Reviewerly/exHarmony huggingface dataset:
Incremental learning of object detection with output merging of compact expert detectors

Object detection has been widely applied and has good performance in recent years. However, it has a practical problem that we have to retrain the network by all images if we want to add a new task. It will require much memory to store images of all tasks and time to retrain the network. If we only use the data set of the new task to fine-tune the original model, it will perform well on the new task but catastrophic forgetting of previous tasks. This paper presents a method to address this issue and achieves incremental learning of object detection when only the current task data set is available each time. The core of our proposed solution is to train an expert detector for each task and merge the output of expert detectors by the merging module. Unlike previous works, our method has no limitation on the number of tasks added and will not cause any loss of mAP. We also use some tricks such as adding pre-classifiers, pruning networks and combining the convolutional layer and batch normalization layer to optimize the model. We conduct extensive experiments on the PASCAL VOC dataset and compare the results with the previous methods, along with a detailed empirical analysis of our approach.
Sigmalaw PBSA - A Deep Learning Model for Aspect-Based Sentiment Analysis for the Legal Domain

Legal information retrieval holds a significant importance to lawyers and legal professionals. Its significance has grown as a result of the vast and rapidly increasing amount of legal documents available via electronic means. Legal documents, which can be considered flat file databases, contain information that can be used in a variety of ways, including arguments, counter-arguments, justifications, and evidence. As a result, developing automated mechanisms for extracting important information from legal opinion texts can be regarded as an important step toward introducing artificial intelligence into the legal domain. Identifying advantageous or disadvantageous statements within these texts in relation to legal parties can be considered as a critical and time consuming task. This task is further complicated by the relevance of context in automatic legal information extraction. In this paper, we introduce a solution to predict sentiment value of sentences in legal documents in relation to its legal parties. The Proposed approach employs a fine-grained sentiment analysis (Aspect-Based Sentiment Analysis) technique to achieve this task. Sigmalaw PBSA is a novel deep learning-based model for ABSA which is specifically designed for legal opinion texts. We evaluate the Sigmalaw PBSA model and existing ABSA models on the SigmaLaw-ABSA dataset which consists of 2000 legal opinion texts fetched from a public online data base. Experiments show that our model outperforms the state-of-the-art models. We also conduct an ablation study to identify which methods are most effective for legal texts.
Blockchain Applications that are Transforming the Society

Since the introduction of digital technology, the drastic changes can be seen in our society in almost all fields from agriculture to aviation; because of this, the communication between person and person, industries, and organizations at various level is rationalized, as technology that took prominent role in financial- and asset-related issues made the living standards of the public better, but the crime rate also drastically increased, and various security breaches and issues came into limelight. Individuals, organizations, industries, government departments, international organizations also faced issues of cybersecurity breaches, hacking, and cryptographical rogue attempts on data and documentation; in this situation, the attempt by hackers in stealing the information and data is common, and day after day, it is increasing which disturbs the society in terms of insecurity and financial losses; to protect the data or information or documents or transactions, the best technology is blockchain technology. Similarly, to handle home and industry automation issues through the Internet and in networked environment, a popular technology came into prominence called Internet of Things (IOT); as it is open-ended and used by many people and hence billions of IOT devices are in use, IOT device environments are to be protected in such a way that at any given situation, these devices should not face any security breaches. Blockchain technology offers advanced security solutions and is used for the digital currency or cryptocurrency security and many other areas including IOT security where unique IOT device identification, device functionality testing, and device field usage can be explored and in turn can make the society digitally safe.
Perfect edge state transfer on cubelike graphs

Perfect (quantum) state transfer has been proved to be an effective model for quantum information processing. In this paper, we give a characterization of cubelike graphs having perfect edge state transfer (PEST, in short). By using a lifting technique, we show that every bent function, and some semi-bent functions as well, can produce some graphs having PEST. Some concrete constructions of such graphs are provided. Notably, using our method, one can obtain some classes of infinite graphs possessing PEST.
Sentiment Analysis of Ojek Online User Satisfaction Based on the Naïve Bayes and Net Brand Reputation Method

