Reviewerly/exHarmony huggingface dataset:
Pods – A novel intelligent energy efficient and dynamic frequency scalings for multi-core embedded architectures in an IoT environment

In the Advent of the Internet of Things (IoT), embedded architecture takes an important dimension in terms of energy and accomplishment. The embedded system needs more and more intelligent algorithms for better performance and energy efficiency to fit into an IoT scenario. Moreover, with the existence of high-performance multi-core embedded architectures, achievements of energy efficiency remains in the dark side of the research. Several algorithms such as dynamic frequency scaling, thread mapping, starvation methodologies were proposed in embedded architectures for efficient usages of clock frequencies and these features were used as the energy saving modes in which the consumption of energy in the embedded architectures are being controlled. But these methods have several backlogs which permits the use of consumption in the embedded architectures. Considering the above features, this paper proposes a new methodology PODS(Predictors for Optimized Dynamic Scaling) which integrates a powerful machine learning algorithm for scaling the clock frequencies by the input workloads and allocation of the core depending based on the workload. The proposed framework PODS has different phases of working namely workload extraction, characterization, and optimization using BAT algorithms and prediction extreme Machine - Learning. The algorithm was tested on ARM/Cortex architectures (Raspberry Pi 3 Model B+), an evaluated algorithm using the IoMT benchmarks and various parameters that include energy consumption, accuracy of detection/prediction was determined and analyzed. It is found that the implementation of the proposed framework in the test is seen resulting between 35 and 40% reduction in the consumption of the power.
Joint consensus and diversity for multi-view semi-supervised classification

As data can be acquired in an ever-increasing number of ways, multi-view data is becoming more and more available. Considering the high price of labeling data in many machine learning applications, we focus on multi-view semi-supervised classification problem. To address this problem, in this paper, we propose a method called joint consensus and diversity for multi-view semi-supervised classification, which learns a common label matrix for all training samples and view-specific classifiers simultaneously. A novel classification loss named probabilistic square hinge loss is proposed, which avoids the incorrect penalization problem and characterizes the contribution of training samples according to its uncertainty. Power mean is introduced to incorporate the losses of different views, which contains the auto-weighted strategy as a special case and distinguishes the importance of various views. To solve the non-convex minimization problem, we prove that its solution can be obtained from another problem with introduced variables. And an efficient algorithm with proved convergence is developed for optimization. Extensive experimental results on nine datasets demonstrate the effectiveness of the proposed algorithm.
Unlearning through Mad Studies: Disruptive pedagogical praxis

Medical discourse currently dominates as the defining framework for madness in educational praxis. Consequently, ideas rooted in a mental health/illness binary abound in higher learning, as both curriculum content and through institutional procedures that reinforce structures of normalcy. While madness, then, is included in university spaces, this inclusion proceeds in ways that continue to pathologize madness and disenfranchise mad people. This paper offers Mad Studies as an alternative entry point for engaging with madness in higher education, arguing that centring madness in pedagogical praxis has the potential to interrupt hegemonic ways of knowing, being, and learning. We illustrate how this disruption is facilitated by examining particular aspects of pedagogical praxis mobilized in Mad Studies, including building curriculum alongside mad community, centring madness in course design and student assessment, and the practice of mad positivity. Ultimately, this approach provides a metacurriculum of unlearning, challenging students to consider how their engagement with madness in the classroom, and beyond, has the potential to disrupt sanist systems of oppression and the normalcy they reconstitute.
Human Action Recognition Using Convolutional Neural Network and Depth Sensor Data

The paper proposes a technique for Human Action Recognition (HAR) that uses a Convolutional Neural Network (CNN). Depth data sequences from the motion sensing devices are converted into images and fed into a CNN rather than using any conventional or statistical method. The initial data was obtained from 10 actions performed by six subjects captured by the Kinect v2 sensor as well as 20 actions performed by 7 subjects from the MSR 3D Action data set. A custom CNN architecture consisting of three convolutional and three max pooling layers followed by a fully connected layer was used. Training, validation, and testing was carried out on a total of 39715 images. An accuracy of 97.23% was achieved on the Kinect data set. On the MSR data set the accuracy was 87.1%.
Assessing the quality of mobile graphical user interfaces using multi-objective optimization

