Reviewerly/exHarmony huggingface dataset:
Construction of a Personalized Learning Platform Based on Intelligent Algorithms for the Integration of Industry and Education under the Framework of the Digital Economy

The era of digital economy puts forward new requirements for personnel training. After nearly ten years of teaching reform and practice, the reform of teaching methods and teaching contents in higher vocational education has achieved certain results. Digital economy is a new economic form with the development of information technology revolution, and it is a series of management practices including competitive strategy, organizational structure and culture. Based on the perspective of digital economy, after more than four years&#x0027; practice and exploration, the research results have found a reproducible and demonstrative way to integrate production and education, cultivate high-quality technical and technical talents in the digital economy-related professional groups such as precision manufacturing, intelligent equipment and new generation information technology. With the advent of digital economy era, the integration of information technology and enterprise management has been further accelerated, and the business activities have been transformed into digitalization, networking and intelligence. In this paper, under the concept of education, in the framework of digital economy, the intelligent algorithm, the integration of personalized learning platform construction, to achieve the purpose of differentiated education personalized learning platform.
Infrastructure as Code for Business Continuity in Institutions of Higher Learning

Running and operating physical secondary back up sites can be expensive. But using the cloud, organizations can lean out some of these associated costs by adopting Infrastructure-as-code (IaC) for business continuity. Business organizations that adopt cloud for business continuity stand to enjoy a number of benefits. These benefits include faster provisioning of computing services, workflow automation, consistency and faster deployment during the cloud migration journey. In spite of the advantages that cloud offers, institutions of higher learning (IHL) in Africa and Kenya in particular, have been slow in adopting it due to various challenges. Therefore, the purpose of this study is to assess the challenges of current configurations and factors that hinder the adoption of cloud for business continuity in institutions of higher learning in Kenya. The paper proposes a framework leveraging on Infrastructure as code and considerations that they must evaluate before adopting cloud for business continuity. Data was collected by use of questionnaires. The questionnaires were given to 92 university staff that had intricate knowledge on cloud computing and analyzed through SPSS. A conceptual framework based on People, Process and Technology (PPT) was used. An experimental study that draws largely from infrastructure as code (IaC) was carried out to simulate a recovery from a local data Centre to Amazon Web Service (AWS) using two cloud formation tools Ansible and Terraform. The experimental results were presented and indicate that, IaC addressed key challenges faced by current setups such as high human resource cost, cumbersome effort, time, higher risk of human error and high maintenance costs. It is hoped the proposed framework will shed light on how IHL can increase their success rates to seamlessly adopt cloud for business continuity.
Multi-Factor Authentication Method for Online Banking Services in South Africa

The banking institutions have adopted innovative banking online service technologies in an effort of increasing service delivery to their customers. However, the escalating threats and evolving attacks to the online banking services, are forcing the banking sectors to always re-evaluate and employ robust online authentication methods. In order to enforce confidentiality, integrity, and unauthorized disclosure of their customers&#x0027; information. Most banking institutions have adopted the Two-Factor-Authentication (2FA) method which requires a username; a password and a code from the bank via a short message to the registered customer number for authentication. This method has proven to be less effective in combating banking online attacks. Thus, user credential harvest attacks are on the rise. In an effort of mitigating these attacks, this paper seeks to develop a more secure online banking Multi-Factor-Authentication (MFA) method in addition to the conventional username and password and customer device identification. While still adopting the traditional authentication methods used for online banking systems; the username and a password or Personal Identification Number (PIN). This paper seeks to incorporate the user biometrics, either a fingerprint or facial recognition depending on the technological features and specifications of their Internet of Things (IoT) device; combined with a bank registered device either via IP address, cookies, or a digital certificate as an extra layer of security for authentication for the online user authentication. In this paper, design science methodology is proposed in order to design the proposed MFA method. In the future, online users from different levels of skill will evaluate the prototype system against existing online authentication methods.
The Reverse Problem of Keystroke Dynamics: Guessing Typed Text with Keystroke Timings Only

Keystroke dynamics can be used to verify the user identity, but also as a means to recover what was typed, through an attack that gains access to the keystroke timings. We propose a method for the task of finding, given only the keystroke timings, whether a medium-sized candidate list of possible texts includes the one to which the timings belong. The timings sample does not need to be short and, with current error rates, the list size can be in the order of one hundred. Previous samples for the candidate texts are not required to train the models. Evaluated using three publicly available datasets, false acceptance and false rejection rates were found to remain below or very near to 1&#x0025; when user samples were available for training. The FAR increases between two- to three-fold when the models are trained with samples from other users, while the FRR jumps to around 15&#x0025;.
Identifying Dynamic Parameters With a Novel Software Design for the M-DOF Collaborative Robot

