nils-herrmann/clean_scirepeval huggingface dataset:
PRIME: A Novel Processing-in-Memory Architecture for Neural Network Computation in ReRAM-Based Main Memory

Processing-in-memory (PIM) is a promising solution to address the "memory wall" challenges for future computer systems. Prior proposed PIM architectures put additional computation logic in or near memory. The emerging metal-oxide resistive random access memory (ReRAM) has showed its potential to be used for main memory. Moreover, with its crossbar array structure, ReRAM can perform matrix-vector multiplication efficiently, and has been widely studied to accelerate neural network (NN) applications. In this work, we propose a novel PIM architecture, called PRIME, to accelerate NN applications in ReRAM based main memory. In PRIME, a portion of ReRAM crossbar arrays can be configured as accelerators for NN applications or as normal memory for a larger memory space. We provide microarchitecture and circuit designs to enable the morphable functions with an insignificant area overhead. We also design a software/hardware interface for software developers to implement various NNs on PRIME. Benefiting from both the PIM architecture and the efficiency of using ReRAM for NN computation, PRIME distinguishes itself from prior work on NN acceleration, with significant performance improvement and energy saving. Our experimental results show that, compared with a state-of-the-art neural processing unit design, PRIME improves the performance by ~2360× and the energy consumption by ~895×, across the evaluated machine learning benchmarks.
Hawkes Processes for Continuous Time Sequence Classification: an Application to Rumour Stance Classification in Twitter

Classification of temporal textual data sequences is a common task in various domains such as social media and the Web. In this paper we propose to use Hawkes Processes for classifying sequences of temporal textual data, which exploit both temporal and textual information. Our experiments on rumour stance classification on four Twitter datasets show the importance of using the temporal information of tweets along with the textual content.
The cerebellum as a liquid state machine

We examined closely the cerebellar circuit model that we have proposed previously. The model granular layer generates a finite but very long sequence of active neuron populations without recurrence, which is able to represent the passage of time. For all the possible binary patterns fed into mossy fibres, the circuit generates the same number of different sequences of active neuron populations. Model Purkinje cells that receive parallel fiber inputs from neurons in the granular layer learn to stop eliciting spikes at the timing instructed by the arrival of signals from the inferior olive. These functional roles of the granular layer and Purkinje cells are regarded as a liquid state generator and readout neurons, respectively. Thus, the cerebellum that has been considered to date as a biological counterpart of a perceptron is reinterpreted to be a liquid state machine that possesses powerful information processing capability more than a perceptron.
Semantics of context-free languages: Correction

Page 137, delete "when ~"j is of the type q(j)" at the end of the proof of the theorem. Page 135, line 14, change T to 3--. In the last formula of (2•4), replace "{(l(Xpo), Xpo)lXpo ~ S}" by "((l(Xpj), Xpj) ll < j <np}". Page 140, line 1, change "p" to "P"; line 13, change "S" to "Z". Page 141, rules 3.1 and 3.2, change "S" to "Y:'. Page 141, rule 6.2, delete "follow (L2)= newsymbol;" and also delete "include follow (L2) in Q;".
Audio Steganography to Protect the Confidential Information: A Survey

Steganography is a science to hide information, it hides a message to another object, and it increases the security of data transmission and archiving it. In the process of steganography, the hidden object in which data is hidden the carrier object and the new object, is called the steganography object. The multiple carriers, such as text, audio, video, image and so can be mentioned for steganography; however, audio has been significantly considered due to the multiplicity of uses in various fields such as the internet. For steganography process, several methods have been developed; including work in the temporary and transformation, each of has its own advantages and disadvantages, and special function. In this paper we mainly review and evaluate different types of audio steganography techniques, advantages and disadvantages.
Sliding-mode control for trajectory-tracking of a Wheeled Mobile Robot in presence of uncertainties

Wheeled Mobile Robots (WMRs) are the most widely used class of mobile robots. This is due to their fast maneuvering, simple controllers and energy saving characteristics. A dynamics-based sliding mode controller for WMR trajectorytracking is proposed. Robustness to external disturbances and parameter uncertainties is achieved. Closed loop real-time results show good performances in trajectory tracking even if for high upper bound of uncertainties.
A cognitive-behavioral model of problematic online gaming in adolescents aged 12-22 years

