Reviewerly/exHarmony huggingface dataset:
Contrastive Data Learning for Facial Pose and Illumination Normalization

Face normalization can be a crucial step when handling generic face recognition. We propose the Pose and Illumination Normalization (PIN) framework with contrast data learning for face normalization. The PIN framework is designed to learn the transformation from a source set to a target set. The source set and the target set compose a contrastive data set for learning. The source set contains faces collected in the wild and thus covers a wide range of variation across illumination, pose, expression and other variables. The target set contains face images taken under controlled conditions and all faces are in frontal pose and balanced in illumination. The PIN framework is composed of an encoder, a decoder and two discriminators. The encoder is made of a state-of-the-art face recognition network and acts as a facial feature extractor, which is not updated during training. The decoder is trained on both the source and target sets, and aims to learn the transformation from the source set to the target set; and therefore, it can transform an arbitrary face into a illumination and pose normalized face. The discriminators are trained to ensure the photo-realistic quality of the normalized face images generated by the decoder. The loss functions employed in the decoder and discriminators are appropriately designed and weighted for yielding better normalization outcomes and recognition performance. We verify the performance of the propose framework on several benchmark databases, and compare with state-of-the-art approaches.
Aggregation Architecture and all-to-one Network for Real-Time Semantic Segmentation

Deep convolutional neural network has demonstrated its out-standing performance in the field of image semantic segmentation. However, the enormous computational complexity of existing high-precision networks limits the application of the model in real-time segmentation tasks. How to achieve a good trade-off between accuracy and speed becomes a challenge. Existing solutions can be roughly divided into three categories according to the network architecture: dilation, encoder-decoder, and multi-pathway, each of which has its advantages. In this paper, we make the following contributions: (i) First, unlike the previous three architectures, we propose a new aggregation architecture as the network back-bone. (ii) Second, a multi-level auxiliary loss design model is used for the training phase, which can improve the model segmentation effect. (iii) According to this aggregation structure, an all-to-one network (ATONet) for real-time semantic segmentation is proposed, which achieves a good trade-off between speed and accuracy by assembling the features of all blocks. (iv) Finally, the proposed network achieves the accuracy of 74.4% and 70.1% mIoU with the inference speed of 42.7 FPS and 93.5 FPS on the Cityscapes and CamVid datasets.
Detection of Cell Nuclei using LadderNet

Cytology which directly examines cells in the early detection of cancer plays an important role but this diagnosis depends on the experience and technology of a pathologist. The problem is that it takes time and the objectivity is poor. An automatic diagnosis system for pathologically diagnosing cancer is necessary to solve these problems. To classify cells into benign or malignant automatically, it is important to detect the cell nucleus from the cell image in advance. This paper proposes a new approach to detect cell nuclei for automatically using LadderNet which is a recent extension model of U-Net used in the Deep Learning Approach.
Research on The Maximization of The Equilibrium Influence of Multi-Information Competition Based on Greedy Algorithm

In the information age, with the continuous improvement of Internet technology, online information has gradually changed people's way of life and work. Therefore, after understanding the background of the spread of multi-information competition, this paper refers to the influence algorithm based on the cost control mechanism, and studies the maximization of the game equilibrium influence of multi-information competition spread based on the greedy algorithm.
Statistical and Geometrical Alignment for Unsupervised Deep Domain Adaptation

The concept of domain adaptation (DA) is based on the principle of information transfer from one domain (usually called source domain) to a different but related other domain (usually called target domain). The past few years have seen a considerable inclination towards the development of deep-domain adaptation approaches. The majority of the existing approaches minimize distribution divergence between both domains to learn shared feature space. One of the shortcomings of existing domain alignment methods is their inability to preserve the original similarity of the data samples in the learned hyperplane. Due to which, data samples far from their respective class distribution or near the edge of it can be incorrectly classified in the hyperplane learned from both domain data distribution. In this work, we propose a novel deep learning approach called Statistical and Geometric Alignment for Unsupervised Deep Domain Adaptation (SGA-UDDA) that minimizes the geometric and statistical shifts between both domains and makes the target domain classifier more robust. A substantial amount of experiments have validated that our proposed SGA-UDDA algorithm surpasses many advanced deep-domain adaptations and statistical domain adaptation methods when the tests were conducted on real-world data-sets.
Improving Identification of System-Directed Speech Utterances by Deep Learning of ASR-Based Word Embeddings and Confidence Metrics