The primary goal of this project was to develop a general identification method via software that can be applied to collaborative robots.To achieve this, the collaborative ultralight robots Kinova Gen2 and Kuka LWR4+ with seven degrees of freedom (M-DOF) were used.Specifically, the ''recursive Newton-Euler'' formulation was used to provide a set of parameters that could describe the body structure and to create a general symbolic representation for collaborative robots.For parameter estimation, the least squares method was used.In addition, trajectories generated with random numbers typically do not produce consistent results; thus, verified trajectories were used.To verify trajectories, real robots were simulated with V-Rep before being executed.When untested trajectories are first tested on robots, undesirable results may occur.This method was convenient for parameter estimation and robot health; saves time; and increases the consistency of results.Algorithms were coded in MATLAB and ROS packages via Python.MATLAB, ROS, and V-Rep worked together in the Ubuntu operating system.The identification methods were modeled, implemented, tested, and validated successfully, and the results for both robots are reported in this article.
Person Re-Identification via Pyramid Multipart Features and Multi-Attention Framework

Video-based person re-identification has become quite attractive due to its importance in many vision surveillance problems. It is a challenging topic due to the inter/intra changes, occlusion, and pose variations involved. In this paper, we propose a pyramid-attentive framework that relies on multi-part features and multiple attention to aggregate features of multi-levels and learns attention-based representations of persons through various aspects. Self-attention is used to strengthen the most discriminative features in the spatial and channel domains and hence capture robust global information. We propose the use of part-relation attention between different multi-granularities of features’ representation to focus on learning appropriate local features. Temporal attention is used to aggregate temporal features. We integrate the most robust features in the global and multi-level views to build an effective convolution neural network (CNN) model. The proposed model outperforms the previous state-of-the art models on three datasets. Notably, using the proposed model enables the achievement of 98.9% (a relative improvement of 2.7% on the GRL) top1 accuracy and 99.3% mAP on the PRID2011, and 92.8% (a relative improvement of 2.4% relative to GRL) top1 accuracy on iLIDS-vid. We also explore the generalization ability of our model on a cross dataset.
DPLRS: Distributed Population Learning Rate Schedule

Deep neural network models perform very brightly in the field of artificial intelligence, but their success is affected by hyperparameters, and the learning rate schedule is one of the most important hyperparameters, while the search for the learning rate schedule is often time-consuming and computationally resource-intensive. In this paper, we proposed Distributed Population Learning Rate Schedule (DPLRS) based on population joint optimization, which uses distributed data parallel deep neural network training to implement a dynamic learning rate schedule optimization strategy based on the population idea, with almost no loss of test accuracy. DPLRS is able to dynamically refine the learning rate schedule during model training instead of following the usual suboptimal strategy. We conducted experiments on typical AlexNet, VGG16, and ResNet18 using the Tianhe-3 supercomputing prototype. The results illustrate that using DPLRS to dynamically update the learning rate can greatly reduce the searching time of the learning rate schedule and meanwhile, can ensure the close performance with the latest population hyperparameter algorithm. Also, In our experiments, DPLRS lead to 123.85x speedup maximum, which prove the effectiveness and robustness of DPLRS.
A Blockchain Cloud Computing Middleware for Academic Manuscript Submission

One of the most important tasks in scientific publishing is the articles' evaluation via the editorial board and the reviewers' community. Additionally, in scientific publishing great concern exists regarding the peer-review process and how it can be further optimised to decrease the time from submission to the first decision, as well as increase the objectivity of the reviewers' remarks ensuring that no bias or human error exists in the reviewing process. In order to address this issue, our article suggests a novice cloud framework for manuscript submission based on blockchain technology that further enhances the anonymity between authors and reviewers alike. Our method covers the whole spectrum of current submission systems capabilities, but it also provides a decentralised solution using open-source tools such as Java Spring that enhance the anonymity of the reviewing process.
Bidirectional Information Transfer Scheme for Joint Intent Detection and Slot Filling

Intent detection and slot filling are two crucial basic tasks in natural language understanding. No matter treat intent detection and slot filling as two separate tasks, or train the two tasks as a joint model using deep learning frameworks, most of the approaches do not completely establish correlation between the intent and slots. Recent joint models build relationship between the two tasks via sharing intermediate representation of network, but we suggest that specific label information of one task is more beneficial to improve the performance of another task. Therefore, a novel bidirectional information transfer model is proposed to build sufficient interaction between intent detection and slot filling, which utilizes more explicit label information extracted from top layer of network. Besides, we introduce a type-aware mechanism to learn discriminative feature from the label information. Experimental results show that our model achieves a better performance on ATIS and SNIPS dataset in most of criteria, and significantly outperforms previous models.
An Efficient 2D Mapping of Quantum Circuits to Nearest Neighbor Designs

