nils-herrmann/clean_scirepeval huggingface dataset:
Validation of heart rate extraction using video imaging on a built-in camera system of a smartphone

As a smartphone is becoming very popular and its performance is being improved fast, a smartphone shows its potential as a low-cost physiological measurement solution which is accurate and can be used beyond the clinical environment. Because cardiac pulse leads the subtle color change of a skin, a pulsatile signal which can be described as photoplethysmographic (PPG) signal can be measured through recording facial video using a digital camera. In this paper, we explore the potential that the reliable heart rate can be measured remotely by the facial video recorded using smartphone camera. First, using the front facing-camera of a smartphone, facial video was recorded. We detected facial region on the image of each frame using face detection, and yielded the raw trace signal from the green channel of the image. To extract more accurate cardiac pulse signal, we applied independent component analysis (ICA) to the raw trace signal. The heart rate was extracted using frequency analysis of the raw trace signal and the analyzed signal from ICA. The accuracy of the estimated heart rate was evaluated by comparing with the heart rate from reference electrocardiogram (ECG) signal. Finally, we developed FaceBEAT, an iPhone application for remote heart rate measurement, based on this study.
Data integration driven ontology design, case study smart city

Methods to design of formal ontologies have been in focus of research since the early nineties when their importance and conceivable practical application in engineering sciences had been understood. However, often significant customization of generic methodologies is required when they are applied in tangible scenarios. In this paper, we present a methodology for ontology design developed in the context of data integration. In this scenario, a targeting ontology is applied as a mediator for distinct schemas of individual data sources and, furthermore, as a reference schema for federated data queries. The methodology has been used and evaluated in a case study aiming at integration of buildings' energy and carbon emission related data. We claim that we have made the design process much more efficient and that there is a high potential to reuse the methodology.
Gender differences in personality: a meta-analysis.

Four meta-analyses were conducted to examine gender differences in personality in the literature (1958-1992) and in normative data for well-known personality inventories (1940-1992). Males were found to be more assertive and had slightly higher self-esteem than females. Females were higher than males in extraversion, anxiety, trust, and, especially, tender-mindedness (e.g., nurturance). There were no noteworthy sex differences in social anxiety, impulsiveness, activity, ideas (e.g., reflectiveness), locus of control, and orderliness. Gender differences in personality traits were generally constant across ages, years of data collection, educational levels, and nations.
Development of a biped walking robot compensating for three-axis moment by trunk motion

The authors have been using the ZMP (@ro Mfoment&oint) as a criterion to distinguish the stability of walking for a biped walking robot which has a trunk. T6 the authors introduce a control method of dynamic biped walking for a biped walking robot to compensate for the three-axis (pitch, roll and yaw-axis) moment on an arbitraryplanned ZMP by trunk motion. The authors developed a biped walking robot and performed a walking experiment with the robot using the control method. The result was a fast dynamic biped walking a t the walking speed of 0.54 slstep with a 0.3 m step on a flat floor. This walking speed is about 50 percent faster than that with the robot which compensates for only the twoaxis (pitch and roll-axis) moment by t runk motion. -z In 1986, the authors developed a biped walking robot WL12 (Waseda Leg No. 12) which has a trunk for the stabilization of walking. At the same time the authors proposed a control method of dynamic biped walking for a biped walking robot to compensate for the two-axis (pitch and roll-axis) moment on an arbitrary planned ZMP by the trunk[l]-[3). In 1990, by using this walking control method, the authors achieved dynamic biped walking at the fastest walking speed of 0.8 s/step with 0.3 m step length on a flat floor[5]. The method was based on the assumption that the contact point between robot foot and the floor would not slide. That is to say, only the pitch-axis and roll-axis moments were compensated for by trunk motion, and the yaw-axis moment was not taken into account in the stabilization of walking. In the walking experiment, however, as the robot walked faster, the yaw-axis moment began to give the robot a spin on the yaw-axis, which became a considerable problem that affected the stability of walking. In contrast, i r was considered that human beings compensate not only for the pitch and roll-axis moment but also for the yaw-axis moment by swinging both arms and rotating the waist to maintain the total stability of walking. 0-7803-0823-9p33/$3.00 (C) 1993 IEEE __ _I------" I -__-.*-Fig. 1 Biped walking robot WL-12RV Therefore, the objective of this study was to develop a biped walking robot which has an ability to compensate for the three-axis moment by trunk motion, to work out a control method of dynamic biped walking for the robot and to realize faster walking than before.
Artificial cooperative search algorithm for numerical optimization problems

