nils-herrmann/clean_scirepeval huggingface dataset:
Edge Agreement: Graph-Theoretic Performance Bounds and Passivity Analysis

This work explores the properties of the edge variant of the graph Laplacian in the context of the edge agreement problem. We show that the edge Laplacian, and its corresponding agreement protocol, provides a useful perspective on the well-known node agreement, or the consensus algorithm. Specifically, the dynamics induced by the edge Laplacian facilitates a better understanding of the role of certain subgraphs, e.g., cycles and spanning trees, in the original agreement problem. Using the edge Laplacian, we proceed to examine graph-theoretic characterizations of the H2 and H∞ performance for the agreement protocol. These results are subsequently applied in the contexts of optimal sensor placement for consensus-based applications. Finally, the edge Laplacian is employed to provide new insights into the nonlinear extension of linear agreement to agents with passive dynamics.
Predicting Tags for StackOverflow Posts

Hashtags created by authors of online content provide a view of a user’s goals and interests. Predicting users’ interests can lead to improved, more user centered human-computer systems. Large-scale behavioral datasets such as Twitter, Facebook, LinkedIn, StackOverflow, and Yelp can be mined and explored to study these hashtags and how they relate to users’ interests. We explored the StackOverflow dataset, and developed an ACT-R inspired Bayesian probabilistic model that can predict the hashtags used by the author of the post. The model is 65% accurate when tasked to predict one tag per post on average. This is achieved by choosing the tag that has the highest log odds of being correct, given the tag’s prior log odds of occurrence and adjusting for the log likelihood ratio of the words in the post being associated with the tag. The model is a successful case showing that ACT-R’s declarative memory retrieval equations scale, and are relevant to task domains that require large-scale knowledge stores.
BIRCH: A New Data Clustering Algorithm and Its Applications

Data clustering is an important technique for exploratory data analysis, and has been studied for several years. It has been shown to be useful in many practical domains such as data classification and image processing. Recently, there has been a growing emphasis on exploratory analysis of very large datasets to discover useful patterns and/or correlations among attributes. This is called data mining, and data clustering is regarded as a particular branch. However existing data clustering methods do not adequately address the problem of processing large datasets with a limited amount of resources (e.g., memory and cpu cycles). So as the dataset size increases, they do not scale up well in terms of memory requirement, running time, and result quality. In this paper, an efficient and scalable data clustering method is proposed, based on a new in-memory data structure called CF-tree, which serves as an in-memory summary of the data distribution. We have implemented it in a system called BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies), and studied its performance extensively in terms of memory requirements, running time, clustering quality, stability and scalability; we also compare it with other available methods. Finally, BIRCH is applied to solve two real-life problems: one is building an iterative and interactive pixel classification tool, and the other is generating the initial codebook for image compression.
Closed Multidimensional Sequential Pattern Mining

We propose a new method, called closed multidimensional sequential pattern mining, for mining multidimensional sequential patterns. The new method is an integration of closed sequential pattern mining and closed itemset pattern mining. Based on this method, we show that (1) the number of complete closed multidimensional sequential patterns is not larger than the number of complete multidimensional sequential patterns (2) the set of complete closed multidimensional sequential patterns covers the complete resulting set of multidimensional sequential patterns. In addition, mining using closed itemset pattern mining on multidimensional information would mine only multidimensional information associated with mined closed sequential patterns, and mining using closed sequential pattern mining on sequences would mine only sequences associated with mined closed itemset patterns
Real-time probabilistic fusion of sparse 3D LIDAR and dense stereo

Real-time 3D perception is critical for localisation, mapping, path planning and obstacle avoidance for mobile robots and autonomous vehicles. For outdoor operation in real-world environments, 3D perception is often provided by sparse 3D LIDAR scanners, which provide accurate but low-density depth maps, and dense stereo approaches, which require significant computational resources for accurate results. Here, taking advantage of the complementary error characteristics of LIDAR range sensing and dense stereo, we present a probabilistic method for fusing sparse 3D LIDAR data with stereo images to provide accurate dense depth maps and uncertainty estimates in real-time. We evaluate the method on data collected from a small urban autonomous vehicle and the KITTI dataset, providing accuracy results competitive with state-of-the-art stereo approaches and credible uncertainty estimates that do not misrepresent the true errors, and demonstrate real-time operation on a range of low-power GPU systems.
Poor Man's Methadone: A Case Report of Loperamide Toxicity.

