Reviewerly/exHarmony huggingface dataset:
Deep Clustering for Mixed-type Data with Frequency Encoding and Doubly Weighted Cross Entropy Loss

Clustering algorithm is unsupervised learning that groups a set of data into distinctive classes according to the similarity between each data sample. Most of previous researches have focused on improving K-prototypes or training proper numerical representations of categorical features using autoencoder. But in this research, we investigate that applying frequency encoding to categorical features can be sufficiently effective. Furthermore, we propose doubly weighted cross entropy loss, DW-CE loss, to find optimal cluster centroid by training fully connected layer. The experiment with two mixed-type datasets, credit approval and heart disease, from UCI repository shows that the proposed clustering with frequency encoding and DW-CE loss provides better performance than existing state of the arts methods in most of cases.
Self-Configured Workflow Platform for MapReduce Job Execution in Cloud Environment

Hadoop MapReduce becomes the essential framework to store, process and analyse big data in large-scale computing environments. The framework provides numerous opportunities to handle data-intensive applications like web crawling and indexing, data mining, anomaly prediction and machine learning. However, manual configurations of the framework parameters expose to human errors and lead to performance overhead and resource consumption penalties. Existing deployment automation solutions intended to mitigate the limitations by only facilitating the application execution workflow without considering crucial conditions that violates the overall performance after setting up the infrastructure parameters. This paper proposes a self-configured workflow platform with a user-friendly collaborating environment that provides automated setups of the framework paraments to obtain ideal performance compared to the native deployment of Hadoop MapReduce. The self-configured workflow platform has been evaluated in experiments on a real Hadoop cloud-based cluster and the results shows that the execution times are comparable to the native application.
E-Learning, Challenges and Opportunities of Instructors in Libyan Higher Institutes

The spread of COVID-19 causes a threat to the whole world. It has affected most human life activities during the last two years. The higher education sector was among the industries massively affected by this pandemic, all educational institutions in Libya were locked down, and a large number of students were left without education. This paper investigates the implementation of E-learning in the Higher Institute of Science and Technology – Khoms in Libya to determine the challenges and limitations the instructors and the institute have faced in implementing E-learning. Thus, a questionnaire was designed and distributed among the instructors in the Institute. This paper discusses several elements of E-learning, including the extent of using E-learning, E-learning skills, benefits of E-learning, and obstacles faced in implementing E-learning. The obtained results show that the IT infrastructure in the Institute must be upgraded, and stable high-speed internet access is required for a smooth implementation of E-learning.
New Datasets and Models for Contextual Reasoning in Visual Dialog

Visual Dialog (VD) is a vision-language task that requires AI systems to maintain a natural question-answering dialog about visual contents. Using the dialog history as contexts, VD models have achieved promising performance on public benchmarks. However, prior VD datasets do not provide sufficient contextually dependent questions that require knowledge from the dialog history to answer. As a result, advanced VQA models can still perform well without considering the dialog context. In this work, we focus on developing new datasets and models to highlight the role of contextual reasoning in VD. We define a hierarchy of contextual patterns to represent and organize the dialog context, enabling quantitative analyses of contextual dependencies and designs of new VD datasts and models. We then develop two new datasets, namely CLEVR-VD and GQA-VD, offering context-rich dialogs over synthetic and realistic images, respectively. Furthermore, we propose a novel neural module network method featuring contextual reasoning in VD. We demonstrate the effectiveness of our proposed datasets and method with experimental results and model comparisons across different datasets. Our code and data are available at https://github.com/SuperJohnZhang/ContextVD .
A Research on the Theory and Technology of Trusted Transaction in Modern Service Industry

This paper mainly discusses the theory and related technology of trusted transactions in modern service industry. Three prohibition principles of trusted transactions in modern service industry are summarized by lucubrate. Through detailed research on the blockchain technology, especially the Consortium Blockchain, we find that its innovation and characteristics meet all the requirements of trusted transactions. After that, we discuss the cryptography and computer principles and technologies behind the three prohibition principles of blockchain supporting trusted transactions in details, implemented on which the smart contract technology is analyzed. On this basis, we put forward the overall framework of trusted transaction based on blockchain technology, and take the e-commerce platform as an example, and the basic design on multilink and microservice framework. Ultimately, we conduct study on big data credit investigation and credit evaluation for trusted transactions under the blockchain system, and make a preliminary discussion on the dynamic penetrating supervision model based on big data of blockchain transactions.
Mitigating Distribution Shift for Multi-Sensor Classification

