Reviewerly/exHarmony huggingface dataset:
SRM: A Sequential Recommendation Model with Convolutional Neural Network and Multiple Features

The sequential recommendation predicts the items that a user may interact in the near future via modeling the user behavior sequences. However, the existing methods adopt shallow networks which cannot extract deep features, and can't fully integrate the user general preferences. We proposed a model named a sequential recommendation model with convolutional neural network and multiple features (SRM), which uses multi-layer convolutional neural network to capture both the point-to-point and union-type sequential patterns. In addition, the model adds the user general preferences and item attributes to improve feature diversity. The experiments on public dataset proved that the SRM model can effectively predict the user's future actions, and the performance of the model is better than the existing sequential recommendation models.
Hierarchical clustering: Visualization, feature importance and model selection

We propose methods for the analysis of hierarchical clustering that fully use the multi-resolution structure provided by a dendrogram. Specifically, we propose a loss for choosing between clustering methods, a feature importance score and a graphical tool for visualizing the segmentation of features in a dendrogram. Current approaches to these tasks lead to loss of information since they require the user to generate a single partition of the instances by cutting the dendrogram at a specified level. Our proposed methods, instead, use the full structure of the dendrogram. The key insight behind the proposed methods is to view a dendrogram as a phylogeny. This analogy permits the assignment of a feature value to each internal node of a tree through an evolutionary model. Real and simulated datasets provide evidence that our proposed framework has desirable outcomes and gives more insights than state-of-art approaches. We provide an R package that implements our methods.
3DVAE-ERSG: 3D Variational Autoencoder for Extremely Rare Signal Generation

Data generation is not data augmentation. Our data generation is a new technique that can synthesize a dataset from a very small number of samples and ensure the quality of its outputs. Recently, this concept has been proposed and applied in a framework called Data Generation Framework for Extremely Rare Case Signals (DGERS) to solve a problem of a limited number of anomaly signals. With the power of DGERS consisting of principal components, including various data augmentation techniques on diverse domains, Signal Fragment Assembler (SFA), Variational Autoencoder (VAE), Data Picker (DP), and Quality Classifier (QC), the generated dataset had the good quality, when evaluated with a performance tester. Nevertheless, the DGERS has not used the full potential of VAE yet. The previous framework used the VAE latent space in only two dimensions. To use a higher potential of VAE, this paper proposed a 3D Variational Autoencoder for Extremely Rare Signal Generation (3DVAE-ERSG). This method increases the dimension of the latent space from 2D to 3D. We also proposed the 3D Data Picker for data exploration. To test this hypothesis, we experimented with the same datasets that the DGERS method was tested before. The results show that our 3DVAE-ERSG can outperform the baseline in most cases.
Enhancing IP Address Geocoding, Geolocating and Visualization for Digital Forensics

Internet Protocol (IP) address holds a probative value to the identification process in digital forensics. The decimal digit is a unique identifier that is beneficial in many investigations (i.e., network, email, memory). IP addresses can reveal important information regarding the device that the user uses during Internet activity. One of the things that IP addresses can essentially help digital forensics investigators in is the identification of the user machine and tracing evidence based on network artifacts. Unfortunately, it appears that some of the well-known digital forensic tools only provide functions to recover IP addresses from a given forensic image. Thus, there is still a gap in answering if IP addresses found in a smartphone can help reveal the user&#x2019;s location and be used to aid investigators in identifying IP addresses that complement the user&#x2019;s physical location. Furthermore, the lack of utilizing IP mapping and visualizing techniques has resulted in the omission of such digital evidence. This research aims to emphasize the importance of geolocation data in digital forensic investigations, propose an IP visualization technique considering several sources of evidence, and enhance the investigation process&#x2019;s speed when its pertained to IP addresses using spatial analysis. Moreover, this research proposes a proof-of-concept (POC) standalone tool that can match critical IP addresses with approximate geolocations to fill the gap in this area.
Digital education as a new pedagogical paradigm

