nils-herrmann/clean_scirepeval huggingface dataset:
WIRELESS BIO-RADAR SENSOR FOR HEARTBEAT AND RESPIRATION DETECTION

In this study, a wireless bio-radar sensor was designed to detect a human heartbeat and respiration signals without direct skin contact. In order to design a wireless bio-radar sensor quantitatively, the signal-to-noise ratio (SNR) in the baseband output of a sensor should be calculated. Therefore, we analyzed the SNR of the wireless bio-radar sensor, considering the signal power attenuation in a human body and all kinds of noise sources. Especially, we measured a residual phase noise of a typical free-running oscillator and used its value for the SNR analysis. Based on these analysis and the measurement results, a compact, low-cost 2.4 GHz direct conversion bio-radar sensor was designed and implemented in a printed circuit board. The demonstrated sensor consists of two printed antennas, a
A Tristate Rigid Reversible and Non-Back-Drivable Active Docking Mechanism for Modular Robotics

This paper proposes a new active bonding mechanism that achieves rigid, reversible, and nonback-drivable coupling between modular mobile robots in a chain formation. The first merit of this interface lies in its ability to operate in three independent modes. In the drive mode, the motor torque is routed to drive the module. In the clamp mode, the motor torque is redirected toward an active joint that enables one module to rotate relative to its neighbors in the formation. In the neutral mode, the motor's rotation achieves alignment between the interface's components prior to the initiation of the drive and clamp modes. The second merit stems from the dual-rod slider rocker (DRSR) mechanism, which toggles between the interface's three modes of operation. The design details of the interface are presented, as well as the optimal kinematic synthesis and dynamic analysis of the DRSR mechanism. Simulation and experimental results validate the DRSR's unique kinematics, as well as the rigidity and the three operation modes of the docking interface.
Vagal influence on working memory and attention.

The aim of the present study was to investigate the effect of vagal tone on performance during executive and non-executive tasks, using a working memory and a sustained attention test. Reactivity to cognitive tasks was also investigated using heart rate (HR) and heart rate variability (HRV). Fifty-three male sailors from the Royal Norwegian Navy participated in this study. Inter-beat-intervals were recorded continuously for 5 min of baseline, followed by randomized presentation of a working memory test (WMT) based on Baddeley and Hitch's research (1974) and a continuous performance test (CPT). The session ended with a 5-min recovery period. High HRV and low HRV groups were formed based on a median split of the root mean squared successive differences during baseline. The results showed that the high HRV group showed more correct responses than the low HRV group on the WMT. Furthermore, the high HRV group showed faster mean reaction time (mRT), more correct responses and less error, than the low HRV group on the CPT. Follow-up analysis revealed that this was evident only for components of the CPT where executive functions were involved. The analyses of reactivity showed a suppression of HRV and an increase in HR during presentation of cognitive tasks compared to recovery. This was evident for both groups. The present results indicated that high HRV was associated with better performance on tasks involving executive function.
A meothod for multi-target range and velocity detection in automotive FMCW radar

FMCW(Frequency Modulation Continuous Wave) radar has many useful applications but a serious problems can occur in multi-target situations. Range-velocity processing should suppress so-called ghost targets and detect missing targets presented by beat frequency shift with Doppler frequency. In this paper, a new method is proposed for effective identification of the correct pairs of beat frequencies received from real targets.
Delicate manipulations with compliant mechanism and electrostatic adhesion

Traditional rigid robotic hand manipulator has been used in many field nowadays due to its advantages of large gripping force and stable performance. However, this kind of rigid manipulator is not suitable for gripping fragile objects since it is motorized and force control can be a problem. It is also not suitable to grip object with different shapes since the manipulator is rigid and not compliant. In this study, a novel manipulator with gripping capability is designed and fabricated. The manipulator combines electrostatic adhesion actuation with soft manipulators. The manipulator has high flexibility and can be compliant to different shapes due to the property of the materials. It is very promising to do delicate manipulations in industry field and biomedical field.
Real Time Data-Driven Approaches for Credit Card Fraud Detection

Credit card fraud causes many financial losses for customer and also for the organization. For this reason, in the past few years, many studies have been performed using machine learning techniques to detect and block fraudulent transactions. This paper introduces two real time data-driven approaches using optimal anomaly detection techniques for credit card fraud detection. The efficiency of this method is studied over a real data set from European credit card holders. Our experiments show that our approaches achieved a high-level of detection accuracy and a low percentage of false alarm rate. Our approaches will bring many benefits for the organizations and for individual users in terms of cost and time efficiency.
Three-dimensional soft-tissue and hard-tissue changes in the treatment of bimaxillary protrusion.