In this paper, we extend our previous work on the detection of system-directed speech utterances. This type of binary classification can be used by virtual assistants to create a more natural and fluid interaction between the system and the user. We explore two methods that both improve the Equal-Error-Rate (EER) performance of the previous model. The first exploits the supplementary information independently captured by ASR models through integrating ASR decoder-based features as additional inputs to the final classification stage of the model. This relatively improves EER performance by 13%. The second proposed method further integrates word embeddings into the architecture and, when combined with the first method, achieves a significant EER performance improvement of 48%, relative to that of the baseline.
A General End-to-End Method for Characterizing Neuropsychiatric Disorders using Free-Viewing Visual Scanning Tasks

The growing availability of eye-gaze tracking technology has allowed for its employment in a wide variety of applications, one of which is the objective diagnosis and monitoring of neuropsychiatric disorders from features of attentional bias extracted from visual scanning patterns. Current techniques in this field are largely comprised of non-generalizable methodologies that rely on domain expertise and study-specific assumptions. In this paper, we present a general, data-driven, end-to-end framework that extracts relevant features of attentional bias from visual scanning behaviour and uses these features to classify between subject groups with standard machine learning techniques. The general framework uses visual scanning data from free-viewing tasks. In these tasks, subjects look at sets of slides with several thematic images while their visual scanning patterns (sets of ordered fixations) are monitored by an eye-tracking system. Subjects' fixations are encoded into relative visual attention maps (RVAMs), and two data-driven methods are proposed to segment regions of interests (ROIs) from RVAMs: 1) using group average RVAMs, and 2) using differences of group average RVAMs. Relative fixation times within the segmented ROIs are then used as input features for a vanilla multilayered perceptron to classify between patient groups. The methods were evaluated on data from two studies: an anorexia nervosa (AN)/healthy controls study (AN study) with 37 subjects, and a bipolar disorder (BD)/major depressive disorder (MDD) study (BD-MDD study) with 73 subjects. Using leave-one-subject-out cross validation, the general methods achieved an area under the receiver operating curve (AUROC) score of 0.935 for the AN study and 0.888 for the BD-MDD study, the latter of which exceeds the performance of the state-of-the-art analysis model designed specifically for the BD-MDD study, which had an AUROC of 0.879. The results validate the proposed framework's efficacy as a generalizable, standard baseline for analyzing visual scanning data.
Unsupervised Feature Learning for Manipulation with Contrastive Domain Randomization

Robotic tasks such as manipulation with visual inputs require image features that capture the physical properties of the scene, e.g., the position and configuration of objects. Recently, it has been suggested to learn such features in an unsupervised manner from simulated, self-supervised, robot interaction; the idea being that high-level physical properties are well captured by modern physical simulators, and their representation from visual inputs may transfer well to the real world. In particular, learning methods based on noise contrastive estimation have shown promising results. To robustify the simulation-to-real transfer, domain randomization (DR) was suggested for learning features that are invariant to irrelevant visual properties such as textures or lighting. In this work, however, we show that a naive application of DR to unsupervised learning based on contrastive estimation does not promote invariance, as the loss function maximizes mutual information between the features and both the relevant and irrelevant visual properties. We propose a simple modification of the contrastive loss to fix this, exploiting the fact that we can control the simulated randomization of visual properties. Our approach learns physical features that are significantly more robust to visual domain variation, as we demonstrate using both rigid and non-rigid objects.
ActPad– A Smart Desk Platform to Enable User Interaction with IoT Devices

ActPad is a desk pad, capable of sensing capacitive touch input in desk setups. Our prototype can sense touches on both, its electrodes and on connected objects. ActPad’s interaction-space is customizable, allowing easy integration and extension of existing desk environments. In smart environments, users may interact with more than one device at the same time. This generates the need for new interaction mechanisms that bundle the control of multiple ubiquitous devices. We support this need through a platform that extends interaction with IoT devices. ActPad accounts for different ways of controlling IoT devices by enabling various modes of interaction – in particular simultaneous, sequential, implicit and explicit – and, hence, a rich input space. As a proof of concept, we illustrate several use cases, including, but not limited to, controlling the browser on a PC, turning lights on/off, switching songs, or preparing coffee.
Texture and exposure awareness based refill for HDRI reconstruction of saturated and occluded areas

