Reviewerly/exHarmony huggingface dataset:
Deep Layer CNN Architecture for Breast Cancer Histopathology Image Detection

In recent years, there are various improvements in computational image processing methods to assist pathologists in detecting cancer cells. Consequently, deep learning algorithm known as Convolutional Neural Network (CNN) has now become a popular method in the application image detection and analysis using histopathology image (images of tissues and cells). This study presents the histopathology image related to breast cancer cells detection (mitosis and non-mitosis). Mitosis is an important parameter for the prognosis/diagnosis of breast cancer. However, mitosis detection in histopathology image is a challenging problem that needs a deeper investigation. This is because mitosis consists of small objects with a variety of shapes, and is easily confused with some other objects or artefacts present in the image. Hence, this study proposed three types of deep layer CNN architecture which are called 6-layer CNN, 13-layer CNN and 17-layer CNN, respectively in detecting breast cancer cells using histopathology image. The aim of this study is to detect the breast cancer cell which is called mitosis from histopathology image using suitable layer in deep layer CNN with the highest accuracy and True Positive Rate (TPR), and the lowest False Positive Rate (FPR) and loss performances. The result shows a promising performance for deep layer CNN architecture of 17-layer CNN is suitable for this dataset with the highest average accuracy, 84.49% and True Positive Rate (TPR), 80.55%; while the least False Positive Rate (FNR), 11.66% and loss 15.50%.
Smile(y) – and your students will smile with you? the effects of emoticons on impressions, evaluations, and behaviour in staff-to-student communication

focuses in his research on behaviour and emotions stemming from online phenomena, recently within education.
Binary Governance: Lessons from the GDPR's Approach to Algorithmic Accountability

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Partially collapsed Gibbs sampling for latent Dirichlet allocation

A latent Dirichlet allocation (LDA) model is a machine learning technique to identify latent topics from text corpora within a Bayesian hierarchical framework. Current popular inferential methods to fit the LDA model are based on variational Bayesian inference, collapsed Gibbs sampling, or a combination of these. Because these methods assume a unimodal distribution over topics, however, they can suffer from large bias when text corpora consist of various clusters with different topic distributions. This paper proposes an inferential LDA method to efficiently obtain unbiased estimates under flexible modeling for heterogeneous text corpora with the method of partial collapse and the Dirichlet process mixtures. The method is illustrated using a simulation study and an application to a corpus of 1300 documents from neural information processing systems (NIPS) conference articles during the period of 2000–2002 and British Broadcasting Corporation (BBC) news articles during the period of 2004–2005.
Academic library as learning space and as collection: A learning commons' effects on collections and related resources and services

A tremendous amount has been written about the library as a learning space and about this model's two most popular outgrowths, the information commons and the learning commons. Little to nothing, however, has been written about how reshaping an academic/research library and repurposing library space affects the library as a collection, its resources, and its collections-related services. This study looks at the immediate impact of opening a learning commons in an academic/research library on circulation, document delivery and interlibrary loan requests for returnables, and on- and off-campus database accesses at one institution. Library Collection Management; Assessment
End-to-end Language Recognition Using Attention Based Hierarchical Gated Recurrent Unit Models

The task of automatic language identification (LID) involving multiple dialects of the same language family on short speech recordings is a challenging problem. This can be further complicated for short-duration audio snippets in the presence of noise sources. In these scenarios, the identity of the language/dialect may be reliably present only in parts of the speech embedded in the temporal sequence. The conventional approaches to LID (and for speaker recognition) ignore the sequence information by extracting long-term statistical summary of the recording assuming an independence of the feature frames. In this paper, we propose to develop an end-to-end neural network framework utilizing short-sequence information in language recognition. A hierarchical gated recurrent unit (HGRU) model with attention module is proposed for incorporating relevance in language recognition, where parts of speech data are weighted more based on their relevance for the language recognition task. Experiments are performed using the language recognition task in NIST LRE 2017 Challenge using clean, noisy and multi-speaker speech data. In these experiments, the proposed approach yields significant improvements over the conventional i-vector based language recognition approaches as well as previously proposed approach to language recognition using recurrent networks.
Fast DENSER: Efficient Deep NeuroEvolution