Facial convexity related to bimaxillary protrusion is prevalent in many populations. Underlying skeletal protrusion combined with increased dentoalveolar protrusion contributes to facial muscle imbalance and lip incompetence, which is undesirable for many patients. In this study, we evaluated the relationship between soft-tissue and hard-tissue changes in an orthodontically treated Asian population.



Twenty-four consecutive adult Asian patients (mean age, 24 years), diagnosed with severe bimaxillary dentoalveolar protrusion, were evaluated using pretreatment and posttreatment cone-beam computed tomography. The patients were treated with 4 first premolar extractions followed by anterior retraction with either skeletal or intraoral anchorage. Serial cone-beam computed tomography radiographs were registered on the entire cranial base and fossa. Soft-tissue and hard-tissue changes were determined through landmark displacement and color mapping.



Upper lip retraction was concentrated between the nasolabial folds and commissures. Lower lip retraction was accompanied by significant redistribution of soft tissues at pogonion. Soft-tissue changes correlated well with regional facial muscle activity. Significant retractions (2-4 mm) of the soft tissues occurred beyond the midsagittal region. Use of skeletal anchorage resulted in 1.5 mm greater lower lip retraction than intraoral anchorage, with greater retraction of the maxillary and mandibular incisor root apices.



Profound soft-tissue changes accompanied retraction of the anterior dentition with both treatment modalities.
MHC class II proteins and disease: a structural perspective

MHC class II molecules on the surface of antigen-presenting cells display a range of peptides for recognition by the T-cell receptors of CD4+ T helper cells. Therefore, MHC class II molecules are central to effective adaptive immune responses, but conversely, genetic and epidemiological data have implicated these molecules in the pathogenesis of autoimmune diseases. Indeed, the strength of the associations between particular MHC class II alleles and disease render them the main genetic risk factors for autoimmune disorders such as type 1 diabetes. Here, we discuss the insights that the crystal structures of MHC class II molecules provide into the molecular mechanisms by which sequence polymorphisms might contribute to disease susceptibility.
Buzz: An extensible programming language for heterogeneous swarm robotics

We present Buzz, a novel programming language for heterogeneous robot swarms. Buzz advocates a compositional approach, offering primitives to define swarm behaviors both from the perspective of the single robot and of the overall swarm. Single-robot primitives include robot-specific instructions and manipulation of neighborhood data. Swarm-based primitives allow for the dynamic management of robot teams, and for sharing information globally across the swarm. Self-organization stems from the completely decentralized mechanisms upon which the Buzz run-time platform is based. The language can be extended to add new primitives (thus supporting heterogeneous robot swarms), and its run-time platform is designed to be laid on top of other frameworks, such as the Robot Operating System. We showcase the capabilities of Buzz by providing code examples, and analyze scalability and robustness of the run-time platform through realistic simulated experiments with representative swarm algorithms.
DEUCE: Write-Efficient Encryption for Non-Volatile Memories

Phase Change Memory (PCM) is an emerging Non Volatile Memory (NVM) technology that has the potential to provide scalable high-density memory systems. While the non-volatility of PCM is a desirable property in order to save leakage power, it also has the undesirable effect of making PCM main memories susceptible to newer modes of security vulnerabilities, for example, accessibility to sensitive data if a PCM DIMM gets stolen. PCM memories can be made secure by encrypting the data. Unfortunately, such encryption comes with a significant overhead in terms of bits written to PCM memory, causing half of the bits in the line to change on every write, even if the actual number of bits being written to memory is small. Our studies show that a typical writeback modifies, on average, only 12% of the bits in the cacheline. Thus, encryption causes almost a 4x increase in the number of bits written to PCM memories. Such extraneous bit writes cause significant increase in write power, reduction in write endurance, and reduction in write bandwidth. To provide the benefit of secure memory in a write efficient manner this paper proposes Dual Counter Encryption (DEUCE). DEUCE is based on the observation that a typical writeback only changes a few words, so DEUCE reencrypts only the words that have changed. We show that DEUCE reduces the number of modified bits per writeback for a secure memory from 50% to 24%, which improves performance by 27% and increases lifetime by 2x.
Variation in the impact of social network characteristics on physical functioning in elderly persons: MacArthur Studies of Successful Aging.

