Reviewerly/exHarmony huggingface dataset:
FDD, Crystal, DSDM—An Educational Perspective

Agile is a time-boxed, iterative methodology to deliver software incrementally from the beginning to the end of the project, instead of delivering only one system at the completion of the software development cycle. Projects are split into smaller pieces of functionality called user stories which are prioritized and then delivered in short cycles known as iterations. The most common agile methodologies widely adopted by software industries are Scrum, Lean, Kanban, XP and Test-Driven development among others. These methodologies have already proven themselves in the software industry and variations also exist to tackle specific development environments. This chapter focuses on less popular methodologies such as FDD, Crystal, DSDM and highlights their application in a creative educational environment. The main objective is to better prepare final year Software Engineering students at the University of Mauritius for an increasingly complex and dynamic global environment. Hands-on activities and software tools to better understand each methodology in an educational context are emphasized in this chapter.
DPCL: Contrastive representation learning with differential privacy

With the proliferation of unlabeled data, increasing efforts have been devoted to unsupervised learning. As one of the most representative branches of unsupervised learning, contrastive learning has made great progress with its high efficiency. Unfortunately, privacy threats to contrastive learning have become sophisticated, making it imperative to develop effective technologies that can deal with such threats. To alleviate the privacy issue in contrastive learning, we propose some novel techniques based on differential privacy, which aim at reducing the high sensitivity of gradient in the private training caused by interactive contrastive learning. Specifically, we add differentially private protection to the connection point related to different per-example gradients, which decreases the sensitivity of the gradients significantly. Our experiments on SimCLR and the Barlow Twins show that our approach is superior since it is more accurate while maintaining the same level of privacy protection.
Audio and Video Matching Zero-Watermarking Algorithm Based on NSCT

In the Internet age, information security is threatened anytime and anywhere and the copyright protection of audio and video as well as the need for matching detection is increasingly strong. In view of this need, this paper proposes a zero-watermarking algorithm for audio and video matching based on NSCT. The algorithm uses NSCT, DCT, SVD, and Schur decomposition to extract video features and audio features and generates zero-watermark stream through synthesis, which is stored in a third-party organization for detection and identification. The detection algorithm can obtain zero watermark from the audio and video to be tested and judge and locate tampering by comparing with the zero watermark of the third party. From the experimental results, this algorithm can not only detect whether the audio and video are mismatched due to tampering attacks but also locate the mismatched audio and video segments and protect the copyright.
Semiconductor technologies and related topics for implementation of electronic reservoir computing systems

Abstract Reservoir computing (RC) is a unique machine learning framework based on a recurrent neural network, which is currently involved in numerous research fields. RC systems are distinguished from other machine learning systems since detailed network designs and weight adjustments are not necessary. This enables the availability of many device and material options to physically implement the system, referred to as physical RC. This review outlines the basics of RC and related issues from an implementation perspective that applies semiconductor electron device technology. A possible interpretation of RC computations is shown using a simple model, and the reservoir network is understood from the viewpoint of network theory. Physical implementation and operation issues are discussed by referring to our experimental investigation of dynamic nodes using a semiconductor tunnel diode with cubic nonlinearity.
A Novel Fusion Scheme for Face Recognition in Challenging Conditions

Literature reveals that by the fusion of global and local descriptors more finer results are earned than either of them alone. Motivated by this, the proposed work introduces the novel fusion scheme by amalgamating the features of three effective descriptors, i.e., PCA, LBP and LPQ. PCA is availed as the global feature extractor and the local feature extraction are carried out by LBP and LPQ. This fusion scheme is termed as the PCA + LBP + LPQ. Prior to amalgamation, z-score normalization is carried out on the respective descriptor. The LBP and LPQ features are attained region wise from corresponding map images. The amalgamated size is on the bigger side; therefore, PCA services are exploited again for compact size. For matching SVMs are availed, and four datasets deployed are ORL, GT, JAFFE and Faces94. The PCA + LBP + LPQ pulls of superb recognition rates than either of PCA, LBP and LPQ. It also overshadow the numerous literature-based techniques.
Feature Enhanced and Context Inference Network for Pancreas Segmentation

Segmenting pancreas from CT images is of great significance for clinical diagnosis and research. Traditional encoder-decoder networks, which are widely used in medical image segmentation, may fail to address low tissue contrast and large variability of pancreas shape and size due to underutilization of multi-level features and context information. To address these problems, this paper proposes a novel feature enhanced and context inference network (FECI-Net) for pancreas segmentation. Specifically, features are enhanced by imposing saliency region constraints to mine complementary regions and details between multi-level features; Gated Recurrent Unit convolution (ConvGRU) is introduced in the decoder to fully contact the context aimed to capture task-relevant fine features. By comparing experimental evaluations on the NIH-TCIA dataset, our method improves IOU and Dice by 5.5% and 4.1% respectively compared to the baseline, which outperforms current state-of-the-art medical image segmentation methods.
Introduction

