nils-herrmann/clean_scirepeval huggingface dataset:
Gradient Response Maps for Real-Time Detection of Textureless Objects

We present a method for real-time 3D object instance detection that does not require a time-consuming training stage, and can handle untextured objects. At its core, our approach is a novel image representation for template matching designed to be robust to small image transformations. This robustness is based on spread image gradient orientations and allows us to test only a small subset of all possible pixel locations when parsing the image, and to represent a 3D object with a limited set of templates. In addition, we demonstrate that if a dense depth sensor is available we can extend our approach for an even better performance also taking 3D surface normal orientations into account. We show how to take advantage of the architecture of modern computers to build an efficient but very discriminant representation of the input images that can be used to consider thousands of templates in real time. We demonstrate in many experiments on real data that our method is much faster and more robust with respect to background clutter than current state-of-the-art methods.
Owner-Centric Protection of Unstructured Data on Smartphones

Modern smartphone apps tend to contain and use vast amounts of data that can be broadly classified as structured and unstructured. Structured data, such as an user’s geolocation, has predefined semantics that can be retrieved by well-defined platform APIs. Unstructured data, on the other hand, relies on the context of the apps to reflect its meaning and value, and is typically provided by the user directly into an app’s interface. Recent research has shown that third-party apps are leaking highly-sensitive unstructured data, including user’s banking credentials. Unfortunately, none of the current solutions focus on the protection of
Body Structure Aware Deep Crowd Counting

Crowd counting is a challenging task, mainly due to the severe occlusions among dense crowds. This paper aims to take a broader view to address crowd counting from the perspective of semantic modeling. In essence, crowd counting is a task of pedestrian semantic analysis involving three key factors: pedestrians, heads, and their context structure. The information of different body parts is an important cue to help us judge whether there exists a person at a certain position. Existing methods usually perform crowd counting from the perspective of directly modeling the visual properties of either the whole body or the heads only, without explicitly capturing the composite body-part semantic structure information that is crucial for crowd counting. In our approach, we first formulate the key factors of crowd counting as semantic scene models. Then, we convert the crowd counting problem into a multi-task learning problem, such that the semantic scene models are turned into different sub-tasks. Finally, the deep convolutional neural networks are used to learn the sub-tasks in a unified scheme. Our approach encodes the semantic nature of crowd counting and provides a novel solution in terms of pedestrian semantic analysis. In experiments, our approach outperforms the state-of-the-art methods on four benchmark crowd counting data sets. The semantic structure information is demonstrated to be an effective cue in scene of crowd counting.
Tensor-Based Learning for Predicting Stock Movements

Stock movements are essentially driven by new information. Market data, financial news, and social sentiment are believed to have impacts on stock markets. To study the correlation between information and stock movements, previous works typically concatenate the features of different information sources into one super feature vector. However, such concatenated vector approaches treat each information source separately and ignore their interactions. In this article, we model the multi-faceted investors’ information and their intrinsic links with tensors. To identify the nonlinear patterns between stock movements and new information, we propose a supervised tensor regression learning approach to investigate the joint impact of different information sources on stock markets. Experiments on CSI 100 stocks in the year 2011 show that our approach outperforms the state-of-the-art trading strategies.
Application of Spatial Data Mining for Agriculture

The research of spatial data is in its infancy stage and there is a need for an accurate method for rule mining. Association rule mining searches for interesting relationships among items in a given data set. This paper enables us to extract pattern from spatial database using k-means algorithm which refers to patterns not explicitly stored in spatial databases. Since spatial association mining needs to evaluate multiple spatial relationships among a large number of spatial objects, the process could be quite costly. An interesting mining optimization method called progressive refinement can be adopted in spatial association analysis. The method first mines large data sets roughly using a fast algorithm and then improves the quality of mining in a pruned data set. The k-means algorithm randomly selects k number of objects, each of which initially represents a cluster mean or center. For each of the remaining objects, an object is assigned to the cluster to which it is most similar, based on the distance between the object and the cluster mean. Then it computes new mean for each cluster. This process iterates until the criterion function converges. The above concept is applied in the area of agriculture where giving the temperature and the rainfall as the initial spatial data and then by analyzing the agricultural meteorology for the enhancement of crop yields and also reduce the crop losses.
An asymmetrical half bridge flyback converter with zero-voltage and zero-current switching

