nils-herrmann/clean_scirepeval huggingface dataset:
Heart Rate Monitoring as an Easy Way to Increase Engagement in Human-Agent Interaction

Physiological sensors are gaining the attention of manufacturers and users. As denoted by devices such as smartwatches or the newly released Kinect 2 – which can covertly measure heartbeats – or by the popularity of smartphone apps that track heart rate during fitness activities. Soon, physiological monitoring could become widely accessible and transparent to users. We demonstrate how one could take advantage of this situation to increase users’ engagement and enhance user experience in human-agent interaction. We created an experimental protocol involving embodied agents – “virtual avatars”. Those agents were displayed alongside a beating heart. We compared a condition in which this feedback was simply duplicating the heart rates of users to another condition in which it was set to an average heart rate. Results suggest a superior social presence of agents when they display feedback similar to users’ internal state. This physiological “similarity-attraction” effect may lead, with little effort, to a better acceptance of agents and robots by the general public.
Autonomous semantic mapping for robots performing everyday manipulation tasks in kitchen environments

In this work we report about our efforts to equip service robots with the capability to acquire 3D semantic maps. The robot autonomously explores indoor environments through the calculation of next best view poses, from which it assembles point clouds containing spatial and registered visual information. We apply various segmentation methods in order to generate initial hypotheses for furniture drawers and doors. The acquisition of the final semantic map makes use of the robot's proprioceptive capabilities and is carried out through the robot's interaction with the environment. We evaluated the proposed integrated approach in the real kitchen in our laboratory by measuring the quality of the generated map in terms of the map's applicability for the task at hand (e.g. resolving counter candidates by our knowledge processing system).
Mining Cohesive Patterns from Graphs with Feature Vectors

The increasing availability of network data is creating a great potential for knowledge discovery from graph data. In many applications, feature vectors are given in addition to graph data, where nodes represent entities, edges relationships between entities, and feature vectors associated with the nodes represent properties of entities. Often features and edges contain complementary information. In such scenarios the simultaneous use of both data types promises more meaningful and accurate results. Along these lines, we introduce the novel problem of mining cohesive patterns from graphs with feature vectors, which combines the concepts of dense subgraphs and subspace clusters into a very expressive problem definition. A cohesive pattern is a dense and connected subgraph that has homogeneous values in a large enough feature subspace. We argue that this problem definition is natural in identifying small communities in social networks and functional modules in Protein-Protein interaction networks. We present the algorithm CoPaM (Cohesive Pattern Miner), which exploits various pruning strategies to efficiently find all maximal cohesive patterns. Our theoretical analysis proves the correctness of CoPaM, and our experimental evaluation demonstrates its effectiveness and
Getting More From the Semiconductor Test: Data Mining With Defect-Cluster Extraction

High-volume production data shows that dies, which failed probe test on a semiconductor wafer, have a tendency to form certain unique patterns, i.e., defect clusters. Identifying such clusters is one of the crucial steps toward improvement of the fabrication process and design for manufacturing. This paper proposes a new technique for defect-cluster identification that combines data mining with a defect-cluster extraction using a Segmentation, Detection, and Cluster-Extraction algorithm. It offers high defect-extraction accuracy, without any significant increase in test time and cost.
Miller Decoding Algorithm Using Sample Recovery and Correlation Method for UHF Gen2 RFID System

In this paper, by analyzing the characteristics of the received signal in both practical and theory environment, a Miller decoding algorithm basing on a sample recovery method and correlation concept is designed. The sample recovery method is designed to apply on the samples of the received signal which often include some degree of distortions caused by noise or timing jitter. Basing on the concept of matched filter, which is obtained by correlating the received signal with a known reference signal to detect the presence of the data symbol in the received signal, a Miller decoding algorithm utilizing correlation is designed. Together with the signal recovery method, the Miller decoding algorithm using correlation shows a good decoding performance under condition of timing jitter and high signal corruption rate caused by noise.
Procedural learning in Parkinson's disease and cerebellar degeneration.