Distribution shift may pose significant challenges in Earth observation, especially when dealing with significantly differ-ent sensors like multispectral optical and Synthetic Aperture Radar (SAR). Deep learning models trained for optical image classification generally do not generalize well for SAR images. This is due to very marked differences between them. Though there is a considerable amount of works on domain adaptation, only few deal with such strong differences. Towards this, we propose a co-teaching based domain adaptation method using dual classifier head, a Multi-layer Perceptron (MLP) classi-fier and a Graph Neural Network (GNN) classifier. The two classifier heads teach each other in an iterative manner, thus gradually adapting both of them for target classification. We experimentally demonstrate the efficacy of the proposed approach on Sentinel 2 (optical) as source and Sentinel 1 (SAR) images as target - both product of Copernicus program of European Space Agency.
Neutron-induced Faults on CNN for Aerial Image Classification on SRAM-based FPGA Using Softcore GPU and HLS

This work evaluates neutron-induced soft errors in an image classification all-convolutional neural network implemented on an SRAM-based FPGA. We compare two implementations, an open-source general-purpose softcore GPU programmed in OpenCL language and a dedicated convolution engine obtained by high-level synthesis coded in C language. Results from a 14.1 MeV neutron irradiation have shown that the HLS architecture presents better reliability than the CNN running on the softcore GPU, due to optimized resources, while also reducing in six times the execution time and in five times the power consumption.
Classification of feasibility of culinary business locations based on the level of building density using euclidean distance algorithm

One of the most in-demand businesses by the public is culinary. Like the business field in general, culinary business still has risks if firms do not pay attention to factors - essential factors in setting up a business. Location selection is one of the important factors that can influence the success of a business. Companies should look for one that matches the strategic and crowded location indicators. Jatiwaringin is one of the areas that correspond to the indicators of strategic and crowded locations. This indicator allows for an intense competition that requires the pioneers to choose the type of business that corresponds to the location that he has determined. This study aims to classify the feasibility of culinary business locations based on the density of buildings. The technique used in this study is to calculate the distance of one point to another using the Euclidean Distance algorithm. The result of this study is the classification of the feasibility of business locations can be done by calculating distances using the euclidean distance algorithm.
Student Satisfaction with Online Learning During Pandemic and After

The pandemic brought many changes that influenced the education process at universities and Comenius University was not an exception. The situation required fast solutions, especially concerning methodological and technical support. This resulted in significant upgrades of all systems. This article displays the views and opinions of Comenius University students on online learning during the past three academic years, including the year 2021 - 2022, when the said education became optional. We summarize advantages and disadvantages of online learning according to our students and compare them to survey results and theories found in literature focused on this field. At the end, we formulate recommendations that might help eliminate weaknesses of online learning and could lead to improving its quality.
Real-Time, Accurate, and Consistent Video Semantic Segmentation via Unsupervised Adaptation and Cross-Unit Deployment on Mobile Device

This demonstration showcases our innovations on efficient, accurate, and temporally consistent video semantic segmentation on mobile device. We employ our test-time unsupervised scheme, AuxAdapt, to enable the segmentation model to adapt to a given video in an online manner. More specifically, we leverage a small auxiliary network to perform weight updates and keep the large, main segmen-tation network frozen. This significantly reduces the computational cost of adaptation when compared to previous methods (e.g., Tent, DVP), and at the same time, prevents catastrophic forgetting. By running AuxAdapt, we can considerably improve the temporal consistency of video segmentation while maintaining the accuracy. We demonstrate how to efficiently deploy our adaptive video segmentation algorithm on a smartphone powered by a Snapdragon® Mobile Platform <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Snapdragon is a product of Qualcomm Technologies, Inc. and/or its subsidiaries., Rather than simply running the entire algorithm on the GPU, we adopt a crossunit deployment strategy. The main network, which will be frozen during test time, will perform inferences on a highly optimized AI accelerator unit, while the small auxiliary net-work, which will be updated on the fly, will run forward passes and back-propagations on the GPU. Such a deployment scheme best utilizes the available processing power on the smartphone and enables real-time operation of our adaptive video segmentation algorithm. We provide example videos in supplementary material.
Data Feel: Exploring Visual Effects in Video Games to Support Sensemaking Tasks