In this paper, the analysis and design of an asymmetrical half bridge flyback DC-DC converter is presented, which can minimize the switching power loss by realizing the zero-voltage switching (ZVS) during the transition between the two switches and the zero-current-switching (ZCS) on the output diode. As a result, high efficiency can be achieved. The principle of the converter operation is explained and analyzed. In order to ensure the realization of ZVS in operation, the required interlock delay time between the gate signals of the two switches, the transformer leakage inductance, and the ZVS range of the output current variation are properly calculated. Experimental results from a 8 V/8 A, 200 kHz circuit are also presented, which verify the theoretical analysis.
Speed Estimation From a Tri-axial Accelerometer Using Neural Networks

We propose a speed estimation method with human body accelerations measured on the chest by a tri-axial accelerometer. To estimate the speed we segmented the acceleration signal into strides measuring stride time, and applied two neural networks into the patterns parameterized from each stride calculating stride length. The first neural network determines whether the subject walks or runs, and the second neural network with different node interactions according to the subject's status estimates stride length. Walking or running speed is calculated with the estimated stride length divided by the measured stride time. The neural networks were trained by patterns obtained from 15 subjects and then validated by 2 untrained subjects' patterns. The result shows good agreement between actual and estimated speeds presenting the linear correlation coefficient r = 0.9874. We also applied the method to the real field and track data.
Streaming Normalization: Towards Simpler and More Biologically-plausible Normalizations for Online and Recurrent Learning

We systematically explored a spectrum of normalization algorithms related to Batch Normalization (BN) and propose a generalized formulation that simultaneously solves two major limitations of BN: (1) online learning and (2) recurrent learning. Our proposal is simpler and more biologically-plausible. Unlike previous approaches, our technique can be applied out of the box to all learning scenarios (e.g., online learning, batch learning, fully-connected, convolutional, feedforward, recurrent and mixed — recurrent and convolutional) and compare favorably with existing approaches. We also propose Lp Normalization for normalizing by different orders of statistical moments. In particular, L1 normalization is well-performing, simple to implement, fast to compute, more biologically-plausible and thus ideal for GPU or hardware implementations. This work was supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF 1231216. 1 ar X iv :1 61 0. 06 16 0v 1 [ cs .L G ] 1 9 O ct 2 01 6 Approach FF & FC FF & Conv Rec & FC Rec & Conv Online Learning Small Batch All Combined Original Batch Normalization(BN) 3 3 7 7 7 Suboptimal 7 Time-specific BN 3 3 Limited Limited 7 Suboptimal 7 Layer Normalization 3 7* 3 7* 3 3 7* Streaming Normalization 3 3 3 3 3 3 3 Table 1: An overview of normalization techiques for different tasks. 3: works well. 7: does not work well.  Fully-connected. Conv: convolutional. Limited: time-specific BN requires recording normalization statistics for each timestep and thus may not generalize to novel sequence length. Layer normalization does not fail on these tasks but perform significantly worse than the best approaches.
The perseverative cognition hypothesis: a review of worry, prolonged stress-related physiological activation, and health.

Perseverative cognition, as manifested in worry and rumination, is a common response to stress, but biopsychological models of stress and health have largely ignored it. These models have generally focused on physiological activation that occurs during stress and have insufficiently addressed effects that occur in anticipation of, or following, stressful events. We argue that perseverative cognition moderates the health consequences of stressors because it can prolong stress-related affective and physiological activation, both in advance of and following stressors. We review evidence that worry, rumination, and anticipatory stress are associated with enhanced cardiovascular, endocrinological, immunological, and neurovisceral activity. The findings yield preliminary support for our hypothesis, suggesting that perseverative cognition might act directly on somatic disease via enhance activation via the cardiovascular, immune, endocrine, and neurovisceral systems.
Low-resolution face recognition: a review

Low-resolution face recognition (LR FR) aims to recognize faces from small size or poor quality images with varying pose, illumination, expression, etc. It has received much attention with increasing demands for long distance surveillance applications, and extensive efforts have been made on LR FR research in recent years. However, many issues in LR FR are still unsolved, such as super-resolution (SR) for face recognition, resolution-robust features, unified feature spaces, and face detection at a distance, although many methods have been developed for that. This paper provides a comprehensive survey on these methods and discusses many related issues. First, it gives an overview on LR FR, including concept description, system architecture, and method categorization. Second, many representative methods are broadly reviewed and discussed. They are classified into two different categories, super-resolution for LR FR and resolution-robust feature representation for LR FR. Their strategies and advantages/disadvantages are elaborated. Some relevant issues such as databases and evaluations for LR FR are also presented. By generalizing their performances and limitations, promising trends and crucial issues for future research are finally discussed.
Service Quality and Perceived Value's Impact on Satisfaction, Intention and Usage of Short Message Service (SMS)

