Reviewerly/exHarmony huggingface dataset:
Using DDMT Teaching Model to Cultivate Critical Thinking in a STEAM Classroom

In an environment disrupted by technology, critical thinking is a crucial 21st Century Skill that allows learners to stay intact when any number of organizations (corporate, political, educational and cultural) try to influence readers to think and act in ways that serve their purposes [1]. It has also been emphasized in the ATC21S [2] project as one of the desired outcomes under ‘Ways of thinking’. In this paper, we aim to share about how Design Based Research and DDMT teaching model can shape a chemistry lesson on water for Grade 5 learners. The lessons will be shaped towards guiding the learners in understanding acidity/alkalinity as required by the national curriculum and also seek to provide an insight into how young learners showcase development of critical thinking in the learning process.
A Technique in Information Retrieval and Bibliometrics to Check the Reliability of an Article Indexing

Many document indexing has been developed since the emergence of the Internet for local, regional, or global authors. Some local document indexing has also been developed for Indonesian authors like Garuda, Neliti, IPI (Indonesian Publication Index), and Sinta (Indonesian Science and Technology Index). The most phenomenal among Indonesian document indexing is Sinta because it is used to score authors that are taken into account when the researcher apply for research grants and promotion. It is also to rank authors, institutions, and journals. Unfortunately, there was no research about Sinta to convince authors that Sinta database is reliable. The purposes of this research are to propose a technique to evaluate document indexing services such as Sinta and to propose some improvements. We used focused web crawler for data gathering. The data then processed by using bibliometrics and information retrievals methods. The results show that there were noises in Sinta's database like duplicate accounts, foreign authors, foreign articles, invalid Scopus articles, difference citation count, and retracted & corrigendum articles. This research also revealed a side effect of Sinta that is unethical ways of publication by Indonesian authors. We also showed that some part of the design of Sinta was inappropriate.
Prediction of the nonsampled units in survey design with the finite population using Bayesian nonparametric mixture model

In the sampling approaches framework, the combination of the information on the sizes of the nonsampled units can help to attain better estimators by using semiparametric models. Sometimes, the design variables that have an important role in the sampling mechanism are not available. Hence predictions require to be adapted for the consequence of selection. To infer the population mean in a sample survey, we study Bayesian nonparametric model with Dirichlet process prior by considering the inverse-probability weights as the only available information. Indeed, we present a Bayesian nonparametric mixture of regression models for the survey outcomes with the weights as predictors and impute the nonsampled units. Finally, the model-based estimators that are derived from the Bayesian (parametric and nonparametric) methods are compared with the design-based estimator based on the simulation approaches.
An Evaluation of Word Frequency Techniques for Text Summarization Using Sentiment Analysis Approach

Digital data has become an important aspect of machine learning and is present in huge volumes on the internet. To use this data efficiently, data handling and processing techniques are required to filter out information from documents and store them. An application of natural language processing, which helps in handling volumes of data, is text summarization. Text summarization helps in condensing documents, and extract the important facts represented in it. There are two techniques in text summarization: abstractive and extractive summarization. Extractive Summarization extracts keywords from the document and combines them to provide a semantically incorrect summary, whereas, Abstractive Summarization produces a semantically correct summary of the text. In this paper, we compare different techniques to identify low and medium frequency words in hotel reviews. We evaluate the techniques based on the correct identification of positive and negative words.
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This article focuses on the new privacy challenges that arise in smart homes. Specifically, the article focuses on inferring the user’s activities—which may, in turn, lead to the user’s privacy—via inferences through device activities and network traffic analysis. We develop techniques that are based on a cryptographically secure token circulation in a ring network consisting of smart home devices to prevent inferences from device activities, via device workflow , i.e., inferences from a coordinated sequence of devices’ actuation. The solution hides the device activity and corresponding channel activities, thus preserving the individual’s activities. We also extend our solution to deal with a large number of devices and devices that produce large-sized data by implementing parallel rings. Our experiments also evaluate the performance in terms of communication overheads of the proposed approach and the obtained privacy.
Dynamic Channel Pruning For Correlation Filter Based Object Tracking

