Reviewerly/exHarmony huggingface dataset:
ATL-BP: A Student Engagement Dataset and Model for Affect Transfer Learning for Behavior Prediction

We propose a video-based transfer learning approach for predicting problem outcomes of students working with an intelligent tutoring system (ITS) by analyzing their faces and gestures. The ability to predict such outcomes enables tutoring systems to adjust interventions and ultimately yield improved student learning. We collected and released a labeled dataset of 2,749 problem-solving interaction samples of 54 students working with an intelligent online math tutor. Our transfer-learning challenge was then to design a representation in the source domain of images obtained from the Internet for facial expression analysis, and transfer this learned representation for human behavior prediction in the domain of webcam videos of students in a classroom environment. We developed a novel facial affect representation and a user-personalized training scheme that unlocks the potential of this representation. We designed several variants of a recurrent neural network that models the temporal structure of video sequences. Our final model, named ATL-BP for Affect Transfer Learning for Behavior Prediction, achieves a relative increase in the mean F-score of 50% over the state-of-the-art method on this new dataset. We also propose an additional set of annotations to predict students’ engagement while solving a specific problem, and present models that can predict such engagement.
Completing Scientific Facts in Knowledge Graphs of Research Concepts

In the last few years, we have witnessed the emergence of several knowledge graphs that explicitly describe research knowledge with the aim of enabling intelligent systems for supporting and accelerating the scientific process. These resources typically characterize a set of entities in this space (e.g., tasks, methods, evaluation techniques, proteins, chemicals), their relations, and the relevant actors (e.g., researchers, organizations) and documents (e.g., articles, books). However, they are usually very partial representations of the actual research knowledge and may miss several relevant facts. In this paper, we introduce SciCheck, a new triple classification approach for completing scientific statements in knowledge graphs. SciCheck was evaluated against nine alternative approaches on seven benchmarks, yielding excellent results. Finally, we provide a real-world use case and applied SciCheck to the Artificial Intelligence Knowledge Graph (AI-KG), a large-scale automatically-generated open knowledge graph including 1.2M statements extracted from the 333K most cited articles in the field of Artificial Intelligence, and generated a new version of this knowledge graph with 300K additional triples.
A Direction-Decoupled Non-Local Attention Network for Single Image Super-Resolution

The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">non-local attention</i> mechanism has often been exploited in deep learning to capture <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">long-range dependencies</i> (LRDs) from the same image for enhancing the performance of various image processing methods. However, the initially proposed non-local attention process inevitably yields extremely-high computation complexity, since <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">all</i> the feature points are involved in computing the LRDs. To address this concern, a recently proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">criss-cross network</i> (CCNet), which has a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">recurrent criss-cross attention</i> (RCCA) module, is used to compute the LRDs by involving only a small set of feature points for significantly reducing computation. Motivated by the RCCA, a novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">direction-decoupled non-local attention</i> (DNA) module is proposed in this paper that is able to further reduce the computation complexity of RCCA by half approximately. To verify the performance of our new non-local attention module, a DNA network is developed for conducting single image super-resolution (SISR). Extensive experimental results have clearly demonstrated the superiority of using our DNA network for SISR when compared with that of state-of-the-art methods.
Methods for activating professional self-determination of a person at the stage of pre-professional development: the experience of foreign countries

The article presents an overview of modern international studies of the problem of activation of professional self-determination of high school students. The results of the study conducted by Chinese scientists aimed at studying cognitive and affective self-esteem and their role in building the educational and professional path of high school students are presented. The effectiveness of applying relevant interventions in the classroom to enhance the professional interest of high school students in STEM professions, a model that combines natural sciences and engineering subjects into a single system, is analyzed in comparison to the study conducted in Germany. Mentoring is considered as a mechanism for professional self-determination in high school, contributing, in the opinion of Swedish scientists, to solving the problem of a shortage of specialists in the labor market. The article gives a comparative analysis of the systems of academic training and initial vocational education, which provides graduates with a smooth transition to the modern labor market. The experience of Denmark is analyzed, which is an institutional context for the study of inequality associated with vocational education.
Low-Complexity Precision-Scalable Multiply-Accumulate Unit Architectures for Deep Neural Network Accelerators

