Reviewerly/exHarmony huggingface dataset:
Neural network-based photovoltaic generation capacity prediction system with benefit-oriented modification

Photovoltaic (PV) generation prediction is a critical technology for integrating solar energy in power systems and markets. Accuracy is the target for most PV prediction models, which represents the minimisation of the average error. However, minimisation of prediction error is to obtain a minimum cost from impact of prediction inaccuracy. The lowest average error may not always relate to the minimum cost. Thus, this paper proposes an integrated PV prediction structure that targets minimum industrial cost from prediction error other than using pure accuracy. The object of machine learning model is modified into the further industrial cost of prediction error, which is the cost of backup generation participation in power dispatch for power grid energy balancing. A feed-forward neural network is selected as typical machine learning model for integration. Additionally, to solve the nesting optimisation problem in network training, an equivalent model is constructed to remove the sub-optimisation and make gradient-based training optimisation feasible. A numerical study shows that the integrated structure leads to prediction results with a lower cost than those of an accuracy-based structure.
Metagraph Aggregated Heterogeneous Graph Neural Network for Illicit Traded Product Identification in Underground Market

The emerging underground markets (e.g., Hack Forums) have been widely used by cybercriminals to trade in illicit products or services, which have played a vital role in the cybercriminal ecosystem. In order to combat the evolving cybercrimes, in this paper, we propose and develop an intelligent framework (named PIdentifier) to automate the analysis of Hack Forums for the identification of illicit product traded in a private contract at the first attempt (to evade the law enforcement, a private contract is made between a vendor and a buyer where the traded product and its detail are invisible). In PIdentifier, based on the large-scale extracted user profiles, user posts and different types of relations within the complex ecosystem in Hack Forums, we first introduce an attributed heterogeneous information network (AHIN) to model the rich semantics and complex relations among multi-typed entities (i.e., vendors, buyers, products, comments and topics). Then, we design different metagraphs to formulate the relatedness between buyers and products based on which a metagraph aggregated heterogeneous graph neural network (denoted as mHGNN) is proposed to learn node representations for illicit traded product identification by attentively propagating and aggregating the neighborhood information defined by the designed metagraphs. Comprehensive experiments are conducted on the real-world dataset collected from Hack Forums. Promising results demonstrate the performance of our proposed PIdentifier framework in illicit traded product identification by comparison with the state-of-the-art baselines.
Recommendation of Influenced Products Using Association Rule Mining: Neo4j as a Case Study

Recommendation systems are now inherent for many business applications to take important business decisions. These systems are built based on the historical data that may be the sales data or customer feedback etc. Customer feedback is very important for any organization as it reflects the view, sentiment of the customers. Online systems allow customers to purchase products at a glance from any e-commerce website. Generally, the potential buyers check the review of the products to take informed decision of purchase. In this work, we attempt to build a recommendation model to find out the influence of a product on another product so that if a user purchases the influential product then the recommender system can recommend the influenced products to the users. In this paper, the recommendation system has been built based on association rule mining. We proposed a new association rule mining technique for quick decision-making and it gives better performance over Apriori algorithm which is one of the most popular approaches for association rule mining. The entire framework has been developed in Neo4j graph data model for doing the data modelling from raw text file and also to perform the analysis. We used real-life customer feedback data of amazon for experimental purpose.
A Novel Approach to Detect Emergency Using Machine Learning

Human activity is always a reflection of its external environmental conditions. If a group of people is in some emergency, then their activities and behaviour will be different as compared to normal conditions. To detect an emergency, Human Activity Recognition (HAR) can play an important role. Human activities such as shouting, running here and there, crying, searching for an exit door can be taken into consideration as an emergency indicator. By detecting the emergency and its degree, the Emergency Management System (EMS) can manage the situation efficiently. In this work, we use machine learning algorithms such as Random Forest (RF), IBK, Bagging, J48 and MLP on WISDM Smartphone and Smartwatch Activity and Biometric Dataset for human activity recognition and RF is found to be the best algorithm with classification accuracy 87.1977% among all other considered techniques.
TextFirewall: Omni-Defending Against Adversarial Texts in Sentiment Classification