SMS, being an almost instantaneous communication medium that connects people, is now a phenomenon that has grown and spread around the globe at an amazing speed. Given the current trend of SMS usage and its potential growth, this paper will provide an insight of the extent to which how service quality and the value perceived by the SMS users have an impact on their extent of the SMS usage in the post SMS adoption phase. Specifically, this article will examine how service quality of the service providers and perceived value affect customer satisfaction and how customer satisfaction will affect their behavioural intention to continue to use SMS which in turn affects the extent of SMS usage in the local context. Using partial-least-squares, an analysis was conducted based on the 150 surveys collected to test for the proposed relationships. The results showed that the tangibles, empathy and assurance dimensions of service quality are antecedents of customer satisfaction and a positive relationship exists between customer satisfaction and customers’ behavioural intentions to continue to use SMS. Additionally, the positive relationship between customers’ behavioural intentions to continue to use SMS and the extent of SMS usage is also significant. These results were similar to the results shown by Cronin and Taylor (1992) studies. The perceived value/customer satisfaction relationship investigated in this research was in line with Fornell et al.(1996) and Cronin et al.(2000) where perceived value was one of the determinants of customer satisfaction. Specially, the results revealed that perceived value, together with tangibles, empathy and assurance aspects of the service quality, played an important role in determining customer satisfaction for SMS. Implications of the above results for research and practice are discussed.
Fabric defect detection using morphological filters

In this paper, a novel defect detection scheme based on morphological filters is proposed to tackle the problem of automated defect detection for woven fabrics. In the proposed scheme, important texture features of the textile fabric are extracted using a pre-trained Gabor wavelet network. These texture features are then used to facilitate the construction of structuring elements in subsequent morphological processing to remove the fabric background and isolate the defects. Since the proposed defect detection scheme requires a few morphological filters only, the amount of computational load involved is not significant. The performance of the proposed scheme is evaluated by using a wide variety of homogeneous textile images with different types of common fabric defects. The test results obtained exhibit accurate defect detection with low false alarms, thus showing the effectiveness and robustness of the proposed detection scheme. In addition, the proposed detection scheme is further evaluated in real time by using a prototyped automated inspection system.
Skin lesion analysis toward melanoma detection: A challenge at the 2017 International symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC)

This article describes the design, implementation, and results of the latest installment of the dermoscopic image analysis benchmark challenge. The goal is to support research and development of algorithms for automated diagnosis of melanoma, the most lethal skin cancer. The challenge was divided into 3 tasks: lesion segmentation, feature detection, and disease classification. Participation involved 593 registrations, 81 pre-submissions, 46 finalized submissions (including a 4-page manuscript), and approximately 50 attendees, making this the largest standardized and comparative study in this field to date. While the official challenge duration and ranking of participants has concluded, the dataset snapshots remain available for further research and development.
New in-line dual- and triple-mode cavity filters with nonresonating nodes

This paper presents new in-line pseudoelliptic bandpass filters with nonresonating nodes. Microwave bandpass filters based on dual- and triple-mode cavities are introduced. In each case, the transmission zeros (TZs) are individually generated and controlled by dedicated resonators. Dual- and triple-mode cavities are kept homogeneous and contain no coupling or tuning elements. A third-order filter with a TZ extracted at its center is designed by cascading two dual-mode cavities. A direct design technique of this filter is introduced and shown to produce accurate initial designs for narrow-band cases. A six-pole filter is designed by cascading two triple-mode cavities. Measured results are presented to demonstrate the validity of this novel approach.
A 10-bit CMOS DAC With Current Interpolated Gamma Correction for LCD Source Drivers

This paper presents a compact 10-bit digital-to-analog converter (DAC) for LCD source drivers. The cyclic DAC architecture is used to reduce the area of LCD column drivers when compared to the use of conventional resistor-string DACs. The current interpolation technique is proposed to perform gamma correction after D/A conversion. The gamma correction circuit is shared by four DAC channels using the interleave technique. A prototype 10-bit DAC with gamma correction function is implemented in 0.35 μm CMOS technology and its average die size per channel is 0.053 mm2, which is smaller than those of the R-DACs with gamma correction function. The settling time of the 10-bit DAC is 1 μs, and the maximum INL and DNL are 2.13 least significant bit (LSB) and 1.30 LSB, respectively.
The importance of parents and other caregivers to the resilience of high-risk adolescents.

