Reviewerly/exHarmony huggingface dataset:
Absolute Anchor-Negative Distance Based Metric Learning for Day-Night Feature Matching

Accurate and robust day-night feature matching is a fundamental challenge for visual localization of autonomous driving cars. Under adverse illumination changing situations, the performance of current handcrafted and CNN-based local features will degrade severely. This problem is caused by the phenomenon of domain gap, which can be alleviated by applying domain transformation to increase the discriminativeness of feature descriptors. However, the standard optimization function of descriptors with hard negative triplets will lead to local minima, which will significantly decrease the robustness of domain transformation on feature matching. To address above challenges, the absolute anchor-negative distance based loss function is proposed, which is named AAN loss. The proposed AAN loss integrates the absolute distance of anchor-negative and anchor-positive samples, to effectively strengthen the discriminativeness of descriptors and prevent converging to local minima. Our proposed method can improve the convergence of the domain transformation and effectively improve the performance of feature matching under adverse illumination conditions. Extensive experiments and evaluations show the improved robustness and efficiency of the proposed method.
Ensemble Learning Based Collaborative Filtering with Instance Selection and Enhanced Clustering

Recommender system is a tool to suggest items to the users from the extensive history of the user's feedback. Though, it is an emerging research area concerning academics and industries, where it suffers from sparsity, scalability, and cold start problems. This paper addresses sparsity, and scalability problems of model-based collaborative recommender system based on ensemble learning approach and enhanced clustering algorithm for movie recommendations. In this paper, an effective movie recommendation system is proposed by Classification and Regression Tree (CART) algorithm, enhanced Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) algorithm and truncation method. In this research paper, a new hyper parameters tuning is added in BIRCH algorithm to enhance the cluster formation process, where the proposed algorithm is named as enhanced BIRCH. The proposed model yields quality movie recommendation to the new user using Gradient boost classification with broad coverage. In this paper, the proposed model is tested on Movielens dataset, and the performance is evaluated by means of Mean Absolute Error (MAE), precision, recall and f-measure. The experimental results showed the superiority of proposed model in movie recommendation compared to the existing models. The proposed model obtained 0.52 and 0.57 MAE value on Movielens 100k and 1M datasets. Further, the proposed model obtained 0.83 of precision, 0.86 of recall and 0.86 of f-measure on Movielens 100k dataset, which are effective compared to the existing models in movie recommendation.
User Sentiment Analysis in the Fintech OVO Review Based on the Lexicon Method

User reviews are important in the new approach to fintech services. To learn this information, a simple sentiment analysis can make the right observations to support the OVO fintech system in analyzing the success of the fintech system. The analysis has several stages, starting from how to extract comment data from the play store, extracting meaningful information from the play store platform, and extracting the data into valuable information. Moreover, accurate topic modeling and document representation is another challenging task in sentiment analysis. We propose a lexicon-based topic modeling in observing user sentiment simply by looking at the number of words that appear. The proposed system retrieves OVO fintech comment data from the Play Store, removes irrelevant content to extract meaningful information, and generates topics and features from the extracted data using NLTK. Data processing using google collab in Python language where data is used freely. Data analysis using the word cloud method, Exploratory Data Analysis (EDA), correlation analysis between words, ordering the number of words in sentences revealed that OVO comments in that period tended to be negative
Predicting Wireless RSSI Using Machine Learning on Graphs

In wireless communications, optimizing the resource allocation requires the knowledge of the state of the channel. This is even more important in device-to-device communications, one typical use case in 5G/6G networks, where such knowledge is hard to obtain at reasonable signaling costs. In this paper, we study the use of graph-based machine learning methods to address this problem. That is to say, we learn to predict the channel state on a given link through measurements on other links, thus decreasing signaling overhead. In particular, we model the problem as a link-prediction one and we consider two representative approaches: Random Dot Product Graphs and Graph Neural Networks. The key point is that these methods consider the geometric structure underlying the data. They thus enable better generalization and require less training data than classic methods, as we show on our evaluation using a dataset of RSSI measurements of real-world Wi-Fi operating networks.
Self-Diagnosis of Radar System State in RSU Applications