In this paper, a new two-population based global search algorithm, the Artificial Cooperative Search Algorithm (ACS), is introduced. ACS algorithm has been developed to be used in solving real-valued numerical optimization problems. For purposes of examining the success of ACS algorithm in solving numerical optimization problems, 91 benchmark problems that have different specifications were used in the detailed tests. The success of ACS algorithm in solving the related benchmark problems was compared to the successes obtained by PSO, SADE, CLPSO, BBO, CMA-ES, CK and DSA algorithms in solving the related benchmark problems by using Wilcoxon Signed-Rank Statistical Test with Bonferroni-Holm correction. The results obtained in the statistical analysis demonstrate that the success achieved by ACS algorithm in solving numerical optimization problems is better in comparison to the other computational intelligence algorithms used in this paper.
Development of a three-dimensional ball rotation sensing system using optical mouse sensors

Robots using ball(s) as spherical wheels have the advantage of omnidirectional motion. Some of these robots use only a single ball as their wheel and dynamically balance on it. However, driving a ball wheel is not straightforward. These robots usually use one or more motor-driven rollers or wheels in frictional contact with the ball wheel. Some slippage can occur with these schemes, so it is important to measure the actual rotation of the ball wheel for good control. This paper proposes one method for measuring the three dimensional rotation of a sphere, which is applicable to the case of a ball wheel. The system measures surface speed by using two or more optical mouse sensors and transforms them into the angular velocity vector of the ball, followed by integration to give the rotational angle. Experiments showed the correctness of this method, yielding an error of approximately 1%, with a 10 ms response.
Residual Networks Behave Like Ensembles of Relatively Shallow Networks

In this work we propose a novel interpretation of residual networks showing that they can be seen as a collection of many paths of differing length. Moreover, residual networks seem to enable very deep networks by leveraging only the short paths during training. To support this observation, we rewrite residual networks as an explicit collection of paths. Unlike traditional models, paths through residual networks vary in length. Further, a lesion study reveals that these paths show ensemble-like behavior in the sense that they do not strongly depend on each other. Finally, and most surprising, most paths are shorter than one might expect, and only the short paths are needed during training, as longer paths do not contribute any gradient. For example, most of the gradient in a residual network with 110 layers comes from paths that are only 10-34 layers deep. Our results reveal one of the key characteristics that seem to enable the training of very deep networks: Residual networks avoid the vanishing gradient problem by introducing short paths which can carry gradient throughout the extent of very deep networks.
The behaviour change wheel: A new method for characterising and designing behaviour change interventions

Improving the design and implementation of evidence-based practice depends on successful behaviour change interventions. This requires an appropriate method for characterising interventions and linking them to an analysis of the targeted behaviour. There exists a plethora of frameworks of behaviour change interventions, but it is not clear how well they serve this purpose. This paper evaluates these frameworks, and develops and evaluates a new framework aimed at overcoming their limitations.



A systematic search of electronic databases and consultation with behaviour change experts were used to identify frameworks of behaviour change interventions. These were evaluated according to three criteria: comprehensiveness, coherence, and a clear link to an overarching model of behaviour. A new framework was developed to meet these criteria. The reliability with which it could be applied was examined in two domains of behaviour change: tobacco control and obesity.



Nineteen frameworks were identified covering nine intervention functions and seven policy categories that could enable those interventions. None of the frameworks reviewed covered the full range of intervention functions or policies, and only a minority met the criteria of coherence or linkage to a model of behaviour. At the centre of a proposed new framework is a 'behaviour system' involving three essential conditions: capability, opportunity, and motivation (what we term the 'COM-B system'). This forms the hub of a 'behaviour change wheel' (BCW) around which are positioned the nine intervention functions aimed at addressing deficits in one or more of these conditions; around this are placed seven categories of policy that could enable those interventions to occur. The BCW was used reliably to characterise interventions within the English Department of Health's 2010 tobacco control strategy and the National Institute of Health and Clinical Excellence's guidance on reducing obesity.



Interventions and policies to change behaviour can be usefully characterised by means of a BCW comprising: a 'behaviour system' at the hub, encircled by intervention functions and then by policy categories. Research is needed to establish how far the BCW can lead to more efficient design of effective interventions.
Anomaly Detection with Attribute Conflict Identification in Bank Customer Data

In commercial banks, data centers often integrates different data sources, which represent complex and independent business systems. Due to the inherent data variability and measurement or execution errors, there may exist some abnormal customer records (data). Existing automatic abnormal customer detection methods are outlier detection which focuses on the differences between customers, and it ignores the other possible abnormal customers caused by the inner features confliction of each customer. In this paper, we designed a method to identify abnormal customer information whose inner attributes are conflicting (confliction detection). We integrate the outlier detection and the confliction identification techniques together, as the final abnormality detection. This can provide a complete and accurate support of customer data for commercial bank's decision making. Finally, we have performed experiments on a dataset from a Chinese commercial bank to demonstrate the effectiveness of our method.
Composing Music by Composing Rules: Design and Usage of a Generic Music Constraint System

