Reviewerly/exHarmony huggingface dataset:
AI and Blockchain for Healthcare Data Security in Smart Cities

The healthcare industry is interested in various AI and blockchain technology characteristics, such as the immutability of data stored in a blockchain. Numerous interesting IoT-based applications are being examined. IoT-based Clinical and biological research will be sped up, biomedical and healthcare data ledgers will be advanced using Blockchain and AI. These evaluations are based on essential Blockchain and AI technology features such as decentralized management, immutable audit trails, data authenticity, resilience, better security, and most importantly, the restoration of charterers’ rights. Blockchain and AI are being used to create innovative and advanced solutions to improve the present standards of medical data handling, sharing, processing, analysis, and classification according to the outcomes. With enhanced efficiency, access control, technical development, privacy protection, and security of operational data processes, blockchain technology is transforming the IoT-based healthcare industry. In this chapter, we proposed AI and Blockchain based framework for improving the data security in the smart cities domain.
Feature-based software design pattern detection

Software design patterns are standard solutions to common problems in software design and architecture. Knowing that a particular module implements a design pattern is a shortcut to design comprehension. Manually detecting design patterns is a time consuming and challenging task, therefore, researchers have proposed automatic design pattern detection techniques. However, these techniques show low performance for certain design patterns. In this work, we introduce a design pattern detection approach, DPDF that improves the performance over the state-of-the-art by using code features with machine learning classifiers to automatically train a design pattern detector. DPDF creates a semantic representation of Java source code using the code features and the call graph, and applies the Word2Vec algorithm on the semantic representation to construct the word-space geometric model of the Java source code. DPDF then builds a machine learning classifier trained on a labelled dataset and identifies software design patterns with over 80% Precision and over 79% Recall. Additionally, we have compared DPDF with two existing design pattern detection techniques namely FeatureMaps & MARPLE-DPD. Empirical results demonstrate that our approach outperforms the existing approaches by approximately 35% and 15% respectively in terms of Precision. The run-time performance also supports the practical applicability of our classifier.
Privacy-Preserving Efficient Federated-Learning Model Debugging

Federated learning allows large amounts of mobile clients to jointly construct a global model without sending their private data to a central server. A fundamental issue in this framework is the susceptibility to the erroneous training data. This problem is especially challenging due to the invisibility of clients’ local training data and training process, as well as the resource constraints. In this paper, we aim to solve this issue by introducing the first FL debugging framework, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FLDebugger</i> , for mitigating test error caused by erroneous training data. The proposed solution traces the global model’s bugs (test errors), jointly through the training log and the underlying learning algorithm, back to first identify the clients and subsequently their training samples that are most responsible for the errors. In addition, we devise an influence-based participant selection strategy to fix bugs as well as to accelerate the convergence of model retraining. The performance of the identification algorithm is evaluated via extensive experiments on a real AIoT system (50 clients, including 20 edge computers, 20 laptops and 10 desktops) and in larger-scale simulated environments. The evaluation results attest to that our framework achieves accurate, privacy-preserving and efficient identification of negatively influential clients and samples, and significantly improves the model performance by fixing bugs.
A multi-party secure encryption-sharing hybrid scheme for image data base on compressed sensing

A multi-party secure encryption-sharing hybrid scheme for image data base on compressed sensing (CS) is proposed. Traditional image compression and encryption schemes only transmit a single secret image in the network, which is easy to lose or be deceived by a single receiver. In order to solve this problem, the secret sharing is introduced to share and transmit the secret image. The plain image is sparsely processed by wavelet transform, zigzag confusion reduces the correlation between pixels, and the measurement matrix generated by the CS and chaotic system is applied to the sparse image to obtain the secret image. Then the secret image is embedded into the carrier image used least significant bit to form a cipher image, which is also a visually secure image. Sharing divides the cipher image into N parts and transmits them to N recipients for secure transmission, and can be recovered only when r(r≪N) recipients choose to recover the image. Experimental results show that the proposed scheme solves the problem of receiver fraud and network transmission insecurity on the basis of ensuring good recovery quality. In addition, the combination of CS and the semi-tensor product reduces the size of the measurement matrix and reduces the computational complexity. The scheme achieves the effect of hiding and encryption, which can resist steganalysis.
Discrete and Soft Prompting for Multilingual Models