To realize the intelligent transportation, environmental awareness of roadside units (RSUs) is of paramount importance. One of the approaches to enable the environmental awareness of RSUs is to equip RSUs with radar systems. However, as more and more radar systems are installed, manually monitoring whether these radar systems work in their normal states becomes impossible. To resolve this issue, a radar system state self-diagnosis method is proposed in this paper by using the radar sensing information with deep learning techniques. Specifically, by using the proposed feature extraction approach, we first effectively convert the huge amount of radar sensing data into useful features. Then, by using the proposed deep neural network to interpret the extracted features, the radar systems can self-diagnose whether there exist faults on the systems. We verify our proposed method via real-world experiments. Results show that our proposed method can accurately diagnose the radar system and report the faults.
An Approach of Enhanced PNCC for Resident Identification Applications

The performance of applying voice control in home automation can significantly drop under multi-resident situations and noisy environments. It therefore requires some appropriate approaches for smart home applications to address the problem of resident identification. Voice recognition, which explores characteristics of voice, is a potential biometric modality for such problem in smart home. In this paper, the power-normalized cepstral coefficient (PNCC) of voice biometrics is applied to identify individuals in smart home. A new technique of power-law nonlinearity and an algorithm of noise suppression based on asymmetric filtering are used to enhance feature extraction and reduce environmental noise. This proposed approach extremely reduces error rate and achieves high performance on different data sets.Duy, D. Dat, N. N. Tram, H. T. M. Son, N. H. Son, N. M.
Legality On Digital Document Using Blockchain Technology: An Exhaustive Study

This research uses Blockchain-based techniques to overcome the problem of easy diploma counterfeiting in Indonesia. Required authentication by the company using staff can save time. Because the Blockchain technique, which has six characteristics, can solve at least four problems, including reducing the number due to peer-to-peer decentralization, and reducing the time spent validating authenticity. To protect the protection system that uses this Blockchain, it is necessary to do library research as one of the previous applications of the methods used in previous researchers and continue the goals of previous researchers. In the future, further research on Blockchain techniques is needed in case of forgery of other documents so that this technology can be adopted and developed further. Based on the literature study conducted in this research, it is concluded that Blockchain technology has a positive impact as a transformation towards new energy and economic acceleration based on green technology, so that digitalization and harmonization of the academic atmosphere can be created, especially through the validity of certificates. The results of this study provides a detailed Experimental Assessment and examines the proposed model for verifying completion certificates using Blockchain Technology.
Cracking SIGABA in less than 24 hours on a consumer PC

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Hand Signs Recognition from Cellphone Camera Captured Images for Deaf-Mute Persons

Vision-based hand gesture recognition is an important task in human–computer interaction research. Sign language recognition through hand gesture images or video frames may facilitate deaf-mute persons communicate with ordinary human beings through computers. In this paper, we report (i) development of a dataset of hand gesture images acquired with handheld digital camera built in a cellphone, (ii) a pipeline for effective segmentation of palm with fingers and orientation correction of segmented region, and (iii) initial benchmark performance with multiple classifiers and multiple folds of experiments. We have obtained highest performance of 99.47% accuracy with RBF kernel-based support vector machine. We would like to release this dataset and make it available for academic, scientific, and non-commercial purposes.
Application of mathematical probabilistic statistical model of base – FFCA financial data processing

Abstract In order to improve the detection and filtering ability for financial data, a data-filtering method based on mathematical probability statistical model, a descriptive statistical analysis model of big data filtering, probability density characteristic statistical design data filtering analysis combined with fuzzy mathematical reasoning, regression analysis according to probability density of financial data distribution, and threshold test and threshold judgment are conducted to realize data filtering. The test results show that the big data filtering and the reliability and convergence of the mathematical model are optimal.
Blockchain And Legal Protection of Copyright Technical Protection Measures for Publications in the Internet Era

In today’s difficult digital copyright era, the communication revolution driven by Internet technology has caused problems such as the weakening of the value foundation of publication copyright works, the proliferation of copyright infringement, and the weakening of copyright awareness. Blockchain is a decentralized and trustless maintenance data. Reliable technology is therefore considered to be the "life-saving straw" for publication copyright. This article uses blockchain technology to re-examine and improve publication copyright protection and legal application in the new era of the Internet, and build a system of balance and integration of publication copyright protection and blockchain technology.
Gaining Location Privacy from Service Flexibility: A Bayesian Game Theoretic Approach