This research presents the design, usage, and evaluation of a highly generic music constraint system called Strasheela. Strasheela simplifies the definition of musical constraint satisfaction problems (CSP) by predefining building blocks required for such problems. At the same time, Strasheela preserves a high degree of generality and is reasonably efficient. Strasheela is highly generic, because it is highly programmable. In particular, three fundamental components are more programmable in Strasheela compared with existing music constraint systems: the music representation, the rule application mechanism, and the search process. Strasheela features an expressive symbolic music representation. This representation supports explicitly storing score information by sets of score objects, their attributes, and their hierarchic nesting. Any information available in the score is accessible from any object in the score and can be used to obtain derived information. The representation is complemented by the notion of variables: score information can be unknown and such information can be constrained. This research proposes a rule formalism which combines convenience and full user control to express which score variable sets are constrained by a given rule. A rule is a first-class function. A rule application mechanism is a higher-order function. A rule application mechanism traverses the score in order to apply a given rule to variable sets. This text presents rule application mechanisms suitable for a large set of musical CSPs and reproduces important mechanisms of existing systems. Strasheela is founded on a constraint programming model which makes the search process programmable at a high-level. The Strasheela user can optimise the search for a particular constraint satisfaction problem by programming a distribution strategy (a dynamic variable and value ordering) independent of the problem definition. Special distribution strategies for efficiently solving various musical CSPs – including complex polyphonic problems – are presented.
Automated detection of dermatological disorders through image-processing and machine learning

Dermatological Diseases are one of the biggest medical issues in 21st century due to it's highly complex and expensive diagnosis with difficulties and subjectivity of human interpretation. In cases of fatal diseases like Melanoma diagnosis in early stages play a vital role in determining the probability of getting cured. We believe that the application of automated methods will help in early diagnosis especially with the set of images with variety of diagnosis. Hence, in this article we present a completely automated system of dermatological disease recognition through lesion images, a machine intervention in contrast to conventional medical personnel based detection. Our model is designed into three phases compromising of data collection and augmentation, designing model and finally prediction. We have used multiple AI algorithms like Convolutional Neural Network and Support Vector Machine and amalgamated it with image processing tools to form a better structure, leading to higher accuracy of 95.3%.
Sentiment Classification with Deep Neural Networks

Sentiment analysis is a subfield of natural language processing (NLP) that attempts to analyze the sentiment of written text. It is is a complex problem that entails different challenges. For this reason, it has been studied extensively. In the past years traditional machine learning algorithms or handcrafted methodologies used to provide state of the art results. However, the recent deep learning renaissance shifted interest towards end to end deep learning models. On the one hand this resulted into more powerful models but on the other hand clear mathematical reasoning or intuition behind distinct models is still lacking. As a result, in this thesis, an attempt to shed some light on recently proposed deep learning architectures for sentiment classification is made. A study of their differences is performed as well as provide empirical results on how changes in the structure or capacity of a model can affect its accuracy and the way it represents and “comprehends” sentences.
Efficient Subgraph Matching on Billion Node Graphs

The ability to handle large scale graph data is crucial to an increasing number of applications. Much work has been dedicated to supporting basic graph operations such as subgraph matching, reachability, regular expression matching, etc. In many cases, graph indices are employed to speed up query processing. Typically, most indices require either super-linear indexing time or super-linear indexing space. Unfortunately, for very large graphs, super-linear approaches are almost always infeasible. In this paper, we study the problem of subgraph matching on billion-node graphs. We present a novel algorithm that supports efficient subgraph matching for graphs deployed on a distributed memory store. Instead of relying on super-linear indices, we use efficient graph exploration and massive parallel computing for query processing. Our experimental results demonstrate the feasibility of performing subgraph matching on web-scale graph data.
CACTI 6 . 0 : A Tool to Understand Large Caches

Future processors will likely have large on-chip caches wit h a possibility of dedicating an entire die for on-chip storage in a 3D stacked model. With the ever gr owing disparity between transistor and wire delay, the properties of such large caches will prim arily depend on the characteristics of the interconnection networks that connect various sub-module s of a cache. CACTI 6.0 is a significantly enhanced version of the tool that primarily focuses on inter connect design for large caches. In addition to strengthening the existing analytical model of the tool f r dominant cache components, CACTI 6.0 includes two major extensions over earlier versions: First , ability to model Non-Uniform Cache Access (NUCA). Second, ability to model different types of wires, s uch as RC based wires with different power, delay, and area characteristics and differential low-swin g buses. The report details the analytical model assumed for the newly added modules along with their validat ion analysis.
Illustrating smooth surfaces