We compared procedural learning, translation of procedural knowledge into declarative knowledge, and use of declarative knowledge in age-matched normal volunteers (n = 30), patients with Parkinson's disease (n = 20), and patients with cerebellar degeneration (n = 15) by using a serial reaction time task. Patients with Parkinson's disease achieved procedural knowledge and used declarative knowledge of the task to improve performance, but they required a larger number of repetitions of the task to translate procedural knowledge into declarative knowledge. Patients with cerebellar degeneration did not show performance improvement due to procedural learning, failed to achieve declarative knowledge, and showed limited use of declarative knowledge of the task to improve their performance. Both basal ganglia and cerebellum are involved in procedural learning, but their roles are different. The normal influence of the basal ganglia on the prefrontal cortex may be required for timely access of information to and from the working memory buffer, while the cerebellum may index and order events in the time domain and be therefore essential for any cognitive functions involving sequences.
A Survey of Optimization Models for Train Routing and Scheduling

The aim of this paper is to present a survey of recent optimization models for the most commonly studied rail transportation problems. For each group of problems, we propose a classification of models and describe their important characteristics by focusing on model structure and algorithmic aspects. The review mainly concentrates on routing and scheduling problems since they represent the most important portion of the planning activities performed by railways. Routing models surveyed concern the operating policies for freight transportation and railcar fleet management, whereas scheduling models address the dispatching of trains and the assignment of locomotives and cars. A brief discussion of analytical yard and line models is also presented. The emphasis is on recent contributions, but several older yet important works are also cited.
Optimal Dynamic Hotel Pricing ∗

We analyze a confidential reservation database provided by a luxury hotel, ”hotel 0”, based in a major US city that enables us to observe individual reservations and cancellations at a daily frequency over a 37 month period. We show how the hotel sets prices for various classes of customers and how its prices vary over time. Hotel pricing is a challenging high-dimensional problem since hotels must not only set prices for each current date, but they must also quote prices for a range of future dates, room types and customer types. Our data reveal the full path of room rates quoted for different types of rooms and customers in advance of the arrival date. We find large within and between week variability in room prices, as well as huge seasonal variations in average daily rates and occupancy rates, not only for the hotel we study but also for its direct competitors. We formulate and estimate a structural model of optimal dynamic hotel pricing using the Method of Simulated Moments (MSM). The estimated model provides accurate predictions of the actual prices set by this firm and resulting paths of bookings and cancellations. Prices quoted for bookings generally decline as the arrival date approaches on nonbusy days, but can increase dramatically in the final days before arrival on busy days when there is a high probability of sell-out. Hotel 0’s prices co-move strongly with its competitors’ prices and we show that a simple price-following strategy where hotel 0 undercuts its competitors’ average price by a fixed percentage provides a good first approximation to its pricing behavior. However we show that simple price-following is suboptimal: when hotel 0 expects to sell out, it is optimal to depart from price-following and increase its price significantly above its competitors. On non-busy days, it is not optimal for hotel 0 to cut its prices in the final days before arrival to try to increase occupancy unless its competitors cut their prices. Though price-following has the superficial appearance of collusive behavior mediated by the use of a commercial revenue management system (RMS), our results suggest that hotel 0’s pricing is competitive and is best described as a rational best response to its beliefs about demand and the prices set by its competitors. In fact hotel 0 regularly disregards the recommended prices of its RMS, which it regards as too low compared to the prices it actually sets.
Quaternion Domain  $k$ -Means Clustering for Improved Real Time Classification of E-Nose Data

This paper proposes a novel clustering technique implemented in the quaternion domain for qualitative classification of E-nose data. The proposed technique is in many ways similar to the popular k-means clustering algorithm. However, computations carried out in the quaternion space have yielded better class separability and higher cluster validity. A pool of possible cluster centers was created by subjecting each initial center to a fixed rotation in the quaternion space. The test samples were then compared with each of the centers in the pool and assigned to an appropriate center using minimum Euclidean distance criterion. The evolving clusters have been evaluated periodically for their compactness and interclass separation using the Davis-Bouldin (DB) index. The set of clusters having minimum DB index was chosen as an optimal one. It was observed that using the proposed technique the inverse DB index remains significantly higher with successive iterations implying a consistent performance on the cluster validity front. Furthermore, clusters formed using quaternion algebra have been observed to have a smaller DB index. Finally, when compared with the traditional k-means algorithm, the proposed technique performed significantly better in terms of percentage classification of unlabeled samples.
A 10-nW 12-bit accurate analog storage cell with 10-aA leakage

Medium-term analog storage offers a compact, accurate, and low-power method of implementing temporary local memory that can be useful in adaptive circuit applications. The performance of these cells is characterized by the sampling accuracy and voltage droop that can be achieved with a given level of die area and power. Hand calculations suggest past implementations have not achieved minimum voltage droop due to uncompensated MOS leakage mechanisms. In this paper, the dominant sources of MOS leakage are experimentally characterized in a standard 1.5-/spl mu/m CMOS process using an on-chip current integration technique, focusing specifically on the 1 fA to 1 aA current range. These measurements reveal an accumulation-mode source-drain coupling mechanism that can easily dominate diode leakage under certain bias conditions and may have limited previous designs. A simple rule-of-thumb is offered for avoiding this leakage effect, leading to a novel ultra-low leakage switch topology. A differential storage cell incorporating this new switch achieves an average leakage of 10 aA at room temperature, an 8/spl times/ reduction over past designs. The cell loses one bit of voltage accuracy, 700 /spl mu/V on a 12-bit scale and 11.3 mV on an 8-bit scale, in 3.3 and 54 min, respectively. This represents a 15/spl times/ increase in hold time at these voltage accuracies over the lowest leakage cell to date, in only 92% of the area. Since the leakage is independent of amplifier bias, the cell can operate on as little as 10 nW of power. Initial measurements also indicate the switch's leakage decreases with the square of process feature size.
Critical success factors for e-learning in developing countries: A comparative analysis between ICT experts and faculty

