Reviewerly/exHarmony huggingface dataset:
Developing Machine Learning Framework to Classify Harmonized System Code. Case Study: Indonesian Customs

Directorate General of Customs and Excise (DGCE), an Indonesian Government agency under the Ministry of Finance, is responsible for ensuring importer or exporter classify their declared goods based on the Harmonized System Code (HS Code). This study aims to find an optimal machine learning framework to classify goods into their HS Code based on the challenges DGCE faced, such as mixed language with an inconsistent pattern of goods descriptions, imbalance multiclass HS Code, and some additional categorical variables. Refer to some previous studies that propose some machine learning models to predict the HS Code based on goods descriptions. This study tries to make some improvements and adjustments in line with the previously mentioned challenges faced by DGCE. Some preprocessing tasks were performed, such as dealing with abbreviations, misspellings, the varying pattern of goods description, and translating Indonesian words into English. One Hot Coding (OHC) is applied to encode nominal and categorical variables. To make features from goods descriptions, we choose Term Frequency - Inverse Document Frequency (TF-IDF) combined with bigrams. As a result, our models show that Random Forest got an F1-score of 79.60% when classifying the HS Code's first four digits, and Multinomial NB got an F1-score of 72.74% when classifying the HS Code's entire digits. Compared to the baseline paper, those scores are 11.26% and 11.36% higher, respectively.
A Meta-Level Hybrid Recommendation Method Based on User Novelty

In current online recommender systems, multiple recommendation methods are commonly performed to meet user personalized needs. However, these methods usually ignore each individual's differentiated latent psychological factors, such as user personality on openness, which reflects the potential probability of selecting new items. Nowadays, more and more users with no clear goals, called blind-novel-hunting users, explore the novel content on pages instead of purchasing to enhance their self-pleasure. This may indicate that people with high novelty tend to try new and unseen things, while ones with low novelty prefer to choose the familiar objects repeatedly. This paper proposes a Meta-level hybrid recommendation method considering of user novelty (UNB-HM). The method could generate the final personalized recommendation TOP-N0/1 by introducing the real-time prediction on openness and a secondary filtering algorithm in accordance with the law of memory forgetting. In order to validate the efficiency to the existing recommendation algorithms, we use e-commercial user behaviors to complete comparative experiments. The results show that, our method is suitable for the user's openness differences, and after the superimposing usage, several algorithms can effectively im-prove the personalized recommendation effectiveness. Addi-tionally, it has also indicated the advantages in scalability and time flexibility
Research on Big Data and Blockchain Mining Methods for Music Fan Users

Starting from the analysis of music fans, through the analysis of the behavior characteristics of music users, this paper establishes a music user fans model, and then uses some basic data (such as users' online records and base station information) for association matching to eliminate some interference factors. Especially with the gradual maturity of big data and block chain technology, we can make full use of block chain technology to solve the problem Research on music fans, finally get the music user list, provide accurate data support for product innovation and marketing strategy.
Hypergraph Regularized SVM and Its Application Emotion Detection

We propose a method attribute called Learning Frame Emotion Detection based on Hypergraph-based Support Vector Machine (SVM). A hypergraph is used in this HISVM to display various emotional relationships in the results. Then the topic of emotional forecasting is cast out as a regularized problem of Hypergraph cutting, where HISVM learns together a sequence of emotional forecasts from the functional space to a Hypergraph Embedded Vector Space that is harmonized with the different emotions. The predictions learned was directly categorized by emotion. This is a compelling strategy-this formulation. Our proposed algorithm will implement other available details, including class labels, flexibly by treating our model as a multifaceted cutting job. Hypergraph Optimized Vector Assistance Machine, we extend our approach to emotion identification. As a significant preprocessing level, selection features are thought to define the most distinctive functions for a compact statistical analysis in many machine learning and model recognition fields. Then we propose a regularized word, a regularized Hypergraph SVM algorithm. We perform tests on various data sets from SVM's machine learning algorithm and two text-based expression grading projects in the real world. Experimental findings show the proposed process's positive results compared to many standard selection methods for emotional characteristics.
Super-Resolution of Level-17 Images Using Generative Adversarial Networks