The aim of this study was to apply the cognitive behavioral model of problematic Internet use to the context of online game use to obtain a better understanding of problematic use of online games and its negative consequences. In total, 597 online game playing adolescents aged 12–22 years participated in this study. Results showed that the cognitive behavioral model of problematic Internet use can also be used in the context of online game use. More specifically, preference for online social interaction, mood regulation and deficient self-regulation appeared to play an important role in predicting negative outcomes from problematic online game use. Together, these cognitions and behaviors explained 79% of the variance of negative outcomes scores. These findings can be used to develop strategies that aim at reducing problematic online game behavior and its negative consequences.
On the Role of Text Preprocessing in Neural Network Architectures: An Evaluation Study on Text Categorization and Sentiment Analysis

In this paper we investigate the impact of simple text preprocessing decisions (particularly tokenizing, lemmatizing, lowercasing and multiword grouping) on the performance of a state-of-the-art text classifier based on convolutional neural networks. Despite potentially affecting the final performance of any given model, this aspect has not received a substantial interest in the deep learning literature. We perform an extensive evaluation in standard benchmarks from text categorization and sentiment analysis. Our results show that a simple tokenization of the input text is often enough, but also highlight the importance of being consistent in the preprocessing of the evaluation set and the corpus used for training word embeddings.
Review of Recommender Systems and Their Application

In the past few decades, the concepts of personalization in the forms of recommender system, information filtering, or customization not only are quickly accepted by the public but also draw considerable attention from enterprises. Therefore, a number of studies based on personalized recommendations have subsequently been produced. Most of these studies apply on E-commerce, website, and information, and some of them apply on teaching, tourism, and TV programs. Because the recent rise of Web 3.0 emphasizes on providing more complete personal information and service through the efficient method, the recommender application gradually develops towards mobile commerce, mobile information, or social network. Many studies have adopted Content-Based (CB), Collaborative Filtering (CF), and hybrid approach as the main recommender style in the analysis. There are few or even no studies that have emphasized on the review of recommendation recently. For this reason, this study aims to collect, analyze, and review the research topics of recommender systems and their application in the past few decades. This study collects the research types and from various researchers. The literature arrangement of this study can help researchers to understand the recommender system researches in a clear sense and in a short time.
Collective Response of Human Populations to Large-Scale Emergencies

Despite recent advances in uncovering the quantitative features of stationary human activity patterns, many applications, from pandemic prediction to emergency response, require an understanding of how these patterns change when the population encounters unfamiliar conditions. To explore societal response to external perturbations we identified real-time changes in communication and mobility patterns in the vicinity of eight emergencies, such as bomb attacks and earthquakes, comparing these with eight non-emergencies, like concerts and sporting events. We find that communication spikes accompanying emergencies are both spatially and temporally localized, but information about emergencies spreads globally, resulting in communication avalanches that engage in a significant manner the social network of eyewitnesses. These results offer a quantitative view of behavioral changes in human activity under extreme conditions, with potential long-term impact on emergency detection and response.
Recurrent Batch Normalization

We propose a reparameterization of LSTM that brings the benefits of batch normalization to recurrent neural networks. Whereas previous works only apply batch normalization to the input-to-hidden transformation of RNNs, we demonstrate that it is both possible and beneficial to batch-normalize the hidden-to-hidden transition, thereby reducing internal covariate shift between time steps. We evaluate our proposal on various sequential problems such as sequence classification, language modeling and question answering. Our empirical results show that our batch-normalized LSTM consistently leads to faster convergence and improved generalization.
Building a Multilingual Named Entity-Annotated Corpus Using Annotation Projection

As developers of a highly multilingual named entity recognition (NER) system, we face an evaluation resource bottleneck problem: we need evaluation data in many languages, the annotation should not be too time-consuming, and the evaluation results across languages should be comparable. We solve the problem by automatically annotating the English version of a multi-parallel corpus and by projecting the annotations into all the other language versions. For the translation of English entities, we use a phrase-based statistical machine translation system as well as a lookup of known names from a multilingual name database. For the projection, we incrementally apply different methods: perfect string matching, perfect consonant signature matching and edit distance similarity. The resulting annotated parallel corpus will be made available for reuse.
A novel approach to mechanical design of articulated fingers for robotic hands

The paper first discusses the reasons why simplified solutions for the mechanical structure of fingers in robotic hands should be considered a worthy design goal. After a brief discussion about the mechanical solutions proposed so far for robotic fingers, a different design approach is proposed. It considers finger structures made of rigid links connected by flexural hinges, with joint actuation obtained by means of flexures that can be guided inside each finger according to different patterns. A simplified model of one of these structures is then presented, together with preliminary results of simulation, in order to evaluate the feasibility of the concept. Examples of technological implementation are finally presented and the perspective and problems of application are briefly discussed.
Software-Defined Networking (SDN): Layers and Architecture Terminology