Fusion of multi-channel representations has played a crucial role in the success of correlation filter (CF) based trackers. But, all channels do not contain useful information for target localization at every frame. During challenging scenarios, ambiguous responses of non-discriminative or unreliable channels lead to erroneous results and cause tracker drift. To mitigate this problem, we propose a method for dynamic channel pruning through online (i.e., at every frame) learning of channel weights. Our method uses estimated reliability scores to compute channel weights, to nullify the impact of highly unreliable channels. The proposed method for learning of channel weights is modeled as a non-smooth convex optimization problem. We then propose an algorithm to solve the resulting problem efficiently compared to off-the-shelf solvers. Results on VOT2018 and TC128 datasets show that proposed method improves the performance of baseline CF trackers.
Predicting QoE of Video Streaming with Network-Context Features

It is always the top priority for network service providers to provide better Quality of Experience (QoE). Huge efforts have been devoted to Adaptive Bit Rate (ABR) streaming, but it is only a passive QoE amelioration method by adapting to bandwidth variations. In this paper, we aim to provide a QoE prediction tool for service providers to improve QoE proactively. We have investigated the impacts of various network conditions on QoE through a large-scale measurement study, and proposed a QoE model from network-context features. The correlation analysis helps to figure out how the network-context features influence QoE. Furthermore, we designed a neural-network learning algorithm to predict QoE with the features of access link, location, CDN and network operators. The prediction algorithm achieves up to 85% accuracy over a large dataset of 2 billion viewing sessions from a commercial video website.
Blockchain, TTP Attacks and Harmonious Relationship with AI

Blockchain and decentralised distributed ledger technologies are being viewed as a mechanism to provide further protection and enhance the security of data by using its properties of immutability, auditability and encryption whilst providing transparency amongst parties who may not know each other; so, operating in a trustless environment. It's true that blockchain has its roots in cryptocurrency applications and is still evolving for that purpose in the financial sector, but many other organisations across different industries are beginning to see the non-crypto use cases where this mechanism to record data that cannot be changed or reversed or apply as smart contracts (as a way to time-stamp transactions between parties) is becoming extremely relevant and purposeful. A variety of industry sectors, besides Finance, has undertaken the use of these distributed technologies and beneficial attributes of blockchain from the healthcare and pharmaceutical, real estate, retail and supply chain, legal and publishing. Organisations have flexible options to run blockchain as permissionless (anyone can join), permissioned (where those need to be invited) or hybrid (a consortium type) and whether data should be held on-chain or off-chain. With industry entering its fourth industrial revolution (Industry 4.0) the addition of blockchain as a complimentary technology has its place and there are some industries very suited to the significant impact it may bring. Also, the advances of Internet of Things, Machine Learning and Artificial Intelligence has meant more pressures on potential impacts to data and the ripple effects that cyber-attacks may cause. This has also become complicated, as cyber-attacks have become much more sophisticated over recent years with the different configuration types and various industry sectors have suffered from a range of these different attack vectors, resulting in some devastating outcomes. These have manifested in the shape of ransomware, malware, manipulation methods, phishing and spear-phishing. Whilst data breaches are a serious incident, in most organisations, there is a growing concern regarding attacks that are designed to have a more destructive effect such as the Ukraine cyber-attack in 2015 that resulted in a shutdown of the power grid or the WannaCry ransomware attack in 2017 that caused widespread chaos with healthcare institutions unable to carry out any tasks since access to data/systems was unavailable.
Dynamic Trilateral Game Model for Attack Graph Security Game

Abstract Internal threats have a huge impact on the attack graph security game. The failure of the MTD model defence measures would be caused by the existence of internal users with certain authority. Dynamic trilateral game model was proposed to extend the original two-part game model. By materializing internal threats, the uncertainty of two-part game model was eliminated, which was expressed as the probability equation used by the players in the observation state process. And the relationship between the offensive and defensive sides became indirect. The user strategy, based on mixed strategy game model, was proposed to increase the coupling between stealth attack and internal threats. The income matrix was dynamically constructed to measure the behavioural outcomes of users and attackers. User behavioural references were obtained through dynamic programming. For the defender, the heuristic strategy in the model reduces the complexity of parties’s behaviour through random sampling. Experiments were carried out on the attack graph model under various game settings. Compared with the two-part game model, our model’s experimental results showed that the cyber security risks were reduced by 17.9% and 18.8% respectively on the strong and weak structural attack graph.
Recovering Decentralized Critical Archival Data From Tampering in Smart City Environment Using Blockchain

