nils-herrmann/clean_scirepeval huggingface dataset:
A framework for cognitive Internet of Things based on blockchain

Internet of Things, cognitive systems, and blockchain technology are three fields which have created numerous revolutions in software development. It seems that a combination among these fields may results in emerging a high potential and interesting field. Therefore, in this paper, we propose a framework for Internet of Things based on cognitive systems and blockchain technology. To the best of our knowledge, there is no framework for Internet of Things based on cognitive systems and blockchain. In order to study the applicability of the proposed framework, a recommender system based on the proposed framework is suggested. Since the proposed framework is novel, the suggested recommender system is novel. The suggested recommender system is compared with the existing recommender systems. The results show that the suggested recommender system has several benefits which are not available in the existing recommender systems.
Identification, characterization, and grounding of gradable terms in clinical text

Gradable adjectives are inherently vague and are used by clinicians to document medical interpretations (e.g., severe reaction, mild symptoms). We present a comprehensive study of gradable adjectives used in the clinical domain. We automatically identify gradable adjectives and demonstrate that they have a substantial presence in clinical text. Further, we show that there is a specific pattern associated with their usage, where certain medical concepts are more likely to be described using these adjectives than others. Interpretation of statements using such adjectives is a barrier in medical decision making. Therefore, we use a simple probabilistic model to ground their meaning based on their usage in context.
Hardware Design of Smart Home Energy Management System With Dynamic Price Response

The smart grid initiative and electricity market operation drive the development known as demand-side management or controllable load. Home energy management has received increasing interest due to the significant amount of loads in the residential sector. This paper presents a hardware design of smart home energy management system (SHEMS) with the applications of communication, sensing technology, and machine learning algorithm. With the proposed design, consumers can easily achieve a real-time, price-responsive control strategy for residential home loads such as electrical water heater (EWH), heating, ventilation, and air conditioning (HVAC), electrical vehicle (EV), dishwasher, washing machine, and dryer. Also, consumers may interact with suppliers or load serving entities (LSEs) to facilitate the load management at the supplier side. Further, SHEMS is designed with sensors to detect human activities and then a machine learning algorithm is applied to intelligently help consumers reduce total payment on electricity without or with little consumer involvement. Finally, simulation and experiment results are presented based on an actual SHEMS prototype to verify the hardware system.
Performance assessment and uncertainty quantification of predictive models for smart manufacturing systems

We review in this paper several methods from Statistical Learning Theory (SLT) for the performance assessment and uncertainty quantification of predictive models. Computational issues are addressed so to allow the scaling to large datasets and the application of SLT to Big Data analytics. The effectiveness of the application of SLT to manufacturing systems is exemplified by targeting the derivation of a predictive model for quality forecasting of products on an assembly line.
Faster Transit Routing by Hyper Partitioning

We present a preprocessing-based acceleration technique for computing bi-criteria Pareto-optimal journeys in public transit networks, based on the well-known RAPTOR algorithm . Our key idea is to first partition a hypergraph into cells, in which vertices correspond to routes (e.g., bus lines) and hyperedges to stops, and to then mark routes sufficient for optimal travel across cells. The query can then be restricted to marked routes and those in the source and target cells. This results in a practical approach, suitable for networks that are too large to be efficiently handled by the basic RAPTOR algorithm.
An Introduction to Journal Phishings and Their Detection Approach

Nowadays, the most important risk and challenge in online system are online scam and phishing attacks. Phishing attacks have been always used to steal important information of users. In this kind of scam, attacker direct victim to fake pages using social engineering techniques, then, starts stealing users` important information such as passwords. In order to confronting these attacks, numerous techniques have been invented which have the ability to confront different kinds of these attacks. Our goal in this paper is to introducing new kind of phishing attacks which are not identifiable by techniques and methods which have been invented to confronting phishing attacks. Unlike other kinds of phishing attacks which target all kinds of users, researchers are the victims of these kinds of journal phishing attacks. Finally, we`ll introduce an approach based on classification algorithms to identify these kind of journal phishing attacks and then we`ll check our suggested approach in error rate.
Deep reinforcement learning with experience replay based on SARSA