High-dynamic-range image (HDRI) displays scenes as vivid as the real scenes. HDRI can be reconstructed by fusing a set of bracketed-exposure low-dynamic-range images (LDRI). For the reconstruction, many works succeed in removing the ghost artefacts caused by moving objects. The critical issue is reconstructing the areas which are saturated due to bad exposure and occluded due to motion with no ghost artefacts. To overcome this issue, this paper proposes texture and exposure awareness based refill. The proposed work first locates the saturated and occluded areas existing in input image set, then refills background textures or patches containing rough exposure and colour information into located areas. Proposed work can be integrated with multiple existing ghost removal works to improve the reconstruction result. Experimental results show that proposed work removes the ghost artefacts caused by saturated and occluded areas in subjective evaluation. For the objective evaluation, the proposed work improves the HDR-VDP-2 evaluation result for multiple conventional works by 1.33% on average.
On Information Asymmetry in Online Reinforcement Learning

In this work, we study the system of two interacting non-cooperative Q-learning agents, where one agent has the privilege of observing the other's actions. We show that this information asymmetry can lead to a stable outcome of population learning, which does not occur in an environment of general independent learners. Furthermore, we discuss the resulted post-learning policies, show that they are almost optimal in the underlying game sense, and provide numerical hints of almost welfare-optimal of the resulted policies.
Mediated Effect of Blended-Learning Strategy in Relationship between Adult Learner-Based Interaction and Learning Effectiveness: New Appointed Fire-Fighter Education

The purpose of this study is to examine the mediated effect of the blended-learning strategy in the relationship between the interaction and learning effectiveness of new firefighters. The Baron and Kenny method of mediated effect analysis was applied to 259 collected samples, and SPSS 18.0 was used to perform linear regression analysis. It was found that the blended-learning group was more effective in learning than the non-blended-learning group. The interaction and learning effectiveness between the targets showed significant results, and the learning strategy of blended-learning exhibited partial mediated effects in the relationship between interaction and learning effectiveness. In particular, the learning attitude of the learning strategy was the combination of learners, learning content, and learning effectiveness during an interaction and partial mediated effects on learning persistence were identified. Based on these results, practical implications on envisioning teaching and learning design from the learner's perspective as a learning strategy to enhance learning effectiveness in a blended-learning situation are presented.
Design and Development of a Loader Bucket Wheelbarrow

This paper briefly introduces the design and analysis of developing the loader bucket at the front of the wheelbarrow to create an assisting system for lifting load into the wheelbarrow. To operate the wheelbarrow, the user has two jobs, which is to manoeuvre the wheelbarrow and scoop the load using a shovel or lifting the load using bare hands. The process of loading and unloading the wheelbarrow could cause discomfort to the waist area for prolonged and repeating work for many hours throughout the day. Moreover, the ergonomic factor is also being considered when using a shovel or even lifting some load using bare hands. Furthermore, to overcome this problem, the wheelbarrow is designed to have its loader bucket at the front of it to do the jobs. The six-point linkages, a pneumatic actuator is assembled with the loader bucket and being controlled by an electric motor. This design has made the jobs easier, effortless, and very safe to the users. The design process starts with data collection of necessities from the users to have a clear problem statement of the current wheelbarrow. Then generations of some ideas of the loader bucket are made, and some design tools assessed them until the final conceptual design created by a CAD software. On a final note, to verify the design, the final design was analysed in terms of its strength through stress and deformation analysis by using a CAD software as well. The result of the whole assembly of loader bucket wheelbarrow after modification is acceptable with the deformation for stress is 6.297e + 07 N/m2, the deformation for displacement is 2.062 mm and the strain is 6.247e-04.
Establishing and developing the system of libraries of the USSR Academy of Sciences regional branches: the 1930s-1945