Aesthetic defects are a violation of quality attributes that are symptoms of bad interface design programming decisions. They lead to deteriorating the perceived usability of mobile user interfaces and negatively impact the User's eXperience (UX) with the mobile app. Most existing studies relied on a subjective evaluation of aesthetic defects depending on end-users feedback, which makes the manual evaluation of mobile user interfaces human-centric, time-consuming, and error-prone. Therefore, recent studies have dedicated their effort to focus on the definition of mathematical formulas that each targets a specific structural quality of the interface. As the UX is tightly dependent on the user profile, the combination and calibration of quality attributes, formulas, and user's characteristics, when defining a defect, are not straightforward. In this context, we propose a fully automated framework which combines literature quality attributes with the user's profile to identify aesthetic defects of MUI. More precisely, we consider the mobile user interface evaluation as a multi-objective optimization problem where the goal is to maximize the number of detected violations while minimizing the detection complexity of detection rules and enhancing the interfaces overall quality in means of guidance and coherence coverage. We conducted a comparative study of several evolutionary algorithms in terms of accurately identifying aesthetic defects. We reported their performance in solving the proposed search-based multi-objective optimization problem. The results confirm the efficiency of the indicator-based evolutionary algorithm in terms of assessing the developers in detecting typical defects and also in generating the most accurate detection rules.
On the expressive power of user-defined effects: Effect handlers, monadic reflection, delimited control

Abstract We compare the expressive power of three programming abstractions for user-defined computational effects: Plotkin and Pretnar’s effect handlers, Filinski’s monadic reflection, and delimited control. This comparison allows a precise discussion about the relative expressiveness of each programming abstraction. It also demonstrates the sensitivity of the relative expressiveness of user-defined effects to seemingly orthogonal language features. We present three calculi, one per abstraction, extending Levy’s call-by-push-value. For each calculus, we present syntax, operational semantics, a natural type-and-effect system, and, for effect handlers and monadic reflection, a set-theoretic denotational semantics. We establish their basic metatheoretic properties: safety, termination, and, where applicable, soundness and adequacy. Using Felleisen’s notion of a macro translation, we show that these abstractions can macro express each other, and show which translations preserve typeability. We use the adequate finitary set-theoretic denotational semantics for the monadic calculus to show that effect handlers cannot be macro expressed while preserving typeability either by monadic reflection or by delimited control. Our argument fails with simple changes to the type system such as polymorphism and inductive types. We supplement our development with a mechanised Abella formalisation.
Robust visual tracking with channel attention and focal loss

Recently, the tracking community leads a fashion of end-to-end feature representation learning for visual tracking. Previous works treat all feature channels and training samples equally during training. This ignores channel interdependencies and foreground–background data imbalance, thus limiting the tracking performance. To tackle these problems, we introduce channel attention and focal loss into the network design to enhance feature representation learning. Specifically, a Squeeze-and-Excitation (SE) block is coupled to each convolutional layer to generate channel attention. Channel attention reflects the channel-wise importance of each feature channel and is used for feature weighting in online tracking. To alleviate the foreground–background data imbalance, we propose a focal logistic loss by adding a modulating factor to the logistic loss, with two tunable focusing parameters. The focal logistic loss down-weights the loss assigned to easy examples in the background area. Both the SE block and focal logistic loss are computationally lightweight and impose only a slight increase in model complexity. Extensive experiments are performed on three challenging tracking datasets including OTB100, UAV123 and TC128. Experimental results demonstrate that the enhanced tracker achieves significant performance improvement while running at a real-time frame-rate of 66 fps.
A New Rule Reduction Method for Fuzzy Modeling

The Takagi-Sugeno fuzzy model (T-S fuzzy model) has been successfully applied to a wide range of problems due to its accurate modeling ability. However, its rule redundancy and dimension disaster remain an open and unsolved issue. In this article, a novel fuzzy modeling method is proposed to reduce the fuzzy rule number without a loss in modeling accuracy. It includes two novel mechanisms: the rule fusion mechanism and the space projection mechanism. The rule fusion mechanism is developed to merge several rules into one according to similarity. In this way, the number of rules will greatly decrease. After the rules are merged, the relationship of data is strongly nonlinear, which renders the local linear model ineffective. In order to alleviate this difficulty, the space projection mechanism is then proposed to project the low-dimensional feature space to a high-dimension space. In this high-dimension space, the relation of data is linear and easily represented by a linear model. In this way, the traditional fuzzy inference is still applicative. With the help of these two mechanisms, a fuzzy model with few rules is constructed, which enables reconstruction of the nonlinear system. A solving method is then developed to determine the optimal parameters of the fuzzy model. Additional analysis and proof show that the proposed method exhibits the uniform approximation ability for strongly nonlinear systems. Case studies not only demonstrate the effectiveness of the proposed method, but also demonstrate its superior modeling performance in comparison to several conventional fuzzy modeling methods, even with the use of fewer rules.
Direct Temporal Mode Measurement for the Characterization of Temporally Multiplexed High Dimensional Quantum Entanglement in Continuous Variables