When using location-based services (LBSs), a user obtains points-of-interest $(\text{P}\text{o}\text{I})$ information by providing the LBS platform with his current geo-location. Such a search also leads to potential privacy leakage if an adversary has access to his geo-data. Traditional k-anonymity mechanisms instruct a user to bear the overhead to report his current location together with k-1 dummy locations to confuse the adversary, which only work well given a large number k. Aware of the common practices that a user is actually flexible in service requirement (e.g., as long as the searched PoIs are within his walking distance), we propose a novel approach to help the user gain location privacy from service flexibility for the case of a small number k. By analyzing the strategic interaction between the user and the adversary in a Bayesian game, we prove that the user with service flexibility should never report his real location for searching PoIs nearby. Instead, he should jointly use all k dummy locations to confuse the adversary’s inference of his real location. Take $k=2$ for example, we manage to show that if the adversary is not likely to access both dummy geo-data, the user should report the two dummy locations at two opposite directions of his real location, and otherwise at the same direction. Perhaps surprisingly, our approach may enable the user to benefit from the adversary’s access to more geo-data. Finally, extensive simulations using some real data show that our mechanism obviously outperforms k anonymity mechanism especially under a small number k.
MySimB’: An Interactive Learning Malay Idioms with Augmented Reality

Currently, learning idioms is included in Level One syllabus for primary school students. However, more often that not students need to traditionally refer to its meaning via physical dictionary. Certain students find learning through this method is outdated and difficult; thus, may tend to demotivate them to learn. Considering the aforementioned issues, an interactive learning tool called, MySimB’ was developed using Augmented Reality approach. The main aim for developing MySimB’ is to assist primary school students in learning standard Malay idioms easily and effectively. Learning idioms through MySimB’ also provides better understanding for primary school students, teachers and parents due to its interactive feature which was attractively designed and developed. With its Augmented Reality approach, students are expected to be more involved with their learning process and have high interest and motivation in learning Malay idioms; thereby, improve students’ achievement.
A Method to Support Continuous Planning at the Team Level

Background: Software development occurs in dynamic contexts subjected to events that must be carried off promptly and may lead to changes of previously defined plans. Although agile methods became popular by focusing on flexibility and constant adaptation, they still lack proper support for the continuous evolution of project plans. Continuous planning refers to the planning process in rapid and parallel cycles so that plans evolve according to the events. Objective: We present COPLAM (Continuous Planning Adoption Method), which supports continuous planning at the team level of agile software development, covering release, iteration, and day cycles, according to the organization’s context and projects’ needs. Method: We applied Design Science Research principles to define COPLAM. Results: We executed a case study to evaluate COPLAM’s ability to support continuous planning practices and the perceptions of usefulness, ease of use, and self-predicted future. The project planner was able to define planning cycles for projects and evolve plans accordingly to events. Conclusions: Initial evidence shows that COPLAM can support the adoption of continuous planning at the team level by fostering plan definition in rapid and parallel planning cycles and the management of events along with plans’ execution.
DARS: Data Augmentation using Refined Segmentation on Computer Vision Tasks

Data augmentation has proven to be effective in improving convolutional neural network performance. As a result, variations of data augmentation methods such as image blending, regional dropout, and copy-paste augmentations has emerged in recent years, enhancing the network's localization and generalization capabilities. However, most data augmentation approach generates unnatural image that contains visible artifacts or strong edges which the network can latch onto. In this paper, we present DARS, a module to refine segmentation in efforts to minimize artifacts for data augmentation. We demonstrated its effectiveness through object classification on Pascal VOC [6] and MS COCO [14] benchmark dataset, with qualitative comparison on refinement from DARS compared to state-of-the-art segmentation models.
Real-time user clickstream behavior analysis based on apache storm streaming

Abstract This paper presents an approach to analyzing consumers’ e-commerce site usage and browsing motifs through pattern mining and surfing behavior. User-generated clickstream is first stored in a client site browser. We build an ingestion pipeline to capture the high-velocity data stream from a client-side browser through Apache Storm, Kafka, and Cassandra. Given the consumer’s usage pattern, we uncover the user’s browsing intent through n-grams and Collocation methods. An innovative clustering technique is constructed through the Expectation-Maximization algorithm with Gaussian Mixture Model. We discuss a framework for predicting a user’s clicks based on the past click sequences through higher order Markov Chains . We developed our model on top of a big data Lambda Architecture which combines high throughput Hadoop batch setup with low latency real-time framework over a large distributed cluster. Based on this approach, we developed an experimental setup for an optimized Storm topology and enhanced Cassandra database latency to achieve real-time responses. The theoretical claims are corroborated with several evaluations in Microsoft Azure HDInsight Apache Storm deployment and in the Datastax distribution of Cassandra. The paper demonstrates that the proposed techniques help user experience optimization, building recently viewed products list, market-driven analyses, and allocation of website resources.
Searching for memory-lighter architectures for OCR-augmented image captioning

