nils-herrmann/clean_scirepeval huggingface dataset:
Attention for Fine-Grained Categorization

This paper presents experiments extending the work of Ba et al. (2014) on recurrent neural models for attention into less constrained visual environments, beginning with fine-grained categorization on the Stanford Dogs data set. In this work we use an RNN of the same structure but substitute a more powerful visual network and perform large-scale pre-training of the visual network outside of the attention RNN. Most work in attention models to date focuses on tasks with toy or more constrained visual environments. We present competitive results for fine-grained categorization. More importantly, we show that our model learns to direct high resolution attention to the most discriminative regions without any spatial supervision such as bounding boxes. Given a small input window, it is hence able to discriminate fine-grained dog breeds with cheap glances at faces and fur patterns, while avoiding expensive and distracting processing of entire images. In addition to allowing high resolution processing with a fixed budget and naturally handling static or sequential inputs, this approach has the major advantage of being trained end-to-end, unlike most current approaches which are heavily engineered.
Deep machine learning provides state-of-the-art performance in image-based plant phenotyping

In plant phenotyping, it has become important to be able to measure many features on large image sets in order to aid genetic discovery. The size of the datasets, now often captured robotically, often precludes manual inspection, hence the motivation for finding a fully automated approach. Deep learning is an emerging field that promises unparalleled results on many data analysis problems. Building on artificial neural networks, deep approaches have many more hidden layers in the network, and hence have greater discriminative and predictive power. We demonstrate the use of such approaches as part of a plant phenotyping pipeline. We show the success offered by such techniques when applied to the challenging problem of image-based plant phenotyping and demonstrate state-of-the-art results (>97% accuracy) for root and shoot feature identification and localization. We use fully automated trait identification using deep learning to identify quantitative trait loci in root architecture datasets. The majority (12 out of 14) of manually identified quantitative trait loci were also discovered using our automated approach based on deep learning detection to locate plant features. We have shown deep learning-based phenotyping to have very good detection and localization accuracy in validation and testing image sets. We have shown that such features can be used to derive meaningful biological traits, which in turn can be used in quantitative trait loci discovery pipelines. This process can be completely automated. We predict a paradigm shift in image-based phenotyping bought about by such deep learning approaches, given sufficient training sets.
Connotation Lexicon: A Dash of Sentiment Beneath the Surface Meaning

Understanding the connotation of words plays an important role in interpreting subtle shades of sentiment beyond denotative or surface meaning of text, as seemingly objective statements often allude nuanced sentiment of the writer, and even purposefully conjure emotion from the readers’ minds. The focus of this paper is drawing nuanced, connotative sentiments from even those words that are objective on the surface, such as “intelligence”, “human”, and “cheesecake”. We propose induction algorithms encoding a diverse set of linguistic insights (semantic prosody, distributional similarity, semantic parallelism of coordination) and prior knowledge drawn from lexical resources, resulting in the first broad-coverage connotation lexicon.
IoT and Cloud Computing in Automation of Assembly Modeling Systems

After the technologies of integrated circuits, personal computers, and the Internet, Internet of Things (IoT) is the latest information technology (IT) that is radically changing business paradigms. However, IoT's influence in the manufacturing sector has yet been fully explored. On the other hand, existing computer-aided software tools are experiencing a bottleneck in dealing with complexity, dynamics, and uncertainties in their applications of modern enterprises. It is argued that the adoption of IoT and cloud computing in enterprise systems (ESs) would overcome the bottleneck. In this paper, the challenges in generating assembly plans of complex products are discussed. IoT and cloud computing are proposed to help a conventional assembly modeling system evolve into an advanced system, which is capable to deal with complexity and changes automatically. To achieve this goal, an assembly modeling system is automated, and the proposed system includes the following innovations: 1) the modularized architecture to make the system robust, reliable, flexible, and expandable; 2) the integrated object-oriented templates to facilitate interfaces and reuses of system components; and 3) the automated algorithms to retrieve relational assembly matrices for assembly planning. Assembly modeling for aircraft engines is used as examples to illustrate the system effectiveness.
A stacked SONOS technology, up to 4 levels and 6nm crystalline nanowires, with Gate-All-Around or independent gates (φ-Flash), suitable for full 3D integration