Precision-scalable deep neural network (DNN) accelerator designs have attracted much research interest. Since the computation of most DNNs is dominated by multiply-accumulate (MAC) operations, designing efficient precision-scalable MAC (PSMAC) units is of central importance. This brief proposes two low-complexity PSMAC unit architectures based on the well-known one, Fusion Unit (FU), which is composed of a few basic units called Bit Bricks (BBs). We first simplify the architecture of BB through optimizing some redundant logic. Then a top-level architecture for PSMAC unit is devised by recursively employing BBs. Accordingly, two low-complexity PSMAC unit architectures are presented for two different kinds of quantization schemes. Moreover, we provide an insight into the decomposed multiplications and further reduce the bitwidths of the two architectures. Experimental results show that our proposed architectures can save up to 44.18% area cost and 45.45% power consumption when compared with the state-of-the-art design.
CrossDet++: Growing Crossline Representation for Object Detection

In object detection, precise object representation is a key factor to successfully classify and locate objects of an image. Existing methods usually use rectangular anchor boxes or a set of points to represent objects. However, these methods either introduce background noise or miss the continuous appearance information inside the object, and thus cause incorrect detection results. In this paper, we propose a novel anchor-free object detection network, called CrossDet++, which uses a set of growing crosslines along horizontal and vertical axes as object representations. An object can be flexibly represented as crosslines in different combinations, which inspires us to select the expressive crossline to effectively reduce the interference of noise. Meanwhile, the crossline representation takes into account the continuous adjacent object information, which is useful to enhance the discriminability of object features and find the object boundaries. Based on the learned crosslines, we propose an axis-query crossline growing module to adaptively capture features of crosslines and query surrounding pixels related to the line features for subsequent growing of crosslines. Their growing offsets and scales can be supervised by a decoupled regression mechanism, which limits the regression target to a specific direction for decreasing the optimization difficulty. During the training, we design a semantic-guided label assignment to emphasize the importance of crossline targets with higher semantic richness, further improving the detection performance. The experiment results demonstrate the effectiveness of our proposed method. Code can be available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/QiuHeqian/CrossDet</uri> .
Exposure-Aware Dynamic Weighted Learning for Single-Shot HDR Imaging

We propose a novel single-shot high dynamic range (HDR) imaging algorithm based on exposure-aware dynamic weighted learning, which reconstructs an HDR image from a spatially varying exposure (SVE) raw image. First, we recover poorly exposed pixels by developing a network that learns local dynamic filters to exploit local neighboring pixels across color channels. Second, we develop another network that combines only valid features in well-exposed regions by learning exposure-aware feature fusion. Third, we synthesize the raw radiance map by adaptively combining the outputs of the two networks that have different characteristics with complementary information. Finally, a full-color HDR image is obtained by interpolating missing color information. Experimental results show that the proposed algorithm significantly outperforms conventional algorithms on various datasets. The source codes and pretrained models are available at https://github.com/viengiaan/EDWL .
pycefr

Python is known to be a versatile language, well suited both for beginners and advanced users. Some elements of the language are easier to understand than others: some are found in any kind of code, while some others are used only by experienced programmers. The use of these elements lead to different ways to code, depending on the experience with the language and the knowledge of its elements, the general programming competence and programming skills, etc. In this paper, we present pycefr, a tool that detects the use of the different elements of the Python language, effectively measuring the level of Python proficiency required to comprehend and deal with a fragment of Python code. Following the well-known Common European Framework of Reference for Languages (CEFR), widely used for natural languages, pycefr categorizes Python code in six levels, depending on the proficiency required to create and understand it. We also discuss different use cases for pycefr: identifying code snippets that can be understood by developers with a certain proficiency, labeling code examples in online resources such as Stackoverflow and GitHub to suit them to a certain level of competency, helping in the onboarding process of new developers in Open Source Software projects, etc. A video shows availability and usage of the tool: https://tinyurl.com/ypdt3fwe.
Recent Advances of Computational Intelligence Techniques for Composing Music

Music exerts a ubiquitous influence on human cultures and daily lives. Composing music is deemed rather complicated because it involves various factors (e.g., instruments, melodies, percussions, and chords) needed to be well coordinated for creating harmony, tension, and emotions. Computational intelligence (CI) has shown its effectiveness in solving complex problems, such as optimization, data modeling, and reasoning. In light of the advantages of CI, a considerable amount of research has been proposed to incorporate CI techniques into music composition applications. The literature shows that evolutionary computation and neural network are very popular in this research area. The present survey reviews the recent studies on music composition using CI techniques, to reflect the methodological advances in the past decade. Particularly, this survey stresses two trends: 1) an increasing interest in deep learning for music composition and 2) the deepened engagement of synergizing domain knowledge, music data, and human interaction. In addition, we provide a taxonomy to classify these studies and discuss the research challenges and future directions.
‘Dial up’ Photonic Integrated Circuit Filter