SARSA, as one kind of on-policy reinforcement learning methods, is integrated with deep learning to solve the video games control problems in this paper. We use deep convolutional neural network to estimate the state-action value, and SARSA learning to update it. Besides, experience replay is introduced to make the training process suitable to scalable machine learning problems. In this way, a new deep reinforcement learning method, called deep SARSA is proposed to solve complicated control problems such as imitating human to play video games. From the experiments results, we can conclude that the deep SARSA learning shows better performances in some aspects than deep Q learning.
Abnormal Event Detection in Videos Using Hybrid Spatio-Temporal Autoencoder

The LSTM Encoder-Decoder framework is used to learn representation of video sequences and applied for detect abnormal event in complex environment. However, it generally fails to account for the global context of the learned representation with a fixed dimension representation and the learned representation is crucial for decoder phase. Based on the LSTM Encoder-Decoder and the Convolutional Autoencoder, we explore a hybrid autoencoder architecture, which not only extracts better spatio-temporal context, but also improves the extrapolate capability of the corresponding decoder with the shortcut connection. The experimental results demonstrate that our approach outperforms lots of state-of-the-art methods on benchmark datasets.
How Much Can We Boost IQ and Scholastic Achievement?

Characteristics, Middle Class, Pregnancy The importance and consequences of raising the average ability level (I0) of the population requires consideration of the ability level that society requires, how the relevant abilities are distributed, and the efficiency of the current educational process. Within the framework of these factors, the document discusses the determinants of mental ability and the measures commonly used, the sources of variance in 10, and the ways in which intelligence might be boosted. The latter include both genetic and non-genetic influences. Such prenatal factors as nutrition, length of pregnancy, maternal stress, and intrauterine environment have and effect on infant intelligence. Fiostnatal environmental influences have not been found to markedly affect 10, with the exception of extreme isolation. In relation to disadvantaged children, whose learning and ability patterns are different from those of middle-class children, it is important that these differences be recognized as a precondition to developing appropriate
The role of diet and exercise for the maintenance of fat-free mass and resting metabolic rate during weight loss.

The incidence of obesity is increasing rapidly. Research efforts for effective treatment strategies still focus on diet and exercise programmes, the individual components of which have been investigated in intervention trials in order to determine the most effective recommendations for sustained changes in bodyweight. The foremost objective of a weight-loss trial has to be the reduction in body fat leading to a decrease in risk factors for metabolic syndrome. However, a concomitant decline in lean tissue can frequently be observed. Given that fat-free mass (FFM) represents a key determinant of the magnitude of resting metabolic rate (RMR), it follows that a decrease in lean tissue could hinder the progress of weight loss. Therefore, with respect to long-term effectiveness of weight-loss programmes, the loss of fat mass while maintaining FFM and RMR seems desirable. Diet intervention studies suggest spontaneous losses in bodyweight following low-fat diets, and current data on a reduction of the carbohydrate-to-protein ratio of the diet show promising outcomes. Exercise training is associated with an increase in energy expenditure, thus promoting changes in body composition and bodyweight while keeping dietary intake constant. The advantages of strength training may have greater implications than initially proposed with respect to decreasing percentage body fat and sustaining FFM. Research to date suggests that the addition of exercise programmes to dietary restriction can promote more favourable changes in body composition than diet or physical activity on its own. Moreover, recent research indicates that the macronutrient content of the energy-restricted diet may influence body compositional alterations following exercise regimens. Protein emerges as an important factor for the maintenance of or increase in FFM induced by exercise training. Changes in RMR can only partly be accounted for by alterations in respiring tissues, and other yet-undefined mechanisms have to be explored. These outcomes provide the scientific rationale to justify further randomised intervention trials on the synergies between diet and exercise approaches to yield favourable modifications in body composition.
Ellipse Detection in Digital Image Data Using Geometric Features

Ellipse detection is an important task in vision based systems because many real world objects can be described by this primitive. This paper presents a fast data driven four stage filtering process which uses geometric features in each stage to synthesize ellipses from binary image data with the help of lines, arcs, and extended arcs. It can cope with partially occluded and overlapping ellipses, works fast and accurate and keeps memory consumption to a minimum.
Schema Inference for Massive JSON Datasets