The society is characterized by the expansion of digital services into all spheres of human life, including education. The digital technology sector plays a leading role in the development of a digital society. The introduction of digital services into education at all its levels is supported by government initiatives as a promising guideline for the transformation of the entire society, taking into account global practices. The research problem is: what are the mechanisms of digital education as a new pedagogical paradigm? The purpose of the research is to substantiate the features of digital education as a new pedagogical paradigm and a component of state educational policy. The research methods are data analysis of Internet sources, scientific works, generalization, systematization. The research results: new educational opportunities through the use of digital resources have been substantiated; the main features of digital education have been identified; the prospects for digital education as an innovative model have been outlined. Key findings: the introduction of digital education into a university environment has been considered in two contexts: as a necessary content of education and as a means of teaching; the main features of digital education focus on consumer preferences, organization of rational user interaction, algorithmized interaction of its users, remote work with information resources, network interaction, etc.; infection for various consumer groups; the ability to operate on different means and compatibility with other applications; the prospects for digital education are the construction of an ergonomic communicative environment of an educational institution, the possession of digital competencies by all subjects of education; motivation of scientific and pedagogical personnel to work in a new educational environment, updating academic disciplines, etc.
Mitigation of poisoning attack in federated learning by using historical distance detection

The federated learning makes it possible for users to jointly train a model while keeps their data stored locally. It is an original privacy preserving machine learning framework. Meanwhile, there exists availability and integrity threats in the framework. There may be malicious clients pretending the benign ones to interfere global model owning to the local model's indifference at server aggregation side. This behavior is named as poisoning attack, which is generally divided into data poisoning and model poisoning. In this paper, we consider a federated learning scenario with one reliable center server and several clients, where existing malicious clients launching poisoning attack. In the scenario we explore the statistical relationship of Euclidean distance among models, including benign versus benign models and malicious versus benign models. Then based the findings and inspired by evolutionary clustering, we design a defense method to screen possible malicious agents and to mitigate their attack before every round's aggregation. The scheme is implemented at the center server side. In the method our mitigation scheme refers to the detection results of both current round and previous round. Lastly we demonstrate the effectiveness of our scheme through experiments of several different scenarios.
Efficient RDF Knowledge Graph Partitioning Using Querying Workload

Data partitioning is an effective way to manage large datasets. While a broad range of RDF graph partitioning techniques has been proposed in previous works, little attention has been given to workload-aware RDF graph partitioning. In this paper, we propose two techniques that make use of the querying workload to detect the portions of RDF graphs that are often queried concurrently. Our techniques leverage predicate co-occurrences in SPARQL queries. By detecting highly co-occurring predicates, our techniques can keep data pertaining to these predicates in the same data partition. We evaluate the proposed partitioning techniques using various real-data and query benchmarks generated by the FEASIBLE SPARQL benchmark generation framework. Our evaluation results show the superiority of the proposed techniques in comparison to previous techniques in terms of better query runtime performances.
Automatic Text Summarization using Maximum Marginal Relevance for Health Ethics Protocol Document in Bahasa

The rapid development of science and technology in the health sector cannot be separated from the support of health research results. Before the research with human as a subject is conducted, every health research in Indonesia is required to make a health ethics protocol document in Bahasa which must comply with basic ethical principles. To determine whether the health research ethics protocol document has met the ethical principles, the health research ethics protocol document will be reviewed by a competent reviewer. The health research ethics protocol document consists of several parts and has a large number of pages, so to conduct a review, reviewers need a long time to understand and analyze the health research ethics protocol document. To reduce the review time, an automatic text summarization (ATS) is needed. ATS extracts important information in health research ethics protocol documents and presents it to reviewers. This research uses cosine similarity and Maximum Marginal Relevance (MMR) and TextRank to summarize the document. The MMR method is considered to have more stable results than TextRank based on the ROUGE evaluation results. The evaluation of result with the ROUGE Toolkit showed F-score value of 19.92% for document 1 and 10.98% for document 2 using MMR.
A 65-nm Energy-Efficient Interframe Data Reuse Neural Network Accelerator for Video Applications

An energy-efficient convolutional neural network (CNN) accelerator is proposed for the video application. Previous works exploited the sparsity of differential (Diff) frame activation, but the improvement is limited as many Diff-frame data is small but non-zero. Processing of irregular sparse data also leads to low hardware utilization. To solve these problems, two key innovations are proposed in this article. First, we implement a hybrid-precision inter-frame-reuse architecture which takes advantage of both low bit-width and high sparsity of Diff-frame data. This technology can accelerate 3.2 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> inference speed with no accuracy loss. Second, we design a conv-pattern-aware processing array that achieves the 2.48 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> –14.2 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> PE utilization rate to process sparse data for different convolution kernels. The accelerator chip was implemented in 65-nm CMOS technology. To the best of our knowledge, it is the first silicon-proven CNN accelerator that supports inter-frame data reuse. Attributed to the inter-frame similarity, this video CNN accelerator reaches the minimum energy consumption of 24.7 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{J}$ </tex-math></inline-formula> /frame in the MobileNet-slim model, which is 76.3% less than the baseline.
ASSET: A Semi-supervised Approach for Entity Typing in Knowledge Graphs