The alluvial fan is conical or funnel-shaped alluvium deposited at the edge of the mountains, whose thickness decreases and width increases from the mountains toward the plains (Pourmorad et al. 2021). The average slope of an alluvial fan is about 5°, but may change by more than 25° (Li et al. 2020). The radius of the alluvial fan may vary from less than a few hundred meters to more than 150 km (Tavanaei et al. 2020). Alluvial fan deposits are often red in color due to the fact that they form in an oxidizing environment (Kumar et al. 2020). The shape of these sediments depends on the tectonic and climatic conditions of the region (Bowman 2019). However, most alluvial fan sediments are formed in areas with arid and semi-arid climates with low vegetation, very low rainfall and rapid erosion. These sediments are not specific to the climate and are formed in different regions (Pourmorad and Jahan 2021). Economically, the identification of alluvial fan deposits can be of particular importance. For example, alluvial sediments can be the center of groundwater accumulation, and most groundwater reservoirs within the sedimentary basin are fed by water from alluvial sediments (Zhang et al. 2020). Most of the gold in the world is also extracted from the deposits of ancient alluvial fans in South Africa, which have remained in placer form. In addition, large amounts of uranium placer are extracted from old alluvial sediments in South African sedimentary basins (Sissakian et al. 2020). This book has a comprehensive study of alluvial fan sediments, which seeks to introduce all the sedimentary and geochemical properties of the alluvial fan. Here, an attempt has been made to study and evaluate the alluvial fan sediments of southwestern Iran as a master case with full practical application. A detailed study including sedimentology, lithology, geochemistry and morphotectonics of alluvial fans of southwestern Iran has been described in this book. These studies will contribute significantly to the identification of similar sedimentary environments and will provide a clear view of the environmental, mineral, agricultural and urban hazards of different regions.
Metric Learning with Distillation for Overcoming Catastrophic Forgetting

In incremental learning, reducing catastrophic forgetting always adds additional weights to the classification layer when a new task comes. Moreover, it focuses on prediction accuracy, ignores mislabeling differences, and causes the learned features to be scattered. This paper removes the softmax and integrates depth metric learning to compute image embeddings, which doesn't need to add extra weights or additional space for new classes. The distances of the data mapped in the old and new task spaces are calculated separately. Then, the distillation of depth metric learning is used to make the two distances as similar as possible to improve the performance of the old knowledge. The experimental results on Cifar10 and Tiny-ImageNet show that the proposed method can effectively alleviate catastrophic forgetting and enhance the effectiveness of incremental learning.
Multi-workflow Scheduling Based on Implicit Information Transmission in Cloud Computing Environment

In the current cloud computing environment, task scheduling and resource allocation are the key and difficult points in the performance improvement. However, there are numerous problems of workflow, such as Montage, Inspiral, Cybershake etc. They have similar workflow structures, which affect the efficiency of task scheduling and resource distribution. In addition, the result obtained by the traditional evolutionary algorithm is the allocation sequence of the virtual machine in the cloud computing environment only for single task, which is a great waste of resources. Aiming at these problems, the multiple workflow tasks are processed in this paper by using implicit information transfer at the same time, that is, to reasonably use the allocation sequence of each task to exchange information so as to share a better virtual machine allocation. Meanwhile, using the potential relationship and differences between different tasks are better able to make population has better convergence and diversity. We proposed a multifactorial evolutionary algorithm based on combinatorial population (CP-MFEA) for multitasking workflows. This paper constructs nine sets of multi-task combination problems, and compares the method with the traditional single-task evolutionary algorithm, the purpose is to describe the superiority of this method clearly. Through the experimental results, we can notice that CP-MFEA’s ability is much more obvious than single-task evolutionary algorithms.
Automated Sensors for Indian Traffic: Challenges and Solutions

Automated traffic detectors can be classified as vehicle detectors/identifiers and vehicle tracking devices. The vehicle detectors/identifiers are mostly location‐based and collect data from the entire vehicle population that crosses the location. However, they miss out the spatial variations. Vehicle tracking devices are usually fixed inside vehicles and can collect spatial parameters such as travel time from that individual vehicle. However, data can be obtained only from vehicles that voluntarily participate by housing the tracking device. This limits the sample size. Although these are proven data collection technologies for traffic conditions elsewhere, they may not work for Indian traffic conditions, with its heterogeneity and lack of lane discipline. Most of the available data collection technologies are limited by the need for lane-based traffic and cannot classify different types of vehicles. This chapter discusses various location-based and spatial/tracking technologies suitable for data collection under Indian traffic conditions.
Prognostic Staging System for Esophageal Cancer Using Lasso, Cox and CS-SVM