The search for Artificial Neural Networks (ANNs) that are effective in solving a particular task is a long and time consuming trial-and-error process where we have to make decisions about the topology of the network, learning algorithm, and numerical parameters. To ease this process, we can resort to methods that seek to automatically optimise either the topology or simultaneously the topology and learning parameters of ANNs. The main issue of such approaches is that they require large amounts of computational resources, and take a long time to generate a solution that is considered acceptable for the problem at hand. The current paper extends Deep Evolutionary Network Structured Representation (DENSER): a general-purpose NeuroEvolution (NE) approach that combines the principles of Genetic Algorithms with Grammatical Evolution; to adapt DENSER to optimise networks of different structures, or to solve various problems the user only needs to change the grammar that is specified in a text human-readable format. The new method, Fast DENSER (F-DENSER), speeds up DENSER, and adds another representation-level that allows the connectivity of the layers to be evolved. The results demonstrate that F-DENSER has a speedup of 20 times when compared to the time DENSER takes to find the best solutions. Concerning the effectiveness of the approach, the results are highly competitive with the state-of-the-art, with the best performing network reporting an average test accuracy of 91.46% on CIFAR-10. This is particularly remarkable since the reduction in the running time does not compromise the performance of the generated solutions.
Augmented Reality Brings the Real World into Natural History Dioramas with Data Visualizations and Bioacoustics at the Carnegie Museum of Natural History

Abstract This paper introduces the AR Perpetual Garden App and highlights reasons why it is unique and important in its use of immersive, augmented reality for informal learning. The design and construction of the application created a virtual diorama based on facts, using realistic 3D models and accurate perspective. Design affordances permit annotations with interactive and multimodal facts and stories, delivering interactive knowledge artifacts. The app combines real and virtual into one holistic and comprehensive learning experience, transferring scientific knowledge to the novice in an instant. The use of immersive augmented reality produced from accurate information, data visualizations, and bioacoustics creates context‐sensitivity experiences inside the gallery and outside in the gardens. Distribution through the Apple and Google app stores to schools and homes can extend the broader impact of the educational mission of the Carnegie Museum of Natural History and its biological research center, the Powdermill Nature Reserve.
Neuromorphic Vision Sensing for CNN-based Action Recognition

Neuromorphic vision sensing (NVS) hardware is now gaining traction as a low-power/high-speed visual sensing technology that circumvents the limitations of conventional active pixel sensing (APS) cameras. While object detection and tracking models have been investigated in conjunction with NVS, there is currently little work on NVS for higher-level semantic tasks, such as action recognition. Contrary to recent work that considers homogeneous transfer between flow domains (optical flow to motion vectors), we propose to embed an NVS emulator into a multi-modal transfer learning framework that carries out heterogeneous transfer from optical flow to NVS. The potential of our framework is showcased by the fact that, for the first time, our NVS-based results achieve comparable action recognition performance to motion-vector or optical-flow based methods (i.e., accuracy on UCF-101 within 8.8% of I3D with optical flow), with the NVS emulator and NVS camera hardware offering 3 to 6 orders of magnitude faster frame generation (respectively) compared to standard Brox optical flow. Beyond this significant advantage, our CNN processing is found to have the lowest total GFLOP count against all competing methods (up to 7.7 times complexity saving compared to I3D with optical flow).
TU-Net and TDeepLab: Deep Learning-Based Terrain Classification Robust to Illumination Changes, Combining Visible and Thermal Imagery

In this paper we propose two novel deep learning-based terrain classification methods robust to illumination changes. The use of cameras is challenged by a variety of factors, of most importance being the changes in illumination. On the other hand, since the temperature of various types of terrains depends on the thermal characteristics of the terrain, the terrain classification can be aided by utilizing the thermal information in addition to visible information. Thus we propose 'TU-Net (Two U-Net)' based on the U-Net and 'TDeepLab (Two DeepLab)' based on DeepLab, which combine visible and thermal images and train the network robust to illumination changes implicitly. To improve the network's learning capability, we expand the proposed methods to the Siamese-based method, which explicitly trains the network to be robust to illumination changes. We also investigate multiple options to fuse the visible and thermal images at at the bottom layer, middle layer, or the top layer of the network. We evaluate the proposed methods with a challenging new dataset consisting of visible and thermal images, which were collected from 10 am till 5 pm (after sunset), and we show the effectiveness of the proposed methods.
Training binary neural networks with knowledge transfer

Binary Neural Networks (BNNs) use binary values for both weights and activations instead of 32 bit floating point numbers typically used in deep neural networks. This reduces the memory footprint by a factor of 32 and allows a very efficient implementation in hardware. BNNs are trained using regular gradient descent but are harder to optimise, take longer to train and generally require a more careful tuning of hyperparameters such as the learning rate decay schedule than floating point versions. We propose to use Knowledge Transfer techniques to make it easier to train BNNs. Knowledge transfer is a general technique that tries to transfer the knowledge stored in a large network (the teacher) to a smaller (student) network. In our case the teacher is a network trained with floating point weights and activations while the student is a BNN. We apply different Knowledge Transfer techniques to the task of training a BNN. We introduce a novel similarity based Knowledge Transfer algorithm and show that this technique results in a higher test accuracy on different benchmark datasets compared to training the BNN from scratch.
Centralized and Clustered Features for Person Re-Identification