Gojek and Grab are the most popular online motorcycle taxis and are often used today in Indonesia, based on Hootsuite's survey. However, it is not yet known how the response from online motorcycle taxi users. So it is necessary to have a sentiment analysis of online motorcycle taxi users whether they are satisfied or dissatisfied with the drivers and Gojek and Grab companies' services. Twitter with 52% active users of all internet users in Indonesia allows users to write various topics so that to find out the level of user satisfaction with Gojek and Grab. Sentiment analysis can be used as a reference for the development of Gojek and Grab services in the future. They measure the level of satisfaction with the Net Brand Reputation (NBR) method from the Naïve Bayes classification results using the rapid miner tool. The rating with accuracy has an accuracy value of 99.80% for Gojek and 99.90% for Grab. This study shows that more tweets have negative opinions compared to positive opinions for Gojek and Grab. Namely 616 positive opinions and 2317 negative opinions for Gojek drivers, 3560 positive opinions and 6419 negative opinions for Gojek Company. 594 positive opinions, and 1866 negative opinions for Grab drivers. As well as 3516 positive opinions and 4407 negative opinions for Grab Companies. So the results of the sentiment analysis of online motorcycle taxi users are dissatisfaction with either the driver or the company.
A Density-Peak-Based Clustering Method for Multiple Densities Dataset

Clustering methods in data mining are widely used to detect hotspots in many domains. They play an increasingly important role in the era of big data. As an advanced algorithm, the density peak clustering (DPC) algorithm is able to deal with arbitrary datasets, although it does not perform well when the dataset includes multiple densities. The parameter selection of cut-off distance dc is normally determined by users’ experience and could affect clustering result. In this study, a density-peak-based clustering method is proposed to detect clusters from datasets with multiple densities and shapes. Two improvements are made regarding the limitations of existing clustering methods. First, DPC finds it difficult to detect clusters in a dataset with multiple densities. Each cluster has a unique shape and the interior includes different densities. This method adopts a step by step merging approach to solve the problem. Second, high densities of points can automatically be selected without manual participation, which is more efficient than the existing methods, which require user-specified parameters. According to experimental results, the clustering method can be applied to various datasets and performs better than traditional methods and DPC.
Data-Driven Approach for Credit Card Fraud Detection with Autoencoder and One-Class Classification Techniques

With the development of e-commerce, payment by credit card has become an essential means for the purchases of goods and services online. Especially, the Manufacturing Sector faces a high risk of fraud online payment. Its high turnover is the reason making this sector is lucrative with fraud. This gave rise to fraudulent activity on the accounts of private users, banks, and other services. For this reason, in recent years, many studies have been carried out using machine learning techniques to detect and block fraudulent transactions. This article aims to present a new approach based on real-time data combining two methods for the detection of credit card fraud. We first use the variational autoencoder(VAE) to obtain representations of normal transactions, and then we train a support vector data description (SVDD) model with these representations. The advantage of the representation learned automatically by the variational autoencoder is that it makes the data smoother, which makes it possible to increase the detection performance of one-class classification methods. The performance evaluation of the proposed model is done on real data from European credit cardholders. Our experiments show that our approach has obtained good results with a very high fraud detection rate.
Estimating Groups of Featured Characters in Comics with Sequence of Characters' Appearance

This paper proposes a method to estimate a group of featured characters during an arbitrary period in comics. Comics have so many aspects of attractiveness such as illustrations and quotes. The storyline, which is one of the attractive of comics, enables us to enjoy the dramas in comics. In comics, the story is driven by characters' activities represented in multimedia forms: emotion, speech, and some other actions. This paper, as a first step to recognize the storyline, tackles the estimation of a group of featured characters in a given period. To compute the storyline of comics with the facility, the proposed method uses a sequence of characters' appearances for each page. The experiment showed that the proposed method outperformed the comparative methods in estimating groups of featured characters with a 0.82 F-value on average while comparative methods showed 0.67 and 0.48. The results showed that sequences of characters' appearance, which are relatively easy to obtain, were sufficient to catch the brief storyline i.e. featured characters in a story.
Coarse ethics: how to ethically assess explainable artificial intelligence

Abstract The integration of artificial intelligence (AI) into human society mandates that their decision-making process is explicable to users, as exemplified in Asimov’s Three Laws of Robotics. Such human interpretability calls for explainable AI (XAI), of which this paper cites various models. However, the transaction between computable accuracy and human interpretability can be a trade-off, requiring answers to questions about the negotiable conditions and the degrees of AI prediction accuracy that may be sacrificed to enable user-interpretability. The extant research has focussed on technical issues, but it is also desirable to apply a branch of ethics to deal with the trade-off problem. This scholarly domain is labelled coarse ethics in this study, which discusses two issues vis-à-vis AI prediction as a type of evaluation. First, which formal conditions would allow trade-offs? The study posits two minimal requisites: adequately high coverage and order-preservation. The second issue concerns conditions that could justify the trade-off between computable accuracy and human interpretability, to which the study suggests two justification methods: impracticability and adjustment of perspective from machine-computable to human-interpretable. This study contributes by connecting ethics to autonomous systems for future regulation by formally assessing the adequacy of AI rationales.
An entropy minimization histogram mergence scheme and its application in image compression