The generative adversarial networks (GAN) deep learning models are being widely used in the field of image processing and its applications such as image generation, feature extraction, image recovery and image super-resolution to name a few. Image super-resolution has a board range of applications like satellite and aerial image analysis, medical image processing, compressed image/video enhancement, etc. This work implements an image super-resolution using generative adversarial network for super-resolution of level-17 low-resolution geospatial images obtained from Indian Remote Sensing (IRS) imagery. The results show that the generated super-resolution image can recuperate photo-realistic textures from low-resolution input pictures. The performance of the model is evaluated with qualitative measure indices such as structural similarity (SSIM) and peak signal-to-noise ratio (PSNR). The performance metric demonstrates that the model can generate images as close to that of the high-resolution image and it also has finer details.
SemGAN: Text to Image Synthesis from Text Semantics using Attentional Generative Adversarial Networks

Text to Image Synthesis is the procedure of automatically creating a realistic image from a particular text description. There are numerous innovative and practical applications for text to image synthesis, including image processing and compute-raided design. Using Generative Adversarial Networks (GANs) alongside the Attention mechanism has led to huge improvements lately. The fine-grained attention mechanism, although powerful, does not preserve the general description information well in the generator since it only attends to the text description at word-level (fine-grained). We propose incorporating the whole sentence semantics when generating images from captions to enhance the attention mechanism outputs. According to experiments, on our model produces more robust images with a better semantic layout. We use the Caltech birds dataset to run experiments on both models and validate the effectiveness of our proposal. Our model boosts the original AttnGAN Inception score by +4.13% and the Fréchet Inception Distance score by +13.93%. Moreover, an empirical analysis is carried out on the objective and subjective measures to: (i) address and overcome the limitations of these metrics (ii) verify that performance improvements are due to fundamental algorithmic changes rather than initialization and fine-tuning as with GANs models.
Implementation of Impact Zachman Framework on Internship Architecture Data Management

The quality of the internship activities can contribute to the participants as experiences it can be used as provisions for student self-development. The Good management consistency needs to be done by the school, so the implementation of the apprenticeship can run as well and provide valuable experience. Moreover, good management certainly requires careful and measured planning. The preparation of participants have to able to adjust the conditions of the internship before implementation needs to considered, so the participants can adaptation with the new environment. The Architectural modeling have to be able on handling the apprenticeship management system on the create right model on solving problem. Through out the Framework test results applied to the apprenticeship data management model, the Zachman Framework which was applied in the development of the apprentice management system architecture model proved to able a positive solution for the implementation of the apprenticeship model. This is determined based on the results of user testing where 73.79% expressed satisfaction with the internship service that is the correlation value between the variable X to Y has a degree of closeness of 0.512 which has a strong meaning.
Multiframe Super-Resolution of Color Images Based on Cross Channel Prior

Color images have a wider range of applications than gray images. There are two ways to extend the traditional super-resolution reconstruction method to color images: Super resolution reconstructs each channel of the color image individually; Change the RGB color bands into YCrCb color bands, then super-resolution reconstructs the luminance component and interpolates the chrominance components.These algorithms cannot effectively utilize the property that the edges and textures are similar in the RGB channels, and the results of those methods may lead to color artifacts. Aiming to solve these problems, we propose a new super-resolution method based on cross channel prior. First, a cross channel prior is proposed to describe the similarity of gradient in RGB channels. Then, a new super-resolution method is proposed for color images via combination of the cross channel prior and the traditional super-resolution methods. Finally, the proposed method reconstructs the color channels alternately. The experimental results show that the proposed method could effectively suppress the generation of color artifacts and improve the quality of the reconstructed images.
Non-Local Single Image DE-Raining Without Decomposition

It is challenging to remove rain steaks from a single rainy image because the rain steaks are spatially varying in the rainy image. On top of a new insight in single image de-raining, a nonlocal de-raining algorithm is proposed in this paper to remove the rain streaks from the rainy image. The rainy image is not decomposed into different layers by the proposed algorithm. Experimental results validate the proposed algorithm.
Is this product easy to control? Liabilities of using difficult-to-pronounce product names

This research studied difficult-to-pronounce product names which are prevalent in certain product categories. In study 1, consumers tried golf balls that varied in name pronounceability but were otherwise identical and, despite direct experience, concluded balls with difficult versus easy-to-pronounce names were less controllable and less preferable. In study 2, consumers were asked to look for a dog that was highly (less highly) controllable for an urban (rural) setting, and the dog with the difficult-to-pronounce name was viewed as less controllable because it seemed less familiar and was less preferred for the urban setting. Study 3 verified the effects of difficult-to-pronounce names on familiarity and controllability perceptions and found preference effects among those with high trait desire for control. Study 4 documented the prevalence of difficult-to-pronounce names on a popular ecommerce site for tires. Overall, our findings indicate managers should avoid using difficult-to-pronounce product names when consumers strongly desire product controllability.
Leveraging AR and Object Interactions for Emotional Support Interfaces

