Reviewerly/exHarmony huggingface dataset:
Face expression recognition based on improved convolutional neural network

Aiming at the problems of huge parameters and network degradation caused by simple linear stacked convolution layers or continuous full connection layers in traditional expression recognition methods, two convolution neural network models are designed through depth separation convolution and residual module respectively to widen and deepen the network. Firstly, model A adopts depth separation convolution instead of regular convolution layer, and the global average pooling layer replaces the final full connection layer, utilizes the methods of dropout, batch normalization, activation function of PReLU and image augmentation to avoid over-fitting effectively. Model B adopts pre-trained ResNet50 model to extract facial features, magnifies the images twice by the SRGAN method. Using ensemble method to fuse model A and B, the accuracy is further improved. To verify the feasibility of the method, the model was tested on the FER2013 facial expression dataset, and the performance was compared with the other facial expression recognition algorithms. The final results showed the improved convolutional neural network (CNN) reached the advanced precision of 73.244% in FER2013 dataset, and the experiment data and the number of model parameters all proved the effectiveness of this method.
JumpinVR: Enhancing Jump Experience in a Limited Physical Space

We introduce a short virtual reality experience highlighting a use-case scenario of distance relocation technique in Redirected Jumping to reduce the size requirements for tracked working space of spatial applications. In our demo, the player traverses a virtual factory by jumping between moving platforms with jump distance scaled by gain.
Come to the Table! Haere mai ki te tēpu!

Come to the Table! explores whether extended realities (XR) can create a bridge between indigenous people (Māori), descendants from European settlers (Pākehā) and people from other ethnicities, by practicing social inclusion. The experience uses real time depth sensing technology and AR/VR displays to enable participants to view and be part of tabletop conversations with people from different cultural backgrounds, in a playful, explorative and powerful way.
The Impact of Thread-Per-Core Architecture on Application Tail Latency

The response time of an online service depends on the tail latency of a few of the applications it invokes in parallel to satisfy the requests. The individual applications are composed of one or more threads to fully utilize the available CPU cores, but this approach can incur serious overheads. The thread-per-core architecture has emerged to reduce these overheads, but it also has its challenges from thread synchronization and OS interfaces. Applications can mitigate both issues with different techniques, but their impact on application tail latency is an open question. We measure the impact of thread-per-core architecture on application tail latency by implementing a key-value store that uses application-level partitioning, and inter-thread messaging and compare its tail latency to Memcached which uses a traditional key-value store design. We show in an experimental evaluation that our approach reduces tail latency by up to 71 % compared to baseline Memcached running on commodity hardware and Linux. However, we observe that the thread-per-core approach is held back by request steering and OS interfaces, and it could be further improved with NIC hardware offload.
Generalized net model of a system for quality assurance in higher education

Ensuring quality of training in higher education is a highly complex activity. It involves a number of processes related to assessment of different educational objects and subjects that are evaluated using different methodologies. The participants in evaluation procedures usually have different rights and responsibilities. Some quality assessment procedures require a preliminary self-evaluation. In addition, e-learning includes a wide variety of virtual learning resources and activities that require an individual assessment approach. The online software assistance of quality assurance activities increases its efficiency, saves time and minimizes the likelihood of making mistakes. The article presents a generalized net model for quality assurance. It offers a flexible approach to modeling different objects and processes for (self) evaluation and accreditation of education. The model aims to offer different options for implementing software applications.
Semantic query transformations for increased parallelization in distributed knowledge graph query processing

Ontologies have become an increasingly popular semantic layer for integrating multiple heterogeneous datasets. However, significant challenges remain with supporting efficient and scalable processing of queries with data linked with ontologies (ontological queries). Ontological query processing queries requires explicitly defined query patterns be expanded to capture implicit ones, based on available ontology inference axioms. However, in practice such as in the biomedical domain, the complexity of the ontological axioms results in significantly large query expansions which present day query processing infrastructure cannot support. In particular, it remains unclear how to effectively parallelize such queries.
Estimating exact energies in quantum simulation without Toffoli gates

Qubitization is a modern approach to estimate Hamiltonian eigenvalues without simulating its time evolution. While in this way approximation errors are avoided, its resource and gate requirements are more extensive: qubitization requires additional qubits to store information about the Hamiltonian, and Toffoli gates to probe them throughout the routine. Recently, it was shown that storing the Hamiltonian in a unary representation can alleviate the need for such gates in one of the qubitization subroutines. Building on that principle, we develop an alternative decomposition of the entire algorithm: without Toffoli gates, we can encode the Hamiltonian into qubits within logarithmic depth.
An Efficient Path Planning Algorithm for Mobile Robots