Social support and social networks have been shown to exert significant effects on health and functioning among elderly persons. Although theorists have speculated that the strength of these effects may differ as a function of sociodemographic characteristics and prior health status, few studies have directly tested the moderating effects of these variables.



Longitudinal data from the MacArthur Study of Successful Aging were used to examine the effects of structural and functional social support on changes in physical functioning over a 7-year period, measured by the Nagi scale, in a sample of initially high-functioning men and women aged 70 to 79 years. Multiple regression analyses were used to test the main effects of social support and social network variables, as well as their interactions with gender, income, and baseline physical performance.



After controlling for potential confounding effects, respondents with more social ties showed less functional decline. The beneficial effects of social ties were stronger for respondents who were male or had lower levels of baseline physical performance.



The effects of social support and social networks may vary according to the individual's gender and baseline physical capabilities. Studies of functional decline among elderly persons should not ignore this population variation in the effects of social networks.
Evaluating Improvements to the Shapelet Transform

The Shapelet tree algorithm was proposed in 2009 as a novel way to find phase independent subsequences which could be used for time series classification. The shapelet discovery algorithm is O(nm), where n is the number of cases, and m is the length of the series. Several methods have sought to increase the speed of finding shapelets. The ShapeletTransform reduces the finding to a single pass, and FastShapelets smooths and reduces the series lengths through PAA and SAX. However neither of these techniques can enumerate all shapelets on the largest of the datasets present in the UCR repository. We first evaluate whether the FastShapelet algorithm is better as a transform, and secondly provide a contract classifier for the shapelet transform, by calculating the number of fundamental operations we can estimate the run time of the algorithm, and sample the data to fulfil this contract. We found that whilst the FastShapeletTransform does drastically reduce the operation count of finding shapelets it is not significantly better than FastShapelets, nor can it compete with the ShapeletTransform. The factory method for sampling the data is competitive with the ShapeletTransform and in some cases we see minor improvements despite being much faster.
The TUM Kitchen Data Set of everyday manipulation activities for motion tracking and action recognition

We introduce the publicly available TUM Kitchen Data Set as a comprehensive collection of activity sequences recorded in a kitchen environment equipped with multiple complementary sensors. The recorded data consists of observations of naturally performed manipulation tasks as encountered in everyday activities of human life. Several instances of a table-setting task were performed by different subjects, involving the manipulation of objects and the environment. We provide the original video sequences, full-body motion capture data recorded by a markerless motion tracker, RFID tag readings and magnetic sensor readings from objects and the environment, as well as corresponding action labels. In this paper, we both describe how the data was computed, in particular the motion tracker and the labeling, and give examples what it can be used for. We present first results of an automatic method for segmenting the observed motions into semantic classes, and describe how the data can be integrated in a knowledge-based framework for reasoning about the observations.
Every Picture Tells a Story: Generating Sentences from Images

Humans can prepare concise descriptions of pictures, focusing on what they find important. We demonstrate that automatic methods can do so too. We describe a system that can compute a score linking an image to a sentence. This score can be used to attach a descriptive sentence to a given image, or to obtain images that illustrate a given sentence. The score is obtained by comparing an estimate of meaning obtained from the image to one obtained from the sentence. Each estimate of meaning comes from a discriminative procedure that is learned using data. We evaluate on a novel dataset consisting of human-annotated images. While our underlying estimate of meaning is impoverished, it is sufficient to produce very good quantitative results, evaluated with a novel score that can account for synecdoche.
Analyzing Emergency Evacuation Strategies for Mass Gatherings using Crowd Simulation And Analysis framework: Hajj Scenario

Hajj is one of the largest mass gatherings where Muslims from all over the world gather in Makah each year for pilgrimage. A mass assembly of such scale bears a huge risk of disaster either natural or man-made. In the past few years, thousands of casualties have occurred while performing different Hajj rituals, especially during the Circumambulation of Kaba (Tawaf) due to stampede or chaos. During such calamitous situations, an appropriate evacuation strategy can help resolve the problem and mitigate further risk of causalities. It is however a daunting research problem to identify an optimal course of action based on several constraints. Modeling and analyzing such a problem of real-time and spatially explicit complexity requires a microscale crowd simulation and analysis framework. Which not only allows the modeler to express the spatial dimensions and features of the environment in real scale, but also provides modalities to capture complex crowd behaviors. In this paper, we propose an Agent-based Crowd Simulation & Analysis framework that incorporates the use of Anylogic Pedestrian library and integrates/interoperate Anylogic Simulation environment with the external modules for optimization and analysis. Hence provides a runtime environment for analyzing complex situations, e.g., emergency evacuation strategies. The key features of the proposed framework include: (i) Ability to model large crowd in a spatially explicit environment at real-scale; (ii) Simulation of complex crowd behavior such as emergency evacuation; (iii) Interoperability of optimization and analysis modules with simulation runtime for evaluating evacuation strategies. We present a case study of Hajj scenario as a proof of concept and a test bed for identifying and evaluating optimal strategies for crowd evacuation
Towards better understanding of gradient-based attribution methods for Deep Neural Networks