It has been shown for English that discrete and soft prompting perform strongly in fewshot learning with pretrained language models (PLMs).In this paper, we show that discrete and soft prompting perform better than finetuning in multilingual cases: Crosslingual transfer and in-language training of multilingual natural language inference.For example, with 48 English training examples, finetuning obtains 33.74% accuracy in crosslingual transfer, barely surpassing the majority baseline (33.33%).In contrast, discrete and soft prompting outperform finetuning, achieving 36.43% and 38.79%.We also demonstrate good performance of prompting with training data in multiple languages other than English.
A whole-slide image (WSI)-based immunohistochemical feature prediction system improves the subtyping of lung cancer

Background Clinically, accurate pathological diagnosis is often challenged by insufficient tissue amounts and the unaffordability of additional immunohistochemical or genetic tests; thus, there is an urgent need for a universal approach to improve the subtyping of lung cancer without the above limitations. Here we aimed to develop a deep learning system to predict the immunohistochemistry (IHC) phenotype directly from whole-slide images (WSIs) to improve the subtyping of lung cancer from surgical resection and biopsy specimens. Methods A total of 1914 patients with lung cancer from three independent hospitals in China were enrolled for WSI-based immunohistochemical feature prediction system (WIFPS) development and validation. Results The WIFPS could directly predict the IHC status of nine subtype-specific biomarkers, including CK7, TTF-1, Napsin A, CK5/6, P63, P40, CD56, Synaptophysin, and Chromogranin A, achieving average areas under the curve (AUCs) of 0.912, 0.906, and 0.888 and overall diagnostic accuracies of 0.925, 0.941, and 0.887 in the validation datasets of total, external surgical resection specimens and biopsy specimens, respectively. The histological subtyping performance of the WIFPS remained comparable with that of general pathologists (GPs), with Cohen's kappa values ranging from 0.7646 to 0.8282. Furthermore, the WIFPS could be trained to not only predict the IHC status of anaplastic lymphoma kinase (ALK), programmed death-1 (PD-1), and programmed death ligand 1 (PD-L1), but also predict EGFR and KRAS mutation status, with AUCs from 0.525 to 0.917, as detected in separate populations. Conclusions In this study, the WIFPS showed its proficiency as a useful complement to traditional histologic subtyping for integrated immunohistochemical spectrum prediction as well as potential in the detection of gene mutations.
CCSA: Cellular Crow Search Algorithm with topological neighborhood shapes for optimization

In evolutionary computation, systematically structuring the population is used to manage the evolution process. Thus controlling the amount of diversity during the algorithm search. Island-based, hierarchical-based, and cellular automata are the most popular structured population models utilized for evolutionary algorithms to improve their diversity and convergence. Specifically, the cellular automata model arranges the population of individuals in a 2D grid spatial structure where the concepts of cells and their neighbors are considered to drive the evolution step. A recent population-based algorithm is proposed called the crow search algorithm (CSA). It stimulates the behavior of crows in keeping their food in hidden places to be retrieved when needed. Like other population-based algorithms, CSA endures from slow convergence because of inadequate diversity. In this paper, the cellular automata model is incorporated with the optimization framework of CSA to control its diversity during the search, thus boosts its efficiency. The proposed method is abbreviated as CCSA. In CCSA, the population is structured as a 2D grid where the active population is iteratively determined. At each iteration, each individual is updated based on current neighboring individuals determined by the topological neighborhood shapes and follows the best neighboring individual in its active population. The CCSA is evaluated using 23 standard benchmark functions well-circulated in the literature. Initially, six cellular topological neighborhood shapes (i.e., L5, L9, C9, C13, C21, and C25) are studied with various problem sizes to investigate their impact on the CCSA convergence. Comparative evaluations against twelve state-of-the-art methods, including four CSA versions, are conducted. The comparative results prove the effectiveness of the proposed CCSA. The diversity of the proposed CCSA has also been stressed using convergence analysis and hamming distance. For further validations, the proposed algorithm is evaluated using three real-world problems published in IEEE-CEC2011 and welded beam design problems. Again, the proposed CCSA yields more fruitful results than other well-established methods using real-world problems. In conclusion, this paper provides an efficient alternative of CSA to perfectly maintain the diversity of the search, which can be further tested using other optimization problems.
Deep Neural Network to Tradeoff between Accuracy and Diversity in a News Recommender System

