Reviewerly/exHarmony huggingface dataset:
Spare simple MKKM with semi‐infinite linear program optimization

Multiple kernel clustering (MKC) optimally combines a group of predefined kernel matrices to improve clustering performance. Although demonstrating promising performance in various applications, most of existing approaches adopt the min–min formulation, which could be sensitive to perturbation with adversarial samples. Moreover, existing MKC algorithms often involve several hypermeters preventing them into further real applications. To address these issues, we propose a parameter-free effective sparse simple multiple kernel k-means algorithm with max–min optimization formulation in this paper. To be specific, we propose to optimize the widely used unsupervised kernel alignment criterion by minimizing the kernel coefficient and maximizing the clustering partition matrix. Unlike traditional min–min formulation, the max–min kernel alignment is robust to adversarial sample perturbation and free of hyper-parameters. An optimization method based on semi-infinite linear program is designed to solve the complicated optimization problem. Extensive experiments on six multiple kernel benchmark data sets demonstrate the effectiveness of the proposed method.
Qutrit based quantum key generation protocol with self checking mechanism

A protocol using qutrit has been conceived of for quantum key generation. Here, key is embedded in the data string as impurity and one of the qutrit states, named as fencing state is used to discriminate the location of key in the main data string. A one way function known to the legitimate users is used to determine the subsequent basis once the fencing state is settled. Since impurity count is same before and after the fencing state, it provides self checking mechanism for detection of error in the quantum channel, if present, thereby providing better security for key extraction. As it needs no privacy amplification, key generation rate is improved to an appreciable extent.
Accurate calibration of beam trajectories in scanning optical imaging systems

We present a calibration method for finding the coordinates of points in the trajectory of the scanning beam in flying-spot imaging devices. Our method is based on laterally translating the field of view on the imaging object plane by introducing additional beam deflections. We show that laterally translating the field of view provides a series of images whose relative translations are equal to the distances between the points in the scanning pattern to be calibrated. We show how these distances are mapped to the coordinates of the trajectory points. As an example, we demonstrate the calibration of the scanning patterns in an optical system with two independent microelectromechanical system based scanners. Our method profits from a large collection of distance measurements to find the trajectory coordinates, thereby minimizing the effect of random sources of uncertainty in the positions of points in the scanning pattern. We have found that we are capable of finding the coordinates of points in the scanning patterns with accuracy greater than the optical resolution of the imaging system.
Student Attendance System Using Face Recognition

Nowadays, face recognition is applied in a wide range of applications. In this research, we apply face recognition to build the student attendance system using advanced deep learning method. The shortcomings of previous process are too many redundant steps, time consuming and depend heavily on human works; this leads to congestion and makes it inconvenient for both users and managers. To address those issues, we use our own deep learning method-convolutional neural network (CNN), a method used in many studies on computer vision. Based on the evaluation of the standard dataset ORL (by AT&T Research Lab) with different methods PCA-NN, LDA-DNN, the proposed method is introduced to combine with Haar Cascade in face detection to achieve the best result. Following that result, we then build an application and apply it in System Administration course of D18HT02 class, Thu Dau Mot University. The results obtained from lecturers who conducted the experiment reveal the promising capability of the proposed method.
On Constructing Enterprise Knowledge Graphs Under Quality and Availability Constraints

Knowledge graph technologies have proven their applicability and usefulness to integrate data silos and answer questions spanning over the different sources. However the integration of data can pose some risks and challenges (security, audit needs, quality control, ...). In this paper we abstract from two client use-cases, one in the banking domain and one in the pharmaceutical domain, to highlight those risks/challenges and propose a generic approach to address them. This approach leverages Semantic web technologies and is implemented using Stardog.
Study on Hybrid Cloud-based Cyber Threat Intelligence Sharing Model Requirements Analysis

Because of the phenomenal growth of internet connectivity and unceasing improvements over hardware and software technology, everything is connected without any boundaries creating the Internet of Things, which becomes part of cyberspace. As the volume of cyberspace is overgrowing, the attackers' capabilities and strategies to find new vulnerabilities are increasing too. In order to defend the attackers from stealing and poisoning critical information of cyberspace, it is crucial to implement Cyber Threat Intelligence (CTI). However, it is not a straightforward process to implement CTI for individuals and organizations because of the resource constraints such as time, capability, and cost. Hence, different stakeholders (individuals or organizations) can collaborate and create a CTI sharing platform to improve their security stances and other organizations. It is well-known that when connected devices are rapidly intensifying in IoT, the cyber threat to information will also rise. So, to own a reliable, scalable, and high-speed CTI sharing platform, the deployment model should be wisely studied. This research carefully analyzed different forms of CTI sharing deployment models: private cloud, public cloud, hybrid cloud, and on-premises. Finally, we suggest the hybrid cloud-based deployment model as a convenient solution for CTI sharing due to its desirable features such as scalability, security, reliability, and high speed.
Model Reduction H∞ Finite Frequency of Takagi-Sugeno Fuzzy Systems