This work explores ways we can leverage augmented reality systems to create meaningful and emotional interactions with physical objects. We explore how hand and eye interactions can be used to trigger meaningful sensory feedback to the user. These investigations show how AR interfaces can interpret the actions of a user to shift the primary purpose of a physical object for emotional support.
Adaptive Proof of Driving Consensus for Intelligent Vehicle Communication

Energy and cost efficiency is a notable drawback in Blockchain Technology as well as in traditional consensus schemes and Blockchain does not have any standard consensus protocol for resource constraint applications such as vehicular communication environment. In this paper, we propose a novel Adaptive Proof of Driving (APoD) consensus algorithm for Blockchain based Vehicular Communication Networks (VCN), especially for resource constraint environments. Our developed algorithm provides secure, trusted and reliable communication environment for vehicles based on reputation. The main contribution of our paper is to improve the power consumption, miner congestion, and cost efficiency of blockchain system for vehicular networks. We evaluate, our proposed approach with the help of congestion less intersection use case for adaptive and secure distributed consensus.
Robust estimation of high-order phase dynamics using Variational Bayes inference

Cycle slips strongly impact the performance of phase tracking algorithm leading, in the worst case, to a permanent loss of lock of the signal. In this paper, we propose a new nonlinear phase estimator to obtain more robust tracks. The latter stems from a Variational Bayes (VB) approximation used within the optimal Bayesian filtering formulation in case of high-order phase dynamics. A comparison with a more conventional technique, namely a Kalman filter based PLL (Phase Lock Loop), is performed in terms of mean square error of the phase estimate and mean time to first slip. Results show that the proposed method outperforms the conventional linear filter with respect to both metrics, especially at low signal-to-noise ratio.
The role of ICT as a media in learning activities before the COVID-19 pandemic: undergraduate perspective

Abstract The crisis caused by COVID-19 has an impact on education in Indonesia, one of which is that classes are postponed due to the implementation of PSBB regulations. The education system is responding to the pandemic with an "emergency e-learning" protocol, with virtual learning. ICT is the main medium of learning with the help of technological resources which is often termed e-learning. Has great potential in the development of the education system by providing freedom of interaction without time and space boundaries. With the results of the descriptive analysis survey, the respondents were 63 students of the Sarjanawiyata Tamansiswa University and Tidar University. Respondents were dominated by female gender as 55 people (87.3%) and male respondents as 8 people (12.7%). Students considered ICT understanding to be very important 61.9% and believed that ICT could expand access to learning 66.7%, with internet usage by 90%, followed by PPT as an ICT tool that was often used by 87.9%. Also, there are obstacles faced by students such as lack of understanding of system operations (40%) and difficulty operating equipment as 36.7%. Thus, knowing students’ expectations will be a consideration for further research. Students have hopes of increasing the use of ICT in lecture activities by 80%-100%, such as increasing internet access by 70% to make it easier and faster.
A JPEG Forensic Detector for Color Bitmap Images

Identification of decompressed JPEG images, especially those compressed with high JPEG quality factors, is a challenging issue in image forensics. Furthermore, the applicability of the existing JPEG forensic detectors in forgery localization is limited by their inability to cope with spatial misalignment in the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$8\times 8$</tex-math></inline-formula> JPEG grid. This paper proposes a novel forensic detector for decompressed JPEG color images stored in the bitmap format. The basic idea is to exploit the chroma upsampling traces introduced during the JPEG decompression of color images. We first show that the chroma upsampling alters the wide-sense stationary nature of the chroma planes and introduces wide-sense cyclostationarity in the upsampled chroma planes of decompressed JPEG images. As a result, unlike in the case of never-compressed images, the distributions of the odd-even and even-odd pixel pair differences are different for the chroma planes of never-compressed and decompressed JPEG images. We use the two-sample Kolmogorov-Smirnov (KS) test to find the similarity between these distributions and thereby to differentiate between never-compressed and decompressed JPEG images. The experimental results on a large set of images, including realistically-forged images, show the efficacy of the proposed detector even in the presence of spatial desynchronization due to image cropping.
Research on the Application of Blockchain in Credit Bank