Sentiment classification has been broadly applied in real life, such as product recommendation and opinion-oriented analysis.Unfortunately, the widely employed sentiment classification systems based on deep neural networks (DNNs) are susceptible to adversarial attacks with imperceptible perturbations into the legitimate texts (also called adversarial texts).Adversarial texts could cause erroneous outputs even without access to the target model, bringing security concerns to systems deployed in safety-critical applications.However, studies on defending against adversarial texts are still in the early stage and not ready for tackling the emerging threats, especially in dealing with unknown attacks.Investigating the minor differences between adversarial texts and legitimate texts and enhancing the robustness of target models are two mainstream ideas for defending against adversarial texts.However, both of them suffer the generalization issue in dealing with unknown adversarial attacks.In this paper, we proposed a general method, called TextFirewall, for defending against adversarial texts crafted by various adversarial attacks, which shows the potential in identifying new developed adversarial attacks in the future.Given a piece of text, our TextFirewall identifies the adversarial text by investigating the inconsistency between the target model's output and the impact value calculated by important words in the text.TextFirewall could be deployed as a third-party tool without modifying the target model and agnostic to the specific type of adversarial texts.Experimental results demonstrate that our proposed TextFirewall effectively identifies adversarial texts generated by the three state-of-the-art (SOTA) attacks and outperforms previous defense techniques.Specifically, TextFirewall achieves an average accuracy of 90.7% on IMDB and 96.9% on Yelp in defending the three SOTA attacks.
Learning adaptive criteria weights for active semi-supervised learning

Batch mode active learning (BMAL) is devoted to training trustful learning models with scarce labeled samples by efficiently asking the ground truth annotations of the most beneficial unlabeled points for supervision with the feedback of an expert. Particularly, BMAL algorithms always sample points based on the decent-designed criteria, such as (un)certainty and representativeness, etc. However, present BMAL approaches consistently are afflicted with one limitation: They simply integrate the sampling criteria with fixed weights to select instances for supervised training, which may yield suboptimal batch acquisition since the criteria values of the plentiful candidate unlabeled samples would fluctuate after retraining the classifier with the newly augmented training set. Instead, the weights of sampling criteria should be allocated appropriately. To overcome this problem, this work proposes a novel Adaptive Criteria Weights batch selection algorithm, abbreviated ACW, which dynamically adjusts the importance of (un)certainty and representativeness to choose critical instances for semi-supervised learning. A submodular function is employed to recognize a diverse mini-batch from the selected batch of samples. We apply our proposed ACW batch sampling algorithm to two types of essential semi-supervised tasks, i.e., semi-supervised classification and semi-supervised clustering. To the best of our knowledge, this work is the first devoted attempt to explore adaptive mechanism of criteria weights in the context of active learning. The superiority and effectiveness of ACW against the present state-of-the-art BMAL approaches have also been demonstrated by the encouraging experimental results.
A Tutorial on Quantum Graph Recurrent Neural Network (QGRNN)

Over the past decades, various neural networks have been proposed with the rapid development of the machine learning field. In particular, graph neural networks using feature-vectors assigned to nodes and edges have been attracting attention in various fields. The usefulness of graph neural networks also affected the field of quantum computing, which led to the birth of quantum graph neural networks composed of parameterized quantum circuits. The quantum graph neural networks have many possibilities as applications from the simulation perspective of quantum dynamics. Among the application models of various quantum graph neural networks, the quantum graph recurrent neural network (QGRNN) is proven to be effective in training the Ising model Hamiltonian. Thus, this paper introduces the concepts of the Ising model, variational quantum eigensolver (VQE) for preparing quantum data, and QGRNN from a software engineer's point of view.
Differentially private regression analysis with dynamic privacy allocation

In recent years, machine learning has reaped huge fruits in the domain of artificial intelligence. However, during the process of model training, machine learning models may be afflicted with the risk of disclosing sensitive information contained in training data. Therefore, there raises an urgent requirement for decreasing the risk of sensitive information leakage. As a novel privacy-preserving mechanism, differential privacy can tackle the shortcoming of traditional privacy model effectively, while providing a provable privacy guarantee. However, the existing literatures on differentially private regression models are limited and lack of the dynamic privacy allocation methods, which may have an influence on the balance between privacy guarantee and model performance. In this paper, we propose an adaptive differentially private regression model to enhance the security guarantee without sacrificing plenty of model utility, which allocates the privacy budget dynamically by using the relevance-based noise imposition mechanism. We add less noise to objective function when input features greatly contribute to the model output, and vice-versa. Theoretical analysis and rigorous experiments demonstrate that our approach not only retains a desirable model utility under a modest privacy budget, but also reduces the potential risk of privacy disclosure.
An Intelligent System for Evaluation of Descriptive Answers