This paper applies the ${A}^{*}$ algorithm commonly used in game development to mobile robot path planning. Although the ${A}^{*}$ algorithm is characterized by optimality, simplicity, and flexibility and it has been widely used in path planning, it also has some disadvantages such as redundant points, large memory overhead, and long computing time. In order to improve efficiency and overcome these disadvantages, this paper first combines the Jump Point Search (JPS) and the ${A}^{*}$ algorithm, and proposes the ${A}^{*}$ algorithm, which reduces the redundant points in the search process and improves the efficiency of path planning. Inspired by the bidirectional search algorithm, the $BJ{A}^{*}$ algorithm is proposed based on the $J{A}^{*}$ algorithm and the improved bidirectional search algorithm, which further improves the speed of the path planning. The simulation results show that the $BJ{A}^{*}$ algorithm is an efficient path planning algorithm for mobile robots, especially in large scenes and scenes requiring high real-time performance.
Posthuman Methodologies <i>for</i> Post-Industrial Cities: A Situated, Speculative, and Somatechnic Venture

This article proposes a posthuman / materialist somatechnics approach which encourages a more nuanced, ethical, and embodied attentiveness to how humans, nature, and materialities are not separate, but actively emerge through entanglements and in co-constitutive relation with one another. Such an attentiveness recognises that we are shaped by the places in which we live and by the many others – human and nonhuman – with whom we live. It also urges the need to reshape research methodologies. To illuminate how we might more closely attend to the places in which we live, learn, teach, inquire, and research, this article offers a series of situated, speculative, and somatechnic engagements arising from our recent ventures in two separate post-industrial cities. The article is framed as a mode of writing otherwise – as a series of experimental elemental essays and the theory-practice diffractive musings they have given rise to. Taken together, the essays and musings aim to contest deficit discourses of post-industrial cities and the multiple bodies who / which inhabit them. The posthuman situated and speculative somatechnics approach we propose offers insights into unexpected and surprising new relations. We hope the elemental essays and musings which follow invite readers to take up the ‘practice of the pause’ in their own places and spaces.
Handwritten Numeral Recognition Using Polar Histogram of Low-Level Stroke Features

The paper focuses on the handwritten numeral recognition of an Indian script, Gujarati, with the reduced dimensions of features. The proposed method employees the low-level stroke (LLS) for feature extraction and the polar histogram method for feature vector generation that enables the reduced size representation of features. The baseline experiments were performed using k-nearest neighbor (k-NN) classifier and the result was improved further using support vector machine (SVM) classifier with radial basis function (RBF) kernel. The method of the Polar histogram of LLS features was also tested on Devanagari and English handwritten numeral datasets. The accuracy of classification for Gujarati, Devanagari, and English are on par with the state-of-the-art methodologies. The experiments were also performed for mixed dataset Gujarati-English, Gujarati-Devanagari, English-Devanagari, and Gujarati-English-Devanagari. In all experiments, the feature vector size is significantly less while the accuracy is not compromised much.
Introduction to IATK

Immersive Analytics is an emerging interdisciplinary research area that investigates the use of nontraditional display and input technology to immerse users in their data. A prominent aspect this research investigates is "immersive data visualization", which uses augmented and virtual reality technology to visually immerse users in their data to facilitate collaboration, exploration and understanding. Currently, there is a lack of simple tools to build interactive data visualizations in immersive environments. The de facto approach is to use off-the-shelf game engines because they allow easy prototyping of 3D user interfaces in these immersive environments. However, game engines do not consider the specialized requirements of data visualization, such as the type and structure of datasets, the breadth of data, especially in the age of "big data", and typical information visualization tasks. IATK: Immersive Analytics Toolkit is an open source visualization toolkit for the Unity game engine that fills this gap. Specifically, IATK: supports an infovis pipeline for virtual and augmented reality environments; visualizes large (up to 1 million) data points at an optimal framerate for immersive applications; and, provides a technology-agnostic model for user interactions with immersive visualizations. This tutorial will introduce participants to the Unity game engine and teach practical skills for implementing immersive data visualisations using IATK.
Progressive Image Restoration Based on CP-ABE With Constant-Size Ciphertext and Constant Bilinear Calculation