In the last few years, tremendous improvement in the field of quantum computing has been witnessed. Although quantum technologies such as ion-trap, NMR, quantum dots have emerged as the promising platforms for implementing quantum circuits but these technologies have faced several design issues. One such important design problem is the nearest neighbor condition, which requires the qubits to interact with adjacent neighbors. This problem can be addressed by adding SWAP gates in the circuit. By doing so, the overhead in the circuit increases thus NN designs with minimum number of SWAP gates need to be developed. Focusing on this, here, in this work, we introduce a heuristic design method for efficient NN realization of quantum circuits in 2D architecture. Our NN transformation process is carried out in two phases, where initially in the first phase, the input circuit is mapped to 2D configuration using a qubit placement strategy and then in the second phase NN designs are obtained through SWAP gate insertion. At the end, the design algorithm has been evaluated over a large set of benchmark functions and the results are compared with some of the existing works. From this comparison, it is seen that our proposed method performs better than the reported works.
GitHub in the Classroom: Lessons Learnt

The decision as to whether or not, and how, to use a Version Control System (VCS) in teaching is complex to make. There are a number of use cases for how a VCS can be used in teaching, there are several VCSs, each VCS has a variety of options for how to access them, each has a number of third-party support systems, and all combinations have different benefits, costs, and challenges. At University of Auckland, we have made significant use of Git and related systems (especially GitHub and GitHub Classroom). In this paper, we offer the lessons we have learned from our collective experience. While we by no means cover all of the possibilities, we hope that instructors considering the use of VCSs, in particular Git, will find the lessons we have learned helpful in making their decisions regarding how to use VCS in teaching.
Multiatlas segmentation

Advancing radiology towards quantitative interpretation relies on innovating with image processing algorithms for context navigation (e.g., "Does this image contain a tumor?"), object localization ("Where is the spleen located?"), and segmentation (e.g., "Which voxels in the image belong to the hippocampus?"). In this chapter, we focus on the most invasive of these questions – segmentation. An algorithm that yields a segmentation of a medical image assigns each voxel in a target medical image to an object class (either deterministically or probabilistically). The results of a segmentation process can be used to address a wide variety of clinical problems from volumetry and shape analysis to radiomics and image-guided interventions. Historically, human efforts for capturing medical image segmentations (e.g., labelers or, herein, raters) have been remarkably resource intensive and subject to high degrees of interrater variability. Hence, medical images paired with their segmentations (so-called maps, charts, or, herein, atlases) are treasured resources. Creating approaches to learn algorithms based on an available and/or feasible collection of atlases has been at the forefront of medical image computing research for the better part of a quarter century. An intuitive and effective family of methods for using existing atlases to create a new algorithm is known as multiatlas segmentation. The core idea underlying multiatlas segmentation is that differences in anatomy as seen through medical images are relatively small and can be compensated through image registration. Hence, the segmentation of an atlas registered to an unseen target represents a reasonable estimation of the true segmentation for that target. When the registration process is repeated for multiple atlases, one would achieve multiple reasonable estimates of a segmentation. The field of multiatlas segmentation centers on capturing and resolving the uncertainty associated with multiple segmentation estimates and optimizing the preprocessing, registration, information integration, and postprocessing steps needed to address complexities of specific anatomies, imaging modalities, and clinical context. The intent of this chapter is to provide historical context for interpretation of modern multiatlas segmentation methods while offering a consistent notation to compare and contrast the evolving literature. Notably, while we highlight salient innovations both from practical and theoretical perspectives, this chapter does not seek to provide a comprehensive literature review.
Subtype classification of malignant lymphoma using immunohistochemical staining pattern