Examination is an efficient and conventional way to test the knowledge gained by the students. The evaluation of examinations is a tiresome and arduous task. Automatic answer-script evaluation makes this task convenient for teachers as it reduces the efforts and time taken. The existing systems provide teachers the facility to conduct automatic objective and short answers exams but evaluating descriptive answers automatically is a challenge. For assessment of descriptive answers, the machine needs to consider the important factors which a human assessor takes into account for manual evaluation. To solve these problems, this research work proposed an intelligent assessment platform which considers the question type, necessary keywords, structural, conceptual and language aspects to evaluate an answer. Proposed model used concept graphs, fuzzy string matching, grammar checking and other similarity metrics for the assessment. The system also generates personalized feedback and analysis reports for students and teachers to promote focused learning.
Automatic Fully-Contextualized Recommendation Extraction from Radiology Reports

Recommendations are a key component of radiology reports. Automatic extraction of recommendations would facilitate tasks such as recommendation tracking, quality improvement, and large-scale descriptive studies. Existing report-parsing systems are frequently limited to recommendations for follow-up imaging studies, operate at the sentence or document level rather than the individual recommendation level, and do not extract important contextualizing information. We present a neural network architecture capable of extracting fully contextualized recommendations from any type of radiology report. We identified six major "questions" necessary to capture the majority of context associated with a recommendation: recommendation, time period, reason, conditionality, strength, and negation. We developed a unified task representation by allowing questions to refer to answers to other questions. Our representation allows for a single system to perform named entity recognition (NER) and classification tasks. We annotated 2272 radiology reports from all specialties, imaging modalities, and multiple hospitals across our institution. We evaluated the performance of a long short-term memory (LSTM) architecture on the six-question task. The single-task LSTM model achieves a token-level performance of 89.2% at recommendation extraction, and token-level performances between 85 and 95% F1 on extracting modifying features. Our model extracts all types of recommendations, including follow-up imaging, tissue biopsies, and clinical correlation, and can operate in real time. It is feasible to extract complete contextualized recommendations of all types from arbitrary radiology reports. The approach is likely generalizable to other clinical entities referenced in radiology reports, such as radiologic findings or diagnoses.
Multimodal Multilevel Fusion for Sequential Protective Behavior Detection and Pain Estimation

In this paper, we present our approach to the FG 2020 EmoPain Challenge for tasks 2 (pain estimation) and 3 (protective behavior detection) from multimodal movement data. We propose to perform sequential protective behavior detection and pain estimation using human movement information. First, we predict the existence of pain, and then use this information along with the multimodal movement data for protective behavior detection. Finally, this information is fused to estimate level of pain. In this work, we apply both early fusion (feature fusion including metadata, modalities, exercises and probabilities) and post-fusion (decision fusion). The proposed approach is encouraging, as it outperforms the baseline, with high margin for both pain estimation and protective behavior detection on the EmoPain challenge 2020 dataset.
Self-Organised Saliency Detection and Representation in Robot Swarms

Self-assembly of shapes using robot swarms has applications spanning architecture, functional materials, and art. Typically, the amount of robotic material needed to represent the shape is relative to the shape's area, with robots filling in the whole shape. To save in robotic material, we explore the ability to automatically aggregate on the frontier of shapes, essentially forming outlines, using a self-organised decentralised mechanism. An image is projected on top of a swarm of robots, and the robots sense the light colour and communicate this information to their neighbours to detect salient features. Results in simulation show the swarm can detect salient features, draw with different line thicknesses, adapt to changes in the feature over time, and scale to 300 robots in reality and up to 1000 robots in simulation. We then show a dynamic performance where robots continuously reconfigure in simulation to form a “video”.
Decision Support System for High Achieving Students Selection Using AHP and TOPSIS

The selection of high-achieving students is one of the schools' efforts to determine potential students who will be prepared to participate in the section of high achieving students at the regional, provincial, national, and international levels. Manual selection activities can have several problems, including it can take a lot of time when many criteria must be used, and many students participate in the selection, and there can be subjective assessments from the selection committee. Based on those problems, a decision support system is proposed using AHP to determine the weight of the criteria and TOPSIS to determine the best alternative. Decision support systems using AHP and TOPSIS can assist the selection committee in selecting high achieving students. The application of a decision support system using AHP and TOPSIS in this study can also resolve problems such as reducing the time required for the selection process and making the selection results more objective.
The effect of synchronous and asynchronous computer-mediated communication (CMC) on learners’ pronunciation achievement