In this paper, we propose an entropy minimization histogram mergence (EMHM) scheme that can significantly reduce the number of grayscales with nonzero pixel populations (GSNPP) without visible loss to image quality. We proved in theory that the entropy of an image is reduced after histogram mergence and that the reduction in entropy is maximized using our EMHM. The reduction in image entropy is good for entropy encoding considering that the minimum average code word length per source symbol is the entropy of the source signal according to Shannon’s first theorem. Extensive experimental results show that our EMHM can significantly reduce the code length of entropy coding, such as Huffman, Shannon, and arithmetic coding, by over 20% while preserving the image subjective and objective quality very well. Moreover, the performance of some classic lossy image compression techniques, such as the Joint Photographic Experts Group (JPEG), JPEG2000, and Better Portable Graphics (BPG), can be improved by preprocessing images using our EMHM.
Feature Selection for Email Phishing Detection Using Machine Learning

Phishing attacks are one of the emerging and devastating cyber-attacks. Email is now considered as an appropriate means of written correspondence. Phishing emails are emails intended to extract sensitive and confidential information from the receiver. Two prong presentations are the aims of this paper; first, the analysis to remove the Phishing features from the email, and second, a notable triumph to reduce the complex data set to a lower dimension with different features. The feature reduction process is based on classification and prediction accuracy. The training and testing dataset is a collection of 1500 data tuples from the SPAMASSASIAN corpus. The validation dataset is made by retrieving the emails from Gmail users. To decide the two class codes, PHISHING and HAM, a total of 2000 emails are used to train, test, and validate the data tuples. In this research, 1000 emails are used to train, 500 emails are used to test the data tuples and validate them. First, the dataset is preprocessed to parse the data using HTML Parsing, Data Cleaning, Stemming, Stop Word Elimination, and Tokenization. By reading each email iteratively from the dataset, the features are extracted. Classifier ensemble strategies have gained the attention of many researchers in the machine learning research community in recent years [1]. Three machine learning classification algorithms are applied to predict the PHISHING and HAM emails such as decision trees (J48), random forest, and logistic regression. It was found that the random forest algorithm works best to separate PHISHING and HAM emails with the precision of a 99% classifier. With 15 feature sets, it fits best, the accuracy of training and validation is calculated to be 95.6% and 99.4%, respectively.
A game co-design method to elicit knowledge for the contextualization of spatial models

A frequent criticism of knowledge-based planning tools is the apparent mismatch between information frameworks used in their spatial models and the information needs of planning actors. Increasingly, these actors are contributing their context-specific knowledge during the development of such tools. Transferring this knowledge from actors to the model remains a challenge. This study establishes a set of design requirements for knowledge elicitation in small group settings and introduces game co-design as a method allowing planning actors and planning support experts to meet halfway between the technology and user domains in the so-called third space. We present an initial case where in three nominal group sessions, actors encountered and critiqued parameterized assumptions of their planning issues in a tangible game environment. Findings indicate that the method can elicit different types of knowledge (divergence) about a spatial system in operationalized terms (formalization). We discuss the potential of tangible game co-design as a modeling as learning exercise and its complementarity to dedicated digital technologies for more holistic planning support.
Detecting aerial objects: drones, birds, and helicopters

Standard object detectors are trained on a wide array of commonplace objects and work out-of-the-box for numerous every-day applications. Training data for these detectors tends to have objects of interest that appear prominently in the scene making them easy to identify. Unfortunately, objects seen by camera sensors in the real-world scenarios typically do not always appear large, in-focus, or towards the center of an image. In the face of these problems, the performance of many detectors lags behind the necessary thresholds for their successful implementation in uncontrolled environments. Specialized applications necessitate additional training data to be reliable in-situ, especially when small objects are likely to appear in the scene. In this paper, we present an object detection dataset consisting of videos that depict helicopter exercises recorded in an unconstrained, maritime environment. Special consideration was taken to emphasize small instances of helicopters relative to the field-of-view and therefore provides a more even ratio of small-, medium-, and large-sized object appearances for training more robust detectors in this specific domain. We use the COCO evaluation metric to benchmark multiple detectors on our data as well as the WOSDETC (Drone Vs. Bird) dataset; and, we compare a variety of augmentation techniques to improve detection accuracy and precision in this setting. These comparisons yield important lessons learned as we adapt standard object detectors to process data with non-iconic views from field-specific applications.
A Maximum Divergence Approach to Optimal Policy in Deep Reinforcement Learning