The daily treats model reduction finite frequency (FFMR) design for Takagi Sugeno (T S) systems.This work is to FFMR design in such a way whether augmented model is steady get a reduced H ∞ index in FF areas with noise is established as a prerequisite.To highlight the importance of suggested process, a practical application has been made.
Research of reading system of the pointer instrument based on embedded system

With the rapid change of society and the continuous development of science and technology, the application of instruments is more and more widely in all kinds of places from daily life to chemical power and aerospace. The pointer instruments in traditional industries are still widely used. But without changing the original equipment structure, the intelligent transformation is still an urgent research direction. The object of this paper is a kind of pointer pressure instrument used in industry. The purpose of this paper is to research the reading system of the pointer instrument based on embedded hardware platform and simple software algorithm. This paper designs an automatic reading system of the value indicated by the dial of the pointer instrument. The system uses embedded hardware devices and adopts the design idea of Linux + Qt + OpenCV. An open source, cross platform and relatively simple image processing algorithm is designed in it. Canny Edge detection, Hough-Circles detection and the Hough-Lines detection are combined in it.
EDUCATION IN NIGERIA DURING THE COVID-19 PANDEMIC: LESSONS FOR POSTERITY

COVID-19 pandemic threw the world into confusion and brought about unprecedented changes in the way people live and do things. A lot of adjustments were made in every sphere of life in order to contain, curtail and survive the deadly virus. Most sectors of many nations including formal and non-formal education at every level were adversely affected due to long periods of restrictions of movement which led to school closures. Unfortunately for Nigerian educational system, ‘End SARS’ protests rocked the whole nation just as schools resumed and were adapting to the new normal, thereby causing people to stay indoors again while some people continued to learn through distant education. The people that could afford it embarked on virtual learning while most learners lost out. These researchers embarked on the study as a way of documenting a very trying period in the history of humans of 21st century especially as it pertains to education in Nigerian. The paper attempted to explain the two key concepts which are COVID-19 and education, discussed education in Nigeria during the worst days of COVID-19 pandemic, state of education in Nigeria in the face of relaxation of restrictions imposed to curb the spread of first wave of covid-19, challenges encountered in online and long distance learning during the COVID-19 lock down/restrictions, lessons from adjustments to COVID-19 situation and way forward for Nigerian education. The paper concludes by asserting that stopping learning whether for school children or adults is not an option in troubled times. Blended learning was recommended as the way forward where appropriate in order to sustain and consolidate on the ICT online adoptions or adaptations and other distance learning strategies utilized in COVID-19 era. The message for posterity is that learning can continue in both favourable and unfavourable situations.
A Deep Reinforcement Learning Method with Action Switching for Autonomous Navigation

Stochastic policy-based deep reinforcement learning (DRL) has successfully gained the widespread application but demands plenty of stochastic exploration to learn the environment at the initial training stage. When the agent is exposed to more complex environment, not only is the methodology inefficient, but its performance may also suffer from the issue of high variance. This paper develops a framework to accelerate the training procedure and reduce the variance by introducing a stochastic switching network, which specifically allows the agent to choose between heuristic actions and actions output by proximal policy optimization (PPO) algorithm. Instead of starting from the random actions, the agent can be effectively guided by the heuristic actions so that the navigation capability of the agent can be rapidly bootstrapped. The vanilla policy gradient (VPG) algorithm is further utilized to train the switching network, which can be jointly trained with the baseline PPO. By the experimental comparison with the baseline PPO in the customized maze environment with openAI Gym toolkit, our method greatly contributes to the more efficient execution of navigation task by means of the heuristic actions for guidance.
Application of BM3D algorithm in CT image denoising of liver cancer

CT images play a vital role in the diagnosis of liver cancer. However, CT images often have significant image noise, which is unfavourable for doctors' diagnoses. In response to this problem, this paper applies the BM3D denoising algorithm to the denoising CT images of liver cancer. The BM3D denoising algorithm first obtains similar blocks through block matching, stacks these similar blocks into three-dimensional blocks, performs collaborative filtering processing, and finally obtains the final clear image through aggregation. Experimental results show that the BM3D algorithm can effectively remove the noise of CT images of liver cancer.
On the Effectiveness of SBSE Techniques