We present the first experimental study of a Gate-All-Around (GAA) SONOS memory architecture with 4-level crystalline nanowire channels (down to 6nm-diameter). The technology is also extended to an independent double gate memory architecture, called φ-Flash. The experimental results with 6nm nanowires show high programming windows (up to 7.4V), making the structure compatible with multilevel operation. Excellent retention even after 104 cycles is achieved. The independent double gate option has otherwise been successfully integrated with 4-level stacked nanowires for multibit applications. The φ-Flash exhibits up to 1.8V ΔVTh between its two gates, demonstrating multibits operation. The basic process to fully disconnect the different nanowires in view of a full 3D integration of a memory array is discussed.
Interference mitigation using uplink power control for two-tier femtocell networks

This paper proposes two interference mitigation strategies that adjust the maximum transmit power of femtocell users to suppress the cross-tier interference at a macrocell base station (BS). The open-loop and the closed-loop control suppress the cross-tier interference less than a fixed threshold and an adaptive threshold based on the noise and interference (NI) level at the macrocell BS, respectively. Simulation results show that both schemes effectively compensate the uplink throughput degradation of the macrocell BS due to the cross-tier interference and that the closed-loop control provides better femtocell throughput than the open-loop control at a minimal cost of macrocell throughput.
Standardization of Automated Analyses of Oculomotor Fixation and Saccadic Behaviors

In an effort toward standardization, this paper evaluates the performance of five eye-movement classification algorithms in terms of their assessment of oculomotor fixation and saccadic behavior. The results indicate that performance of these five commonly used algorithms vary dramatically, even in the case of a simple stimulus-evoked task using a single, common threshold value. The important contributions of this paper are: evaluation and comparison of performance of five algorithms to classify specific oculomotor behavior; introduction and comparison of new standardized scores to provide more reliable classification performance; logic for a reasonable threshold-value selection for any eye-movement classification algorithm based on the standardized scores; and logic for establishing a criterion-based baseline for performance comparison between any eye-movement classification algorithms. Proposed techniques enable efficient and objective clinical applications providing means to assure meaningful automated eye-movement classification.
Enhancing attention through training

Attention can be improved by repetition of a specific task that involves an attention network (network training), or by exercise or meditation that changes the brain state (state training). We first review the concept of attention networks that link changes in orienting, alerting and executive control to brain networks. Network training through video games or computer exercises can improve aspects of attention. The extent of transfer beyond the trained task is a controversial issue. Mindfulness is a form of meditation that keeps attention focused on the current moment. Some forms of meditation have been shown to improve executive attention reduce stress and produce specific brain changes. Additional research is needed to understand the limits and mechanisms of these effects.
Changes in brain activity related to eating chocolate: from pleasure to aversion.

We performed successive H(2)(15)O-PET scans on volunteers as they ate chocolate to beyond satiety. Thus, the sensory stimulus and act (eating) were held constant while the reward value of the chocolate and motivation of the subject to eat were manipulated by feeding. Non-specific effects of satiety (such as feelings of fullness and autonomic changes) were also present and probably contributed to the modulation of brain activity. After eating each piece of chocolate, subjects gave ratings of how pleasant/unpleasant the chocolate was and of how much they did or did not want another piece of chocolate. Regional cerebral blood flow was then regressed against subjects' ratings. Different groups of structures were recruited selectively depending on whether subjects were eating chocolate when they were highly motivated to eat and rated the chocolate as very pleasant [subcallosal region, caudomedial orbitofrontal cortex (OFC), insula/operculum, striatum and midbrain] or whether they ate chocolate despite being satiated (parahippocampal gyrus, caudolateral OFC and prefrontal regions). As predicted, modulation was observed in cortical chemosensory areas, including the insula and caudomedial and caudolateral OFC, suggesting that the reward value of food is represented here. Of particular interest, the medial and lateral caudal OFC showed opposite patterns of activity. This pattern of activity indicates that there may be a functional segregation of the neural representation of reward and punishment within this region. The only brain region that was active during both positive and negative compared with neutral conditions was the posterior cingulate cortex. Therefore, these results support the hypothesis that there are two separate motivational systems: one orchestrating approach and another avoidance behaviours.
Improving deep neural network ensembles using reconstruction error

Ensemble learning of neural network is a learning paradigm where ensembles of several neural networks show improved generalization capabilities that outperform those of single networks. For deep learning of multi-layer neural networks, ensemble learning is still applicable. In addition, characteristics of deep neural networks can provide potential opportunities to improve the performance of traditional neural network ensembles. In this paper, we propose an ensemble criterion of deep neural networks that is based on the reconstruction error and present two strategies to solve the most important issues in ensemble learning of neural networks: component dataset sampling and output averaging. Component training datasets are selected according to the reconstruction error instead of random bootstrap sampling or re-weighting. Moreover, for each testing instance, we can compute the reconstruction error yielded by the sub-model simultaneously with the output. The reconstruction error is used as the weights in output averaging. From the perspectives of prediction interval and confidence interval, we demonstrated that smaller reconstruction error could ensure smaller prediction interval. We also incorporate the famous structure ensemble approach “Dropout” into the proposed approach to achieve the best performance. We conduct experiments on classification and regression datasets to validate the effectiveness of our approach.
Vision-based autonomous load handling for automated guided vehicles

