nils-herrmann/clean_scirepeval huggingface dataset:
Recommender Systems Research: A Connection-Centric Survey

Recommender systems attempt to reduce information overload and retain customers by selecting a subset of items from a universal set based on user preferences. While research in recommender systems grew out of information retrieval and filtering, the topic has steadily advanced into a legitimate and challenging research area of its own. Recommender systems have traditionally been studied from a content-based filtering vs. collaborative design perspective. Recommendations, however, are not delivered within a vacuum, but rather cast within an informal community of users and social context. Therefore, ultimately all recommender systems make connections among people and thus should be surveyed from such a perspective. This viewpoint is under-emphasized in the recommender systems literature. We therefore take a connection-oriented perspective toward recommender systems research. We posit that recommendation has an inherently social element and is ultimately intended to connect people either directly as a result of explicit user modeling or indirectly through the discovery of relationships implicit in extant data. Thus, recommender systems are characterized by how they model users to bring people together: explicitly or implicitly. Finally, user modeling and the connection-centric viewpoint raise broadening and social issues—such as evaluation, targeting, and privacy and trust—which we also briefly address.
SLA-Aware Resource Scaling for Energy Efficiency

Cloud data centers (CDCs) with abundant resource capacities have prevailed in the past decade. However, these CDCs often struggle to efficiently deal with resource provisioning in terms of performance and energy efficiency. In this paper, we present Energy-Based Auto Scaling (EBAS) as a new resource auto-scaling approach—that takes into account Service Level Agreement (SLA)—for CDCs. EBAS proactively scales resources at the CPU core level in terms of both the number and frequency of cores. It incorporates the dynamic voltage and frequency scaling (DVFS) technique to dynamically adjust CPU frequencies. The proactive decisions on resource scaling are enabled primarily by the CPU usage prediction model and the workload consolidation model of EBAS. The experimental results show that EBAS can save energy on average by 14% compared with the Linux governor. In particular, EBAS contributes to enhancing DVFS by making it aware of SLA conditions, which leads to savings of computing power and in turn energy.
A Universal Denoising Framework With a New Impulse Detector and Nonlocal Means

Impulse noise detection is a critical issue when removing impulse noise and impulse/Gaussian mixed noise. In this paper, we propose a new detection mechanism for universal noise and a universal noise-filtering framework based on the nonlocal means (NL-means). The operation is carried out in two stages, i.e., detection followed by filtering. For detection, first, we propose the robust outlyingness ratio (ROR) for measuring how impulselike each pixel is, and then all the pixels are divided into four clusters according to the ROR values. Second, different decision rules are used to detect the impulse noise based on the absolute deviation to the median in each cluster. In order to make the detection results more accurate and more robust, the from-coarse-to-fine strategy and the iterative framework are used. In addition, the detection procedure consists of two stages, i.e., the coarse and fine detection stages. For filtering, the NL-means are extended to the impulse noise by introducing a reference image. Then, a universal denoising framework is proposed by combining the new detection mechanism with the NL-means (ROR-NLM). Finally, extensive simulation results show that the proposed noise detector is superior to most existing detectors, and the ROR-NLM produces excellent results and outperforms most existing filters for different noise models. Unlike most of the other impulse noise filters, the proposed ROR-NLM also achieves high peak signal-to-noise ratio and great image quality by efficiently removing impulse/Gaussian mixed noise.
Relevance-Based Language Models

We explore the relation between classical probabilistic models of information retrieval and the emerging language modeling approaches. It has long been recognized that the primary obstacle to effective performance of classical models is the need to estimate a relevance model: probabilities of words in the relevant class. We propose a novel technique for estimating these probabilities using the query alone. We demonstrate that our technique can produce highly accurate relevance models, addressing important notions of synonymy and polysemy. Our experiments show relevance models outperforming baseline language modeling systems on TREC retrieval and TDT tracking tasks. The main contribution of this work is an effective formal method for estimating a relevance model with no training data.
Style-based inverse kinematics

This paper presents an inverse kinematics system based on a learned model of human poses. Given a set of constraints, our system can produce the most likely pose satisfying those constraints, in real-time. Training the model on different input data leads to different styles of IK. The model is represented as a probability distribution over the space of all possible poses. This means that our IK system can generate any pose, but prefers poses that are most similar to the space of poses in the training data. We represent the probability with a novel model called a Scaled Gaussian Process Latent Variable Model. The parameters of the model are all learned automatically; no manual tuning is required for the learning component of the system. We additionally describe a novel procedure for interpolating between styles.Our style-based IK can replace conventional IK, wherever it is used in computer animation and computer vision. We demonstrate our system in the context of a number of applications: interactive character posing, trajectory keyframing, real-time motion capture with missing markers, and posing from a 2D image.
LoRa Scalability: A Simulation Model Based on Interference Measurements

