Reviewerly/exHarmony huggingface dataset:
An Explainable Deep Network Framework with Case-Based Reasoning Strategies for Survival Analysis in Oncology

Survival analysis models the time-to-event information while handling data censorship. Such technique yields extensive applications in carcinoma treatment and prediction. Deep neural networks (DNNs) are appealing for survival analysis because of their non-linear nature. However, DNNs are often described as “black box” models because they are hard or practically impossible to explain. In this study, we propose an explainable deep network framework for survival prediction in cancer. We utilize strategies from nearest neighbor retrieval in case-based reasoning (CBR) to provide useful insights into the inner workings of the deep network. First, we use an autoencoder network to reconstruct the features of a training input. We create a prototype layer, which can store the weight vector following the encoded input and receive the output from the encoder. In this deep survival model network, the total loss function consists of four terms: the negative log partial likelihood function of the Cox model, the autoencoder loss, and two interpretability prototype distance terms. We use an adaptive weights approach to combine the four loss terms. The network that the prototype layer learns during training naturally comes with an explanation for each prediction. We also introduce Shapley Additive Explanation (SHAP) to explain each feature importance for the model predictions. The results of this study demonstrate on two cancer methylation data sets that the developed approach is effective.
A Cluster-Based Facial Image Anonymization Method Using Variational Autoencoder

Existing methods for face de-identification often cause inevitable damage to the utility of facial information. The anonymized facial images can hardly be applied in practical applications. In this work, we propose a cluster-based generative model that conceals the identity of images while preserving the utility of facial images. We extract facial features in the first stage, then classify images into several clusters. Four naive protection methods, blindfold, mosaic, cartoon and mosaic, are adopted to form facial image inputs without private information. Along with the four de-identified images, a random facial image in the same cluster is also chosen as another input for better preservation of facial features. We train a novel model with multiple inputs, called Multi-stage Utility Maintenance-Variational AutoEncoder (MsUM-VAE), generating a facial image using the mentioned multi-inputs. The output of the model retains a large portion of the facial characteristics, but cannot be distinguished from the original image dataset, avoiding the disclosure of privacy. We perform numerous evaluations on the CelebA dataset to showcase the effectiveness of our model, and the findings indicate that the model surpasses conventional techniques for obscuring identity and maintaining the utility of images.
ColCompNeT: Deep Learning-Based Colorization-Based Coding Network

Converting a grayscale image to a visually plausible and perceptually meaningful color image is an exciting research topic in computer vision and graphics. However, predicting the chrominance channels from a grayscale image is an ill-posed problem, especially when the objective is to produce the same color as in the ground truth image, like for the application of compression using colorization or video/image restoration. Compression using colorization efficiently reduces the storage space and transmission bandwidth by two-thirds and maintains the image’s visual quality. However, colorizing decompressed gray images exactly as ground truth with maximum accuracy is a challenging task. In this paper, learning-based CNN network ColCompNeT has been proposed in which concept of parallel training of colorization and compression network is utilized. With the proposed algorithm, a maximum bit saving of 36.45% is achieved with the improved objective and subjective performance when compared with the state-of-the-art methods.
Robust Watermarking Algorithm for Medical Volume Data Based on PJFM and 3D-DCT

With the rapid development of information technology and digital image processing, digital imaging technology is gradually mature and widely used in the field of medicine. The use of computed tomography (CT) and magnetic resonance imaging (MRI) for diagnosis has become an indispensable and effective auxiliary means of modern medical diagnosis. It not only facilitates the diagnosis of doctors and real-time information exchange, but also effectively solves the problem of uneven distribution of medical resources. However, medical data is vulnerable to various malicious attacks when spreading and sharing information on the network, and there is a risk of disclosure of patient privacy. To solve this problem, this paper proposes a robust watermarking algorithm for medical volume data based on deformed Jacobi-Fourier moments and 3D discrete cosine transform. The algorithm first used PJFM moment to extract the features of each slice of 3D medical volume data. Then the feature matrix was transformed by 3D-DCT to combine the contour information and detail information of each slice, and the visual feature vector was obtained by applying perceptual hash technology. In addition, the proposed algorithm used Logistic Map to preprocess and encrypt the original watermark image, and utilized zero watermarking technology to embed,, which further improved the security and practicability of the algorithm. Simulation results show that the proposed algorithm has good robustness against common and geometric attacks, especially resist rotation attacks, cropping attacks and compression attacks.
Value Investing in Cryptoassets