This study identifies the critical success factors that influence the acceptance of e-learning systems in developing countries. E-learning is a popularmode of delivering educationalmaterials in higher education by universities throughout the world. This study identifies multiple factors that influence the success of elearning systems from the literature and compares the relative importance among two stakeholder groups in developing countries, ICT experts and faculty. This study collected 76 usable responses using the Delphi method and Analytic Hierarchy Process (AHP) approach. The results reveal 6 dimensions and 20 critical success factors for e-learning systems in developing countries. Findings illustrate the importance of curriculum design for learning performance. Technology awareness, motivation, and changing learners’ behavior are prerequisites for successful e-learning implementations. Several recommendations are provided to aid the implementation of e-learning systems for developing countries which have relevance for researchers and practitioners. Limitations as well as possible research directions are also discussed.
The YouTube Lens: Crowdsourced Personality Impressions and Audiovisual Analysis of Vlogs

Despite an increasing interest in understanding human perception in social media through the automatic analysis of users' personality, existing attempts have explored user profiles and text blog data only. We approach the study of personality impressions in social media from the novel perspective of crowdsourced impressions, social attention, and audiovisual behavioral analysis on slices of conversational vlogs extracted from YouTube. Conversational vlogs are a unique case study to understand users in social media, as vloggers implicitly or explicitly share information about themselves that words, either written or spoken cannot convey. In addition, research in vlogs may become a fertile ground for the study of video interactions, as conversational video expands to innovative applications. In this work, we first investigate the feasibility of crowdsourcing personality impressions from vlogging as a way to obtain judgements from a variate audience that consumes social media video. Then, we explore how these personality impressions mediate the online video watching experience and relate to measures of attention in YouTube. Finally, we investigate on the use of automatic nonverbal cues as a suitable lens through which impressions are made, and we address the task of automatic prediction of vloggers' personality impressions using nonverbal cues and machine learning techniques. Our study, conducted on a dataset of 442 YouTube vlogs and 2210 annotations collected in Amazon's Mechanical Turk, provides new findings regarding the suitability of collecting personality impressions from crowdsourcing, the types of personality impressions that emerge through vlogging, their association with social attention, and the level of utilization of nonverbal cues in this particular setting. In addition, it constitutes a first attempt to address the task of automatic vlogger personality impression prediction using nonverbal cues, with promising results.
Experiments on Local Positioning with Bluetooth

This paper presents a design and an implementation of the Bluetooth local positioning application. Positioning is based on received power levels, which are converted to distance estimates according to a simple propagation model. The extended Kalman filter computes 3D position estimate on the basis of distance estimates. With the used Bluetooth hardware, the mean absolute error of positioning was measured to be 3.76 m. The accuracy can be improved if Bluetooth devices are able to measure received power levels more precisely.
A Secure Robust Gray Scale Image Steganography Using Image Segmentation

Steganography is the art of hiding a secret message in some kind of media. The main goal is not to hide only the secret message but also the existence of communication and secure data transferring. There are a lot of methods that were utilized for building the steganography; such as LSB (Least Significant Bits), Discrete Cosine Transform (DCT), Discrete Fourier Transform, Spread-Spectrum Encoding, and Perceptual Masking, but all of them are challenged by steganalysis. This paper proposes a new technique for Gray Scale Image Steganography that uses the idea of image segmentation and LSB to deal with such problem. The proposed method deals with different types of images by converting them to a virtual gray scale 24 bitmaps, finds out the possible segments inside image and then computes the possible areas for each segment with boundaries. Any intruder trying to find the transformed image will not be able to understand it without the correct knowledge about the transformation process. The knowledge is represented by the key of image segmentation, key of data distribution inside segment (area selection), key of mapping within each area segment, key agreement of cryptography method, key of secret message length and key of message extension. Our method is distinguishing oneself by one master key to generate the area selection key, pixels selection keys and cryptography key. Thus, the existence of secret message is hard to be detected by the steganalysis. Experiment results show that the proposed technique satisfied the main requirements of steganography; visual appearance, modification rate, capacity, undetectability, and robustness against extraction (security). Also it achieved the maximum capacity of cover image with a modification rate equals 0.04 and visual quality for stego-image comparable to cover image.
3D Field D: Improved Path Planning and Replanning in Three Dimensions