LoRa is a long-range, low power, low bit rate and single-hop wireless communication technology. It is intended to be used in Internet of Things (IoT) applications involving battery-powered devices with low throughput requirements. A LoRaWAN network consists of multiple end nodes that communicate with one or more gateways. These gateways act like a transparent bridge towards a common network server. The amount of end devices and their throughput requirements will have an impact on the performance of the LoRaWAN network. This study investigates the scalability in terms of the number of end devices per gateway of single-gateway LoRaWAN deployments. First, we determine the intra-technology interference behavior with two physical end nodes, by checking the impact of an interfering node on a transmitting node. Measurements show that even under concurrent transmission, one of the packets can be received under certain conditions. Based on these measurements, we create a simulation model for assessing the scalability of a single gateway LoRaWAN network. We show that when the number of nodes increases up to 1000 per gateway, the losses will be up to 32%. In such a case, pure Aloha will have around 90% losses. However, when the duty cycle of the application layer becomes lower than the allowed radio duty cycle of 1%, losses will be even lower. We also show network scalability simulation results for some IoT use cases based on real data.
Detection of SQL injection and XSS attacks in three tier web applications

Web applications are used on a large scale worldwide, which handles sensitive personal data of users. With web application that maintains data ranging from as simple as telephone number to as important as bank account information, security is a prime point of concern. With hackers aimed to breakthrough this security using various attacks, we are focusing on SQL injection attacks and XSS attacks. SQL injection attack is very common attack that manipulates the data passing through web application to the database servers through web servers in such a way that it alters or reveals database contents. While Cross Site Scripting (XSS) attacks focuses more on view of the web application and tries to trick users that leads to security breach. We are considering three tier web applications with static and dynamic behavior, for security. Static and dynamic mapping model is created to detect anomalies in the class of SQL Injection and XSS attacks.
On-Line Adaptation of a Signal Predistorter through Dual Reinforcement Learning

Several researchers have demonstrated how neural networks can be trained to compensate for nonlinear signal distortion in e.g. digital satellite communications systems. These networks, however, require that both the original signal and its distorted version are known. Therefore, they have to be trained o -line, and they cannot adapt to changing channel characteristics. In this paper, a novel dual reinforcement learning approach is proposed that can adapt on-line while the system is performing. Assuming that the channel characteristics are the same in both directions, two predistorters at each end of the communication channel co-adapt using the output of the other predistorter to determine their own reinforcement. Using the common Volterra Series model to simulate the channel, the system is shown to successfully learn to compensate for distortions up to 30%, which is signi cantly higher than what might be expected in an actual channel.
Customer Purchase Behavior Prediction from Payment Datasets

With the advances in the development of mobile payments, a huge amount of payment data are collected by banks. User payment data offer a good dataset to depict customer behavior patterns. A comprehensive understanding of customers' purchase behavior is crucial to developing good marketing strategies, which may trigger much greater purchase amounts. For example, by exploring customer behavior patterns, given a target store, a set of potential customers is able to be identified.
 In other words, personalized campaigns at the right time and in the right place can be treated as the last stage of consumption. Here we propose a probability graphical model that exploits the payment data to discover customer purchase behavior in the spatial, temporal, payment amount and product category aspects, named STPC-PGM. As a result, the mobility behavior of an individual user could be predicted with a probabilistic graphical model that accounts for all aspects of each customer's relationship with the payment platform. To achieve real time advertising, we then develop an online framework that efficiently computes the prediction results. Our experiment results show that STPC-PGM is effective in discovering customers' profiling features, and outperforms the state-of-the-art methods in purchase behavior prediction. In addition, the prediction results are being deployed in the marketing of real-world credit card users, and have presented a significant growth in the advertising conversion rate.
Total quality management and sustainable competitive advantage

Although it is generally accepted that Total Quality Management (TQM) can generate a sustainable competitive advantage, there is, surprisingly, little or no theory to underpin that belief. Therefore, the primary purpose of this paper is to explore the validity of the claim. By drawing on the market-based theory of competitive advantage, resource-based theory of the firm, and systems theory, we are able to conclude that the belief is warranted. We deduce that the content of TQM is capable of producing a costor differentiation-based advantage, and that the tacitness and complexity that are inherent in the process of TQM have the potential to generate the barriers to imitation that are necessary for sustainability. D 2000 Elsevier Science Inc. All rights reserved.
CVSS Attack Graphs