Understanding the flow of information in Deep Neural Networks (DNNs) is a challenging problem that has gain increasing attention over the last few years. While several methods have been proposed to explain network predictions, there have been only a few attempts to compare them from a theoretical perspective. What is more, no exhaustive empirical comparison has been performed in the past. In this work, we analyze four gradient-based attribution methods and formally prove conditions of equivalence and approximation between them. By reformulating two of these methods, we construct a unified framework which enables a direct comparison, as well as an easier implementation. Finally, we propose a novel evaluation metric, called Sensitivity-n and test the gradient-based attribution methods alongside with a simple perturbation-based attribution method on several datasets in the domains of image and text classification, using various network architectures.
Generating narrative variation in interactive fiction

GENERATING NARRATIVE VARIATION IN INTERACTIVE FICTION Nick Montfort Mitchell P. Marcus and Gerald Prince A general method for the generation of natural language narrative is described. It allows the expression, or narrative discourse, to vary independently of the underlying events and existents that are the narrative’s content. Specifically, this variation is accomplished in an interactive fiction (IF) system which replies to typed input by narrating what has happened in a simulated world. IF works have existed for about 30 years as forms of text-based computer simulation, instances of dialog systems, and examples of literary art. Theorists of narrative have carefully distinguished between the level of underlying content (corresponding to the simulated world in interactive fiction) and that of expression (corresponding to the textual exchange between computer arnd user) since the mid-1960s, when the field of narratology began to develop, but IF systems have not yet made use of this distinction. The current project contributes new techniques for automatic narration by building on work done in computational linguistics, specifically natural language generation, and in narratology. First, types of narrative variation that are possible in IF are identified and formalized in a way that is suitable for a natural language generation system. An architecture for an IF system is then described and implemented; the result allows multiple works of interactive fiction to be realized and, using a general plan for narrating, allows them to be narrated in different ways during interaction. The system’s ability to generate text is considered in a pilot evaluation. Plans for future work are also discussed. They include publicly released systems for IF development and narratology education, adding a planning capability that uses actors’ individual perspectives, and adapting automatic narration to different sorts of interactive systems.
Discriminative n-gram language modeling

This paper describes discriminative language modeling for a large vocabulary speech recognition task. We contrast two parameter estimation methods: the perceptron algorithm, and a method based on maximizing the regularized conditional log-likelihood. The models are encoded as deterministic weighted finite state automata, and are applied by intersecting the automata with word-lattices that are the output from a baseline recognizer. The perceptron algorithm has the benefit of automatically selecting a relatively small feature set in just a couple of passes over the training data. We describe a method based on regularized likelihood that makes use of the feature set given by the perceptron algorithm, and initialization with the perceptron’s weights; this method gives an additional 0.5% reduction in word error rate (WER) over training with the perceptron alone. The final system achieves a 1.8% absolute reduction in WER for a baseline first-pass recognition system (from 39.2% to 37.4%), and a 0.9% absolute reduction in WER for a multi-pass recognition system (from 28.9% to 28.0%).
The Development Process of the Semantic Web and Web Ontology

This paper deals with the semantic web and web ontology. The existing ontology development processes are not catered towards casual web ontology development, a notion analogous to standard web page development. Ontologies have become common on the World-Wide Web[2]. Key features of this process include easy and rapid creation of ontological skeletons, searching and linking to existing ontologies and a natural language-based technique to improve presentation of ontologies[6]. Ontologies, however, vary greatly in size, scope and semantics. They can range from generic upper-level ontologies to domain-specific schemas. The success of the Semantic Web is based on the existance of numerous distributed ontologies, using which users can annotate their data, thereby enabling shared machine readable content. This paper elaborates the stages in a casual ontology development process. Key wordsToolkits; ontology; semantic web; language-based; web ontology.