A large modern organization usually has a large number of computers and smart devices continuously deployed to store and share critical data among the users inside and outside the organization. Critical data storage needs to be secure and scalable for such an organization. While current cloud-based services offer a centralized view and control over the IT operations of these smart devices, it is prone to attacks, data tampering, and rogue administrator problems. In this paper, an approach using blockchain to recovering critical data from tampering is presented. Although our approach incurs a large amount of overhead due to the use of blockchain, our approach is shown to recover the critical data from all the data tampering. Simulation of our approach used in a smart city environment is conducted to show that the overhead of our approach is compared favorably with that of using a centralized cloud-based approach. Our approach is suitable for recovering critical archival data, instead of real-time data, because the speed is not a very important factor for recovering critical archival data from tampering.
Attention-Based User Temporal Model for Recommendation

For the traditional recommendation system, the user model is represented by the user's historical behaviors, but the user's interest is not static, and the user's interest drift affects the final recommendation effect of the recommendation system. However, most of the work to solve the drift of interest is to design long-term and short-term models for users, Using RNNbased models with different time spans to learn an overall embedding of user history sequences. It is not possible to dynamically monitor user interest drift. In this work, we propose a user model named Attention-Based User Temporal Model (AUTM). The key design of our work is to use the attention model to dynamically assign importance weights to user embedding of RNN network outputs at different times, and then combine the static user embedding. Compared to a set of baselines in real-word dataset, our model shows better performance in prediction precision and Area Under Curve (AUC) score.
A RDA-Based Deep Reinforcement Learning Approach for Autonomous Motion Planning of UAV in Dynamic Unknown Environments

Abstract Autonomous motion planning (AMP) in dynamic unknown environments emerges as an urgent requirement with the prosperity of unmanned aerial vehicle (UAV). In this paper, we present a DRL-based planning framework to address the AMP problem, which is applicable in both military and civilian fields. To maintain learning efficiency, a novel reward difference amplifying (RDA) scheme is proposed to reshape the conventional reward functions and is introduced into state-of-the-art DRLs to constructs novel DRL algorithms for the planner’s learning. Different from conventional motion planning approaches, our DRL-based methods provide an end-to-end control for UAV, which directly maps the raw sensory measurements into high-level control signals. The training and testing experiments demonstrate that our RDA scheme makes great contributions to the performance improvement and provides the UAV good adaptability to dynamic environments.
DGA domain name detection based on BiGRU-MCNN

Aiming at the problem that DGA domain name is difficult to detect effectively, a hybrid model based on bidirectional gated recurrent unity and multi-channel convolutional neural network is proposed for DGA domain name detection. The model consists of three parts: the character embedding layer, the feature extraction layer and the classification prediction layer. The character embedding layer completes the automatic encoding of the input characters; the feature extraction layer uses BiGRU to learn the dependency between data features and the MCNN uses different neural network channels to learn information from various aspects to obtain deep hidden information for automatic extraction. The characteristics of the input characters; the classification layer uses a three-layer fully connected neural network to achieve automatic prediction classification of DGA domain names. The experimental results show that the model achieves an accuracy of 92.27%, which improves the accuracy of detection compared with other deep learning methods.
Simulation of Quantum Radar Cross Section for 3-D Targets

Quantum radar has attracted extensive attentions since it was proposed. As an important parameter of quantum radar, quantum radar cross section (QRCS) is defined from the framework of quantum electrodynamics. More recently, many researches related to the computational simulation of QRCS are reported. However, the calculations are limited to the two-dimensional (2-D) target and extremely time-consuming. In this paper, we propose a universal method to analyze the QRCS of 3-D targets. Furthermore, an efficient parallel technology is implemented, leading to remarkable improvement on calculation efficiency. Our method presented here can be helpful to the rapid analysis of QRCS and object identification.
The Principles of Developing a Differential Video Controlling Scheme Based on the Use of Intelligent Agents

The article outlines the basic principles of building the video intensity controlling systems in real time, based on the use of multi-agent approach. The differential scheme involves using of the different sets of options for the basic codec, depending on the content of the video data fragments, received for processing. In the framework of the multi-agent approach, at each of the transformation steps in the basic coding technology, uses a separate intelligent agent that performs exclusively one of the processing functions. At the same time, the decision on the choice of certain processing parameters for the agent at each stage makes on the basis of the processing model. In turn, the processing model is a set of laws that associate with the selected processing options the obtained values of the intensity of the video stream as well as the magnitude of the error.
Graph Convolutional Neural Networks to Classify Whole Slide Images