The paper presents a method for automatically detecting pallets and estimating their position and orientation. For detection we use a sliding window approach with efficient candidate generation, fast integral features and a boosted classifier. Specific information regarding the detection task such as region of interest, pallet dimensions and pallet structure can be used to speed up and validate the detection process. Stereo reconstruction is employed for depth estimation by applying Semi-Global Matching aggregation with Census descriptors. Offline test results show that successful detection is possible under 0.5 seconds.
Evaluation of SiC BJTs for High-Power DC–DC Converters

The design of a 200-A, all-SiC power-module-based on bipolar junction transistor devices is described, and the impact of the module is assessed on the performance of a 50-kW dc-dc converter for electric vehicle applications, particularly the overall weight and efficiency. Using a hard-switching dual-interleaved topology, which has proven high efficiency and high-power density capability, the operation of a 50-kW, 75-kHz all-SiC converter is compared with that of an insulated-gate bipolar transistor-based silicon converter, switching at 25 kHz, each providing 600-V output. The results show that the total losses are reduced by almost 40%, whilst the overall weight is reduced by 27%, achieving a power density of 10.5 kW/kg. Experimental results of the SiC converter operating at 220-600 V, 52.8 kW are provided, showing an efficiency of 97%.
The Linguistic Annotation Framework: a standard for annotation interchange and merging

This paper overviews the International Standards Organization Linguistic Annotation Framework (ISO LAF) developed in ISO TC37 SC4. We describe the XML serialization of ISO LAF, the Graph Annotation Format (GrAF) and discuss the rationale behind the various decisions that were made in determining the standard. We describe the structure of the GrAF headers in detail and provide multiple examples of GrAF representation for text and multi-media. Finally, we discuss the next steps for standardization of interchange formats for linguistic annotations.
Accelerating Critical Section Execution with Asymmetric Multicore Architectures

To improve the performance of a single application on Chip Multiprocessors (CMPs), the application must be split into threads which execute concurrently on multiple cores. In multi-threaded applications, critical sections are used to ensure that only one thread accesses shared data at any given time. Critical sections can serialize the execution of threads, which significantly reduces performance and scalability.
 This paper proposes Accelerated Critical Sections (ACS), a technique that leverages the high-performance core(s) of an Asymmetric Chip Multiprocessor (ACMP) to accelerate the execution of critical sections. In ACS, selected critical sections are executed by a high-performance core, which can execute the critical section faster than the other, smaller cores. As a result, ACS reduces serialization: it lowers the likelihood of threads waiting for a critical section to finish. Our evaluation on a set of 12 critical-section-intensive workloads shows that ACS reduces the average execution time by 34% compared to an equal-area 32T-core symmetric CMP and by 23% compared to an equal-area ACMP. Moreover, for 7 out of the 12 workloads, ACS improves scalability by increasing the number of threads at which performance saturates.
Research of the Machine Vision Based PCB Defect Inspection System

Machine vision-based PCB defect inspection system is designed to meet high speed and high precision requirement in PCB manufacture industry field, which is the combination of software and hardware. This paper firstly introduced the whole system structure and the principle of vision detection, while described the relevant key technologies used during the PCB defect inspection, finally implemented one set of test system with the key technologies mentioned. The experimental results show that the defect of PCB can be effectively inspected, located and recognized with the key technologies.
A Survey on Metric Learning for Feature Vectors and Structured Data

The need for appropriate ways to measure the distance or similarity between data is ubiquitous in machine learning, pattern recognition and data mining, but handcrafting such good metrics for specific problems is generally difficult. This has led to the emergence of metric learning, which aims at automatically learning a metric from data and has attracted a lot of interest in machine learning and related fields for the past ten years. This survey paper proposes a systematic review of the metric learning literature, highlighting the pros and cons of each approach. We pay particular attention to Mahalanobis distance metric learning, a well-studied and successful framework, but additionally present a wide range of methods that have recently emerged as powerful alternatives, including nonlinear metric learning, similarity learning and local metric learning. Recent trends and extensions, such as semi-supervised metric learning, metric learning for histogram data and the derivation of generalization guarantees, are also covered. Finally, this survey addresses metric learning for structured data, in particular edit distance learning, and attempts to give an overview of the remaining challenges in metric learning for the years to come.
Human action recognition using transfer learning with deep representations