Extracting trusted label from unlabeled data and enhancing the discriminative ability of features are essential issues in person re-identification. The latest study shows that the progressive unsupervised learning has a good performance, and its advantage is specially shown in the ability to select reliable samples from the unlabeled dataset by clustering pedestrian features. Since the features extracted from the same pedestrian samples by the convolutional neural network trained by a softmax loss function may not have good clustering properties, a centralized clustering loss function (CCLF) is proposed in the letter. During the training process, CCLF makes the features of the same category close to the clustering center selected by $k$-means++, and implements it by adding a distance penalty term on softmax. In the evaluation stage, the study finds that the features have better clustering characteristics and the discrimination is enhanced. The experiments were evaluated on datasets CHUK03, Market1501, and DukeMTMC-reID to verify the superiority of CCLF.
Encoding Materials and Data for Iterative Personalization

Data is changing how we design consumer products. Shoe production is a prime example of this; foot size, footstep pressure and personal preferences can be used to design personalized shoes. Research done around metamaterials, programming materials and computational composites illustrate the possibilities of creating complex data & material relationships. These new relationships allow us to look at future products almost like software apps, becoming a kind of product service systems, where the focus is on its iterative personalization improvement over time. Can we create systems of such data driven objects that in turn allow us to design new objects that are informed by the data trail? In this paper we report on four RtD project iterations that explore this challenge and provide a set of insights on how to close this new iterative loop.
Beyond Schematic Capture

Printed Circuit Board (PCB) design tools are critical in helping users build non-trivial electronics devices. While recent work recognizes deficiencies with current tools and explores novel methods, little has been done to understand modern designers and their needs. To gain better insight into their practices, we interview fifteen electronics designers of a variety of backgrounds. Our open-ended, semi-structured interviews examine both overarching design flows and details of individual steps. One major finding was that most creative engineering work happens during system architecture, yet current tools operate at lower abstraction levels and create significant tedious work for designers. From that insight, we conceptualize abstractions and primitives for higher-level tools and elicit feedback from our participants on clickthrough mockups of design flows through an example project. We close with our observation on opportunities for improving board design tools and discuss generalizability of our findings beyond the electronics domain.
A Multifeature Hierarchical Locating Algorithm for Hexagon Nut of Railway Fasteners

Fasteners are an important part of railway security. The application of computer vision and image processing on fastener detection has become a research hot spot. However, most of the researches have focused on the missing and damage detection of fasteners up to now. In this paper, a method based on image processing is proposed to locate the center of the hexagon nut in the railway fastener. The precise position of the hexagon nut can be used for the automatic replacement of the nut, which is of great significance for the automatic railway construction and maintenance. The locating method proposed in this paper can be divided into two steps. First, the improved Canny detector which combines surrounding suppression and iterative excitation is used to extract the contours of the fastener. Then, a multifeature hierarchical locating (MHL) algorithm is proposed, which integrates the circle contour of the gasket and the hexagon features of the nut. The experimental results show that the improved Canny detector suppresses noises effectively and highlights the contours of fastener. The MHL algorithm in the next step improves the locating accuracy remarkably while guaranteeing the robustness meantime.
No-reference perceptual quality assessment of stereoscopic images based on binocular visual characteristics

Perceptual quality assessment of distorted stereoscopic images has attracted considerable attention and become an important yet challenging task in 3D multimedia applications. In this paper, we propose a blind/no-reference stereoscopic 3D image quality assessment scheme that utilizes binocular visual characteristics. The design of this scheme is motivated by studies on the perception of distorted stereoscopic images. The major technical contribution of this paper is the introduction of binocular features and binocular energy for stereoscopic image quality prediction. To be more specific, the proposed scheme extracts the local magnitude, local phase and visual saliency from a stereopair as binocular features. In addition, the binocular energy responses are also obtained as quality-predictive features. After feature extraction, a statistical regression model is used to map the features of distorted stereoscopic image to its subjective quality score. The effectiveness of the designed metric is verified on the publicly available 3D image quality assessment databases. Experimental results show that the proposed scheme achieves superiority over other related algorithms in terms of consistent alignment with subjective assessment of stereoscopic 3D images.
<scp>FPGA</scp> implementation of chaos‐based high‐speed true random number generator

