nils-herrmann/clean_scirepeval huggingface dataset:
A Performance Analysis of Incentive Mechanisms for Cooperative Computing

As more devices gain Internet connectivity, more information needs to be exchanged between them. For instance, cloud servers might disseminate instructions to clients, or sensors in the Internet of Things might send measurements to each other. In such scenarios, information spreads faster when users have an incentive to contribute data to others. While many works have considered this problem in peer-to-peer scenarios, none have rigorously theorized the performance of different design choices for the incentive mechanisms. In particular, different designs have different ways of "bootstrapping" new users (distributing information to them) and preventing "free-riding" (receiving information without uploading any in return). We classify incentive mechanisms in terms of reciprocity-, altruism-, and reputation-based algorithms, and then analyze the performance of these three basic and three hybrid algorithms. We show that the algorithms lie along a tradeoff between fairness and efficiency, with altruism and reciprocity at the two extremes. The three hybrids all leverage their component algorithms to achieve similar efficiency. The reputation hybrids are the most fair and can nearly match altruism's bootstrapping speed, but only the reciprocity/reputation hybrid can match reciprocity's zero-tolerance for free-riding. It therefore yields better fairness and efficiency when free-riders are present. We validate these comparisons with extensive experimental results.
Modern Intrusion Detection, Data Mining, and Degrees of Attack Guilt

This chapter examines the state of modern intrusion detection, with a particular emphasis on the emerging approach of data mining. The discussion paralleIs two important aspects of intrusion detection: general detection strategy (misuse detection versus anomaly detection) and data source (individual hosts versus network trafik). Misuse detection attempts to match known patterns of intrusion , while anomaly detection searches for deviations from normal behavior . Between the two approaches, only anomaly detection has the ability to detect unknown attacks. A particularly promising approach to anomaly detection combines association mining with other forms of machine learning such as classification. Moreover, the data source that an intrusion detection system employs significantly impacts the types of attacks it can detect. There is a tradeoff in the level of detailed information available verD. Barbará et al. (ed .), Applications of Data Mining in Computer Security
Terrain-adaptive locomotion skills using deep reinforcement learning

Reinforcement learning offers a promising methodology for developing skills for simulated characters, but typically requires working with sparse hand-crafted features. Building on recent progress in deep reinforcement learning (DeepRL), we introduce a mixture of actor-critic experts (MACE) approach that learns terrain-adaptive dynamic locomotion skills using high-dimensional state and terrain descriptions as input, and parameterized leaps or steps as output actions. MACE learns more quickly than a single actor-critic approach and results in actor-critic experts that exhibit specialization. Additional elements of our solution that contribute towards efficient learning include Boltzmann exploration and the use of initial actor biases to encourage specialization. Results are demonstrated for multiple planar characters and terrain classes.
One shot learning gesture recognition from RGBD images

We present a system to classify the gesture from only one learning example. The inputs are duo-modality, i.e. RGB and depth sensor from Kinect. Our system performs morphological denoising on depth images and automatically segments the temporal boundaries. Features are extracted based on Extended-Motion-History-Image (Extended-MHI) and the Multi-view Spectral Embedding (MSE) algorithm is used to fuse duo modalities in a physically meaningful manner. Our approach achieves less than 0.3 in Levenshtein distance in CHALEARN Gesture Challenge validation batches .
Structured Models for Fine-to-Coarse Sentiment Analysis

In this paper we investigate a structured model for jointly classifying the sentiment of text at varying levels of granularity. Inference in the model is based on standard sequence classification techniques using constrained Viterbi to ensure consistent solutions. The primary advantage of such a model is that it allows classification decisions from one level in the text to influence decisions at another. Experiments show that this method can significantly reduce classification error relative to models trained in isolation.
Explainable Prediction of Medical Codes from Clinical Text

Clinical notes are text documents that are created by clinicians for each patient encounter. They are typically accompanied by medical codes, which describe the diagnosis and treatment. Annotating these codes is labor intensive and error prone; furthermore, the connection between the codes and the text is not annotated, obscuring the reasons and details behind specific diagnoses and treatments. We present an attentional convolutional network that predicts medical codes from clinical text. Our method aggregates information across the document using a convolutional neural network, and uses an attention mechanism to select the most relevant segments for each of the thousands of possible codes. The method is accurate, achieving precision@8 of 0.71 and a Micro-F1 of 0.54, which are both better than the prior state of the art. Furthermore, through an interpretability evaluation by a physician, we show that the attention mechanism identifies meaningful explanations for each code assignment.
Text-based domain ontology building using Tf-Idf and metric clusters techniques