Software-Defined Networking (SDN) refers to a new approach for network programmability, that is, the capacity to initialize, control, change, and manage network behavior dynamically via open interfaces. SDN emphasizes the role of software in running networks through the introduction of an abstraction for the data forwarding plane and, by doing so, separates it from the control plane. This separation allows faster innovation cycles at both planes as experience has already shown. However, there is increasing confusion as to what exactly SDN is, what the layer structure is in an SDN architecture, and how layers interface with each other. This document, a product of the IRTF Software-Defined Networking Research Group (SDNRG), addresses these questions and provides a concise reference for the SDN research community based on relevant peer-reviewed literature, the RFC series, and relevant documents by other standards organizations. Haleplidis, et al. Informational [Page 1] RFC 7426 (IRTF). Documents approved for publication by the IRSG are not a candidate for any level of Internet Standard; see Section 2 of RFC 5741. Information about the current status of this document, any errata, and how to provide feedback on it may be obtained at
Winograd Schemas and Machine Translation

A Winograd schema is a pair of sentences that differ in a single word and that contain an ambiguous pronoun whose referent is different in the two sentences and requires the use of commonsense knowledge or world knowledge to disambiguate. This paper discusses how Winograd schemas and other sentence pairs could be used as challenges for machine translation using distinctions between pronouns, such as gender, that appear in the target language but not in the source. A Winograd schema (Levesque, Davis, and Morgenstern 2012) is a pair of sentences, or of short texts, called the elements of the schema, that satisfy the following constraints: 1. The two elements are identical, except for a single word or two or three consecutive words. 2. Each element contains a pronoun. There are at least two noun phrases in the element that, grammatically, could be the antecedents of this pronoun. However, a human reader will reliably choose one of these as plausible and reject the rest as implausible. Thus, for a human reader, the resolution of the pronoun in each element is unambiguous. 3. The correct resolution of the pronoun is different in the two sentences. 4. Computationally simple strategies, such as those based on single word associations in text corpora or selectional restrictions, will not suffice to disambiguate either element. Rather, both disambiguation require some amount of world knowledge and of commonsense reasoning. The following is an example of a Winograd schema: A. The trophy doesn't fit in the brown suitcase because it's too large. B. The trophy doesn't fit in the brown suitcase because it's too small.
Managing Disruption Risks in Supply Chains

T are two broad categories of risk affecting supply chain design and management: (1) risks arising from the problems of coordinating supply and demand, and (2) risks arising from disruptions to normal activities. This paper is concerned with the second category of risks, which may arise from natural disasters, from strikes and economic disruptions, and from acts of purposeful agents, including terrorists. The paper provides a conceptual framework that reflects the joint activities of risk assessment and risk mitigation that are fundamental to disruption risk management in supply chains. We then consider empirical results from a rich data set covering the period 1995–2000 on accidents in the U.S. Chemical Industry. Based on these results and other literature, we discuss the implications for the design of management systems intended to cope with supply chain disruption risks.
Network Intrusion Detection System for Denial of Service Attack based on Misuse Detection

In a wireless network system the security is a main concern for a user. It is basically suffering from mainly two security attacks i) Virus Attack ii) Intruders. Intruder does not only mean it want to hack the private information over the network, it also includes using a node bandwidth and increasing the Delay of Service for other host over the network. This paper is basically based on such type of attack. This paper reviews the comparison of different Intrusion Detection System. On the behalf of the reviewed work we proposed a new Network Intrusion System that will mainly detects the most prominent attack of Wireless Network i.e. DoS Attack. The proposed system is an intelligent system that will detect the intrusion dynamically on the bases of Misuse Detection which has very less false negative. The system not only detects the intruders by the IP address, it detects the system with its contents also.
Scalable Object Detection for Stylized Objects

Following recent breakthroughs in convolutional neural networks and monolithic model architectures, state-ofthe-art object detection models can reliably and accurately scale into the realm of up to thousands of classes. Things quickly break down, however, when scaling into the tens of thousands, or, eventually, to millions or billions of unique objects. Further, bounding box-trained end-to-end models require extensive training data. Even though – with some tricks using hierarchies – one can sometimes scale up to thousands of classes, the labor requirements for clean image annotations quickly get out of control. In this paper, we present a two-layer object detection method for brand logos and other stylized objects for which prototypical images exist. It can scale to large numbers of unique classes. Our first layer is a CNN from the Single Shot Multibox Detector family of models that learns to propose regions where some stylized object is likely to appear. The contents of a proposed bounding box is then run against an image index that is targeted for the retrieval task at hand. The proposed architecture scales to a large number of object classes, allows to continuously add new classes without retraining, and exhibits state-of-the-art quality on a stylized object detection task such as logo recognition.
Historical biogeography, ecology and species richness.