Attack models and attack graphs are efficient tools to describe and analyse attack scenarios aimed at computer networks. More precisely, attack graphs give all possible scenarios for an attacker to reach a certain goal, exploiting vulnerabilities of the targeted network. Nevertheless they give no information about the damages induced by these attacks, nor about the probability of exploitation of these scenarios. In this paper, we propose to combine attack graphs and CVSS framework, in order to add damage and exploitability probability information. Then, we define a notion of risk for each attack scenario, which is based on quantitative information added to attack graphs.
Mirror Neuron and Theory of Mind Mechanisms Involved in Face-to-Face Interactions: A Functional Magnetic Resonance Imaging Approach to Empathy

Empathy allows emotional psychological inference about other person's mental states and feelings in social contexts. We aimed at specifying the common and differential neural mechanisms of self- and other-related attribution of emotional states using event-related functional magnetic resonance imaging. Subjects viewed faces expressing emotions with direct or averted gaze and either focused on their own emotional response to each face (self-task) or evaluated the emotional state expressed by the face (other-task). The common network activated by both tasks included the left lateral orbito-frontal and medial prefrontal cortices (MPFC), bilateral inferior frontal cortices, superior temporal sulci and temporal poles, as well as the right cerebellum. In a subset of these regions, neural activity was significantly correlated with empathic abilities. The self- (relative to the other-) task differentially activated the MPFC, the posterior cingulate cortex (PCC)/precuneus, and the temporo-parietal junction bilaterally. Empathy-related processing of emotional facial expressions recruited brain areas involved in mirror neuron and theory-of-mind (ToM) mechanisms. The differential engagement of the MPFC, the PCC/precuneus, and temporo-parietal regions in the self-task indicates that these structures act as key players in the evaluation of one's own emotional state during empathic face-to-face interaction. Activation of mirror neurons in a task relying on empathic abilities without explicit task-related motor components supports the view that mirror neurons are not only involved in motor cognition but also in emotional interpersonal cognition. An interplay between ToM and mirror neuron mechanisms may hold for the maintenance of a self-other distinction during empathic interpersonal face-to-face interactions.
Register Allocation Via Coloring

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Evaluation of Road Marking Feature Extraction

This paper proposes a systematic approach to evaluate algorithms for extracting road marking features from images. This specific topic is seldom addressed in the literature while many road marking detection algorithms have been proposed. Most of them can be decomposed into three steps: extracting road marking features, estimating a geometrical marking model, tracking the parameters of the geometrical model along an image sequence. The present work focuses on the first step, i.e. feature extraction. A reference database containing over 100 images of natural road scenes was built with corresponding manually labeled ground truth images (available at http://www.lcpc.fr/en/produits/ride/). This database enables to evaluate and compare extractors in a systematic way. Different road marking feature extraction algorithm representing different classes of techniques are evaluated: thresholding, gradient analysis, and convolution. As a result of this analysis, recommendations are given on which extractor to choose according to a specific application.
Analytical loss model of power MOSFET

An accurate analytical model is proposed in this paper to calculate the power loss of a metal-oxide semiconductor field-effect transistor. The nonlinearity of the capacitors of the devices and the parasitic inductance in the circuit, such as the source inductor shared by the power stage and driver loop, the drain inductor, etc., are considered in the model. In addition, the ringing is always observed in the switching power supply, which is ignored in the traditional loss model. In this paper, the ringing loss is analyzed in a simple way with a clear physical meaning. Based on this model, the circuit power loss could be accurately predicted. Experimental results are provided to verify the model. The simulation results match the experimental results very well, even at 2-MHz switching frequency.
A Survey on Successors of LEACH Protocol

Even after 16 years of existence, low energy adaptive clustering hierarchy (LEACH) protocol is still gaining the attention of the research community working in the area of wireless sensor network (WSN). This itself shows the importance of this protocol. Researchers have come up with various and diverse modifications of the LEACH protocol. Successors of LEACH protocol are now available from single hop to multi-hop scenarios. Extensive work has already been done related to LEACH and it is a good idea for a new research in the field of WSN to go through LEACH and its variants over the years. This paper surveys the variants of LEACH routing protocols proposed so far and discusses the enhancement and working of them. This survey classifies all the protocols in two sections, namely, single hop communication and multi-hop communication based on data transmission from the cluster head to the base station. A comparitive analysis using nine different parameters, such as energy efficiency, overhead, scalability complexity, and so on, has been provided in a chronological fashion. The article also discusses the strong and the weak points of each and every variants of LEACH. Finally the paper concludes with suggestions on future research domains in the area of WSN.
Superfamilies of evolved and designed networks.