This paper explores the use of visual effects common in video games that support a range of tasks that are similar in many ways to analysis tasks supported in visual analytics tools. While some visual effects are meant to increase engagement or to support a game’s overall visual design, we find that in many games visual effects are used throughout gameplay in order to assist a player in reasoning about the game world. In this work, we survey popular games across a range of categories (from casual games to “Triple A” games), focusing specifically on visual effects that support a player’s sensemaking within the game world. Based on our analysis of these games, we identify a range of tasks that could benefit from the use of “data feel,” and advocate for the continued investigation of visual effects and their application in data visualization software tools.
Question Answering Using Semantic Query Graphs: A Replication Study

Semantic methods for natural language question answering are better suited for domain-specific applications than their deep-learning counterparts because they can utilize domain-specific structured data and do not require the large training datasets typically necessary for deep learning. Zou et al. describe a method for RDF-based question answering in which the input question is transformed into a semantic query graph that is subsequently mapped to a subgraph of the domain knowledge graph containing the answer. In this work we replicate this approach and evaluate it using the Question Answering over Linked Data (QALD) dataset. Of the 131 relevant questions in this dataset, the method answers 41 questions, 23 of which are answered correctly, achieving an Fl-score of 0.201. Integration of state-of-the-art named entity recognition (NER) techniques provide a path forward for improving performance.
The Efficacy of Using Round Table Technique in Teaching Writing Descriptive Text to Indonesian English as Foreign Language (EFL) Students

This research aims at finding out the efficacy of using round table technique in teaching writing especially descriptive text to Indonesian English as Foreign Language (EFL) students. The main question of this research: Is the use of Round Table technique effective in teaching conditional sentences to Indonesian EFL students?. The research can be an addition to the persisting teaching writing in English particularly the descriptive text through the use of Round Table technique. This research was conducted at First semester students of the English Study Program of Nusa Cendana University, Kupang. The population of this research was First semester students of the English Study Program of Nusa Cendana University, Kupang. The research used a pre-experimental method with a pre-test and post-test design. The pre-test was given to find out the basic ability of the students in mastering writing descriptive text and the post-test given to find out the students’ improvement in mastering writing descriptive text after giving the treatment by using round table technique. The findings show that using round table technique is effective in teaching writing descriptive text to Indonesian EFL students. It is supported by the result of the significance test through SPSS 20 program that the P was 0.00. Therefore, it is recommended to use the round table technique in developing students’ writing descriptive of Indonesian EFL students.
Analysing WhatsApp and Instagram as Blended Learning Tools

This chapter explores, from a theoretical point of view, the literature investigating the use of WhatsApp and Instagram applications to foster language learning as well as the outcomes of the studies carried out in these two fields. The possibilities of these two apps to develop blended learning environments are also regarded. The theoretical frameworks in the existing literature for the possible implementation of these social networking tools in classroom contexts are considered. Findings indicate a growing number of studies making use of the WhatsApp application to develop either blended learning models or online tasks in foreign language courses while the research exploring the use of Instagram for language development remains very scarce.
GNNQ: A Neuro-Symbolic Approach to Query Answering over Incomplete Knowledge Graphs

Abstract Real-world knowledge graphs (KGs) are usually incomplete—that is, miss some facts representing valid information. So, when applied to such KGs, standard symbolic query engines fail to produce answers that are expected but not logically entailed by the KGs. To overcome this issue, state-of-the-art ML-based approaches first embed KGs and queries into a low-dimensional vector space, and then produce query answers based on the proximity of the candidate entity and the query embeddings in the embedding space. This allows embedding-based approaches to obtain expected answers that are not logically entailed. However, embedding-based approaches are not applicable in the inductive setting, where KG entities (i.e., constants) seen at runtime may differ from those seen during training. In this paper, we propose a novel neuro-symbolic approach to query answering over incomplete KGs applicable in the inductive setting. Our approach first symbolically augments the input KG with facts representing parts of the KG that match query fragments, and then applies a generalisation of the Relational Graph Convolutional Networks (RGCNs) to the augmented KG to produce the predicted query answers. We formally prove that, under reasonable assumptions, our approach can capture an approach based on vanilla RGCNs (and no KG augmentation) using a (often substantially) smaller number of layers. Finally, we empirically validate our theoretical findings by evaluating an implementation of our approach against the RGCN baseline on several dedicated benchmarks.
Prototype Augmentation with Dummy Samples

