise by repeatedly filling the interstices between four mutually tangent circles with further tangent circles. We observe that there exist Apollonian packings which have strong integrality properties, in which all circles in the packing have integer curvatures and rational centers such that (curvature)×(center) is an integer vector. This series of papers explain such properties. A Descartes configuration is a set of four mutually tangent circles with disjoint interiors. An Apollonian circle packing can be described in terms of the Descartes configuration it contains. We describe the space of all ordered, oriented Descartes configurations using a coordinate system MD consisting of those 4× 4 real matrices W with W QDW = QW where QD is the matrix of the Descartes quadratic form QD = x 2 1 + x 2 2 + x 2 3 + x 2 4 − 12(x1 + x2 + x3 + x4) and QW of the quadratic form QW = −8x1x2 + 2x3 + 2x4. On the parameter space MD the group Aut(QD) acts on the left, and Aut(QW ) acts on the right, giving two different “geometric” actions. Both these groups are isomorphic to the Lorentz group O(3, 1). The right action
RAM-Based Reconfigurable Implementation of the MD6 Hash Function

Recent breakthroughs in cryptanalysis of standard hash functions like SHA-1 and MD5 raise the need for alternatives. The MD6 hash function is developed by a team led by Professor Ronald L. Rivest in response to the call for proposals for a SHA-3 cryptographic hash algorithm by the National Institute of Standards and Technology. The hardware performance evaluation of hash chip design mainly includes efficiency and flexibility. In this paper, a RAM-based reconfigurable FPGA implantation of the MD6-224/256/384 /512 hash function is presented. The design achieves a throughput ranges from 118 to 227 Mbps at the maximum frequency of 104MHz on low-cost Cyclone III device. The implementation of MD6 core functionality uses mainly embedded Block RAMs and small resources of logic elements in Altera FPGA, which satisfies the needs of most embedded applications, including wireless communication. The implementation results also show that the MD6 hash function has good reconfigurability.
Textual Entailment with Structured Attentions and Composition

Deep learning techniques are increasingly popular in the textual entailment task, overcoming the fragility of traditional discrete models with hard alignments and logics. In particular, the recently proposed attention models (Rocktäschel et al., 2015; Wang and Jiang, 2015) achieves state-of-the-art accuracy by computing soft word alignments between the premise and hypothesis sentences. However, there remains a major limitation: this line of work completely ignores syntax and recursion, which is helpful in many traditional efforts. We show that it is beneficial to extend the attention model to tree nodes between premise and hypothesis. More importantly, this subtree-level attention reveals information about entailment relation. We study the recursive composition of this subtree-level entailment relation, which can be viewed as a soft version of the Natural Logic framework (MacCartney and Manning, 2009). Experiments show that our structured attention and entailment composition model can correctly identify and infer entailment relations from the bottom up, and bring significant improvements in accuracy.
DenseCap: Fully Convolutional Localization Networks for Dense Captioning

We introduce the dense captioning task, which requires a computer vision system to both localize and describe salient regions in images in natural language. The dense captioning task generalizes object detection when the descriptions consist of a single word, and Image Captioning when one predicted region covers the full image. To address the localization and description task jointly we propose a Fully Convolutional Localization Network (FCLN) architecture that processes an image with a single, efficient forward pass, requires no external regions proposals, and can be trained end-to-end with a single round of optimization. The architecture is composed of a Convolutional Network, a novel dense localization layer, and Recurrent Neural Network language model that generates the label sequences. We evaluate our network on the Visual Genome dataset, which comprises 94,000 images and 4,100,000 region-grounded captions. We observe both speed and accuracy improvements over baselines based on current state of the art approaches in both generation and retrieval settings.
Transparent Voting Platform Based on Permissioned Blockchain

Since 2004, different research was handling the challenges in the centralized voting systems, e-voting protocols and recently the decentralized voting. So electronic voting puts forward some difficulties regarding the voter anonymity, the secure casting of the votes and to prevent the voting process from frauding. The Decentralized property of the technology called"blockchain"could have the solution for many of the challenges in voting research area and brings a new secure mechanism of safe and transparent voting. In this paper, a broad comparison between ongoing voting systems has studied by analyzing their structure and the drawbacks that should consider in future to improve the whole election process from keeping the privacy of the voter, casting a vote with the possibility to check if it was counted correctly to publishing the results. The result of the paper will give a new approach to extend the target of the election from small scale to large scale despite the fact of Ethereum limitation which can cast on the blockchain just five votes per minute. The primary challenge is to find an answer for this question:"How to balance between voter privacy and transparency without breaking the important rule where the voter can proof for a specific candidate that he voted for him in a bribe situation?".