Abstract A true random number generator (TRNG) is a basic building block of many modern cryptographic systems. As field programmable gate array (FPGA) has a flexible architecture and low‐cost test cycle, hence, it becomes an ideal platform for hardware implementation of digital systems. This paper presents an FPGA implementation of a high‐speed TRNG that is based on a chaotic oscillator at 100 MHz frequency with speed of 1600 Mbps. The experimental results show that the proposed generator is faster and more compact than the existing chaotic ring‐oscillator‐based TRNGs, and further, it is verified that the generated bit sequences pass all TRNG tests in National Institute of Standards and Technology (NIST SP 800‐22). The proposed TRNG is implemented in two FPGA families: Nexys 4 DDR XC7A100TCSG‐1 (Artix 7) and Basys 3 XC7A35T1CPG236C (Artix 7) Xilinx Vivado v.2017.3 design suite.
Supergene remobilization of Au in Au-bearing regolith related to orogenic deposits: A case study from Kazakhstan

Our study of the primary and supergene gold from the Suzdal and Raigorodok deposits in Kazakhstan confirms that during the formation of the regolith in both deposits, invisible and fine gold is released from the host sulfide minerals during their oxidation and destruction; and transported in the form of soluble thiosulfate complexes by near-surface solutions. Upon contact with a geochemical barrier, gold can be deposited as crystalline aggregates. In the deposition of this gold the role of geochemical barriers, such as variations of redox, pH and sorption are very important. Evidence of the past processes of dissolution, redistribution and aggregation of gold in the regolith in these deposits is provided by the following features: 1) The presence of dissolution and growth structures on the surface of newly formed gold; 2) The size of the gold grains increases and newly formed gold microcrystals appear on the surface of the grains; 3) Changes in the composition of native gold (e.g. decrease of silver content); 4) The accumulation of gold in subhorizontal localized layers within the regolith; 5) Gold association with supergene minerals (limonite, jarosite, goethite, Mn-hydroxides, opal, gypsum, calcite, clay minerals, etc.). This detailed study of the specific compositional and structural features of supergene gold and the physicochemical conditions of regolith environment allowed the recognition of the releasing, dissolution, transfer and aggregation processes of gold in the regolith.
The influence of snippet length on user behavior in mobile web search

Purpose Web search is more and more moving into mobile contexts. However, screen size of mobile devices is limited and search engine result pages face a trade-off between offering informative snippets and optimal use of space. One factor clearly influencing this trade-off is snippet length. The purpose of this paper is to find out what snippet size to use in mobile web search. Design/methodology/approach For this purpose, an eye-tracking experiment was conducted showing participants search interfaces with snippets of one, three or five lines on a mobile device to analyze 17 dependent variables. In total, 31 participants took part in the study. Each of the participants solved informational and navigational tasks. Findings Results indicate a strong influence of page fold on scrolling behavior and attention distribution across search results. Regardless of query type, short snippets seem to provide too little information about the result, so that search performance and subjective measures are negatively affected. Long snippets of five lines lead to better performance than medium snippets for navigational queries, but to worse performance for informational queries. Originality/value Although space in mobile search is limited, this study shows that longer snippets improve usability and user experience. It further emphasizes that page fold plays a stronger role in mobile than in desktop search for attention distribution.
A Novel Inpainting Algorithm for Recovering Landsat-7 ETM+ SLC-OFF Images Based on the Low-Rank Approximate Regularization Method of Dictionary Learning With Nonlocal and Nonconvex Models

On May 31, 2003, the scan line corrector (SLC) of the Enhanced Thematic Mapper Plus (ETM+) on-board the Landsat-7 satellite failed, resulting in strips of data lost in all ETM+ images acquired since then. In this paper, we proposed a novel inpainting algorithm for recovering the ETM+ SLC-off images. The two slopes of the boundaries of each missing stripe were extracted through the Hough transform, ignoring the slope of the edge of the strip that overlaps the edge of the image. An adaptive dictionary was then developed and trained using ETM+ SLC-on images acquired before May 31, 2003 so that the physical characteristics and geometric features of the ground coverage of the data-missing strips can be considered during recovery. To make the algorithm computationally efficient, data-missing strips were repaired along their slope directions by using the logdet $\left ({\cdot }\right)$ low-rank nonconvex model along with the dictionary. The algorithm was tested using the simulated ETM+ SLC-off images created from a multiband ETM+ SLC-on image file and compared to the high accuracy low-rank tensor completion (HaLRTC), logDet, and tensor nuclear norm (TNN) algorithms. The results show that the ETM+ images restored using the new algorithm have lower RMSE, higher PSNR and structure similarity (SSIM) values, and better visualization. These results indicate that the new algorithm performs better than the other three algorithms and can efficiently and accurately restore the data-missing stripes.