Sharing photos on an open social platform with one-to-many features increases the leakage risk of information of the shared photos. Ciphertext-policy attribute-based encryption (CP-ABE) is a one-to-many public key cryptosystem, and its access policy can be set by data owners. Therefore, if we share the protected image on the social platform and encrypt the restoration parameters of the shared image with CP-ABE, then only the accessor whose attribute set satisfies the access policy of CP-ABE can decrypt the ciphertext corresponding to his/her attribute level and then restore the image with the corresponding sharpness level. Consequently, image information protection is realized in a one-to-many scenario. However, for the existing CP-ABE, only when further shortening its ciphertext, reducing the number of bilinear pairing calculations during decryption, and ensuring flexible access policies is it beneficial for CP-ABE to be widely used in popular smart terminals that can run social software. Furthermore, existing algorithms for restoring images also have room for improvement in terms of time and space requirements. For this reason, we propose a progressive image restoration strategy based on the improved CP-ABE in this paper. For the improvement of CP-ABE, we design a new encryption scheme to achieve a shorter constant-size ciphertext, construct an auxiliary function to help the independent authority centers generate private keys for users in new ways, design a decryption algorithm with only one bilinear pairing calculation, and provide the update algorithms for attribute revocation. Subsequently, we build a distributed CP-ABE based on our improved CP-ABE, and the correctness and security of the proposed CP-ABE algorithm are also proven. In addition, we follow the tree access policy to support the access policy, including AND, OR, NOT and threshold operations simultaneously, to adapt to the requirement of some user attributes that undergo frequent changes. Moreover, based on the atmospheric scattering model, we adopt three algorithms to restore the protected image in the dehazing mode according to the sharpness parameters. Compared with the existing literature, we provide users with not only a flexible and efficient access control strategy, shorter constant-size ciphertext and fewer bilinear calculations but also multiple algorithms for restoring images. The experimental results for six privilege levels demonstrate the superiority of our algorithms in protecting image information.
K-Nearest Neighbor for colon cancer identification

Abstract Colon cancer or colorectal cancer is a type of cancer that attacks the last part of the human digestive system. Lymphoma and carcinoma are types of cancer that attack humans colon. Colon cancer causes deaths about half a million people every year. In Indonesia, colon cancer is the third largest cancer case for women and second in men. Unhealthy lifestyles such as minimum consumption of fiber, rarely exercising and lack of awareness for early detection are factors that cause high cases of colon cancer. The aim of this study is to produce a system that can detect and classify images into type of colon cancer lymphoma, carcinoma, or normal. The designed system used 198 data colon cancer tissue pathology, consist of 66 images for Lymphoma cancer, 66 images for carcinoma cancer and 66 for normal/healthy colon condition. This system will classify colon cancer starts from image preprocessing, feature extraction using Principal Component Analysis (PCA) and classification using K-Nearest Neighbor (K-NN) method. Several stages in preprocessing are resized, convert RGB image to grayscale, edge detection, and last histogram equalization. Tests will be done by trying some K-NN input parameter settings. The result of this study is an image processing system that can detect and classify type of colon cancer with high accuracy and low computation time.
Randomspace-Based Fuzzy C-Means for Topic Detection on Indonesia Online News

Topic detection is a process used to analyze words in a collection of textual data to determine the topics in the collection, how they relate to each other, and how they change from time to time. Fuzzy C-Means (FCM) and Kernel-based Fuzzy C-Means (KFCM) method are clustering method that is often used in topic detection problems. Both FCM and KFCM can group dataset into multiple clusters on a low-dimensional dataset, but fail on high-dimensional dataset. To overcome this problem, dimension reduction is carried out on the dataset before topic detection is carried out using the FCM or KFCM method. In this study, the national news account's tweets dataset on Twitter were used for topic detection using the Randomspace-based Fuzzy C-Means (RFCM) method and Kernelized Randomspace-based Fuzzy C-Means (KRFCM) method. The RFCM and KRFCM learning methods are divided into two steps, which are reducing the dimension of the dataset into a lower-dimensional dataset using random projection and conducting the FCM learning method on the RFCM and the KFCM learning method on KRFCM. After obtaining the topics, then an evaluation is carried out by calculating the coherence value on the topics. The coherence value used in this study uses the Pointwise Mutual Information (PMI) unit. The study was conducted by comparing the average PMI values of RFCM and KRFCM with Eigenspace-based Fuzzy C-Means (EFCM) and Kernelized Eigenspace-based Fuzzy C-Means (KRFCM). The results obtained using national news account's tweets showed that the RFCM and KRFCM methods offered faster running time for a dimensional reduction but had smaller average PMI values compared to the average PMI values generated by the EFCM and KEFCM learning methods.
Sparse Model-Agnostic Meta-Learning Algorithm for Few-Shot Learning

Few-shot meta learning aims to obtain a prior from previous experiences, which is well used for new tasks during meta-test phase. The model-agnostic meta-learning (MAML) algorithm in [3] achieves this goal by finding a proper initial parameter at meta-training phase and this initialization could be quickly adapted to a new task by using the gradient descent at meta-test phase. However, it suffers from the problem of high computational complexity. In this paper, we propose a sparse model-agnostic meta-learning (SMAML) algorithm to alleviate the complexity problem and enhance the efficiency, where <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$L_{1}$</tex> regularization is considered as an extra constraint. Experimental results on classification show that our well-placed modification indeed improves the computational efficiency in comparison to the original MAML algorithm.
Geology and mapping of laterites, South Eastern Desert, Egypt: Based on field and ASTER data approach