We present an interpolation-based planning and replanning algorithm that is able to produce direct, low-cost paths through three-dimensional environments. Our algorithm builds upon recent advances in 2D grid-based path planning and extends these techniques to 3D grids. It is often the case for robots navigating in full three-dimensional environments that moving in some directions is significantly more difficult than others (e.g. moving upwards is more expensive for most aerial vehicles). Thus, we also provide a facility to incorporate such characteristics into the planning process. Along with the derivation of the 3D interpolation function used by our planner, we present a number of results demonstrating its advantages and real-time capabilities
Algorithms for data warehouse design to enhance decision-making

On-line analytical processing (OLAP) queries are strongly affected by the amount data needed to be accessed from the disk. Therefore, there is a need to employ techniques that can facilitate efficient execution of these queries. There has been a lot of work to optimize the performance of relational data warehouses. Among the two fragmentation techniques, vertical fragmentation is often considered more complicated than horizontal, it nearly impossible to obtain an optimal solution. Data partitioning concept that has been studied in the context of relational databases aims to reduce query execution time and facilitate the parallel execution of queries. In this paper, we develop a new framework based on genetic algorithm for applying the partitioning technique on relational DW schema (star schema) to minimize the total query execution cost. We develop an analytical cost model for executing a set of OLAP queries on a partitioned star schema. We conduct experiments to evaluate the utility of partitioning in efficiently executing OLAP queries. Finally, we show how partitioning can be used to facilitate parallel execution of OLAP queries.  Partitioning, warehouse, OLAP queries, Genetic algorithm, penalty function, query optimization.
Biologically inspired multi-layered synthetic skin for tactile feedback in prosthetic limbs

The human body offers a template for many state-of-the-art prosthetic devices and sensors. In this work, we present a novel, sensorized synthetic skin that mimics the natural multi-layered nature of mechanoreceptors found in healthy glabrous skin to provide tactile information. The multi-layered sensor is made up of flexible piezoresistive textiles that act as force sensitive resistors (FSRs) to convey tactile information, which are embedded within a silicone rubber to resemble the compliant nature of human skin. The top layer of the synthetic skin is capable of detecting small loads less than 5 N whereas the bottom sensing layer responds reliably to loads over 7 N. Finite element analysis (FEA) of a simplified human fingertip and the synthetic skin was performed. Results suggest similarities in behavior during loading. A natural tactile event is simulated by loading the synthetic skin on a prosthetic limb. Results show the sensors' ability to detect applied loads as well as the ability to simulate neural spiking activity based on the derivative and temporal differences of the sensor response. During the tactile loading, the top sensing layer responded 0.24 s faster than the bottom sensing layer. A synthetic biologically-inspired skin such as this will be useful for enhancing the functionality of prosthetic limbs through tactile feedback.
Understanding IEEE 1451-Networked smart transducer interface standard - What is a smart transducer?

This article introduces the IEEE 1451 standard for networked smart transducers. It discusses the concepts of smart transducers, IEEE 1451 smart transducers, the architecture of the IEEE 1451 family of standards, application of IEEE 1451, and example implementations of the IEEE 1451 standards. In conclusion, the IEEE 1451 suite of standards provides a set of standard interfaces for networked smart transducers, helping to achieve sensor plug and play and interoperability for industry and government.
The optimal training load for the development of dynamic athletic performance.

This study was performed to determine which of three theoretically optimal resistance training modalities resulted in the greatest enhancement in the performance of a series of dynamic athletic activities. The three training modalities included 1) traditional weight training, 2) plyometric training, and 3) explosive weight training at the load that maximized mechanical power output. Sixty-four previously trained subjects were randomly allocated to four groups that included the above three training modalities and a control group. The experimental groups trained for 10 wk performing either heavy squat lifts, depth jumps, or weighted squat jumps. All subjects were tested prior to training, after 5 wk of training and at the completion of the training period. The test items included 1) 30-m sprint, 2) vertical jumps performed with and without a countermovement, 3) maximal cycle test, 4) isokinetic leg extension test, and 5) a maximal isometric test. The experimental group which trained with the load that maximized mechanical power achieved the best overall results in enhancing dynamic athletic performance recording statistically significant (P < 0.05) improvements on most test items and producing statistically superior results to the two other training modalities on the jumping and isokinetic tests.