Entity typing in knowledge graphs (KGs) aims to infer missing types of entities and might be considered one of the most significant tasks of knowledge graph construction since type information is highly relevant for querying, quality assurance, and KG applications. While supervised learning approaches for entity typing have been proposed, they require large amounts of (manually) labeled data, which can be expensive to obtain. In this paper, we propose a novel approach for KG entity typing that leverages semi-supervised learning from massive unlabeled data. Our approach follows a teacher-student paradigm that allows combining a small amount of labeled data with a large amount of unlabeled data to boost performance. We conduct several experiments on two benchmarking datasets (FB15k-ET and YAGO43k-ET). Our results demonstrate the effectiveness of our approach in improving entity typing in KGs. Given type information for only 1% of entities, our approach ASSET predicts missing types with a F1-score of 0.47 and 0.64 on the datasets FB15k-ET and YAGO43k-ET, respectively, outperforming supervised baselines.
Quora Question Pairs Using XG Boost

Quora is a place to gain and share knowledge. It's a platform to ask questions and connect with people who contribute unique insights and quality answers. In September 2018, Quora reportedly hit 300 million monthly users. With over 300 million people visiting Quora every month, it’s no surprise that many people ask duplicated questions, that is, questions that have same intent. Multiple questions with the same intent can cause seekers to spend more time finding the best answer to their question and make writers feel they need to answer multiple versions of the same question. Quora values canonical questions because they provide a better experience to active seekers and writers, and offer more value to both of these groups in the long term. The main aim of this work is to apply various natural language processing (NLP) concepts for feature extraction from the given dataset and apply machine learning model XG Boost to predict the similarity.
Physical Layer Communication Security in Smart Cities: Challenges and Threats Identification

Communication technologies will have tremendous presence in modern smart cities, providing means for massive data collection, plethora of services and technical management and control. However, as the complexity of the underlying communication systems increases, so do the security challenges and risks, which raises concerns about the smart city technology in general. Standard communication security approaches implemented in conventional wireless and wired networks are typically focused only on specific protocols and communication schemes, and, hence, are not well grounded for smart cities from the complexity and diversity point of view. In this paper, we discuss physical layer communication security challenges and risks in smart cities. We argue that, besides higher-level information protection, an appropriate framework for intrusion detection and threat/attack classification and identification is of utmost importance. For this purpose, we propose to use methods based on applying automatic modulation classification (AMC) systems. We provide overview of the appropriate AMC schemes, and discuss their properties, advantages and disadvantages in tackling the security challenges.
Symbolic DNN-Tuner

Hyper-Parameter Optimization (HPO) occupies a fundamental role in Deep Learning systems due to the number of hyper-parameters (HPs) to be set. The state-of-the-art of HPO methods are Grid Search, Random Search and Bayesian Optimization. The first two methods try all possible combinations and random combination of the HPs values, respectively. This is performed in a blind manner, without any information for choosing the new set of HPs values. Bayesian Optimization (BO), instead, keeps track of past results and uses them to build a probabilistic model mapping HPs into a probability density of the objective function. Bayesian Optimization builds a surrogate probabilistic model of the objective function, finds the HPs values that perform best on the surrogate model and updates it with new results. In this paper, we improve BO applied to Deep Neural Network (DNN) by adding an analysis of the results of the network on training and validation sets. This analysis is performed by exploiting rule-based programming, and in particular by using Probabilistic Logic Programming. The resulting system, called Symbolic DNN-Tuner, logically evaluates the results obtained from the training and the validation phase and, by applying symbolic tuning rules, fixes the network architecture, and its HPs, therefore improving performance. We also show the effectiveness of the proposed approach, by an experimental evaluation on literature and real-life datasets.
Aspect Based Sentiment Analysis: Restaurant Online Review Platform in Indonesia with Unsupervised Scraped Corpus in Indonesian Language