The news recommender systems are marked by a few unique challenges specific to the news domain. These challenges emerge from rapidly evolving readers’ interests over dynamically generated news items that continuously change over time. News reading is driven by a blend of a reader’s long-term and short-term interests. In addition, diversity is required in a news recommender system to keep the reader engaged in the reading process and get them exposed to different views and opinions. This paper proposes a deep neural network that jointly learns news and user representation in a unified framework. It learns the news representation (features) from the headlines, snippets (body) and taxonomy (category, subcategory) of news. The attention mechanism learns a reader’s long-term interests from the complete click history, short-term interests from recent clicks via LSTMs and diverse interests. We also apply different levels of attention to our model. We conduct extensive experiments on two news datasets to demonstrate the effectiveness of our approach.
Quantifying and alleviating political bias in language models

Current large-scale language models can be politically biased as a result of the data they are trained on, potentially causing serious problems when they are deployed in real-world settings. In this paper, we first describe metrics for measuring political bias in GPT-2 generation, and discuss several interesting takeaways: 1) The generation of vanilla GPT-2 model is mostly liberal-leaning, 2) Such political bias depends on the sensitive attributes mentioned in the context, and 3) Priming the generation with a explicit political identifier, the extent of political bias is imbalanced (between liberal and conservative). We then propose a reinforcement learning (RL) framework for mitigating such political biases in generated text: By using rewards from word embeddings or a classifier, our RL framework guides debiased generation without having access to the training data or requiring the model to be retrained. In empirical experiments on three attributes sensitive to political bias (gender, location, and topic), our methods reduced bias according to both our metrics and human evaluation, while maintaining readability and semantic coherence.
Fixation classification: how to merge and select fixation candidates

Eye trackers are applied in many research fields (e.g., cognitive science, medicine, marketing research). To give meaning to the eye-tracking data, researchers have a broad choice of classification methods to extract various behaviors (e.g., saccade, blink, fixation) from the gaze signal. There is extensive literature about the different classification algorithms. Surprisingly, not much is known about the effect of fixation and saccade selection rules that are usually (implicitly) applied. We want to answer the following question: What is the impact of the selection-rule parameters (minimal saccade amplitude and minimal fixation duration) on the distribution of fixation durations? To answer this question, we used eye-tracking data with high and low quality and seven different classification algorithms. We conclude that selection rules play an important role in merging and selecting fixation candidates. For eye-tracking data with good-to-moderate precision (RMSD < 0.5∘), the classification algorithm of choice does not matter too much as long as it is sensitive enough and is followed by a rule that selects saccades with amplitudes larger than 1.0∘ and a rule that selects fixations with duration longer than 60 ms. Because of the importance of selection, researchers should always report whether they performed selection and the values of their parameters.
Stream-learn — open-source Python library for difficult data stream batch analysis

Stream-learn is a Python package compatible with scikit-learn and developed for the drifting and imbalanced data stream analysis. Its main component is a stream generator, which allows producing a synthetic data stream that may incorporate each of the three main concept drift types (i.e., sudden, gradual and incremental drift) in their recurring or non-recurring version, as well as static and dynamic class imbalance. The package allows conducting experiments following established evaluation methodologies (i.e., Test-Then-Train and Prequential). Besides, estimators adapted for data stream classification have been implemented, including both simple classifiers and state-of-the-art chunk-based and online classifier ensembles. The package utilises its own implementations of prediction metrics for imbalanced binary classification tasks to improve computational efficiency.
Virtual Mouse using Hand Gestures

Gesture-controlled laptops and computers have recently gained a lot of traction. Leap motion is the name for this technique. Waving our hand in front of our computer/laptop allows us to manage certain of its functionalities. Over slides and overheads, computer-based presentations have significant advantages. Audio, video, and even interactive programmes can be used to improve presentations. Unfortunately, employing these techniques is more complicated than using slides or overheads. The speaker must operate various devices with unfamiliar controls (e.g., keyboard, mouse, VCR remote control). In the dark, these devices are difficult to see, and manipulating them causes the presentation to be disrupted. Hand gestures are the most natural and effortless manner of communicating. The camera’s output will be displayed on the monitor. The concept is to use a simple camera instead of a classic or standard mouse to control mouse cursor functions. The Virtual Mouse provides an infrastructure between the user and the system using only a camera. It allows users to interface with machines without the use of mechanical or physical devices, and even control mouse functionalities. This study presents a method for controlling the cursor’s position without the need of any electronic equipment. While actions such as clicking and dragging things will be carried out using various hand gestures. As an input device, the suggested system will just require a webcam. The suggested system will require the use of OpenCV and Python as well as other tools. The camera’s output will be presented on the system’s screen so that the user can further calibrate it.
Towards Big Data Security Framework by Leveraging Fragmentation and Blockchain Technology