The area between wadi Dungash–wadi Shait in the South Eastern Desert of Egypt comprises ophiolitic nappes structurally overthrust on island arc‐related metasedimentary–metavolcanic rocks, which are traversed by several granitoids, gabbroic and basaltic rocks. This paper focuses on uncovering laterite zones utilizing field geological, mineralogical and geochemical attributes of the investigation region. Laterites are distributed at the border between Neoproterozoic and Phanerozoic rocks. The laterite deposits are developed after the emplacement of Natash flows as a result of lateritization process in tropical regions, during the Late Cretaceous. The laterite sections comprise a sequence from the top to the base, oxide laterite, plasmic laterite, saprolith and bedrock. The laterite sections consist of hematite and goethite together with small quantity of gibbsite. Geochemically, the laterite profiles are marked by an increase in Fe content and decrease in Si from the top to the bottom. Fe 2 O 3 is the most abundant with 30.09–54.67 wt.%, Al 2 O 3 vary from 18.01 to 32.78 wt.%, and SiO 2 vary from 15.05 to 19.43 wt.%. Advanced Spaceborne Thermal Emission and Reflection Radiometer images are utilised to characterize a primer laterite zone. The band ratio 4/5 is found to be efficient in defining the laterite soil in the studied area.
Text-Based Image Style Transfer and Synthesis

In this work, we propose a novel framework for synthesizing the stylization of text-based images. It is composed of several steps without supervision. Initially, the style image is segmented to foreground and background images. Then, the main color of foreground is accumulated and assigned to the stroke-based binarized geometric shape like text, symbols and icons to be content image. The foreground image is considered as the target style and transferred to the content image by image style transfer neural network. Meanwhile, the blank space in segmented background image is filled by image inpainting-based partial convolution neural network. Finally, the composition of the stylized geometric shape and the complete background image is accomplished by texture synthesis. The features of edge, color, texture, etc., are explored for the stylization of multiple geometric shapes. It explains the rationality of our proposed method on binarized geometric shape style transfer. The extensive experiments on various tasks, such as visual-textual presentation synthesis, icon/symbol rendering and structure-guided image inpainting, demonstrate the effectiveness of the proposed method.
Towards a Better Understanding of Team-Driven Dynamics in Agile Software Projects

In agile software development, proper team structures and sprint estimations are crucial aspects to reach high-performance outcomes. Performance can vary due to the influence of social-driven team factors. Resulting in team dynamics with the focus on human factors are usually difficult to capture and thus often not monitored. However, their impact can impede the planning and fulfillment of sprints. Data on team behavior should be simplified to track, analyze, and interpret as sprint influences are important to understand. We provide a centralized solution that extends JIRA functionally and continuously captures sprint characteristics in the daily working environment of teams. In this paper, we describe a JIRA plugin that enables the assessment of team behavior in combination with exploratory analyses. The tool became approached with six software projects and a total of 53 undergraduate students. Characterizations made with the plugin can reveal sprint and team dynamics over time, involving development performance and team-related measures. The feature comes with a feedback mechanism for teams that visualize and implicates the sprint dependencies. The approach reveals a set of team-related sprint dynamics, its systematically capturing, and characterization. With the achieved solution, team leader and developer can be supported to understand the ongoing sprint and team-driven dynamics better. Thus, they can keep track of their habits for future sprint planning and team adjustment impacts.
SAR Image Generation with Semantic-Statistical Convolution

SAR image due to its nature of coherent imaging manifests both deterministic semantic information and speckle-like statistical textures. It is necessary to have a general representation scheme of the semantic-statistical two-layer hierarchy of SAR image so that semantic and textural information can be separated. Inspired by the correlated clutter simulation method proposed by Bustos et al [1]–[2], this paper studies a semantic-statistical convolution scheme to generate a SAR image from a semantic map. For each terrain type, we estimate the intensity distribution and correlated texture model and then generate textures with correlated clutter. The method is tested on actual SAR images of E-SAR data including urban and forest areas and Flevoland AirSAR data with 15 terrains.
Scalable Identity and Key Management for Publish-Subscribe Protocols in the Internet-of-Things

Publish-Subscribe protocols such as the Message Queuing Telemetry Transport (MQTT) protocol are considered scalable, lightweight, and one-size-fits-all solutions for the Internet-of-Things (IoT) networking. MQTT has been widely adopted in the Industrial IoT to automate distributed power grid equipment such as smart meters and sensors. Such protocols are being adopted rapidly, without much attention being paid to security. Although these protocols support client-side TLS certificates, operators of ten do not enable these features, fearing performance and availability issues. Moreover, managing these certificates would be yet another challenging problem.