Ecology and historical (phylogeny-based) biogeography have much to offer one another, but exchanges between these fields have been limited. Historical biogeography has become narrowly focused on using phylogenies to discover the history of geological connections among regions. Conversely, ecologists often ignore historical biogeography, even when its input can be crucial. Both historical biogeographers and ecologists have more-or-less abandoned attempts to understand the processes that determine the large-scale distribution of clades. Here, we describe the chasm that has developed between ecology and historical biogeography, some of the important questions that have fallen into it and how it might be bridged. To illustrate the benefits of an integrated approach, we expand on a model that can help explain the latitudinal gradient of species richness.
Three Models to Relate Detachment of Low Form Fouling at Laboratory and Ship Scale

Since fouling-release coating systems do not prevent settlement, various methods to quantify the tenacity of adhesion of fouling organisms on these systems have been offered. One such method is the turbulent channel flow apparatus. The question remains how the results from laboratory scale tests relate to the self-cleaning of a ship coated with a fouling-release surface. This paper relates the detachment strength of low form fouling determined in the laboratory using a turbulent channel flow to the conditions necessary for detachment of these organisms in a turbulent boundary layer at ship scale. A power-law formula, the ITTC-57 formula, and a computational fluid dynamics (CFD) model are used to predict the skin-friction at ship scale. The results from all three methods show good agreement and are illustrated using turbulent channel flow data for sporelings of the green macrofouling alga Enteromorpha growing on a fouling-release coating.
Smart wearable systems: Current status and future challenges

Extensive efforts have been made in both academia and industry in the research and development of smart wearable systems (SWS) for health monitoring (HM). Primarily influenced by skyrocketing healthcare costs and supported by recent technological advances in micro- and nanotechnologies, miniaturisation of sensors, and smart fabrics, the continuous advances in SWS will progressively change the landscape of healthcare by allowing individual management and continuous monitoring of a patient's health status. Consisting of various components and devices, ranging from sensors and actuators to multimedia devices, these systems support complex healthcare applications and enable low-cost wearable, non-invasive alternatives for continuous 24-h monitoring of health, activity, mobility, and mental status, both indoors and outdoors. Our objective has been to examine the current research in wearable to serve as references for researchers and provide perspectives for future research.



Herein, we review the current research and development of and the challenges facing SWS for HM, focusing on multi-parameter physiological sensor systems and activity and mobility measurement system designs that reliably measure mobility or vital signs and integrate real-time decision support processing for disease prevention, symptom detection, and diagnosis. For this literature review, we have chosen specific selection criteria to include papers in which wearable systems or devices are covered.



We describe the state of the art in SWS and provide a survey of recent implementations of wearable health-care systems. We describe current issues, challenges, and prospects of SWS.



We conclude by identifying the future challenges facing SWS for HM.
TrueSkill 2: An improved Bayesian skill rating system

Online multiplayer games, such as Gears of War and Halo, use skill-based matchmaking to give players fair and enjoyable matches. They depend on a skill rating system to infer accurate player skills from historical data. TrueSkill is a popular and effective skill rating system, working from only the winner and loser of each game. This paper presents an extension to TrueSkill that incorporates additional information that is readily available in online shooters, such as player experience, membership in a squad, the number of kills a player scored, tendency to quit, and skill in other game modes. This extension, which we call TrueSkill2, is shown to significantly improve the accuracy of skill ratings computed from Halo 5 matches. TrueSkill2 predicts historical match outcomes with 68% accuracy, compared to 52% accuracy for TrueSkill.
The functional neuroanatomical correlates of response variability: evidence from a response inhibition task

Intra-individual performance variability may be an important index of the efficiency with which executive control processes are implemented, Lesion studies suggest that damage to the frontal lobes is accompanied by an increase in such variability. Here we sought for the first time to investigate how the functional neuroanatomy of executive control is modulated by performance variability in healthy subjects by using an event-related functional magnetic resonance imaging (ER-fMRI) design and a Go/No-go response inhibition paradigm. Behavioural results revealed that individual differences in Go response time variability were a strong predictor of inhibitory success and that differences in mean Go response time could not account for this effect. Task-related brain activation was positively correlated with intra-individual variability within a distributed inhibitory network consisting of bilateral middle frontal areas and right inferior parietal and thalamic regions. Both the behavioural and fMRI data are consistent with the interpretation that those subjects with relatively higher intra-individual variability activate inhibitory regions to a greater extent, perhaps reflecting a greater requirement for top-down executive control in this group, a finding that may be relevant to disorders of executive/attentional control.