Esophageal cancer is a heterogeneous malignant tumor with high mortality. Design constructing an effective prognostic staging system would help to improve the prognosis of patients. In this paper, blood indexes and TNM stages of patients with esophageal cancer are analyzed using the Lasso algorithm and Cox Proportional Hazards analysis. Two indicators significantly correlated with postoperative survival time and status of esophageal cancer patients are screened out. PI prognostic index is established by Cox Proportional Hazards Models. PI prognostic system can effectively predict the survival status of patients, which has been verified by ROC curve. PI prognostic staging system was established by ROC curve results. PI prognostic staging system was validated using kaplan-Meier curves, $$P<0.0001$$ . Cuckoo search algorithm-support vector machine can accurately predict PI prognostic staging system, and the accuracy of 10-fold cross-validation is 97.6721%. Therefore, the constructed PI prognostic staging system has the value of predicting the risk level of esophageal cancer patients.
Collaborative Content Construction

This work presents a proposal of pedagogical architecture called Collaborative Content Construction, which relies on the collective construction of knowledge through relational pedagogy, supported by the genetic epistemology of Jean Piaget. Pedagogical architectures represent a way of contributing to innovation in the use of technological support in learning. This proposal includes a methodology that simultaneously builds, collectively and interactively, knowledge, and also leads to the development of materials that benefit participants even after the end of the activity, because in addition to learning in the process, we expect resulting learning material to be more attractive to the students since it would be developed by their peers.
Geology of the Study Area

It is vital to have sufficient knowledge of the geology of the region, including the type of sediments, sediment origin, formation and morphology of the region, and other geological and environmental information about the region (Pourmorad and Jahan 2021). In terms of sedimentary and tectonic properties, Iran has several basins and sub-basins, of which the most important is the Zagros Basin (Fig. 2.1). The Zagros Basin is characterized by a thick sequence of 7–14 km of sedimentary sediments in large dimensional areas along the north-northeastern edge of the Arabian plate (Bagheri Moghadam and Kharazian 2020). The Zagros Basin has been a stable part of the Gondwana subcontinent during the Paleozoic, and the inactive margin was formed during the Mesozoic (Alavi 2004). The collision between the Iranian and Arabian plates during the Cenozoic led to the formation of the fold-thrust Zagros belt and the associated Foreland Basin (Alizadeh et al. 2020). This folded-trust belt has long been considered due to its huge oil and gas resources and extensive studies have been conducted on it (Pash et al. 2020). Due to the geographical location of the study areas, which is in the folded Zagros, in this chapter we have tried to explain the Zagros tectonics (structural evolution of the Neottis Basin) and the Zagros stratigraphy.
Cross-Validation of Circular Economy Causal Network Maps

The circular economy (CE) paradigm has significant potential in addressing resource depletion challenges. CE aims to attain a cyclic economy where materials are recirculated. Identification of its drivers and barriers facilitate the transition towards CE. Mapping its causal networks are essential in understanding their interrelationships. The decision-making trial and evaluation laboratory (DEMATEL) possesses the ability to map causal networks, utilizing perception on pairwise relationships for mapping. This utilization relies on subjective perceptions, resulting in biases. Fuzzy cognitive maps (FCMs) also possess the ability to map causal networks. FCMs have a similar architecture with neural networks, allowing calibration according to empirical data. Integrating both frameworks yield an empirically trained causal network. A hybrid DEMATEL-FCM framework is developed and is applied in country-level economies. The proposed hybrid approach is capable of mapping an initial causal network using DEMATEL and then training the network through FCM. The network attained an accuracy of 73.20% on training and 73.81% on testing, revealing economic attractiveness and consumer demand as significant to material footprint reduction. The DEMATEL-FCM framework is applicable to the validation of causal networks. Findings from this work may be used for the assessment of driver interrelationships for CE.
Strain Measurement of Aluminium in Tensometer Using Digital Image Correlation (DIC)