Loperamide, a common over-the-counter antidiarrheal drug and opioid derivative, is formulated to act upon intestinal opioid receptors. However, at high doses, loperamide crosses the blood-brain barrier and reaches central opioid receptors in the brain, leading to central opiate effects including euphoria and respiratory depression. We report the case of a young man found dead in his residence with a known history of drug abuse. At autopsy, the only significant findings were a distended bladder and bloody oral purge. Drug screening found nontoxic levels of alprazolam, fluoxetine, and marijuana metabolites. Liquid chromatography time-of-flight mass spectrometry found an unusual set of split isotope peaks consistent with chlorine. On the basis of autopsy and toxicological findings, loperamide toxicity was suspected because of its opioid properties and molecular formula containing chlorine. A sample of loperamide was analyzed by liquid chromatography time-of-flight mass spectrometry, resulting in a matching mass and retention time to the decedent's sample. Subsequently, quantitative testing detected 63 ng/mL of loperamide or more than 6 times of therapeutic peak concentration. Cause of death was determined as "toxic effects of loperamide with fluoxetine and alprazolam." Because of its opioid effects and easy accessibility, loperamide is known as "poor man's methadone" and may go undetected at medical and forensic drug screening.
Current-mode sensorless control of single-phase brushless DC fan motors

This paper proposes a new control scheme for the implementation of a low-cost and high efficiency sensorless speed control IC for single-phase brushless dc (BLDC) fan motors. The proposed control scheme detects the zero-crossing-point (ZCP) of the measured back-EMF to generate commutation signals without Hall sensor. A current mode soft switching scheme is used to smooth the current spikes induced by the reduction of the back-EMF when the rotor is crossing the commutation boundary. An adaptive blanking time control strategy is used to adjust the time interval to ensure correct detection of the ZCP. An open-loop constant Ampere/Hertz ramping control scheme is developed for the startup control from zero speed and switching to sensorless mode once the ZCP is detected. The proposed sensorless control scheme has been verified by using computer simulation based on a developed single-phase BLDC fan motor model. Experimental verification of the proposed control scheme has been carried out by using digital implementation technique. Experimental results reveal the superior performance of the proposed control scheme.
Generalized Constraint Neural Network Regression Model Subject to Linear Priors

This paper is reports an extension of our previous investigations on adding transparency to neural networks. We focus on a class of linear priors (LPs), such as symmetry, ranking list, boundary, monotonicity, etc., which represent either linear-equality or linear-inequality priors. A generalized constraint neural network-LPs (GCNN-LPs) model is studied. Unlike other existing modeling approaches, the GCNN-LP model exhibits its advantages. First, any LP is embedded by an explicitly structural mode, which may add a higher degree of transparency than using a pure algorithm mode. Second, a direct elimination and least squares approach is adopted to study the model, which produces better performances in both accuracy and computational cost over the Lagrange multiplier techniques in experiments. Specific attention is paid to both “hard (strictly satisfied)” and “soft (weakly satisfied)” constraints for regression problems. Numerical investigations are made on synthetic examples as well as on the real-world datasets. Simulation results demonstrate the effectiveness of the proposed modeling approach in comparison with other existing approaches.
Semantic Sentiment Analysis Challenge at ESWC2017

Sentiment Analysis is a widely studied research field in both research and industry, and there are different approaches for addressing sentiment analysis related tasks. Sentiment Analysis engines implement approaches spanning from lexicon-based techniques, to machine learning, or involving syntactical rules analysis. Such systems are already evaluated in international research challenges. However, Semantic Sentiment Analysis approaches, which take into account or rely also on large semantic knowledge bases and implement Semantic Web best practices, are not under specific experimental evaluation and comparison by other international challenges. Such approaches may potentially deliver higher performance, since they are also able to analyze the implicit, semantics features associated with natural language concepts. In this paper, we present the fourth edition of the Semantic Sentiment Analysis Challenge, in which systems implementing or relying on semantic features are evaluated in a competition involving large test sets, and on different sentiment tasks. Systems merely based on syntax/word-count or just lexicon-based approaches have been excluded by the evaluation. Then, we present the results of the evaluation for each task and show the winner of the most innovative approach award, that combines several knowledge bases for addressing the sentiment analysis task.
Commonsense Reasoning Based on Betweenness and Direction in Distributional Models

Several recent approaches use distributional similarity for making symbolic reasoning more flexible. While an important step in the right direction, the use of similarity has a number of inherent limitations. We argue that similarity-based reasoning should be complemented with commonsense reasoning patterns such as interpolation and a fortiori inference. We show how the required background knowledge for these inference patterns can be obtained from distributional models.
On-Line Flatness Measurement in the Steelmaking Industry