Photonic integrated circuits (PICs) are capable of providing advanced signal processing functions in small footprints; however, their operation is affected by fabrication tolerances in addition to dynamically changing thermal and acoustic effects. Thus, it can be difficult to tune PICs to accurately implement desired functionality, even those with a relatively small number of photonic components (e.g. 10). In this contribution, we demonstrate a ‘dial-up’ PIC filter that can accurately and reliably implement a wide range of filter functions. It uses an on-chip optical reference path, which allows the phase and amplitude of each tap in a finite impulse response (FIR) filter to be individually determined. The fractional delay of the reference path is chosen to be half the delay between the FIR filter's taps, which upon Fourier transformation enables us to uniquely identify the phase and amplitude of each tap in the FIR filter. This information is used in an independent feedback algorithm to control each tap to ensure that a desired (‘dialed-up’) response is accurately implemented. To demonstrate the flexibility of the method, we experimentally demonstrate functions of a sinc-shaped filter with a phase step, a Hilbert transform and low- and high-pass filters.
Salient-VPR: Salient Weighted Global Descriptor for Visual Place Recognition

Visual place recognition (VPR) is a widely investigated but challenging problem, which must deal with the twin problems of changed appearance caused by varying weather conditions, illumination, and seasons, as well as dynamic objects in complex environments. In this paper, we proposed Salient-VPR by combining image retrieval, semantic information, and saliency cues to achieve accurate estimations. We provides a novel formulation for combining the local semantic features into global descriptors. Unlike traditional global descriptors that typically summarize the whole visual content in the images or image regions, we aggregate dense local semantic descriptors based on pixel-level semantic scores to form global semantic descriptors. Then we calculate predicted saliency descriptors to learn the representation of static objects from images. These salient descriptions are learned from a dataset tailored for VPR missions. Lastly, we introduce a late-fusion module, which increases the stability of the descriptor and avoids performance degradation caused by the limitations of semantic and saliency prediction. Our method outperforms traditional global descriptors in experiments, attaining state-of-the-art visual location recognition scores on a variety of challenging datasets such as Pitts30k, MSLS, and Tokyo24/7.
Research on Joint Extraction Method of Entity and Relation Triples Based on Hierarchical Cascade Labeling

As an important research field of artificial intelligence, knowledge graph develops rapidly, and triplet extraction is the key to the construction of a knowledge graph.The traditional pipeline extraction method will bring the error of entity recognition into the relationship extraction and affects the extraction effect.Besides, the traditional pipeline extraction method cannot solve the SEO (Single Entity Overlap) and EPO (Entity Pair Overlap) problems.Inspired by this, we compare the advantages and disadvantages of the mainstream methods of entity and relationship triples joint extraction, propose a new joint extraction method of entity relation triples based on a hierarchical cascade labeling model (named HCL model), and the HCL model is based on multi neural network cooperation.Further, we construct a balanced sampling Chinese dataset about the entity and relational triplet extraction which contains SEO and EPO.We carry out the experiments on the balanced data set, and the F1 value of the HCL model reaches 65.4% better than other baseline models.
Locally Smoothed Gaussian Process Regression

We develop a novel framework to accelerate Gaussian process regression (GPR). In particular, we consider localization kernels at each data point to down-weigh the contributions from other data points that are far away, and we derive the GPR model stemming from the application of such localization operation. Through a set of experiments, we demonstrate the competitive performance of the proposed approach compared to full GPR, other localized models, and deep Gaussian processes. Crucially, these performances are obtained with considerable speedups compared to standard global GPR due to the sparsification effect of the Gram matrix induced by the localization operation.
Toward Robust Facial Authentication for Low-Power Edge-AI Consumer Devices