Relationships between 43 high-risk adolescents and their caregivers were examined qualitatively. Parents and other formal and informal caregivers such as youth workers and foster parents were found to exert a large influence on the behaviors that bolster mental health among high-risk youth marginalized by poverty, social stigma, personal and physical characteristics, ethnicity, and poor social or academic performance. Participants' accounts of their intergenerational relationships with caregivers showed that teenagers seek close relationships with adults in order to negotiate for powerful self-constructions as resilient. High-risk teens say they want the adults in their lives to serve as an audience in front of whom they can perform the identities they construct both inside and outside their homes. This pattern was evident even among youth who presented as being more peer-than family-oriented. The implications of these findings to interventions with caregivers and teens is discussed.
Self-Directed Learning in Problem-Based Learning and its Relationships with Self-Regulated Learning

This study investigated the role of self-directed learning (SDL) in problem-based learning (PBL) and examined how SDL relates to self-regulated learning (SRL). First, it is explained how SDL is implemented in PBL environments. Similarities between SDL and SRL are highlighted. However, both concepts differ on important aspects. SDL includes an additional premise of giving students a broader role in the selection and evaluation of learning materials. SDL can encompass SRL, but the opposite does not hold. Further, a review of empirical studies on SDL and SRL in PBL was conducted. Results suggested that SDL and SRL are developmental processes, that the “self” aspect is crucial, and that PBL can foster SDL. It is concluded that conceptual clarity of what SDL entails and guidance for both teachers and students can help PBL to bring forth self-directed learners.
A 2.6 mW/Gbps 12.5 Gbps RX With 8-Tap Switched-Capacitor DFE in 32 nm CMOS

A low-power receiver circuit in 32 nm SOI CMOS is presented, which is intended to be used in a source-synchronous link configuration. The design of the receiver was optimized for power owing to the assumption that a link protocol enables a periodic calibration during which the circuit does not have to deliver valid data. In addition, it is shown that the transceiver power and the effect of high-frequency transmit jitter can be reduced by implementing a linear equalizer only on the receive side and avoiding a transmit feed-forward equalizer (TX-FFE). On the circuit level, the receiver uses a switched-capacitor (SC) approach for the implementation of an 8-tap decision-feedback equalizer (DFE). The SC-DFE improves the timing margin relative to previous DFE implementations with current feedback, and leads to a digital-style circuit implementation with compact layout. The receiver was measured at data rates up to 13.5 Gb/s, where error free operation was verified with a PRBS-31 sequence and a channel with 32 dB attenuation at Nyquist. With the clock generation circuits amortized over eight lanes, the receiver circuit consumes 2.6 mW/Gbps from a 1.1 V supply while running at 12.5 Gb/s.
Contactless and Cuffless Monitoring of Blood Pressure on a Chair Using E-Textile Materials

Blood pressure (BP) can be measured cufflessly and noninvasively by using pulse transit time, which is obtained from simultaneously recorded electrocardiogram (ECG) and photoplethysmogram (PPG). To apply this BP monitoring technique in daily life, an office chair was modified into a contactless and cuffless BP-monitoring device using e-textile materials. In this study, twelve normotensive subjects, including 6 males and 6 females, were recruited for testing this BP-monitoring chair. During the experiment, exercise was carried out to obtain a wider range of BP. In total, 62 pairs of systolic BP (SBP) and diastolic BP (DBP) estimations were made. Readings from a clinically approved cuff-based automatic BP monitor was used as reference. The results showed that the estimated SBP and DBP differed from their references by -6.3plusmn10.5 mmHg and -0.7plusmn4.9 mmHg respectively. It was found that the SBP estimation errors mainly came from the data obtained immediately after exercise. Further improvement should be made to minimize those errors. However, this concept is potentially useful in self BP monitoring for home healthcare.
Monitor alarm fatigue: standardizing use of physiological monitors and decreasing nuisance alarms.

Reliance on physiological monitors to continuously "watch" patients and to alert the nurse when a serious rhythm problem occurs is standard practice on monitored units. Alarms are intended to alert clinicians to deviations from a predetermined "normal" status. However, alarm fatigue may occur when the sheer number of monitor alarms overwhelms clinicians, possibly leading to alarms being disabled, silenced, or ignored.



Excessive numbers of monitor alarms and fear that nurses have become desensitized to these alarms was the impetus for a unit-based quality improvement project.



Small tests of change to improve alarm management were conducted on a medical progressive care unit. The types and frequency of monitor alarms in the unit were assessed. Nurses were trained to individualize patients' alarm parameter limits and levels. Monitor software was modified to promote audibility of critical alarms.



Critical monitor alarms were reduced 43% from baseline data. The reduction of alarms could be attributed to adjustment of monitor alarm defaults, careful assessment and customization of monitor alarm parameter limits and levels, and implementation of an interdisciplinary monitor policy.



Although alarms are important and sometimes life-saving, they can compromise patients' safety if ignored. This unit-based quality improvement initiative was beneficial as a starting point for revamping alarm management throughout the institution.