Search-Based Software Engineering is now a mature area with numerous techniques developed to tackle some of the most challenging software engineering problems, from requirements to design, testing, fault localisation, and automated program repair. SBSE techniques have shown promising results, giving us hope that one day it will be possible for the tedious and labour intensive parts of software development to be completely automated, or at least semi-automated. In this talk, I will focus on the problem of objective performance evaluation of SBSE techniques. To this end, I will introduce Instance Space Analysis (ISA), which is an approach to identify features of SBSE problems that explain why a particular instance is difficult for an SBSE technique. ISA can be used to examine the diversity and quality of the benchmark datasets used by most researchers, and analyse the strengths and weaknesses of existing SBSE techniques. The instance space is constructed to reveal areas of hard and easy problems, and enables the strengths and weaknesses of the different SBSE techniques to be identified. I will present on how ISA enabled us to identify the strengths and weaknesses of SBSE techniques in two areas: Search-Based Software Testing and Automated Program Repair. Finally, I will end my talk with potential future directions of the objective assessment of SBSE techniques.
Model Fusion of LightGBM and SAKT for Knowledge Tracking

Knowledge tracking is to model the student’s learning interaction records so that we can evaluate the student’s learning state with relative accuracy and can adjust the students’ learning schedule appropriately or formulate a more reasonable learning plan. However, this task is very challenging. The challenges are mainly in two aspects. On the one hand, each individual is different, and on the other hand, handling data is complex. Currently, there are two main ideas for knowledge tracking, namely machine learning or deep learning. For such ”knowledge tracking” problems, our idea is to combine traditional machine learning solutions and neural network sequence models to obtain better results in such problems. We proposed the LGBM+SAKT model fusion method, which organically combines machine learning and deep learning, which not only makes full use of as many data features as possible, but also extracts and represents data features at a deep level without losing readability. We complete this task in a knowledge tracking competition on Kaggle, scoring at least 1.8% over every single model at the end.
A Change-point Wavelet Method to Hard Drives Running Status

Traditional statistical methods are currently used to analyze the feature of hard drives failure, but difficult to model the failure feature for random events with small probability of hard drives failure or datasets with unbalanced and missing values. Therefore, this paper presents a change-point wavelet transform (CPWT) method, which can extract the indicative SMART change-point features of failed hard drives. Because wavelet has the ability of denoting local signal characteristics in the time domain and frequency domain, we use the wavelet transform method to extract the sudden change features of the time series, to reveal the characteristics of the change-point wavelet. Then, we use convolution network and long short-term memory (CNN-LSTM) model, to verify the effectiveness of the change-point wavelet extracted for failure prediction. The experimental results show that the change-point wavelet evaluates the health of the hard drives, the accuracy and precision of prediction are 91.56% and 90.78% respectively, confirming that the change-point wavelet has a certain predictive ability for hard drives failures, which provides a basis for cloud storage failure judgment and prediction.
Applications of Anomaly Detection in Manufacturing

Anomaly detection is one of the very predominant activities in manufacturing and it has many practical applications, like scrap reduction, quality testing and compliance testing. Many machine learning applications are available for anomaly detection in the literature and they are performing a decently good job. In the current study, we are describing various applications of anomaly detection in manufacturing domains, like automotive, oil and gas, pharma and consumer goods, and also some of the latest methods available in the literature to perform anomaly detection. We also highlighted some of the gaps and future scope of work in anomaly detection topics.
Simultaneous Contextualization and Interpretation with Keyword Awareness

Most natural-language-processing methods are designed for estimating context given an entire set of sentences at once. However, dialogue is incremental in nature. SCAIN (Simultaneous Contextualization and Interpretation) is an algorithm for incremental dialogue processing. Along with the progress of the dialogue, it can solve the interdependence problem in which the interpretation of words depends on the context, and the context is determined by the interpreted words. However, SCAIN cannot process texts that contain more words insignificant to context estimation such as in longer texts. We propose SCAIN with keyword extraction (SCAIN/KE), which extracts keywords that contribute to context estimation and eliminates the effect of insignificant words so that it can process longer texts. In the case study, SCAIN/KE updates context and interpretation better than SCAIN and obtains the keywords that contribute to context estimation better than other statistical methods. In the experiments, we evaluated SCAIN/KE on solving the ambiguity of polysemous words using the Wikipedia disambiguation pages. The results indicate that SCAIN/KE is more accurate than SCAIN.
A Cross-Media Retrieval Method Based on Semisupervised Learning and Alternate Optimization