Shape is a key characteristic to determine the quality of outgoing flat-rolled products in the steel industry. It is greatly influenced by flatness, a feature to describe how the surface of a rolled product approaches a plane. Flatness is of the utmost importance in steelmaking, since it is used by most downstream processes and customers for the acceptance or rejection of rolled products. Flatness sensors compute flatness measurements based on comparing the length of several longitudinal fibers of the surface of the product under inspection. Two main different approaches are commonly used. On the one hand, most mechanical sensors measure the tensile stress across the width of the rolled product, while manufacturing and estimating the fiber lengths from this stress. On the other hand, optical sensors measure the length of the fibers by means of light patterns projected onto the product surface. In this paper, we review the techniques and the main sensors used in the steelmaking industry to measure and quantify flatness defects in steel plates, sheets and strips. Most of these techniques and sensors can be used in other industries involving rolling mills or continuous production lines, such as aluminum, copper and paper, to name a few. Encompassed in the special issue, State-of-the-Art Sensors Technology in Spain 2013, this paper also reviews the most important flatness sensors designed and developed for the steelmaking industry in Spain.
Neural basis of speech perception.

The functional neuroanatomy of speech processing has been difficult to characterize. One major impediment to progress has been the failure to consider task effects when mapping speech-related processing systems. We summarize a dual-stream model of speech processing that addresses this situation. In this model, a ventral stream processes speech signals for comprehension, and a dorsal stream maps acoustic speech signals to parietal and frontal-lobe articulatory networks. The model assumes that the ventral stream is largely bilaterally organized, although there are important computational differences between the left- and right-hemisphere systems, whereas the dorsal stream is strongly left-hemisphere-dominant.
Secure and light IoT protocol (SLIP) for anti-hacking

In the elemental technologies, it is necessary to realize the Internet service of things (IoT), sensors and devices, network, platform (hardware platforms, open software platform, such as specific OS platforms). Web services, data analysis and prediction, big data processing, such as security and privacy protection technology, there are a variety of techniques. These elements technology provide a specific function. The element technology is integrated with each other. However, by several techniques are integrated, it can be problems with integration of security technologies that existed for each element technology. Even if individual technologies basic security features are constituting Internet Services (CIA: Confidentiality, integrity, authentication or authorization). It also offers security technology not connected to each other. Therefore, I will look at the security technology and proposes a lightweight routing protocol indispensable for realizing a secure Internet services things.
Health and well-being benefits of spending time in forests: systematic review

Numerous studies have reported that spending time in nature is associated with the improvement of various health outcomes and well-being. This review evaluated the physical and psychological benefits of a specific type of exposure to nature, forest therapy.



A literature search was carried out using MEDLINE, PubMed, ScienceDirect, EMBASE, and ProQuest databases and manual searches from inception up to December 2016.  "Forest" or "Shinrin -Yoku" or "Forest bath" AND "Health" or "Wellbeing". The methodological quality of each randomized controlled trials (RCTs) was assessed according to the Cochrane risk of bias (ROB) tool.



Six RCTs met the inclusion criteria. Participants' ages ranged from 20 to 79 years. Sample size ranged from 18 to 99. Populations studied varied from young healthy university students to elderly people with chronic disease. Studies reported the positive impact of forest therapy on hypertension (n = 2), cardiac and pulmonary function (n = 1), immune function (n = 2), inflammation (n = 3), oxidative stress (n = 1), stress (n = 1), stress hormone (n = 1), anxiety (n = 1), depression (n = 2), and emotional response (n = 3). The quality of all studies included in this review had a high ROB.



Forest therapy may play an important role in health promotion and disease prevention. However, the lack of high-quality studies limits the strength of results, rendering the evidence insufficient to establish clinical practice guidelines for its use. More robust RCTs are warranted.
Multiscale Approaches To Music Audio Feature Learning

Content-based music information retrieval tasks are typically solved with a two-stage approach: features are extracted from music audio signals, and are then used as input to a regressor or classifier. These features can be engineered or learned from data. Although the former approach was dominant in the past, feature learning has started to receive more attention from the MIR community in recent years. Recent results in feature learning indicate that simple algorithms such as K-means can be very effective, sometimes surpassing more complicated approaches based on restricted Boltzmann machines, autoencoders or sparse coding. Furthermore, there has been increased interest in multiscale representations of music audio recently. Such representations are more versatile because music audio exhibits structure on multiple timescales, which are relevant for different MIR tasks to varying degrees. We develop and compare three approaches to multiscale audio feature learning using the spherical K-means algorithm. We evaluate them in an automatic tagging task and a similarity metric learning task on the Magnatagatune dataset.
A MapReduce Approach to NoSQL RDF Databases