Recent years have seen the widespread use of JSON as a data format to represent massive data collections. JSON data collections are usually schemaless. While this ensures several advantages, the absence of schema information has important negative consequences: the correctness of complex queries and programs cannot be statically checked, users cannot rely on schema information to quickly figure out structural properties that could speed up the formulation of correct queries, and many schema-based optimizations are not possible. In this paper we deal with the problem of inferring a schema from massive JSON data sets. We first identify a JSON type language which is simple and, at the same time, expressive enough to capture irregularities and to give complete structural information about input data. We then present our main contribution, which is the design of a schema inference algorithm, its theoretical study and its implementation based on Spark, enabling reasonable schema inference time for massive collections. Finally, we report about an experimental analysis showing the effectiveness of our approach in terms of execution time, precision and conciseness of inferred schemas, and scalability.
GHz UWB automotive short range radar – Spectrum allocation and technology trends

Automotive UWB (Ultra-Wideband) short range radar (SSR) is on the market as a key technology for novel comfort and safety systems. SiGe based 79 GHz UWB SRR will be a definite candidate for the long term substitution of the 24 GHz UWB SRR. This paper will give an overview of the finished BMBF joint project KOKON and the recently started successing project RoCC, which concentrate on the development of this technology and sensor demonstrators. In both projects, the responsibilities of Daimler AG deal with application based sensor specification, test and evaluation of realized sensor demonstrators. Recent UWB SRR frequency regulation approaches and activitites will be introduced. Furthermore, some first results of Daimler activities within RoCC will be presented, dealing with the packaging and operation of these sensors within the complex car environment.
Depth of field rendering via adaptive recursive filtering

We present a new post-processing method for rendering high-quality depth-of-field effects in real time. Our method is based on a recursive filtering process, which adaptively smooths the image frame with local depth and circle of confusion information. Unlike previous post-filtering approaches that rely on various convolution kernels, the behavior of our filter is controlled by a weighting function defined between two neighboring pixels. By properly designing this weighting function, our method produces spatially-varying smoothed results, correctly handles the boundaries between in-focus and out-of-focus objects, and avoids rendering artifacts such as intensity leakage and blurring discontinuity. Additionally, our method works on the full frame without resorting to image pyramids. Our algorithms runs efficiently on graphics hardware. We demonstrate the effectiveness of the proposed method with several complex scenes.
A review of the use of psychophysiological methods in game research

This article reviews the psychophysiological method in game research. Psychophysiological measurements provide an objective, continuous, real-time, noninvasive, precise and sensitive way to assess the game experience. However, the best results require controlled experiments with careful monitoring of variables, large enough sample sizes and expertise in electrical signal processing. We briefly explain the theory behind the method and present the most useful measures: electromyography (EMG), electrodermal activity (EDA), electroencephalography (EEG) and cardiac measures. We review previous studies that have used psychophysiological measures in game research and illustrate some future directions. Our article covers several research lines using the psychophysiological method in game studies, and offers a comprehensive list of references for those interested in the field. KIVIKANGAS, J. Matias, et al. A review of the use of psychophysiological methods in game research. Journal of Gaming & Virtual Worlds, 2011, vol. 3, no. 3, p. 181-199 DOI : 10.1386/jgvw.3.3.181_1
Cloud-assisted body area networks: state-of-the-art and future challenges

Body Area Networks (BANs) are emerging as enabling technology for many human-centered application domains such as health-care, sport, fitness, wellness, ergonomics, emergency, safety, security, and sociality. A BAN, which basically consists of wireless wearable sensor nodes usually coordinated by a static or mobile device, is mainly exploited to monitor single assisted livings. Data generated by a BAN can be processed in real-time by the BAN coordinator and/or transmitted to a server-side for online/offline processing and long-term storing. A network of BANs worn by a community of people produces large amount of contextual data that require a scalable and efficient approach for elaboration and storage. Cloud computing can provide a flexible storage and processing infrastructure to perform both online and offline analysis of body sensor data streams. In this paper, we motivate the introduction of Cloud-assisted BANs along with the main challenges that need to be addressed for their development and management. The current state-of-the-art is overviewed and framed according to the main requirements for effective Cloud-assisted BAN architectures. Finally, relevant open research issues in terms of efficiency, scalability, security, interoperability, prototyping, dynamic deployment and management, are discussed.
Danmaku vs. Forum Comments: Understanding User Participation and Knowledge Sharing in Online Videos