Digital Image Correlation (DIC) is an evolving optical image technique to measure deformation. DIC technique correlates a series of images captured during deformation. Based on the position of speckle pattern on the image, the displacement and strain field are estimated. The aluminium sample is prepared as per ASTM standard added with speckle pattern spray-painted on its surface. A simple stand-alone CCD camera—lighting system is used to record the deformation along uniaxial tensile testing carried out in Tensometer. A series of captured images during deformation are tracked using open-source DIC program in MATLAB software. The static and dynamic analysis of specimen is also carried out in ANSYS workbench. The stress and strain values obtained from the experimental readings (Tensometer) and the image correlations (DIC) closely match each other whereas simulation results obtained in ANSYS have 16% error compared with experimental. This error may be reduced by decreasing the element size and reducing errors in meshing and loading.
Self-attention Convolution for Sparse to Dense Depth Completion

Depth completion from a sparse set of depth measurements and a single RGB image has been shown to be an effective method for generating high-quality depth images. However, traditional convolutional neural network methods tend to interpolate and replicate the output from the surrounding depth values. The underutilization of sparse information leads to blurred boundaries and loss of structural information. To further improve the accuracy of depth completion, we extend the original U-shaped network by self-attention convolution to extract more useful information from the sparse depth measurements. The experimental results validate the effectiveness of self-attention convolution using the U-net architecture on the NYUv2 depth dataset. The accuracy of the proposed method has been improved by 16.9% compared to the original Unet network.
Few Shot Learning with Embedding Alignment and Standardization

Unlike the classical approaches of developing Machine Learning models with a huge amount of data, few-shot learning (FSL) refers to the learning with a limited amount of training data. In practice, FSL learns dissimilarity between instances, allowing it to generalize to new tasks with fewer data. In recent years, many state-of-the-art (SoA) FSL models, such as Matching Net, Relational Net, and Prototypical Net etc., have been proposed in learning the dissimilarity metric in euclidean or in latent space. Recent works are trying to achieve higher accuracy by increasing depth of the embedding module(e.g. WRN, ResNet12 or higher) and complexity in embedding transformation and adaptation. We propose a “Few Shot Learning with embedding alignment and standardization” (FEAS) as an approach for learning the dissimilarity measure which maximizes the inter class dissimilarity, keeping embedding modules shallow and embedding adaptation simpler. It generalizes the classification tasks by learning feature level bias, applying instance level standardization 1 . The results show that the proposed model improves results over protoTypical networks by 6.1% and over SimpleShot by 8.01% at miniImageNet (5-way 5-shot, convNet).
Handwritten Bengali Digit Classification Using Deep Learning

Bengali Character Recognition has recently become a field of increased academic interest due to its wide and varying applications. In this paper, a recognition system based on transfer learning has been proposed for robust recognition of Bengali numeric digits. Several preprocessing techniques have been used on the images to aid in the training process. ResNet-18 has been used for the transfer learning. All deep learning applications have been achieved by the extensive use of the PyTorch library. The dataset used has been chosen for its unbiasedness, which allows the proposed model to perform better on digits it has never encountered before. The proposed model has shown state of the art performance, with an accuracy of 92% in just 25 epochs. Thus, the proposed model doesn’t only provide a decent accuracy, but it does so with lesser number of parameters than other leading approaches along with taking fewer epochs to reach the final result.
A Reconstruction-Based Visual-Acoustic-Semantic Embedding Method for Speech-Image Retrieval

Speech-image retrieval aims at learning the relevance between image and speech.Prior approaches are mainly based on bi-modal contrastive learning, which can not alleviate the cross-modal heterogeneous issue between visual and acoustic modalities well. To address this issue, we propose a visual-acoustic-semantic embedding (VASE) method. First, we propose a tri-modal ranking loss by taking advantage of semantic information corresponding to the acoustic data, which introduces the auxiliary alignment to enhance the alignment between image and speech. Second, we introduce a cycle-consistency loss based on feature reconstruction. It can further alleviate the heterogeneous issue between different data modalities ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , visual-acoustic, visual-textual and acoustic-textual). Extensive experiments have demonstrated the effectiveness of our proposed method. In addition, our VASE model achieves state-of-the-art performance on the speech-image retrieval task on the Flickr8K [Harwath and Glass, 2015]s and Places [Harwath <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , 2018] datasets.
A Novel Approach in Breast Cancer Diagnosis with Image Processing

To declare breast lumps as cancerous through only segmentation techniques is not possible since mammography reports should be followed by a biopsy. We propose an Otsu method with two threshold values followed by an iterative approach to split up the image into cancerous and non-cancerous regions since both techniques are time efficient. Setting a threshold value with Otsu and Iterative approach no doubt differentiates the image into cancerous and non-cancerous but the challenging task is to set the threshold value, so by setting a minimal threshold value 0.5 and 0.8 the algorithm performs well and an area that has been damaged by disease is estimated as well by implementing the various Matlab functions. This research will assist the radiologists to analyze properly the mammography report act accordingly on the affected part.