Complex biological, technological, and sociological networks can be of very different sizes and connectivities, making it difficult to compare their structures. Here we present an approach to systematically study similarity in the local structure of networks, based on the significance profile (SP) of small subgraphs in the network compared to randomized networks. We find several superfamilies of previously unrelated networks with very similar SPs. One superfamily, including transcription networks of microorganisms, represents "rate-limited" information-processing networks strongly constrained by the response time of their components. A distinct superfamily includes protein signaling, developmental genetic networks, and neuronal wiring. Additional superfamilies include power grids, protein-structure networks and geometric networks, World Wide Web links and social networks, and word-adjacency networks from different languages.
Antibody-dependent cellular cytotoxicity mediated by cetuximab against lung cancer cell lines.

Epidermal growth factor receptor (EGFR) is commonly overexpressed in lung cancer. Cetuximab is a chimeric mouse-human antibody targeted against EGFR. Compared with its inhibitory properties, its immunologic mechanisms have not been well studied. In this study, we investigated the antibody-dependent cellular cytotoxicity (ADCC) activity of cetuximab against lung cancer cell lines.



We studied the correlation between EGFR expression in lung cancer cell lines and the ADCC activity of cetuximab as well as the influence of interleukin-2 and chemotherapy on the ADCC activity. EGFR expression was measured by a quantitative flow cytometric analysis and immunohistochemistry. The ADCC activity was assessed by a 4-h (51)Cr release assay. Peripheral blood mononuclear cells, purified T cells, natural killer (NK) cells, and monocytes from healthy donors or lung cancer patients were used as effector cells.



Fresh peripheral blood mononuclear cells exhibited cetuximab-mediated ADCC activity against lung cancer cell lines at a low concentration of cetuximab (0.25 microg/mL). A logarithmic correlation was observed between the number of EGFRs and ADCC activity. Even low EGFR expression, which was weakly detectable by immunohistochemistry, was sufficient for maximum ADCC activity, and further increases in EGFR expression on the target cells had no further effect on the ADCC activity. In addition, ADCC activity was enhanced by interleukin-2 mainly through activation of NK cells and was less susceptible to immunosuppression by chemotherapy than NK activity in lung cancer patients.



These observations suggest the importance of ADCC activity as an immunologic mechanism of cetuximab in biological therapy for lung cancer patients.
Control of a humanoid robot by a noninvasive brain-computer interface in humans.

We describe a brain-computer interface for controlling a humanoid robot directly using brain signals obtained non-invasively from the scalp through electroencephalography (EEG). EEG has previously been used for tasks such as controlling a cursor and spelling a word, but it has been regarded as an unlikely candidate for more complex forms of control owing to its low signal-to-noise ratio. Here we show that by leveraging advances in robotics, an interface based on EEG can be used to command a partially autonomous humanoid robot to perform complex tasks such as walking to specific locations and picking up desired objects. Visual feedback from the robot's cameras allows the user to select arbitrary objects in the environment for pick-up and transport to chosen locations. Results from a study involving nine users indicate that a command for the robot can be selected from four possible choices in 5 s with 95% accuracy. Our results demonstrate that an EEG-based brain-computer interface can be used for sophisticated robotic interaction with the environment, involving not only navigation as in previous applications but also manipulation and transport of objects.
Indra: A Word Embedding and Semantic Relatedness Server

In recent years word embedding/distributional semantic models evolved to become a fundamental component in many natural language processing (NLP) architectures due to their ability of capturing and quantifying semantic associations at scale. Word embedding models can be used to satisfy recurrent tasks in NLP such as lexical and semantic generalisation in machine learning tasks, finding similar or related words and computing semantic relatedness of terms. However, building and consuming specific word embedding models require the setting of a large set of configurations, such as corpus-dependant parameters, distance measures as well as compositional models. Despite their increasing relevance as a component in NLP architectures, existing frameworks provide limited options in their ability to systematically build, parametrise, compare and evaluate different models. To answer this demand, this paper describes INDRA, a multi-lingual word embedding/distributional semantics framework which supports the creation, use and evaluation of word embedding models. In addition to the tool, INDRA also shares more than 65 pre-computed models in 14 languages.