The paper describes the methodology used to develop a construction domain ontology, taking into account the wealth of existing semantic resources in the sector ranging from dictionaries to thesauri. Given the characteristics and settings of the construction industry, a modular, architecture-centric approach was adopted to structure and develop the ontology. The paper argues that taxonomies provide an ideal backbone for any ontology project. Therefore, a construction industry standard taxonomy was used to provide the seeds of the ontology, enriched and expanded with additional concepts extracted from large discipline-oriented document bases using information retrieval (IR) techniques.
Design principles and improvement of cost function based energy aware routing algorithms for wireless sensor networks

Cost function based routing has been widely studied in wireless sensor networks for energy efficiency improvement and network lifetime elongation. However, due to the complexity of the problem, existing solutions have various limitations. In this paper, we analyze the inherent factors, design principles and evaluation methods for cost function based routing algorithms. Two energy aware cost based routing algorithms named Exponential and Sine Cost Function based Route (ESCFR) and Double Cost Function based Route (DCFR) have been proposed in this paper. For ESCFR, its cost function can map small changes in nodal remaining energy to large changes in the function value. For DCFR, its cost function takes into consideration the end-to-end energy consumption, nodal remaining energy, resulting in a more balanced and efficient energy usage among nodes. The performance of the cost function design is analyzed. Extensive simulations demonstrate the proposed algorithms have significantly better performance than existing competing algorithms.
Mind the Gap : Why do people act environmentally and what are the barriers to pro-environmental behavior ?

Numerous theoretical frameworks have been developed to explain the gap between the possession of environmental knowledge and environmental awareness, and displaying pro-environmental behavior. Although many hundreds of studies have been undertaken, no denitive explanation has yet been found. Our article describes a few of the most inuential and commonly used analytical frameworks: early US linear progression models; altruism, empathy and prosocial behavior models; and nally, sociological models. All of the models we discuss (and many of the ones we do not such as economic models, psychological models that look at behavior in general, social marketing models and that have become known as deliberative and inclusionary processes or procedures (DIPS)) have some validity in certain circumstances. This indicates that the question of what shapes pro-environmental behavior is such a complex one that it cannot be visualized through one single framework or diagram. We then analyze the factors that have been found to have some inuence, positive or negative, on pro-environmental behavior such as demographic factors, external factors (e.g. institutional, economic, social and cultural) and internal factors (e.g. motivation, pro-environmental knowledge, awareness, values, attitudes, emotion, locus of control, responsibilities and priorities). Although we point out that developing a model that tries to incorporate all factors might neither be feasible nor useful, we feel that it can help illuminate this complex eld. Accordingly, we propose our own model based on the work of Fliegenschnee and Schelakovsky (1998) who were inuenced by Fietkau and Kessel (1981).
A preliminary study on hybrid sentiment model for customer purchase intention analysis in socialcommerce

With the usage of Web 2.0 technologies, the internet users share their views via social media resulting in a large amount of raw data for which data mining techniques are needed to extract valuable knowledge. An example of people's views that can be extracted from the users' comments(tweets) is their interest or preference on different products, services, events etc. This research work aims to study on the customer intention to purchase in social-commerce(s-commerce). Finding out the intention of customer to purchase in s-commerce is important due to the major role being brought by the customers in marketing. Sentiment analysis is seen to be the most appropriate method to extract customer's opinions. This paper presents the fundamental part of the research for developing a hybrid sentiment analysis model by using machine learning approach and lexicon-based approach to analyze communications especially using Malay language on social network portals to determine the customer intention to purchase in s-commerce. Precision, recall, accuracy and f-measure metrics are proposed for the computing and comparing the results of the experiments.
Shift: A Zero FLOP, Zero Parameter Alternative to Spatial Convolutions