For the image classification problem, the construction of appropriate training data is important for improving the generalization ability of the classifier in particular when the size of the training data is small. We propose a method that quantitatively evaluates the typicality of a hematoxylin-and-eosin (H&E)-stained tissue slide from a set of immunohistochemical (IHC) stains and applies the typicality to instance selection for the construction of classifiers that predict the subtype of malignant lymphoma to improve the generalization ability.We define the typicality of the H&E-stained tissue slides by the ratio of the probability density of the IHC staining patterns on low-dimensional embedded space. Employing a multiple-instance-learning-based convolutional neural network for the construction of the subtype classifier without the annotations indicating cancerous regions in whole slide images, we select the training data by referring to the evaluated typicality to improve the generalization ability. We demonstrate the effectiveness of the instance selection based on the proposed typicality in a three-class subtype classification of 262 malignant lymphoma cases.In the experiment, we confirmed that the subtypes of typical instances could be predicted more accurately than those of atypical instances. Furthermore, it was confirmed that instance selection for the training data based on the proposed typicality improved the generalization ability of the classifier, wherein the classification accuracy was improved from 0.664 to 0.683 compared with the baseline method when the training data was constructed focusing on typical instances.The experimental results showed that the typicality of the H&E-stained tissue slides computed from IHC staining patterns is useful as a criterion for instance selection to enhance the generalization ability, and this typicality could be employed for instance selection under some practical limitations.
Power Distribution Network Capacitive Decoupling for Side-Channel Resistance

In power side-channel attacks (SCA), the adversary observes the power leakage at the external power pin to reverse engineer the secrets embedded in sensitive circuits. Mitigation techniques are often integrated into the logic to make power consumption data independent. In this paper, we develop a new design strategy for designing the on-chip power distribution network using a decoupling capacitance to thwart the power side-channel attack. The decoupling capacitances are introduced along the power lanes in a distributed fashion, to suppress the data-leakage from the sensitive-circuit. To facilitate the computer-aided design of such PDNs, we also develop approximate heuristics to extract feature vectors from the current (I) profile of the internal logic blocks, and for feature vector propagation over the on-chip power distribution network. We study the sensitivity of decoupling capacitance value and its placement over the power distribution network. Finally, we evaluate the side-channel resistance with and without decoupling capacitance using Spice level simulations. Machine Learning (ML) classifiers are used to quantify the side-channel strength in terms of success rate for power side-channel adversary. A 100pf decoupling capacitance, in some cases, reduces the ML success rate from 80&#x0025; to 21&#x0025; to provide significant SCA resistance.
Advancing Chinese Event Detection via Revisiting Character Information

Recently, character information has been successfully introduced into the encoder-decoder event detection model to relieve the trigger-word mismatch problem, thus achieving impressive results in the languages without natural delimiters (i.e., Chinese). However, it is introduced into the encoder or the decoder separately, which makes the advantage of character information not be captured and represented adequately for event detection. In this article, we proposed a novel method to model character information in both the encoding and decoding stages to advance the neural event detection model. In particular, the proposed method can encode both words and characters and predict their event types jointly and further leverage interactions between word and its characters to optimize the inference. Experimental results show that the proposed model outperforms previous event detection methods on the ACE2005 Chinese benchmark. We release our code at Github. 1
Recommendation System for Elective Courses using Content-based Filtering and Weighted Cosine Similarity

Each study program requires students to take several elective courses. The appropriateness of the elective courses taken with the student's abilities can be one of the factors for the success of student studies. This research focuses on building a content-based filtering recommendation system that provides several elective courses recommendation according to the student's academic history. The proposed recommendation systems' results are based on preprocessed word items from courses taken by the user. The weighted cosine similarity between the elective courses syllabus and the user profiles is calculated. Moreover, the experiment employed a dataset of the CSUGM course syllabus. The proposed recommendation system is evaluated in two ways, i.e., questionnaire method and validation method. The questionnaire method obtains an assessment of system performance, hence the validation method to get the average accuracy. The questionnaire was conducted by involving thirty students of the CSUGM undergraduate program. The experimental results show that the proposed recommendation system has a good performance proven by the percentage of recommendation diversity 81.67%. Furthermore, the accuracy of the proposed recommendation system has an average of 64%.
Unsupervised Anomalous Sound Detection For Machine Condition Using Memory-augmented Deep Autoencoder

Anomalous sound detection (ASD) for machine condition is the task to identify whether the sound emitted from a target machine is normal or anomalous. The idea is to detect unknown anomalous sounds under the condition that only normal sound samples have been provided as training data. The original anomalous sound detection for machine condition uses a deep autoencoder(AE) to learn a normal model that makes anomalous data emerge to more reconstruction error. However, it has been found that sometimes the autoencoder generalizes so well that it can also reconstruct anomalies well. In this paper, we present a memory-augmented deep autoencoder(MemAE) method for machine abnormal sound detection to alleviate the problem. Compared with the traditional AE based method, the accuracy AUC of MemAE in the three machines of Toy-conveyor, Slide rail and Valve are increased by 11%, 3% and 7%.