Whole slide images (WSIs) are the digitization of histology slides and are increasingly used by pathologists to detect the cancerous regions and make diagnosis for patients. Recent machine learning and deep learning algorithms have shown great success in automated classification of WSIs. However, given the computational challenge associated with processing high resolution WSIs, conventional techniques rely on a patch-based approach, and subsequently aggregate extracted features using firs-order statistics. However, as cancerous regions are clustered together, such an approach ignores the spatial relationships within each tile. Here, we present a novel application of graph convolutional networks (GCNs) to analyze WSIs. GCNs are powerful deep neural networks for modelling node relationships in a graph. To capture the spatial information, we model each tile as a node in the graph, and aggregate features by applying GCNs to the graph. The classification results on real-world histopathology datasets shows improved performance over conventional methods, and highlights the potential of graph-based methods in biomedical data analytics.
A NB-IoT based intelligent combiner box for PV arrays integrated with short-term power prediction using extreme learning machine and similar days

Abstract The grid-connected photovoltaic (PV) power stations are instability and volatility due to meteorological factors. A way to improve this problem is PV power forecasting. This paper proposed an improved short-term PV power prediction model that combines an extreme learning machine (ELM) neural network and similar day method. Firstly, a narrow-band Internet of Things (NB-IoT) intelligent combiner box data monitoring system is designed to collect multivariate meteorological factors and original PV output power datasets in different seasons. Secondly, the corresponding training set is selected according to the season type of the forecast day, and the Euclidean distance (ED) between the training set and the forecasting day is calculated, and the M-day with a small Euclidean distance is selected. Then, the N-day similar day data is divided among the M days as the new training set input, and the P-day optimal similar day data and the multivariate meteorological of the prediction day are divided as test set inputs. Finally, the ELM neural network prediction model is used to predict the output power of the predicted day. The experimental results show that the proposed method has the highest prediction accuracy in contrast to other two prediction models.
Gender Recognition from Real-Life Images

Pattern Recognition from images has always been a prime research area when machine learning is talked about. Gender recognition from real-life images is a similar area which poses many challenges since humans are efficient in identifying gender but it is not easy to accomplish the same task with computers. Most of the social interactions depend upon gender perception. In this paper, a machine learning based approach has been discussed to recognize gender from real-life images. Various classifiers have been trained on 17,500 images from the Adience benchmark with multiple train and test data splits. The popular machine learning technique Principal component analysis is used to represent the face image in a low dimensional space with the help of a feature vector. The experimental results have shown that the maximum accuracy achieved is 90% from support vector machine using a radial basis function on a 70–30% split of training and testing data.
Local Image Feature Extraction using Stacked-Autoencoder in the Bag-of-Visual Word modelling of Images

The Bag-of-Visual Words has been recognised as an effective mean of representing images for image classification. However, its reliance on hand crafted image feature extraction algorithms often results in significant computational overhead, and poor classification accuracies. Therefore, this paper presents a Bag-of-Visual Word Modelling in which Image Feature Extraction is achieved using Deep Feature Learning via Stacked-Autoencoder. The proposed method is tested using three image collections constituted from the Caltech 101 image collection and the results confirm the ability of deep feature learning to yield optimum image categorisation performance.
Hybrid Regional Feature Cutting Network for Thyroid Ultrasound Images Classification

With the increasing incidence of thyroid diseases, the detection and classification of thyroid nodule has attracted more and more attention. Deep learning has achieved promising results in computer-aided diagnosis due to the advantages of obtaining high-dimensional features. In this paper, we proposed hybrid cutting network (HCN) based on regional feature cutting method for feature extraction and classification of thyroid ultrasound images. Firstly, we build a backbone network for feature extraction. Next, we add a regional feature cutting (RFC) layer in the network to perform multiple cutting on the image feature matrix, so as to reduce the impact on classification caused by the similarity of local features between benign and malignant nodules. Finally, based on the local feature matrix of different size, we utilize N-first voting method to construct a hybrid classification voting network with three branches for final classification prediction. Experimental results show that the proposed method has achieved 97% accuracy, 95.74% precision, 100% recall, and 97.82% f1-measure in the open access public dataset, outperforming other current mainstream models.
Research on User Clustering Algorithm Based on Improved ROCK Algorithm

Abstract This paper analyzes the merits and demerits of the ROCK algorithm firstly, then indicates that the merits of Rock is easy to cluster categorical database such as Mushroom, and the demerits is that the similarity formula sim of this algorithm depends on the intuition of domain experts. The improved ROCK gets the result of clustering by calculating the similarity using Jaccard coefficient based on the reason which is that the bigger figure of the similarity is, the more alike the object is. Then we can implement personality recommendation according to the result.