Current State-of-the-Art image captioning systems that can read and integrate read text into the generated descriptions need high processing power and memory usage, which limits the sustainability and usability of the models (as they require expensive and very specialized hardware). The present work introduces two alternative versions (L-M4C and L-CNMT) of top architectures (on the TextCaps challenge), which were mainly adapted to achieve near-State-of-The-Art performance while being memory-lighter when compared to the original architectures, this is mainly achieved by using distilled or smaller pre-trained models on the text-and-OCR embedding modules. On the one hand, a distilled version of BERT was used in order to reduce the size of the text-embedding module (the distilled model has 59% fewer parameters), on the other hand, the OCR context processor on both architectures was replaced by Global Vectors (GloVe), instead of using FastText pre-trained vectors, this can reduce the memory used by the OCR-embedding module up to a 94% . Two of the three models presented in this work surpassed the baseline (M4C-Captioner) of the challenge on the evaluation and test sets, also, our best lighter architecture reached a CIDEr score of 88.24 on the test set, which is 7.25 points above the baseline model.
Exploring Artificial Intelligence methods for recognizing human activities in real time by exploiting inertial sensors

The aim of this work is to present two different algorithmic pipelines for human activity recognition (HAR) in real time, exploiting inertial measurement unit (IMU) sensors. Various learning classifiers have been developed and tested across different datasets. The experimental results provide a comparative performance analysis based on accuracy and latency during fine-tuning, training and prediction. The overall accuracy of the proposed pipeline reaches 66 % in the publicly available dataset and 90% in the in-house one.
Exploring Support Vector Machines for Big Data Analyses

The traditional support vector machines perform well in classification and prediction on small and medium-sized data sets, but there are some problems such as the low training efficiency and the low accuracy in large sample number, high dimension and large-scale data sets. Meanwhile, with the rise of distributed computing platforms such as the Spark suitable for big data analyses, more and more scholars at home and abroad turn their research direction to the distributed machine learning algorithms Therefore, in order to carry out the research on support vector machine for big data analyses, this paper explores the related researches and current situations of support vector machine, including: in-depth analysis of the algorithm principle of support vector machines, systematical investigation of the improved methods of support vector machines for the big data analyses, and distributed support vector machines under the Spark platform. Then, combined with the parallelization mechanism of the Spark, the some future research directions of support vector machine are investigated: for optimizing the accuracy of training results, some special matrix calculation skills should be added; In term of the research on SVM under the Spark platform, some better optimization methods from the perspective of dimension and partition can be found.
Semi-Supervised Segmentation of Renal Pathology: An Alternative to Manual Segmentation and Input to Deep Learning Training

Kidney biopsy interpretation is the gold standard for the diagnosis and prognosis for kidney disease. Pathognomonic diagnosis hinges on the correct assessment of different structures within a biopsy that is manually visualized and interpreted by a renal pathologist. This laborious undertaking has spurred attempts to automate the process, offloading the consumption of temporal resources. Segmentation of kidney structures, specifically, the glomeruli, tubules, and interstitium, is a precursory step for disease classification problems. Translating renal disease decision making into a deep learning model for diagnostic and prognostic classification also relies on adequate segmentation of structures within the kidney biopsy. This study showcases a semi-automated segmentation technique where the user defines starting points for glomeruli in kidney biopsy images of both healthy normal and diabetic kidney disease stained with Nile Red that are subsequently partitioned into four areas: background, glomeruli, tubules and interstitium. Five of 30 biopsies that were segmented using the semi-automated method were randomly selected and the regions of interest were compared to the manual segmentation of the same images. Dice Similarity Coefficients (DSC) between the methods showed excellent agreement; Healthy (glomeruli: 0.92, tubules: 0.86, intersititium: 0.78) and diabetic nephropathy: (glomeruli: 0.94, tubules: 0.80, intersititium: 0.80). To our knowledge this is the first semi-automated segmentation algorithm performed with human renal biopsies stained with Nile Red. Utility of this methodology includes further image processing within structures across disease states based on biological morphological structures. It can also be used as input into a deep learning network to train semantic segmentation and input into a deep learning algorithm for classification of disease states.