This paper aims to explore and solve the difficult problems faced in the development of traditional credit banks, and analyze the adaptability of blockchain technology in credit banks. The credit chain was designed with Beihang Chain as a prototype, and the transaction speed, scalability and credibility were improved through technologies such as domain indexing, dual-chain concurrency and parallel Byzantine protocol. Then the credit chain is applied to the credit bank architecture to provide an effective solution for the construction of credit bank.
Towards Efficient 3D Point Cloud Scene Completion via Novel Depth View Synthesis

3D point cloud completion has been a long-standing challenge at scale, and corresponding per-point supervised training strategies suffered from cumbersome annotations. 2D supervision has recently emerged as a promising alternative for 3D tasks, but specific approaches for 3D point cloud completion still remain to be explored. To overcome these limitations, we propose an end-to-end method that directly lifts a single depth map to a completed point cloud. With one depth map as input, a multiway novel depth view synthesis network (NDVNet) is designed to infer coarsely completed depth maps under various viewpoints. Meanwhile, a geometric depth perspective rendering module is introduced to utilize the raw input depth map to generate a re-projected depth map for each view. Therefore, the two parallelly generated depth maps for each view are further concatenated and refined by a depth completion network (DCNet). The final completed point cloud is fused from all refined depth views. Experimental results demonstrate the effectiveness of our proposed approach composed of aforementioned components, to produce high-quality, state-of-the-art results on the popular SUNCG benchmark.
Making Sense of Online Discussions: Can Automated Reports help?

Enabling healthier online deliberation around issues of public concerns is an increasingly vital challenge in nowadays society. Two fundamental components of a healthier deliberation are: i. the capability of people to make sense of what they read, so that their contribution can be relevant; and ii. the improvement of the overall quality of the debate, so that noise can be reduced and useful signals can inform collective decision making. Platform designers often resort to computational aids to improve these two processes. In this paper, we examine automated reporting as promising mean of improving sensemaking in discussion platforms. We compared three approaches to automated reporting: an abstractive summariser, a template report and an argumentation highlighting system. We then evaluated improvements in sensemaking of participants and the perception on overall quality of the debate. The study suggests that argument mining technologies are particularly promising computational aids to improve sense making and perceived quality of online discussion, thanks to their capability to combine computational models for automated reasoning with users’ cognitive needs and expectation of automated reporting.
Multi-Objective Exploration for Proximal Policy Optimization

In Reinforcement Learning, the reward is one of the main components to optimize the strategy. While other approaches are based on a simple scalar reward to get an optimal policy, we propose a model learning the designated reward in numerous conditions. Our method, which we call multi-objective exploration for proximal policy optimization (MOE-PPO), alleviates the dependence on the reward design by executing the Preferent Surrogate Objective (PSO). We also make full use of Curiosity Driven Exploration to increase exploration ability. Our experiments test MOE-PPO in the Super Mario Bros environment designed by OpenAIGym with three criteria to illustrate our approach's effectiveness. The result shows that MOE-PPO outperforms other on-policy algorithms under many conditions.
JINSense: Repurposing Electrooculography Sensors on Smart Glass for Midair Gesture and Context Sensing

In this work, we explore a new sensing technique for smart eyewear equipped with Electrooculography (EOG) sensors. We repurpose the EOG sensors embedded in a JINS MEME smart eyewear, originally designed to detect eye movement, to detect midair hand gestures. We also explore the potential of sensing human proximity, rubbing action and to differentiate materials and objects using this sensor. This new found sensing capabilities enable a various types of novel input and interaction scenarios for such wearable eyewear device, whether it is worn on body or resting on a desk.
Generalized Local Attention Pooling for Deep Metric Learning

Deep metric learning has been key to recent advances in face verification and image retrieval amongst others. These systems consist on a feature extraction block (extracts feature maps from images) followed by a spatial dimensionality reduction block (generates compact image representations from the feature maps) and an embedding generation module (projects the image representation to the embedding space). While research on deep metric learning has focused on improving the losses for the embedding generation module, the dimensionality reduction block has been overlooked. In this work, we propose a novel method to generate compact image representations which uses local spatial information through an attention mechanism, named Generalized Local Attention Pooling (GLAP). This method, instead of being placed at the end layer of the backbone, is connected at an intermediate level, resulting in lower memory requirements. We assess the performance of the aforementioned method by comparing it with multiple dimensionality reduction techniques, demonstrating the importance of using attention weights to generate robust compact image representations. Moreover, we compare the performance of multiple state-of-the-art losses using the standard deep metric learning system against the same experiment with our GLAP. Experiments showcase that the proposed Generalized Local Attention Pooling mechanism outperforms other pooling methods when compared with current state-of-the-art losses for deep metric learning.