We present a new set of algorithms for line-art rendering of smooth surfaces. We introduce an efficient, deterministic algorithm for finding silhouettes based on geometric duality, and an algorithm for segmenting the silhouette curves into smooth parts with constant visibility. These methods can be used to find all silhouettes in real time in software. We present an automatic method for generating hatch marks in order to convey surface shape. We demonstrate these algorithms with a drawing style inspired by A Topological Picturebook by G. Francis.
Heart Disease Prediction Using Classification with Different Decision Tree Techniques

Data mining is one of the essential areas of research that is more popular in health organization. Data mining plays an effective role for uncovering new trends in healthcare organization which is helpful for all the parties associated with this field. Heart disease is the leading cause of death in the world over the past 10 years. Heart disease is a term that assigns to a large number of medical conditions related to heart. These medical conditions describe the irregular health condition that directly affects the heart and all its parts. The healthcare industry gathers enormous amount of heart disease data which are not “mined” to discover hidden information for effective decision making. Data mining techniques are useful for analyzing the data from many different dimensions and for identifying relationships. This paper explores the utility of various decision tree algorithms in classify and predict the disease.
Escher sphere construction kit

M.C. Escher created a myriad of amazing planar tessellations, yet only a few three-dimensional ones such as his wooden fish ball and dodecahedral flower. We have developed an interactive program to design and manufacture “Escher Spheres” sets of tiles that can be assembled into spherical balls. The user chooses from a set of predefined symmetry groups and then deforms the boundaries of the basic domain tile; all corresponding points based on the chosen symmetry class move concurrently, instantly showing the overall result. The interior of the tile can be embellished with a bas-relief. Finally the tile is radially extruded and output as a solid model suitable for free-form fabrication.
Building an open-source robotic stereotaxic instrument.

This protocol includes the designs and software necessary to upgrade an existing stereotaxic instrument to a robotic (CNC) stereotaxic instrument for around $1,000 (excluding a drill), using industry standard stepper motors and CNC controlling software. Each axis has variable speed control and may be operated simultaneously or independently. The robot's flexibility and open coding system (g-code) make it capable of performing custom tasks that are not supported by commercial systems. Its applications include, but are not limited to, drilling holes, sharp edge craniotomies, skull thinning, and lowering electrodes or cannula. In order to expedite the writing of g-coding for simple surgeries, we have developed custom scripts that allow individuals to design a surgery with no knowledge of programming. However, for users to get the most out of the motorized stereotax, it would be beneficial to be knowledgeable in mathematical programming and G-Coding (simple programming for CNC machining). The recommended drill speed is greater than 40,000 rpm. The stepper motor resolution is 1.8°/Step, geared to 0.346°/Step. A standard stereotax has a resolution of 2.88 μm/step. The maximum recommended cutting speed is 500 μm/sec. The maximum recommended jogging speed is 3,500 μm/sec. The maximum recommended drill bit size is HP 2.
SOMKE: Kernel Density Estimation Over Data Streams by Sequences of Self-Organizing Maps

In this paper, we propose a novel method SOMKE, for kernel density estimation (KDE) over data streams based on sequences of self-organizing map (SOM). In many stream data mining applications, the traditional KDE methods are infeasible because of the high computational cost, processing time, and memory requirement. To reduce the time and space complexity, we propose a SOM structure in this paper to obtain well-defined data clusters to estimate the underlying probability distributions of incoming data streams. The main idea of this paper is to build a series of SOMs over the data streams via two operations, that is, creating and merging the SOM sequences. The creation phase produces the SOM sequence entries for windows of the data, which obtains clustering information of the incoming data streams. The size of the SOM sequences can be further reduced by combining the consecutive entries in the sequence based on the measure of Kullback-Leibler divergence. Finally, the probability density functions over arbitrary time periods along the data streams can be estimated using such SOM sequences. We compare SOMKE with two other KDE methods for data streams, the M-kernel approach and the cluster kernel approach, in terms of accuracy and processing time for various stationary data streams. Furthermore, we also investigate the use of SOMKE over nonstationary (evolving) data streams, including a synthetic nonstationary data stream, a real-world financial data stream and a group of network traffic data streams. The simulation results illustrate the effectiveness and efficiency of the proposed approach.