Human action recognition is an imperative research area in the field of computer vision due to its numerous applications. Recently, with the emergence and successful deployment of deep learning techniques for image classification, object recognition, and speech recognition, more research is directed from traditional handcrafted to deep learning techniques. This paper presents a novel method for human action recognition based on a pre-trained deep CNN model for feature extraction & representation followed by a hybrid Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) classifier for action recognition. It has been observed that already learnt CNN based representations on large-scale annotated dataset could be transferred to action recognition task with limited training dataset. The proposed method is evaluated on two well-known action datasets, i.e., UCF sports and KTH. The comparative analysis confirms that the proposed method achieves superior performance over state-of-the-art methods in terms of accuracy.
Social exclusion decreases prosocial behavior.

In 7 experiments, the authors manipulated social exclusion by telling people that they would end up alone later in life or that other participants had rejected them. Social exclusion caused a substantial reduction in prosocial behavior. Socially excluded people donated less money to a student fund, were unwilling to volunteer for further lab experiments, were less helpful after a mishap, and cooperated less in a mixed-motive game with another student. The results did not vary by cost to the self or by recipient of the help, and results remained significant when the experimenter was unaware of condition. The effect was mediated by feelings of empathy for another person but was not mediated by mood, state self-esteem, belongingness, trust, control, or self-awareness. The implication is that rejection temporarily interferes with emotional responses, thereby impairing the capacity for empathic understanding of others, and as a result, any inclination to help or cooperate with them is undermined.
An alternative view of the mental lexicon

An essential aspect of knowing language is knowing the words of that language. This knowledge is usually thought to reside in the mental lexicon, a kind of dictionary that contains information regarding a word's meaning, pronunciation, syntactic characteristics, and so on. In this article, a very different view is presented. In this view, words are understood as stimuli that operate directly on mental states. The phonological, syntactic and semantic properties of a word are revealed by the effects it has on those states.
Dimensions and Metrics for Evaluating Recommendation Systems

recommendation systems support users and developers of various computer and software systems to overcome information overload, perform information discovery tasks and approximate computation, among others. They have recently become popular and have attracted a wide variety of application scenarios from business process modelling to source code manipulation. Due to this wide variety of application domains, different approaches and metrics have been adopted for their evaluation. In this chapter, we review a range of evaluation metrics and measures as well as some approaches used for evaluating recommendation systems. The metrics presented in this chapter are grouped under sixteen different dimensions, e.g., correctness, novelty, coverage. We review these metrics according to the dimensions to which they correspond. A brief overview of approaches to comprehensive evaluation using collections of recommendation system dimensions and associated metrics is presented. We also provide suggestions for key future research and practice directions. Iman Avazpour Faculty of ICT, Centre for Computing and Engineering Software and Systems (SUCCESS), Swinburne University of Technology, Hawthorn, Victoria 3122,
The poppy humanoid robot: Leg design for biped locomotion

We introduce a novel humanoid robotic platform designed to jointly address three central goals of humanoid robotics: 1) study the role of morphology in biped locomotion; 2) study full-body compliant physical human-robot interaction; 3) be robust while easy and fast to duplicate to facilitate experimentation. The taken approach relies on functional modeling of certain aspects of human morphology, optimizing materials and geometry, as well as on the use of 3D printing techniques. In this article, we focus on the presentation of the design of specific morphological parts related to biped locomotion: the hip, the thigh, the limb mesh and the knee. We present initial experiments showing properties of the robot when walking with the physical guidance of a human.
TIPS: context-aware implicit user identification using touch screen in uncontrolled environments

Due to the dramatical increase in popularity of mobile devices in the last decade, more sensitive user information is stored and accessed on these devices everyday. However, most existing technologies for user authentication only cover the login stage or only work in restricted controlled environments or GUIs in the post login stage. In this work, we present TIPS, a Touch based Identity Protection Service that implicitly and unobtrusively authenticates users in the background by continuously analyzing touch screen gestures in the context of a running application. To the best of our knowledge, this is the first work to incorporate contextual app information to improve user authentication. We evaluate TIPS over data collected from 23 phone owners and deployed it to 13 of them with 100 guest users. TIPS can achieve over 90% accuracy in real-life naturalistic conditions within a small amount of computational overhead and 6% of battery usage.