Robust authentication for low-power consumer devices without a keyboard remains a challenge.The recent availability of low-power neural accelerator hardware, combined with improvements in neural facial recognition algorithms provides enabling technology for low-power, on-device facial authentication.The present research work explores a number of approaches to test the robustness of a state-of-the-art facial recognition (FR) technique, Arcface for such end-to-end applications.As extreme lighting conditions and facial pose are the two more challenging scenarios for FR we focus on these.Due to the general lack of large-scale multiple-identity datasets, GAN-based re-lighting and pose techniques are used to explore the effects on FR performance.These results are further validated on the best available multi-identity datasets -MultiPIE and BIWI.The results show that FR is quite robust to pose variations up to 45-55 degrees, but the outcomes are not definitive for the tested lighting scenarios.For lighting, the tested GAN-based relighting augmentations show significant effects on FR robustness.However, the lighting scenarios from MultiPIE dataset -the best available public dataset -show some conflicting results.It is unclear if this is due to an incorrectly learned GAN relighting transformation or, alternatively, to mixed ambient/directional lighting scenes in the dataset.However, it is shown that the GAN-induced FR errors for extreme lighting conditions can be corrected by fine-tuning the FR network layers.The conclusions support the feasibility of implementing a robust authentication method for low-power consumer devices.
Lupa

In this paper, we present Lupa --- a platform for large-scale analysis of the programming language usage. Lupa is a command line tool that uses the power of the IntelliJ Platform under the hood, which gives it access to powerful static analysis tools used in modern IDEs. The tool supports custom analyzers that process the rich concrete syntax tree of the code and can calculate its various features: the presence of entities, their dependencies, definition-usage chains, etc. Currently, Lupa supports analyzing Python and Kotlin, but can be extended to other languages supported by IntelliJ-based IDEs. We explain the internals of the tool, show how it can be extended and customized, and describe an example analysis that we carried out with its help: analyzing the syntax of ranges in Kotlin.
A Secure Solution for a Blockchain-Based Consortium Promotional Scheme

Promotional schemes, such as promotional points and coupons, are highly effective marketing tools. Through these schemes, merchants can obtain customer loyalty or attract new customers, and customers can obtain benefits when purchasing goods or services. Therefore, the use of promotional schemes is considered to be a win-win strategy. Promotional points are becoming an increasingly popular way of providing customers with discounts or gifts to incentivise the purchase of some products. However, some security issues should be addressed: forgery, double-spending, privacy, etc. Blockchain is gaining popularity in academic research and business applications, as it has the potential to change business models in numerous sectors of the economy. The characteristics of blockchain (security, immutability, efficiency, etc.) can help to provide secure solutions for blockchain-based applications in the marketing field. In this paper, we propose a multimerchant, blockchain-based promotional point scheme that allows points to be transferred between customers and preserves customers’ privacy.
An Efficient Deep-Learning-Based Super-Resolution Accelerating SoC With Heterogeneous Accelerating and Hierarchical Cache

This article presents an energy-efficient accelerating system-on-chip (SoC) for super-resolution (SR) image reconstruction on a mobile platform. With the rise of contactless communication and streaming services, the need for SR is growing. As one of the most basic low-level image processing algorithms, SR can reconstruct high-quality images from low-quality images which are noisy, compressed, or with damaged pixels. However, a massive amount of computation and considerable precision of pixel data pose challenges for acceleration in a resource and bandwidth constrained platform. SR has high energy consumption and long latency. While previous neural processing units (NPUs) reduced the precision to increase the efficiency and accelerate convolutional neural network (CNN) computation, few of them concentrated on both the output image quality and the performance of the entire system. The proposed SR SoC restores the high-quality image using a precision-optimized SR algorithm on an energy-efficient accelerating architecture and cache subsystem. It contributes three algorithm-hardware co-optimized features: 1) heterogeneous accelerating architecture (HAA) with only 8-bit floating-point (FP)-and-fixed-point (FXP) hybrid-precision for SR task; 2) tile-based hierarchical cache (THC) subsystem for the low energy and small footprint cost layer fusion; and 3) heterogeneous L1 data lifetime-aware optimized cache (DLOC) for the energy-efficient on-chip memory access. The prototype of SR SoC is fabricated in 65-nm technology and occupies a 10.0-mm2 die area. The proposed SR SoC can maintain the high reconstruction quality while consuming only 19% of the energy of an FXP16 system with homogeneous NPU. As a result, the SR SoC presents <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.6\times $ </tex-math></inline-formula> higher energy efficiency than the previous SR targeting NPU and achieves 107-frame-per-second (fps) framerates running <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4\times $ </tex-math></inline-formula> SR image generation to full high definition (FHD) scale at only 0.92-mJ/frame energy consumption.