The world is facing a growth in the amount and variety of data generated by both users and machines. Despite the exponential increases, the tools and technologies developed to manage these data volumes are not intended to meet security and data protection requirements. Additionally, most of the current big data security systems are offered by a centralized third party, which is vulnerable to many security threats. Blockchain technology plays a significant role by addressing modern technology concerns such as decentralization, non-tampering, trust, data ownership, and traceability, making it great potential to protect personal information. This research presents a new big data security solution empowered by blockchain technology and incorporates fragmentation, encryption, and access control techniques. Our proposed fragmentation algorithm takes into account the data owner’s demand for encryption to be added to the fragmentation process. Furthermore, data fragments will be stored in the distributed manner offered by the big data environment, resulting in an additional layer of data protection. In order to achieve an optimal security solution, we aim to enhance big data security with acceptable overhead and avoid the encryption overhead for non-sensitive and low-sensitive data portions. We present the results of our implemented techniques to highlight that the overheads (in terms of computation time) introduced by our solution are negligible relative to its security and privacy gains.
An Instrumented Glove for Recognition of Brazilian Sign Language Alphabet

This paper presents the development and analysis of a system for Brazilian Sign Language (Libras) recognition, which is composed of an instrumented glove and an acquisition system. The instrumented glove has five flex sensors, an inertial sensor, and two contact sensors (made with silver-coated fabric). For data acquisition, five volunteers performed gestures in Libras. In the proposed segmentation technique, the collected data were segmented into three periods (fixed amounts of signal samples): construction period, alphabet gesture, and relaxation period. The periods of alphabet gesture are submitted to segmentation, and the classifier was designed to recognize the 26 letters of alphabet. Different classifiers were employed to classify the gestures and analyze the system in different scenarios. For the classification in group of sensors, the highest performance obtained was an accuracy of 96.15% with Random Forest (RF) classifier.
KAMA: 3D Keypoint Aware Body Mesh Articulation

We present KAMA, a 3D Keypoint Aware Mesh Articulation approach that allows us to estimate a human body mesh from the positions of 3D body keypoints. To this end, we learn to estimate 3D positions of 26 body keypoints and propose an analytical solution to articulate a parametric body model, SMPL, via a set of straightforward geometric transformations. Since keypoint estimation directly relies on image clues, our approach offers significantly better alignment to image content when compared to state-of-the-art approaches. Our proposed approach does not require any paired mesh annotations and provides accurate mesh fittings through 3D keypoint regression only. Results on the challenging 3DPW and Human3.6M show that our approach yields state-of-the-art body mesh fittings.
Continual learning classification method with the weighted k-nearest neighbor rule for time-varying data space based on the artificial immune system

Supervised learning classification methods play an important role in various fields. However, most of them cannot effectively classify the data from time-varying data spaces, for they lack continual learning ability. Inspired by the intelligent mechanism that memory cells of the biological immune system can evolve with the evolution of invaders, a continual learning classification method with the weighted k-nearest neighbor rule for time-varying data space (WKNN-CLCMTVD) is proposed. Memory cells selected by the weighted k-nearest neighbor rule are used to identify the type of testing data. Memory cells are continuously cultivated, updated, and eliminated through learning testing data to improve the classification ability of WKNN-CLCMTVD. It degenerates into a standard supervised learning classification method when all data independent of time. Take experiments on twenty benchmark datasets, a 2-dimensional synthetic dataset, and XJTU-SY rolling element bearing accelerated life test datasets to evaluate the performance of WKNN-CLCMTVD. Results show that it has better classification ability for time-invariant data and outperforms the other methods for time-varying data space. Results also show that its running speed is far faster than that of other continual learning classification methods.
On the Performance of Crowd-Specific Detectors in Multi-Pedestrian Tracking

In recent years, several methods and datasets have been proposed to push the performance of pedestrian detection in crowded scenarios. In this study, three crowd-specific detectors are combined with a general tracking-by-detection approach to evaluate their applicability in multi-pedestrian tracking. Investigating the relation between detection and tracking accuracy, we make the interesting observation that in spite of a high detection capability, the performance in tracking can be poor and analyze the reasons behind that. However, one of the examined approaches can significantly boost the tracking performance on two benchmarks under different training configurations. It is shown that combining crowd-specific detectors with a simple tracking pipeline can achieve promising results, especially in challenging scenes with heavy occlusion. Although our tracker only relies on motion cues and no visual information is considered, applying the strong detections from the crowd-specific model, state-of-the-art results on the challenging MOT17 and MOT20 benchmarks are obtained.