Danmaku is a new video comment feature that is gaining popularity. Unlike typical forum comments that are displayed with user names below videos, danmaku comments are overlaid on the screen of videos without showing users' information. Prior work studied forum comments and danmaku separately, and little work compared how these two features were used. We collected 38,399 danmaku comments and 16,414 forum comments posted in 2017 on 30 popular videos on Bilibili.com. We examined the usage of these two features in terms of user participation, language used, and ways of sharing knowledge. We found that more users posted danmaku comments, and they also posted these more frequently than forum comments. Even though, in total, more negative language was used in danmaku comments than in forum comments, active users appeared to post more positive comments in danmaku. There was no such correlation in forum comments. It is interesting to find that danmaku and forum comments enabled knowledge sharing in a complementary manner, where danmaku comments involved more explicit knowledge sharing and forum comments exhibited more tacit knowledge sharing. We discuss design implications to promote social interactions for online video systems.
Intelligent sleep stage mining service with smartphones

Sleep quality plays a significant role in personal health. A great deal of effort has been paid to design sleep quality monitoring systems, providing services ranging from bedtime monitoring to sleep activity detection. However, as sleep quality is closely related to the distribution of sleep duration over different sleep stages, neither the bedtime nor the intensity of sleep activities is able to reflect sleep quality precisely. To this end, we present Sleep Hunter, a mobile service that provides a fine-grained detection of sleep stage transition for sleep quality monitoring and intelligent wake-up call. The rationale is that each sleep stage is accompanied by specific yet distinguishable body movements and acoustic signals. Leveraging the built-in sensors on smartphones, Sleep Hunter integrates these physical activities with sleep environment, inherent temporal relation and personal factors by a statistical model for a fine-grained sleep stage detection. Based on the duration of each sleep stage, Sleep Hunter further provides sleep quality report and smart call service for users. Experimental results from over 30 sets of nocturnal sleep data show that our system is superior to existing actigraphy-based sleep quality monitoring systems, and achieves satisfying detection accuracy compared with dedicated polysomnography-based devices.
Electromagnetic simulation of 3D stacked ICs: Full model vs. S-parameter cascaded based model

Three-dimensional electromagnetic simulation models are often simplified and/or segmented in order to reduce the simulation time and memory requirements without sacrificing the accuracy of the results. This paper investigates the difference between full model and S-parameter cascaded based model of 3D stacked ICs with the presence of Through Silicon Vias. It is found that the simulation of the full model is required for accurate results, however, a divide and conquers (segmentation) approach can be used for preliminary post layout analysis. Modeling guidelines are discussed and details on the proper choice of ports, boundary conditions, and solver technology are highlighted. A de-embedding methodology is finally explored to improve the accuracy of the cascaded/segmented results.
Dual Poisson-Disk Tiling: An Efficient Method for Distributing Features on Arbitrary Surfaces

This paper introduces a novel surface-modeling method to stochastically distribute features on arbitrary topological surfaces. The generated distribution of features follows the Poisson disk distribution, so we can have a minimum separation guarantee between features and avoid feature overlap. With the proposed method, we not only can interactively adjust and edit features with the help of the proposed Poisson disk map, but can also efficiently re-distribute features on object surfaces. The underlying mechanism is our dual tiling scheme, known as the dual Poisson-disk tiling. First, we compute the dual of a given surface parameterization, and tile the dual surface by our specially-designed dual tiles; during the pre-processing, the Poisson disk distribution has been pre-generated on these tiles. By dual tiling, we can nicely avoid the problem of corner heterogeneity when tiling arbitrary parameterized surfaces, and can also reduce the tile set complexity. Furthermore, the dual tiling scheme is non-periodic, and we can also maintain a manageable tile set. To demonstrate the applicability of this technique, we explore a number of surface-modeling applications: pattern and shape distribution, bump-mapping, illustrative rendering, mold simulation, the modeling of separable features in texture and BTF, and the distribution of geometric textures in shell space.
Scalable Topical Phrase Mining from Text Corpora

While most topic modeling algorithms model text corpora with unigrams, human interpretation often relies on inherent grouping of terms into phrases. As such, we consider the problem of discovering topical phrases of mixed lengths. Existing work either performs post processing to the results of unigram-based topic models, or utilizes complex n-gramdiscovery topic models. These methods generally produce low-quality topical phrases or suffer from poor scalability on even moderately-sized datasets. We propose a different approach that is both computationally efficient and effective. Our solution combines a novel phrase mining framework to segment a document into single and multi-word phrases, and a new topic model that operates on the induced document partition. Our approach discovers high quality topical phrases with negligible extra cost to the bag-of-words topic model in a variety of datasets including research publication titles, abstracts, reviews, and news articles.