Within the field of second language acquisition, pronunciation has received relatively little attention. This lack of research can also be seen in the general lack of systematic pedagogical materials for pronunciation training, effectively reinforcing the “marginalized” nature of pronunciation in the second language classroom (Lee, Plonsky & Saito, 2019). Accordingly, the current study investigated the use of synchronous/asynchronous computer-mediated communication as a method of L2 pronunciation instruction for the phonemic and lexical features of pronunciation. Broadly, the current study examined the effectiveness of face-to-face, synchronous, and asynchronous voice computer-mediated communication on EFL learners’ pronunciation achievement. In so doing, the study utilized an experimental design with 15 participants in the control group and 30 participants in two experimental groups. There were two types of tests in this study: lexical stress test, and phonemic discrimination test. The results showed the positive effect of CMC-oriented instruction compared with F-F instruction on EFL learners’ pronunciation development; however, not a significant difference was found between the two CMC instructional modes. The Syn group, on the other hand, tended to vary their pronunciation achievements more than the other groups. Indeed, technology holds great potential for pronunciation training, particularly in terms of maximizing opportunities for practice and exposure to oral language.
Federated Learning For Cyber Security: SOC Collaboration For Malicious URL Detection

Managed security service providers increasingly rely on machine-learning methods to exceed traditional, signature-based threat detection and classification methods. As machine-learning often improves with more data available, smaller organizations and clients find themselves at a disadvantage: Without the ability to share their data and others willing to collaborate, their machine-learned threat detection will perform worse than the same model in a larger organization. We show that Federated Learning, i.e. collaborative learning without data sharing, successfully helps to overcome this problem. Our experiments focus on a common task in cyber security, the detection of unwanted URLs in network traffic seen by security-as-a-service providers. Our experiments show that i) Smaller participants benefit from larger participants ii) Participants seeing different types of malicious traffic can generalize better to unseen types of attacks, increasing performance by 8% to 15% on average, and up to 27% in the extreme case. iii) Participating in Federated training never harms the performance of the locally trained model. In our experiment modeling a security-as-a service setting, Federated Learning increased detection up to 30% for some participants in the scheme. This clearly shows that Federated Learning is a viable approach to address issues of data sharing in common cyber security settings.
Traffic Lights Analysis and Simulation Using Fuzzy Inference System of Mamdani on Three-Signaled Intersections.

As one of the main characters at road intersections, traffic lights have lost its limelight and functions as of late. The unbalanced timing on its green light/green time duration settings with less to no regard to actual traffic in each lane becomes one of its main problems. Therefore in this paper, the author will formulate a more dynamic green time setting system by using Fuzzy Inference System type Mamdani. To help with the analyzing process, a desktop-based application is designed to simulate the green light duration setting for the traffic light based on the previously conducted analysis. The green light/green time output obtained during the implementation of this application will be further compared to the data obtained from transportation office of DKI Jakarta (Dinas Perhubungan Provinsi DKI Jakarta). From this comparison, it can be deduced that the output results from the implementation of this methods show a more dynamic value.
Dense Docked Ship Detection via Spatial Group-Wise Enhance Attention in SAR Images

Target detection for dense docked SAR ships has always been a challenge. First of all, dense docked ships are generally in the port area, and the interference in the land area is large. Secondly, the adjacent ships are easily detected as a ship in the detection, or will be suppressed during the Non Maximal Suppression (NMS) process, causing the targets to be lost. This paper proposes a target detection method for dense docked ships based on CenterNet. At the same time, Spatial Group-wise Enhance (SGE) attention module is added to CenterNet in this paper. SGE reduces the amount of calculation by grouping channels, and at the same time strengthens the spatial features of each group to extract more semantic features. The enhanced feature map is sent to the detection network for dense docked SAR ship target detection. The proposed method is verified on the dataset SAR-ship-Dataset, and the experimental results show that the method in this paper has better detection performance for dense docked ships.
Cryptoanalysis of an Authenticated Data Structure Scheme With Public Privacy-Preserving Auditing

In this letter, we point out that the privacy-preserving adaptive trapdoor hash authentication tree scheme (published in IEEE TIFS, doi: 10.1109/TIFS.2020.2986879) can be invalidated by an adversarial cloud server: if the outsourced data is arbitrarily modified, the cloud server still can pass the third-party auditor's auditing.