Field-orthogonal temporal mode analysis of optical fields is recently developed for a new framework of quantum information science. But so far, the exact profiles of the temporal modes are not known, which makes it difficult to achieve mode selection and de-multiplexing. Here, we report a novel method that measures directly the exact form of the temporal modes. This in turn enables us to make mode-orthogonal homodyne detection with mode-matched local oscillators. We apply the method to a pulse-pumped, specially engineered fiber parametric amplifier and demonstrate temporally multiplexed multi-dimensional quantum entanglement of continuous variables in telecom wavelength. The temporal mode characterization technique can be generalized to other pulse-excited systems to find their eigen modes for multiplexing in temporal domain.
Complex CNN-Based Equalization for Communication Signal

In this paper, we address the application of deep learning in signal equalization, presenting an end-to-end learned method based on convolutional neural network (CNN) to directly recover a communication signal from a noised signal influenced by wireless channel. Different from real-valued tasks, complex-valued tasks can be hardly resolved by normally sequential real-valued CNN. Alternatively, we propose a simply mixed cascade structure to replace the traditional equalization methods in communication systems, i.e., multi-modulus algorithm, least mean square method and recursive least square method. Additionally, we generate a noised dataset consisting of known modulation signals in digital communication signal by simulation. The effects of multi-path fading, additive white Gaussian noise (AWGN), frequency and phase offset and symbol rate are taken into consideration. Furthermore, we proved the proposed method obtaining better performance over the traditional equalization method.
Design and implementation of an open source Greek POS Tagger and Entity Recognizer using spaCy

This paper proposes a machine learning approach to part-of-speech tagging and named entity recognition for Greek, focusing on the extraction of morphological features and classification of tokens into a small set of classes for named entities. The architecture model that was used is introduced. The greek version of the spaCy platform was added into the source code, a feature that did not exist before our contribution, and was used for building the models. Additionally, a part of speech tagger was trained that can detect the morphology of the tokens and performs higher than the state-of-the-art results when classifying only the part of speech. For named entity recognition using spaCy, a model that extends the standard ENAMEX type (organization, location, person) was built. Certain experiments that were conducted indicate the need for flexibility in out-of-vocabulary words and there is an effort for resolving this issue. Finally, the evaluation results are discussed.
Strategies to Reduce the Expert Supervision Required for Deep Learning-Based Segmentation of Histopathological Images

The emergence of computational pathology comes with a demand to extract more and more information from each tissue sample. Such information extraction often requires the segmentation of numerous histological objects (e.g., cell nuclei, glands, etc.) in histological slide images, a task for which deep learning algorithms have demonstrated their effectiveness. However, these algorithms require many training examples to be efficient and robust. For this purpose, pathologists must manually segment hundreds or even thousands of objects in histological images, i.e., a long, tedious and potentially biased task. The present paper aims to review strategies that could help provide the very large number of annotated images needed to automate the segmentation of histological images using deep learning. This review identifies and describes four different approaches: the use of immunohistochemical markers as labels, realistic data augmentation, Generative Adversarial Networks (GAN), and transfer learning. In addition, we describe alternative learning strategies that can use imperfect annotations. Adding real data with high-quality annotations to the training set is a safe way to improve the performance of a well configured deep neural network. However, the present review provides new perspectives through the use of artificially generated data and/or imperfect annotations, in addition to transfer learning opportunities.
Effects of Mobile Augmented Reality (MAR) towards Students’ Visualization Skills when Learning Orthographic Projection

Recent study shows an increasing interest towards the implementation of augmented reality in teaching and learning. Majority of the studies in this area focuses on how this technology could impact students’ performance and various skills including visualization skills. Hence, the purpose of this study was to examine the differences in students’ visualization skills after utilizing Mobile Augmented Reality (MAR) and conventional learning in orthographic projection classroom. This study involved 60 students and was conducted in Universiti Teknologi Malaysia: 30 students in experimental group used MAR; 30 students in control group used conventional learning. In this study, mental rotation skills were measured using Purdue Spatial Visualization Test of Rotation (PSVT:R) due to its importance to ensure students understanding in orthographic projection. Results from this study showed a significant difference between students in both group where students in experimental group displayed a significantly higher level of visualization skills in terms of mental rotation skills compared to students in control group. At the end of their study, respondents were given a set of questionnaires to obtain their feedback on the use of MAR. Based on students’ positive feedback on MAR, the result proved that it can be an effective spatial visualization tool. MAR is also practical to be used in classrooms since it has low production cost.
‘Project &amp; Excite’ Modules for Segmentation of Volumetric Medical Scans