As introduced at the beginning of this book, value investing, or fundamental analysis, focuses on fundamentals. It identifies and measures the pillars giving real economic value to an asset. Value investors compare the asset’s value with the asset’s price and invest when the latter is significantly lower than the former. Gains materialize for the investor when the price eventually converges toward the underlying value.
Dynamic Hand Gesture Recognition Using mYOLO-CSRT and HGCNN for Human–Machine Interaction

For human–computer interaction, object detection and recognition are central tasks. In this paper, modified You only Look once (mYOLO) algorithm has been proposed to detect the region of interest. The proposed detection algorithms have a residual that prevents the detection algorithm from vanishing gradient problems and can see part of interest effectively in any challenging condition. The channel and spatial reliability tracking (CSRT) has been used for tracking the region of interest. A new CNN model hand gesture convolution neural network (HGCNN) model has been proposed for recognition of the gesture. The recognition accuracy of the proposed model is 99.83%. Raspberry Pi 3B+ is used to interact between humans and supporting devices.
Research on Attribute-Based Privacy-Preserving Computing Technologies

Privacy-preserving computing is an important topic in epidemic prevention, credit report and education resource sharing. Data analysis is the basis for realizing the overall speed increase of returning to work and production. This paper comprehensively summarizes the current academic research on privacy-preserving computing. In this work, we systematically summarize the four aspects of the related schemes on the attribute-based encryption schemes: keyword search, symmetric search, PSI, homomorphic encryption. We classify the schemes and compare the same-type schemes accordingly. Finally, we present the directions and challenges for our future work. This paper is intended to provide convenience for the subsequent research on privacy-preserving computing.
Video Transcript Extraction and Summarization Using Transfer Learning

The number of videos accessible on web platforms is continuously growing. Global access to the content is provided, especially in videos or audio. Unlike images, where data can be gathered from a single frame, videos require a viewer to watch the entire thing to fully understand the context, and this presents a significant challenge to information extraction. Previously, seq2seq models were used to predict the summaries of paragraphs or transcripts. But recent advancements in transfer learning bypass the accuracy obtained by the standard NLP algorithms such as text rank and seq2seq models. Also, the summarization type focused on by most of the standard models is extractive summarization. Therefore, Hugging Face Transformers are used in this study to summarize the generated transcripts as they help us to perform abstractive summarization and also give greater accuracy than previous standard NLP algorithms and models.The system proposed in this paper converts audio chunks from input videos into transcripts. The summarization model, based on natural language processing and transfer learning, is then given these transcripts. This paper presents a comparative analysis for some transformer models and selects the one which performs better than other models and incorporates it in the proposed system. The developed model accepts user-supplied video links as input and outputs a summary like description of video.
Facelets

This chapter covers using Faces 4.0’s built-in templating engine, called Facelets. Compared to using plain Faces, you can use Facelets to further modularize your frontend code and improve code quality, reuse, and maintenance.
Improving MIL Video Anomaly Detection Concatenating Deep Features of Video Clips

In this work we apply the MIL ranking loss proposed in Sultani et al. (2018) in a different way than the original work. Videos are disposed into clips with fixed length, rather than a fixed number of segments. Furthermore, feature vectors are obtained by concatenating feature vectors from two adjacent segments. We argue that this procedure makes it easier for the algorithm to distinguish between normal and anomalous segment scores. Since with fixed-length clips the number of segments varies within videos, we balance the video mini-batch during the training phase with the same number of segments, for the smallest video in the mini-batch. Experiments with Shanghaitech dataset show that this procedure is able to improve performance when compared against the original proposal.
Implementation of E-Commerce Application with Analytics

E-commerce applications play a very crucial role in the medical field these days. With so many medicines being available for illness, it is very difficult to track the pattern of medicines to find the best medicine for illness. A lot of research works are being carried out on medical pattern prediction, as the clinic could not predict which drugs would be dispensed frequently.
EthereumX: Improving Signature Security With Randomness Preprocessing Module