In recent years, the increased need to house and process large volumes of data has prompted the need for distributed storage and querying systems. The growth of machine-readable RDF triples has prompted both industry and academia to develop new database systems, called “NoSQL,” with characteristics that differ from classical databases. Many of these systems compromise ACID properties for increased horizontal scalability and data availability. This thesis concerns the development and evaluation of a NoSQL triplestore. Triplestores are database management systems central to emerging technologies such as the Semantic Web and linked data. A triplestore comprises data storage using the RDF (resource description framework) data model and the execution of queries written in SPARQL. The triplestore developed here exploits an opensource stack comprising, Hadoop, HBase, and Hive. The evaluation spans several benchmarks, including the two most commonly used in triplestore evaluation, the Berlin SPARQL Benchmark, and the DBpedia benchmark, a query workload that operates an RDF representation of Wikipedia. Results reveal that the join algorithm used by the system plays a critical role in dictating query runtimes. Distributed graph databases must carefully optimize queries before generating MapReduce query plans as network traffic for large datasets can become prohibitive if the query is executed naively.
Disassortative Degree Mixing and Information Diffusion for Overlapping Community Detection in Social Networks (DMID)

In this paper we propose a new two-phase algorithm for overlapping community detection (OCD) in social networks. In the first phase, called disassortative degree mixing, we identify nodes with high degrees through a random walk process on the row-normalized disassortative matrix representation of the network. In the second phase, we calculate how closely each node of the network is bound to the leaders via a cascading process called network coordination game. We implemented the algorithm and four additional ones as a Web service on a federated peer-to-peer infrastructure. Comparative test results for small and big real world networks demonstrated the correct identification of leaders, high precision and good time complexity. The Web service is available as open source software.
A programmable and virtualized network & IT infrastructure for the internet of things: How can NFV & SDN help for facing the upcoming challenges

The Internet of Things (IoT) revolution has major impacts on the network & Information Technology (IT) infrastructure. As IT in the past decade, network virtualization is simultaneously on its way with for instance Network Functions Virtualization (NFV) and Software-Defined Networking (SDN). NFV and SDN are approaches enhancing the infrastructure agility thus facilitating the design, delivery and operation of network services in a dynamic and scalable manner. IoT will push the infrastructure to its limit with numerous and diverse requirements to fulfill, we therefore believe that the agility brought by the combination of NFV and SDN is essential to face the IoT revolution. In this article, we first highlight some IoT challenges that the network & IT infrastructure will face. The NFV and SDN benefits are presented from a network operator point of view. Following a description of the IoT ecosystem and a recall of some of the IoT stakeholders expectations, a new multi-layered IoT architecture involving SDN and NFV and based upon network & IT resources is put forward. Finally, the article illustrates how the proposed architecture is able to cope with the identified IoT challenges.
Daylight Sensing LED Lighting System

Adaptation of artificial light based on the available amount of daylight is known to be effective for energy savings. To achieve such daylight control, the state-of-the-art lighting systems use external photodetectors. The photodetector measures the combined contribution of artificial light and daylight, and closed-loop control schemes are used to determine the dimming levels of luminaires to produce the right amount of artificial light. In this paper, we propose LED luminaires that can perform the dual function of illumination and daylight sensing, obviating the need of additional photodetectors. We present a daylight sensing luminaire prototype and consider two driving protocols for sensing and illumination. An open-loop control scheme is then considered for daylight control. The proposed system is shown to be more robust to reflectance changes in comparison with a photodetector-based closed-loop lighting control system.
Liability and Computer Security: Nine Principles

The conventional wisdom is that security priorities should be set by risk analysis. However, reality is subtly different: many computer security systems are at least as much about shedding liability as about minimising risk. Banks use computer security mechanisms to transfer liability to their customers; companies use them to transfer liability to their insurers, or (via the public prosecutor) to the taxpayer; and they are also used to shift the blame to other departments (“we did everything that GCHQ/the internal auditors told us to”). We derive nine principles which might help designers avoid the most common pitfalls.
Are Cyberbullies Less Empathic? Adolescents' Cyberbullying Behavior and Empathic Responsiveness

Meta-analyses confirm a negative relationship between aggressive behavior and empathy, that is, the ability to understand and share the feelings of others. Based on theoretical considerations, it was, therefore, hypothesized that a lack of empathic responsiveness may be characteristic for cyberbullies in particular. In the present study, 2.070 students of Luxembourg secondary schools completed an online survey that included a cyberbullying questionnaire(4) and a novel empathy short scale. According to the main hypothesis, analyses of variances indicated that cyberbullies demonstrated less empathic responsiveness than non-cyberbullies. In addition, cyberbullies were also more afraid of becoming victims of cyberbullying. The findings confirm and substantially extend the research on the relationship between empathy and aggressive behavior. From an educational point of view, the present findings suggest that training of empathy skills might be an important tool to decrease cyberbullying.