Fully Convolutional Neural Networks (F-CNNs) achieve state-of-the-art performance for image segmentation in medical imaging. Recently, squeeze and excitation (SE) modules and variations thereof have been introduced to recalibrate feature maps channel- and spatial-wise, which can boost performance while only minimally increasing model complexity. So far, the development of SE has focused on 2D images. In this paper, we propose 'Project & Excite' (PE) modules that base upon the ideas of SE and extend them to operating on 3D volumetric images. 'Project & Excite' does not perform global average pooling, but squeezes feature maps along different slices of a tensor separately to retain more spatial information that is subsequently used in the excitation step. We demonstrate that PE modules can be easily integrated in 3D U-Net, boosting performance by $$5\%$$ Dice points, while only increasing the model complexity by $$2\%$$ . We evaluate the PE module on two challenging tasks, whole-brain segmentation of MRI scans and whole-body segmentation of CT scans. Code: https://github.com/ai-med/squeeze_and_excitation .
Risk as affect: The affect heuristic in cybersecurity

Risk perception is an important driver of netizens’ (Internet users’) cybersecurity behaviours, with a number of factors influencing its formation. It has been argued that the affect heuristic can be a source of variation in generic risk perception. However, a major shortcoming of the supporting research evidence for this assertion is that the central construct, affect, has not been measured or analysed. Moreover, its influence in the cybersecurity domain has not yet been tested. The contribution of the research reported in this paper is thus, firstly, to test the affect heuristic while measuring its three constructs: affect, perceived risk and perceived benefit and, secondly, to test its impact in the cybersecurity domain. By means of two carefully designed studies (N = 63 and N = 233), we provide evidence for the influence of the affect heuristic on risk perception in the cybersecurity domain. We conclude by identifying directions for future research into the role of affect and its impact on cybersecurity risk perception.
Toward Driver Face Recognition in the Intelligent Traffic Monitoring Systems

This paper models the driver face recognition problem under the intelligent traffic monitoring systems as severe illumination variation face recognition with single sample problem. Firstly, in the point of view of numerical value sign, the current illumination invariant unit is derived from the subtraction of two pixels in the face local region, which may be positive or negative, we propose a generalized illumination robust (GIR) model based on positive and negative illumination invariant units to tackle severe illumination variations. Then, the GIR model can be used to generate several GIR images based on the local edge-region or the local block-region, which results in the edge-region based GIR (EGIR) image or the block-region based GIR (BGIR) image. For single GIR image based classification, the GIR image utilizes the saturation function and the nearest neighbor classifier, which can develop EGIR-face and BGIR-face. For multi GIR images based classification, the GIR images employ the extended sparse representation classification (ESRC) as the classifier that can form the EGIR image based classification (GIRC) and the BGIR image based classification (BGIRC). Further, the GIR model is integrated with the pre-trained deep learning (PDL) model to construct the GIR-PDL model. Finally, the performances of the proposed methods are verified on the Extended Yale B, CMU PIE, AR, self-built Driver and VGGFace2 face databases. The experimental results indicate that the proposed methods are efficient to tackle severe illumination variations.
LightAnchors

Augmented reality requires precise and instant overlay of digital information onto everyday objects. We present our work on LightAnchors, a new method for displaying spatially-anchored data. We take advantage of pervasive point lights - such as LEDs and light bulbs - for both in-view anchoring and data transmission. These lights are blinked at high speed to encode data. We built a proof-of-concept ap-plication that runs on iOS without any hardware or software modifications. We also ran a study to characterize the performance of LightAnchors and built eleven example demos to highlight the potential of our approach.
A multi-task learning model with adversarial data augmentation for classification of fine-grained images

A large set of training samples is a prerequisite to effectively learn deep neural networks for image classification. When the labeled samples are scarce, it often fails to produce a promising model. Data augmentation is a widely used technique to overcome the issue, which enlarges the training samples with label invariant transformations, e.g., rotation, flip and random crop etc. However, the diversity of images generated by standard data augmentation is quite limited and thus the final improvement on classification accuracy is not much, especially for fine-grained classification problem. In this paper, we propose a two-stage generative adversarial network, namely Fine-grained Conditional Adversarial Network (F-CGAN), which can produce class-dependent synthetic images with fine-grained details. Moreover, to leverage the synthetic images for fine-grained classification, we develop a multi-task learning classifier, which categorizes training images and synthetic images simultaneously. Experimental results on CUB Birds and Stanford Dogs data sets show that the proposed method indeed improves the classification accuracy.