Ethereum leverages ECDSA as the digital signature scheme to validate transactions. From the provable security standpoint, ECDSA built on an 80-bit security Elliptic Curve group can achieve at most 50-bit concrete security, rather than 80-bit security, due to its reduction loss for <inline-formula><tex-math notation="LaTeX">$2^{30}$</tex-math></inline-formula> signature queries in security analysis. The state-of-the-art ECDSA scheme comes with no de facto formal security guarantee. Although there have been many signatures with higher concrete security, their structures are quite different from ECDSA and a total replacement of the signature field in Ethereum will incur high deployment cost. In this work, we present EthereumX without compromising the signature structure in Ethereum while achieves better security. The security gain is built on top of a new technique named randomness preprocessing module (RPM), which can securely pre-generate and verify randomness with the help of Ethereum. Calling RPM allows to pre-select randomness, which will be used for the subsequent signature, and to verify the randomness, assuring that it is previously generated. We give an instantiation with formal security guarantee and prove that it can be improved to 80-bit concrete security under the same discrete logarithm assumption as ECDSA. From this instantiated scheme, we implement EthereumX via a deployment into a locally simulated network. Experiment results show that EthereumX costs 5 seconds for a block generation which is equal to Ethereum, and generates/verifies at least <inline-formula><tex-math notation="LaTeX">$17017/10623$</tex-math></inline-formula> transactions per second that is practical enough in application, even if they are slightly slower than Ethereum which generates/verifies at least <inline-formula><tex-math notation="LaTeX">$17908/11257$</tex-math></inline-formula> transactions per second. We also mention that RMP can be applied to other DL-based signatures for the security improvement.
Weighted Pathfinding in the Paparazzi Problem with Dynamic Obstacles

The paper investigates the use of the A* Algorithm in a weighted and dynamic paparazzi problem. The performance of different heuristic functions are evaluated, namely the Manhattan, Euclidean and Chebyshev using four and eight neighboring nodes. Tests are conducted on different sized maps. Obstacles and terrain structures with different weights (corresponding to different speeds) are placed fixed on the map. Dynamic obstacles are represented by security guards that move randomly on the map. Overall, we observe lower costs if the paparazzo is able to move diagonally on larger maps. In addition, the Manhattan heuristic performs best in terms of total cost in a predominant number of simulations. On the contrary, the selection of the heuristic has no significant impact on the overall execution time of the simulation.
A Survey of the State-of-the-Art and Some Extensions of Recommender System Based on Big Data

Recommender systems (RSs) based on big data have been shown to be very powerful tools for solving the information overload to assist the choice-making when dealing with the massive amount information in the age of big data and artificial intelligence. This paper presents an overview of the state-of-art RS that can be classified into four categories: content-based algorithms (CR), collaborative filtering-based algorithms (CF), and knowledge-based algorithms (KR), as well as hybrid recommendation-based algorithms (HR). The popular CF-based recommender algorithms are especially focused by classifying them into the memory-based algorithms, and model-based algorithms as they show the advantages of great rating prediction without contextual features compared to the rest of RS approaches. By reviewing the current RS and understanding their limitations, the emerging solutions or possible extensions that would improve recommendation capabilities involving deep learning, knowledge graphs, and parallel computing techniques are significantly discussed for future RS research direction. At the same time, by identifying current problems, some possible solutions will be shown in the last part.
Texas K-12 Teachers Technology Needs Assessment

This chapter describes a participatory action research study in which teachers enrolled in a doctoral program investigated the technology usage of 73 K-12 teachers in Texas schools. Participant responses during focus group conversations reveal their teaching challenges and needs during the 2020–2021 pandemic-related move to remote learning. Findings suggest digital tools, practices, and attitudes that can strengthen education in the future.
Research on Data Cleaning Method for Dispatching and Operation of Cascade Hydropower Stations

High-quality datasets are the basis for refined scheduling and operation of cascade hydropower stations, but the data quality is often affected by many factors, including data collection and transmission equipment failures, data storage system defects, and so on. Consequently, useless or wrong data often appear in the datasets and hurt the operations of cascade hydropower stations. Data cleaning is a key method to improve data quality. In this study, a two-stage data cleaning framework is used to detect and correct abnormal data. Firstly, many unsupervised machine learning methods are applied to detect abnormal data and abnormal data variation in the dataset. Secondly, the abnormal data are corrected by the proposed univariate data interpolation method. This data cleaning framework is applied in the water level datasets of Three Gorges–Gezhouba cascade hydropower stations. The results demonstrate that the two-stage data cleaning framework based on the isolation forest algorithm and random forest regression can effectively detect and correct the abnormal data in the water level datasets. The false detection rate and recall rate of the abnormal data detection method are 0.003 and 0.98, respectively, and the mean square error and goodness of fit $${\text{R}}^{2}$$ of the correction model in the test, sets are 0.092 and 0.998, respectively, which can meet the data quality requirements of cascade hydropower stations.
Speech Recognition in Healthcare: A Comparison of Different Speech Recognition Input Interactions