Neural networks rely on convolutions to aggregate spatial information. However, spatial convolutions are expensive in terms of model size and computation, both of which grow quadratically with respect to kernel size. In this paper, we present a parameter-free, FLOP-free "shift" operation as an alternative to spatial convolutions. We fuse shifts and point-wise convolutions to construct end-to-end trainable shift-based modules, with a hyperparameter characterizing the tradeoff between accuracy and efficiency. To demonstrate the operation's efficacy, we replace ResNet's 3x3 convolutions with shift-based modules for improved CIFAR10 and CIFAR100 accuracy using 60% fewer parameters; we additionally demonstrate the operation's resilience to parameter reduction on ImageNet, outperforming ResNet family members. We finally show the shift operation's applicability across domains, achieving strong performance with fewer parameters on image classification, face verification and style transfer.
Forensic analysis of social networking applications on mobile devices

The increased use of social networking applications on smartphones makes these devices a goldmine for forensic investigators. Potential evidence can be held on these devices and recovered with the right tools and examination methods. This paper focuses on conducting forensic analyses on three widely used social networking applications on smartphones: Facebook, Twitter, and MySpace. The tests were conducted on three popular smartphones: BlackBerrys, iPhones, and Android phones. The tests consisted of installing the social networking applications on each device, conducting common user activities through each application, acquiring a forensically sound logical image of each device, and performing manual forensic analysis on each acquired logical image. The forensic analyses were aimed at determining whether activities conducted through these applications were stored on the device’s internal memory. If so, the extent, significance, and location of the data that could be found and retrieved from the logical image of each device were determined. The results show that no traces could be recovered from BlackBerry devices. However, iPhones and Android phones store a significant amount of valuable data that could be recovered and used by forensic investigators. a 2012 A. Marrington, N. Al Mutawa & I. Baggili.
Correlated Nonideal Effects of Dark and Light I–V Characteristics in a-Si/c-Si Heterojunction Solar Cells

a-Si/c-Si (amorphous Silcon/crystalline Silicon) heterojunction solar cells exhibit several distinctive dark and light I-V nonideal features. The dark I-V of these cells exhibits unusually high ideality factors at low forward-bias and the occurrence of a “knee” at medium forward-bias. Nonidealities under illumination, such as the failure of superposition and the occurrence of an “S-type” curve, are also reported in these cells. However, the origin of these nonidealities and how the dark I-V nonidealities manifest themselves under illumination, and vice versa, have not been clearly and consistently explained in the current literature. In this study, a numerical framework is used to interpret the origin of the dark I-V nonidealities, and a novel simulation technique is developed to separate the photo-current and the contact injection current components of the light I-V. Using this technique, the voltage dependence of photo-current is studied to explain the failure of the superposition principle and the origin of the S-type light I-V characteristics. The analysis provides a number of insights into the correlations between the dark I-V and the light I-V. Finally, using the experimental results from this study and from the current literature, it is shown that these nonideal effects indeed affect the dark I-V and the light I-V in a predictable manner.
MAXIMUM POWER POINT TRACKING OF PV ARRAYS UNDER PARTIAL SHADING CONDITION USING SEPIC CONVERTER

There exists a variety of maximum power point tracking (MPPT) techniques, each having its own merits and demerits. Under partialshading conditions, the conventional tracking techniques fail to guarantee successful tracking of the global maximum power, ie the conventional MPPT methods such as perturb and observe and incremental conductance may converge on local maximum power point resulting in significant reduction of power generated. This paper discusses about an improved technique for tracking global maximum power point of photovoltaic arrays that has better performance under partial shading conditions. The first stage in this method is to find out global maximum power point among the local maxima. Once the global maximum power point is found then by adjusting the duty ratio, the voltage corresponding to maximum power can be found out. The control is then transferred to perturb and observe algorithm stage. This technique could be applied for both stand alone and grid connected PV system. A comparison study between a SEPIC converter and a buck boost converter with the above mentioned algorithm has also been carried out in order to verify the performance of both the converters. The above mentioned converters have been designed for 150W at a switching frequency of 10 KHz. Modified algorithm has been simulated using MATLAB/Simulink and results are obtained. Partial shading condition was modelled in MATLAB/Simscape and analysed the solar array characteristics under various shading conditions. From the simulation results it was found that the SEPIC converter is much more efficient and is highly suitable for photo voltaic applications.
No-Frills Human-Object Interaction Detection: Factorization, Appearance and Layout Encodings, and Training Techniques