Meta-learning is proven to be powerful for helping models make quick adaptations to new tasks. However, the lack of data severely constrains the further improvement. As data augmentation has been an effective and commonly used approach to reach state-of-art performance in image classification tasks, different strategies for applying data augmentation to meta-learning have emerged. One common combination of data augmentation and meta-learning is performing different transformations on images(e.g. horizontal flip, color jitter, and random crop) before feeding them to the feature extractor. Another way is using generative models, such as GAN, to expand the available dataset and alleviate the negative effects of lack of data. These methods either have limited boosts or have difficulties to converge during training and require significant extra computation. In this paper, we propose a novel method using a modified Variational Auto Encoder(VAE) to generate dummy samples from the support set. Our data augmentation method is performed in the feature space to reduce computation steps. Combined with prototypical networks we call our method as Prototype Augmentation with Dummy Samples(PADS). Experiments are carried out both in standard few-shot image classification and cross-domain scenarios and we achieved significant improvement with comparison to the baseline.
Research on 3D Face Recognition Technology Based on Multi Feature Fusion from the Perspective of Aesthetics

Face recognition is still one of the research hotspots in the field of pattern recognition and artificial intelligence. Compared with two-dimensional face images, three-dimensional face model can provide key depth information, which is less affected by pose and illumination, and has better adaptability to the change of expression. Through the research of 3D face recognition method, the difficulties of 2D face recognition are overcome. Aiming at the shortage of the existing 3D face data set, this paper designs a 3D face recognition method, and proposes a data set synthesized by 3dmm. At the same time, by transforming the expression and shape on the data set, enough face samples can be synthesized to obtain 3D face point cloud data with good expression ability, the accuracy of face recognition is improved by the 3D face recognition technology of multi feature fusion. For 3D face data, this paper uses face point cloud, which avoids the loss of key information when mapping from 3D face to 2D face, and avoids the huge data of 3D face voxel network.
Professional Shame as a Socio-Psychological Mechanism for Marginalization in Engineering Education

Through examining participants' experiences of shame and subsequent struggle to belong and claim identity as an engineer, we seek to address efforts in bolstering diversity, equity, and inclusion that may be hindered by the permeation of professional shame in the experience of minoritized students.We see these findings as critical in giving insight on how minoritization occurs and so that equity can become a systemic objective for everyone in the engineering community rather than the burden only on the shoulders of those who are marginalized by the community.
Flexible Co-design Practice in Accommodating “Making” as a Method to Express Creativity

This article discusses a case study of a bamboo church construction project in Malang, Indonesia. This project employed a co-design method involving all church congregation members and was facilitated by the ASF ID (Architecture San Frontieres Indonesia) team. The co-design process tried to accommodate participants to unleash their creativity to shape design decisions. One of the important findings from the ground is that the participant’s creative potential is better accommodated by “making” rather than “drawing.” Participants can better maximise their creativity when directly embodying their ideas into 3D prototypes on the field site rather than designing through drawings first in the studio. This tendency is supported by the “craftsmanship” culture owned by most of the participants. This article concludes that the flexibility of the co-design process in this case study shapes by the role of participants. The participants have other ways of being creative and accommodated in the co-design process.
A New Method to Measure Similarity of Words in Japanese Twitter Based on Related Images

Twitter, as a popular form of social media in Japan, has emerged as a valuable data resource for various important social network analysis tasks. However, Japanese tweets often contain nonstandard words and variant notations, owing to which several words with the same meaning may be written differently. The use of such words will generate the sparsity problem and decrease the accuracy of similarity measures between users. Furthermore, the performance of user or tweet recommendations may be deteriorated. Therefore, words with the same meaning must be unified in the preprocessing step. In this research, assuming that words with the same meaning have similar or common related images, we propose a method to use word-related images to measure the similarity between words. A manually annotated Japanese data set is created to evaluate the proposed method. Experimental results indicate that the proposed method outperforms the existing methods in most cases.
Research on Mask RCNN based on rotating self-supervised learning

Traffic sign perception is an important part of the field of autonomous driving perception, at present stage, sensors such as cameras, ultrasonic radar, and lidar are usually used to recognize traffic signs. The task of object detection based on deep learning is to find objects of interest in images or videos, and to detect their positions and sizes at the same time. The field of supervised learning is booming in recent years, some researchers have combined self-supervised learning with some downstream tasks in computer vision (such as classification, object detection, semantic segmentation, etc.), and achieved good results. In this article, we design a Mask RCNN object detection network model combined with rotating self-supervised learning, and call it Rot-Mask RCNN, then train the model on the data set that collected by the Hilens Kit camera, and finally test its accuracy. The mAP value can reach 0.9, which fully proves the effectiveness of the method. Data set will be made public later.