The article is devoted to organizing the work of the USSR Academy of Sciences (AS) libraries in the 1930s, while creating a network of scientific branches and bases of AS in the country regions. The main goal of the created library system was to provide scientists working far from Moscow and Leningrad with scientific publications according with the specialization of newly created scientific institutions. The article attempts to recall how the expansion of the system of academic libraries in the USSR has started, and provides real data on their work. The authors overview the academic organizations created on the periphery, describes the processes of acquisitions and methodological management of libraries of branches and bases of the USSR Academy of Sciences.The principles of formation of science library network proved to be effective and the system of academic libraries expanded successfully for decades. The factual base of the article were the documents found in the archives of the Library of Natural Sciences RAS, in the Moscow and St. Petersburg Archives of the Russian Academy of Sciences, in some regional archives and materials available on the Internet sites. The authors used a general scientific methodology, applying source study, archaeographic and analytical-synthetic methods of working with archival documents. The article is devoted to organizing the work of the USSR Academy of Sciences (AS) libraries in the 1930s, while creating a network of scientific branches and bases of AS in the country regions. The main goal of the created library system was to provide scientists working far from Moscow and Leningrad with scientific publications according with the specialization of newly created scientific institutions. The article attempts to recall how the expansion of the system of academic libraries in the USSR has started, and provides real data on their work. The authors overview the academic organizations created on the periphery, describes the processes of acquisitions and methodological management of libraries of branches and bases of the USSR Academy of Sciences.The principles of formation of science library network proved to be effective and the system of academic libraries expanded successfully for decades. The factual base of the article were the documents found in the archives of the Library of Natural Sciences RAS, in the Moscow and St. Petersburg Archives of the Russian Academy of Sciences, in some regional archives and materials available on the Internet sites. The authors used a general scientific methodology, applying source study, archaeographic and analytical-synthetic methods of working with archival documents.
Large-Scale Instance Selection using Center of Principal Components

One-hot encoding is one of the most popular data preprocessing methods used to convert categorical data into numerical data before applying a training classification model. Drawbacks of one-hot encoding are its high memory consumption and large storage requirements when the training set has many categorical columns. To overcome these drawbacks, we proposed the PC-CS method to reduce the training set size by only selecting principal components center as a representative instance of each disjoint partition. The proposed method was compared with two instance selection methods and the full training model as the baseline model. We used five datasets from the UCI dataset repository website. Evaluation of the classification performance involved four classifier algorithms: decision tree, naive Bayes, support vector machine, and logistic regression. The average classification performance of the proposed PC-CS method was approximately 2% higher than those of the other algorithms tested, while the reduction rate was about 5% higher.
IllumiWand: Improving 3D Interaction with Monoscopic Displays Through a Projected Physical 3D Pointer

This work presents a system concept and implementation that targets the improvement of spatial perception when interacting with virtual objects displayed perspectively on a monoscopic display. The IllumiWand is an illuminated rod that can be partially made transparent to occlude or show (parts of) virtual objects the rod intersects with. As the rod is operated in front of the 2D display, it affords spatial perception of objects in the volume that can be swept with it between the user and the display. Our concept extends previous work on hover space systems by using the volume in front of a display as an additional medium to provide spatial visual cues.
Decoupled Self-attention Module for Person Re-identification

Person re-identification aims to identify the same person from different cameras, which needs to integrate whole-body information and capture global correlation. However, convolutional neural network is able to only capture short-distance information because of the size of filters. Self-attention is introduced to capture long-distance correlation, but inner-product similarity calculation in self-attention mingles semantic response and semantic difference together. Semantic difference is more important for person re-identification, because it is robust to illumination without the effect of semantic response. However, we find the scale of norms measuring semantic response is much larger than angle measuring semantic difference by decoupling inner-product similarity into norms and angle. To balance the importance of semantic response and semantic difference in self-attention, we propose the decoupled self-attention module for person re-identification to make the most of self-attention. Extensive experiments show that the decoupled self-attention module obtains significant performance with easier convergence and stronger robustness.
International Training Programs IT Security System for Specialists in Onboard Systems

The relevance of the research topic of this article is due to the fact that the problem of developing and implementing distance learning technologies in the field of higher education is particularly acute for the Russian education system due to increasing requirements for the quality, volume and pace of the educational process and dissatisfaction with the existing system of higher education. The primary task in the process of implementation and functioning of distance learning mechanisms is to ensure information security. The authors developed models and processes of activities related to distance learning at the University. The problems of information protection in the developed models are solved using the most modern technologies and approaches. An important aspect of the educational activity of a modern University is the implementation of training programs with the possibility of obtaining two state diplomas. Such programs involve training in internal or external exchange, and widely use remote technologies.
Research on Intelligent Online Test System of Linear Algebra Based on Mathematical Modeling

Linear algebra is a professional basic course for science and engineering students in colleges and universities. This course focuses on discussing the linear theory of finite dimensional space, which is highly logical and abstract. Examination is an important way and means to urge students to study consciously and check the effects of teaching and learning. It is also an effective guiding measure for learning behavior, and it is also a method often used to distinguish, discover and select talents. It is an important measure to integrate the idea of mathematical modeling into the reform of mathematics teaching in colleges and universities. Based on the idea of mathematical modeling, this paper calculates the assessment parameters by MCMC method, and realizes the accurate test of online learning achievement. Experimental results show that user satisfaction and performance are high.