Model-free reinforcement learning algorithms based on entropy regularized have achieved good performance in control tasks. Those algorithms consider using the entropy-regularized term for the policy to learn a stochastic policy. This work provides a new perspective that aims to explicitly learn a representation of intrinsic information in state transition to obtain a multimodal stochastic policy, for dealing with the tradeoff between exploration and exploitation. We study a class of Markov decision processes (MDPs) with divergence maximization, called divergence MDPs. The goal of the divergence MDPs is to find an optimal stochastic policy that maximizes the sum of both the expected discounted total rewards and a divergence term, where the divergence function learns the implicit information of state transition. Thus, it can provide better-off stochastic policies to improve both in robustness and performance in a high-dimension continuous setting. Under this framework, the optimality equations can be obtained, and then a divergence actor-critic algorithm is developed based on the divergence policy iteration method to address large-scale continuous problems. The experimental results, compared to other methods, show that our approach achieved better performance and robustness in the complex environment particularly. The code of DivAC can be found in https://github.com/yzyvl/DivAC.
A Scalable Track-Before-Detect Method With Poisson/Multi-Bernoulli Model

We propose a scalable track-before-detect (TBD) tracking method based on a Poisson/multi-Bernoulli model. To limit computational complexity, we approximate the exact multi-Bernoulli mixture posterior probability density function (pdf) by a multi-Bernoulli pdf. Data association based on the sum-product algorithm and recycling of Bernoulli components enable the detection and tracking of low-observable objects with limited computational resources. Our simulation results demonstrate a significantly improved tracking performance compared to a state-of-the-art TBD method.
Regolith characterization and landscape evolution for geochemical exploration of the covered Yamarna Terrane, Western Australia

The easternmost Archaean Yamarna Terrane is the least explored terrane under transported cover in the Yilgarn Craton of Western Australia. Our aims are to understand the evolution of the paleolandscape, determine the weathering history and depositional processes, and the provenance, mineralogy and geochemistry of the Permian to Quaternary transported cover. This is a necessary step for selecting the appropriate sample media to vector towards buried mineral deposits in this terrane. The Permian cover was deposited in broad, structurally controlled valleys that were carved into and partly to completely truncate the previously weathered Archaean rocks. UPb dating of zircon grains indicates that the main provenance of the Permian cover on the Yamarna Terrane is the Albany-Fraser Orogen, with limited contribution from the Yilgarn Craton, Musgrave Province and Antarctica. The Permian cover of the paleovalleys wedges out laterally towards pre-existing, weathered basement paleohighs, capped by lateritic residuum. The Permian cover is deeply weathered, and consists of upper ferruginous (mottled, brecciated, pisolitic and nodular) and lower kaolinitic, poorly lithified sandstones and mudstones with a basal gravel at the bottom. Above the basal unconformity between the Permian and weathered Archaean, the Rb/Zr ratio decreases sharply and the Permian cover shows negative Eu anomalies (Eu/Eu*) compared to positive Eu/Eu* in the underlying weathered and fresh mafic Archaean rocks, suggesting distal felsic source rocks for the Permian cover. In places, where Au increases, the base of the Permian cover still shows positive Eu/Eu* anomalies, suggesting physical dispersion from proximal mineralized mafic rocks. The Post-Permian weathering and erosion processes produced an extensive layer of Au-bearing detrital ferruginous clasts derived from lateritic residuum and ferruginous pisoliths and nodules from the Permian cover. These ferruginous grains occur as loose or cemented by Fe oxides forming ferricrete. Ferricrete is not necessarily related to the underlying weathered dolerite compared to the lateritic residuum. This is shown, in some places, by a sharp decrease in the Ti/Zr ratio, high HFSE, negative Eu/Eu*, high LREE/HREE compared to the underlying weathered dolerite suggest an unconformity and physical dispersion from weathered felsic source rocks. The Au-enrichment in the ferricrete derived from the felsic rocks occurred after deposition as a result of hydromorphic dispersion by groundwater. The weathered Permian cover and Archaean rock are covered by aeolian sand within which Au is concentrated in authigenic pisoliths. These pisoliths were formed by periodical water saturation during seasonal water table fluctuation. The unconformities and the ferruginous regolith materials are relevant to Au exploration through cover in the Yamarna Terrane and are highly recommended for regional-scale systematic sampling.
Robust Image-based Landing Control of a Quadrotor on an Unknown Moving Platform Using Circle Features

This paper addresses the robust image-based landing control problem for a landing system, including an underactuated quadrotor and an unknown moving platform. Firstly, an image kinematics is constructed by using an image moment based on a circle feature in the virtual image plane, which decouples the landing system into a translation and a rotation control module. Exploiting the funnel control techniques with barrier Lyapunov functions (BLFs), the proposed IBVS control law does not depend on relative height needed by IBVS-like approaches and the explicit knowledge of moving platforms. Besides, it guarantees feature errors with prescribed transient and steady-state behavior. Finally, A simulation study is presented to verify the efficacy of the proposed control scheme.