With the continuous advancement in Internet technology, we are gradually stepping into an era of big data where a large amount of multimedia data is produced every day at any given time. In order to properly utilize these data, the research on big data is also constantly evolving. Cross-media retrieval is a prime example, aiming at retrieving various forms of data, for example, text, image, audio, video, and other forms. The most difficult task for cross-media retrieval lies in the potential correlation between different modalities data and how to overcome the semantic gap. This paper proposes a cross-media retrieval method based on semisupervised learning and alternate optimization (SMDCR) to overcome the abovementioned difficulties, thereby improving the retrieval accuracy. The main advantage of this method is to make full use of the degree of correlation between the semantic information of the labeled data and unlabeled data. Simultaneously, we combine the linear regression term, correlation analysis term, and feature selection term into a joint cross-media learning framework. Furthermore, the projection matrices are trained with the alternate optimization method. Finally, experimental results on two public datasets demonstrate the effectiveness of the proposed method.
Unprovability results for clause set cycles

The notion of clause set cycle abstracts a family of methods for automated inductive theorem proving based on the detection of cyclic dependencies between clause sets. By discerning the underlying logical features of clause set cycles, we are able to characterize clause set cycles by a logical theory. We make use of this characterization to provide practically relevant unprovability results for clause set cycles that exploit different logical features.
Risk Framework for Bitcoin Custody Operation with the Revault Protocol

Our contributions with this paper are twofold. First, we elucidate the methodological requirements for a risk framework of custodial operations and argue for the value of this type of risk model as complementary with cryptographic and blockchain security models. Second, we present a risk model in the form of a library of attack-trees for Revault – an open-source custody protocol. The model can be used by organisations as a risk quantification framework for a thorough security analysis in their specific deployment context. Our work exemplifies an approach that can be used independent of which custody protocol is being considered, including complex protocols with multiple stakeholders and active defence infrastructure.
Improved 3D Morphable Model for Facial Action Unit Synthesis

To overcome the limitation of the conventional 3D face model on the synthesis of local facial expression movements, this paper proposes an improved 3D face model that combines facial action coding system (FACS) and 3D morphable model (3DMM). Our proposed 3D face model can be used for 3D facial expression synthesis with local action units (AUs). To be specific, AUs are introduced as prior knowledge into the 3D face model to capture the anatomically defined muscle movements of different facial expressions. Our proposed model extracts the parameters of a single AU, which can also be used to represent AU information to facilitate AU recognition. The qualitative and quantitative experimental results demonstrate that our proposed model can generate 3D faces with specific AU labels. These AU parameters of our proposed model perform better in AU classification than the global expression parameters of conventional 3DMM.
Supporting Proactive Refactoring: An Exploratory Study on Decaying Modules and Their Prediction

Code smells can be detected using tools such as a static analyzer that detects code smells based on source code metrics. Developers perform refactoring activities based on the result of such detection tools to improve source code quality. However, such an approach can be considered as reactive refactoring, i.e., developers react to code smells after they occur. This means that developers first suffer the effects of low-quality source code before they start solving code smells. In this study, we focus on proactive refactoring, i.e., refactoring source code before it becomes smelly. This approach would allow developers to maintain source code quality without having to suffer the impact of code smells. To support the proactive refactoring process, we propose a technique to detect decaying modules, which are non-smelly modules that are about to become smelly. We present empirical studies on open source projects with the aim of studying the characteristics of decaying modules. Additionally, to facilitate developers in the refactoring planning process, we perform a study on using a machine learning technique to predict decaying modules and report a factor that contributes most to the performance of the model under consideration.
Design and Implementation of a Vision- and Grating-Sensor-Based Intelligent Unmanned Settlement System

In this article, a new vision- and grating-sensor-based intelligent unmanned settlement (IUS) system is proposed for convenience stores to automatically recognize the shopping behavior of customers, record their identities, and generate invoices. First, we design a new IUS architecture, which includes a shelf module and exit module. To achieve automatic settlement for each customer, a shopping event detection method is proposed. In this method, a vision-based human pose estimation algorithm is used to detect a human form standing in front of a shelf. The hand actions of each customer are detected by a grating sensor, and an image recognition method based on a convolutional neural network (CNN) is applied to recognize the items in the hands of customers. To reduce the image annotation workload, we propose a semisupervised training method for the recognition network. Based on hand action detection and item recognition, a shopping event recognition method is designed for the system, and a facial image of the customer corresponding to each shopping behavior is captured. Finally, each detected shopping event is added to the invoice of the corresponding customer via a facial recognition method. To verify the effectiveness of the proposed IUS system, we have built a handheld item image dataset and a shopping event dataset for an unmanned convenience store. The experimental results show that the proposed system can accurately recognize shopping behaviors and generate invoices.