Speech recognition (SR) in healthcare has the potential to improve clinical documentation and communication among healthcare providers. In this respect, there are different ways of voice interaction. The purpose of this study is to compare four different speech recognition input interactions for healthcare form filling, without implying a specific SR algorithm, and thus to measure which interaction is the best in terms of time spent, error rate, and participant preference. We conducted a user study involving 15 participants, divided into three groups according to their computer usage expertise. During the experiment, participants filled out forms using four different SR interactions and traditional typing. The results showed that the interaction in which participants say all the data in a single sentence has the lowest time spent by participants, with an 83% advantage over the traditional keyboard/mouse interaction. Despite this, the System Usability Scale questionnaire revealed that the participants preferred the interaction in which they converse with a vocal assistant. Our results also found that SR interaction reduces the difference in input time between occasional and expert users.
Investment Vehicles

Investors in cryptoassets need to choose where to invest. Options are available, from pure investment on the blockchain to custodians investing on one’s behalf. This chapter investigates these options and the benefits they provide.
On Diverse and Serendipitous Item Recommendation: A Reinforced Similarity and Multi-objective Optimization-Based Composite Recommendation Framework

The commercial applicability of the recommendation system (RS) motivates researchers to broaden their focus from beyond accurate recommendations to diverse, novel, and serendipitous recommendations. To improve the capabilities of the personalized recommendation list of the user, we need to view the RS as the multi-objective optimization-based recommendation system. The superiority of the multi-objective recommendation model be subject to address three significant issues: (a) traditional rating evaluation approach has limited competency towards data sparsity issue which in turn affects fitness value, (b) strength of elementary objective functions lack in judging the comprehensive strength of associated component metrics, and (c) relevant recommendations failed to surprise the user with an unexpected yet novel recommendation. Hence, this research proposed a multi-objective recommendation framework (MORF) that jointly optimizes diversity and serendipity metrics with recommendation accuracy. Furthermore, the MORF integrates the reinforced similarity-based implicit similarity computation and rating prediction model to overcome the data sparsity issue in traditional rating metrics. Within MORF, we design three conflicting objective functions to develop the recommendation system’s capability to produce a diverse, surprising, yet relevant recommendation. The generated Pareto front over two benchmark data sets describes the trade-off recommendation solution. Finally, the proposed MORF is evaluated and compared with other baselines in terms of mean precision, diversity, and novelty.
Evaluating Generative Adversarial Networks for Gurumukhi Handwritten Character Recognition (CR)

With the invention of deep learning, optical CR (OCR) has achieved state-of-the-art performance for Latin, Arabic and Chinese scripts. However for regional languages, lack of large and high-quality training data needed to train the deep neural networks is a major barrier for the usage of deep learning. To produce a diverse synthetic data similar to realistic data, a class of deep networks called Generative Adversarial Networks (GANs) were proposed. GANs have performed efficiently for non Indic scripts and in various other fields like object detection, image translation, etc. Gurumukhi script is one of the regional Indic scripts widely used in North India and some other parts of the world like Canada, USA, etc. In this work, we have evaluated the power of GANs for generating the character dataset of regional language of India. Experimental work has been done using variants of GANs to generate the synthetic data for Gurumukhi script. Deep convolution GAN (DCGAN), Wasserstein GAN (WGAN), Least square GAN (LSGAN) and Conditional GAN (CGAN) have been evaluated for the Gurumukhi handwritten characters dataset. GANs with different network structures, training methods, loss functions and optimizers are used and compared for Gurumukhi handwritten character dataset to find the optimal parameters and results.
IESCM: Image Encryption Using Signcryption and Chaotic Maps

Image encryption is very important in concealing the actual image from third parties by encrypting it with various and efficient algorithms. It also plays an important role in concealing a patient’s medical images in the medical sector. Medical images are regarded to be the most important and sensitive data, and they must be encrypted in order to be impenetrable to cryptographic attacks. In this paper, we make use of elliptical curve and chaotic maps signcryption for encryption and decryption of medical images. Signcryption is a method which can provide reliability to the images which are very sensitive.
Apriori Versus FP-Growth for Recommendation System

Market basket analysis, or MBA, is a technique for determining the strength of an association rule between two or more products purchased at the same time, as well as patterns of when two or more occurrences occur at the same time. If the item “One” is purchased, then item “Two” is most likely to be purchased, according to Market Basket Analysis. The rules are hypo-thetical because they are developed from the data's co-occurrence frequencies. Frequency is the percentage of baskets that include the objects of interest. Pricing, product placement, and product suggestions to customers based on their purchases can all be influenced by these ideas. The goal of this research is to compare the performance of the Apriori and FP-growth algorithms. The Apriori approach generates candidate frequent itemsets, while the FP-growth algorithm bypasses candidate generation and builds a tree using a cost-effective and efficient ‘divide and conquer’ strategy.