The paper has designed a dynamic dashboard that will show a summarized information of restaurants in Indonesia on four distinct metrics which are Food, Service, Ambience and Covid Safety. Each metrics shown will have their own ratings which shows the detailed score for each aspect of the restaurant. The data inside the dashboard have been developed by using semi supervised learning of aspect-based sentiment analysis approach. The idea is to analyze past reviews/comments of each restaurant in the current restaurant's online review platform and extract the sentiment as well as the aspect of each of the reviews. The restaurant lists and the reviews have been collected through web scraping method on one of the most used online review platforms in Indonesia which is Tripadvisor. Scraped data has been cleaned through several process of data pre-processing by utilizing Sastrawi and NLTK library for Indonesian languages. The machine learning tools that will extract the aspect and sentiments in every of the reviews will be built by applying Monkeylearn machine learning platform through APIs. Cleaned datasets have been imported into the platform for data annotations of model training to identify the set of words belongs in each aspect categories as well as their sentiment values. Although after reaching the end of the analysis, this paper has concluded that accuracy of the analysis may not be ideal due to lack of negative sentiment dataset being gathered which affects the model during the training process. In conclusion, the feature has successfully been built and implemented as well as deployed into a web server which supported by Ngrok services however, there are still more room for improvement regarding the analysis of the model.
Prior-guided GAN-based interactive airplane engine damage image augmentation method

Deep learning-based methods have achieved remarkable success in object detection, but this success requires the availability of a large number of training images. Collecting sufficient training images is difficult in detecting damages of airplane engines. Directly augmenting images by rotation, flipping, and random cropping cannot further improve the generalization ability of existing deep models. We propose an interactive augmentation method for airplane engine damage images using a prior-guided GAN to augment training images. Our method can generate many types of damages on arbitrary image regions according to the strokes of users. The proposed model consists of a prior network and a GAN. The Prior network generates a shape prior vector, which is used to encode the information of user strokes. The GAN takes the shape prior vector and random noise vectors to generate candidate damages. Final damages are pasted on the given positions of background images with an improved Poisson fusion. We compare the proposed method with traditional data augmentation methods by training airplane engine damage detectors with state-of-the-art object detectors, namely, Mask R-CNN, SSD, and YOLO v5. Experimental results show that training with images generated by our proposed data augmentation method achieves a better detection performance than that by traditional data augmentation methods.
Unbalanced Data Set Classification Based on Convolutional Neural Network

When a data set contains different categories of data and the number of elements contained in the categories differs greatly, we call this type of data set an unbalanced data set. When classifying imbalanced data sets, it is difficult to correctly classify important minority data using traditional algorithms. How to better classify minority data has become a difficult point. The use of convolutional neural networks can solve this point well. Therefore, the purpose of this article is to study the classification of unbalanced data sets based on convolutional neural networks. This article first analyzes the structure and algorithm of the convolutional neural network. Combining with the current research status of imbalanced data set classification, the difficulties and performance evaluation methods are discussed. Finally, the classification of imbalanced data sets is researched and analyzed with convolutional neural network. This paper systematically expounds the classification model of convolutional neural network, the computational complexity of convolutional neural network, and the analysis of parameters and models of convolutional neural network applied to the classification of imbalanced data sets. And use observation method, comparative method and other research methods to carry out experimental investigation on the research of this article. Experimental research shows that the CNN algorithm has better precision and recall results for different data set samples in the classification process than the SVM algorithm and the K-means algorithm, and the classification is more accurate.
Erasures Repair for Decreasing Monomial-Cartesian and Augmented Reed-Muller Codes of High Rate

In this work, we present linear exact repair schemes for one or two erasures in decreasing monomial-Cartesian codes (DM-CC), a family of codes which provides a framework for polar codes. In the case of two erasures, the positions of the erasures should satisfy a certain restriction. We present families of augmented Reed-Muller (ARM) and augmented Cartesian codes (ACar) which are families of evaluation codes obtained by strategically adding vectors to Reed-Muller and Cartesian codes, respectively. We develop repair schemes for one or two erasures for these families of augmented codes. Unlike the repair scheme for two erasures of DM-CC, the repair scheme for two erasures for the augmented codes has no restrictions on the positions of the erasures. When the dimension and base field are fixed, we give examples where ARM and ACar codes provide a lower bandwidth (resp., bitwidth) in comparison with Reed-Solomon (resp., Hermitian) codes. When the length and base field are fixed, we give examples where ACar codes provide a lower bandwidth in comparison with ARM. Finally, we analyze the asymptotic behavior when the augmented codes achieve the maximum rate.