We show that with an appropriate factorization, and encodings of layout and appearance constructed from outputs of pretrained object detectors, a relatively simple model outperforms more sophisticated approaches on humanobject interaction detection. Our model includes factors for detection scores, human and object appearance, and coarse (box-pair configuration) and optionally fine-grained layout (human pose). We also develop training techniques that improve learning efficiency by: (i) eliminating train-inference mismatch; (ii) rejecting easy negatives during mini-batch training; and (iii) using a ratio of negatives to positives that is two orders of magnitude larger than existing approaches while constructing training mini-batches. We conduct a thorough ablation study to understand the importance of different factors and training techniques using the challenging HICO-Det dataset [4].
Comparative analysis of UFMC technology in 5G networks

The article dwells upon a new promising technology Universal Filtered Multi-Carrier (UFMC) for future 5G mobile networks. The technology is compared with OFDM technology used in 4G networks, and its effectiveness is analyzed. A disadvantage of OFDM technology is a necessity to use a cyclic prefix, which reduces data transmission rate. Due to UFMC technology, the cyclic prefix can be rejected, and thereby a spectral efficiency of mobile network can be increased in comparison with OFDM technology. Also, UFMC technology is less sensitive to estimation errors of frequency and time shifts.
Feasibility Study on the Use of Biometrics in an Entitlement Scheme

This report examines the feasibility of using biometrics as a means of establishing a unique identity, to support the proposed entitlement scheme under development by the United Kingdom Passport Service (UKPS) and Driver and Vehicle Licensing Agency (DVLA). Biometrics identification systems measure physiological and behavioural characteristics of a person, and use these measurements to reliably distinguish one person from another. The main examples considered in this study are fingerprint, iris and face image recognition. Biometric identification can assist in the issue of entitlement cards, passports, driving licences and other identity documents in two ways. On the initial issue of such documents, biometrics can be used to check that applicants are not erroneously issued documents using two different identity details; this is the main focus of this study. Secondly, when an entitlement card, passport or driving licence is being used, biometrics can help confirm that it is being used by the correct person. The purpose of the study is to assess the feasibility of fingerprint, iris and face recognition technologies for these applications, to identify unknowns and the risks associated with the use of biometrics in such a national identity scheme, and to make recommendations for how some of these risks might be addressed should such a scheme proceed. Biometric methods do not offer 100% certainty of authentication of individuals. The success of any deployment of a system using biometric methods depends, therefore, on many factors such as: the degree of the 'uniqueness' of the biometric measure, technical and social factors, user interfaces, and provision of secure backup systems for those situations and individual s where the biometric will not work effectively. The main findings of the study are: a In principle, fingerprint or iris recognition can provide the identification performance required for unique identification over the entire UK adult population. In the case of fingerprint recognition, the system would require the enrolment of at least four fingers, whereas for iris recognition both irises should be registered. However, the practicalities of deploying either iris or fingerprint recognition in such a scheme are far from straightforward. b Such a system would be a groundbreaking deployment for this kind of biometric application. Not only would it be one of the largest deployments to date, but aspects of its performance would be far more demanding than those of similarly sized systems; such existing systems are either not applied in the civil sector, or operate …
Modeling and Control of a Articulated Robot Arm with Embedded Joint Actuators

Nowadays human-cooperative robot (HCR) become to be used in the industry and cost-effective manufacturing of it is required. The UR5 robot uses only three type of common-structured rotating joint actuators, which differs from size and power. So this robot become famous cost-effective HCR. The embedded rotating joint actuator for UR5-type HCR is known as a "smart actuator", because only connection of them with links becomes a 6-DOF robot manipulator. Recent development of networked motor drive technology with EtherCAT communication makes this connection and operation of robot manipulator easy. The analysis about forward and inverse kinematics for UR5 is found in many literatures. With help of these materials, the dynamic simulation about a smart actuator for UR5 robot is done in this paper. The co-simulation of motor drive and manipulator dynamics is done with Matlab / Simulink and Simscape Multibody. The results shows that the developed smart actuator is good to substitute the joint actuator of UR5 robot.
A review on memristor applications

This article presents a review on the main applications of the fourth fundamental circuit element, named "memristor", which had been proposed for the first time by Leon Chua and has recently been developed by a team at HP Laboratories led by Stanley Williams. In particular, after a brief analysis of memristor theory with a description of the first memristor, manufactured at HP Laboratories, we present its main applications in the circuit design and computer technology, together with future developments.