Reviewerly/exHarmony huggingface dataset:
Mining Pareto-optimal counterfactual antecedents with a branch-and-bound model-agnostic algorithm

Mining counterfactual antecedents became a valuable tool to discover knowledge and explain machine learning models. It consists of generating synthetic samples from an original sample to achieve the desired outcome in a machine learning model, thus helping to understand the prediction. An insightful methodology would explore a broader set of counterfactual antecedents to reveal multiple possibilities while operating on any classifier. Therefore, we create a tree-based search that requires monotonicity from the objective functions (a.k.a. cost functions); it allows pruning branches that will not improve the objective functions. Since monotonicity is only required for the objective function, this method can be used for any family of classifiers (e.g., linear models, neural networks, and decision trees). However, additional classifier properties speed up the tree search when it foresees branches that will not result in feasible actions. Moreover, the proposed optimization generates a diverse set of Pareto-optimal counterfactual antecedents by relying on multi-objective concepts. The results show an algorithm with working guarantees that enumerates a wide range of counterfactual antecedents. It helps the decision-maker understand the machine learning decision and finds alternatives to achieve the desired outcome. The user can inspect these multiple counterfactual antecedents to find the most suitable one and better understand the prediction.
Differentially private release of event logs for process mining

The applicability of process mining techniques hinges on the availability of event logs capturing the execution of a business process. In some use cases, particularly those involving customer-facing processes, these event logs may contain private information. Data protection regulations restrict the use of such event logs for analysis purposes. One way of circumventing these restrictions is to anonymize the event log to the extent that no individual can be singled out using the anonymized log. This article addresses the problem of anonymizing an event log in order to guarantee that, upon release of the anonymized log, the probability that an attacker may single out any individual represented in the original log does not increase by more than a threshold. The article proposes a differentially private release mechanism, which samples the cases in the log and adds noise to the timestamps to the extent required to achieve the above privacy guarantee. The article reports on an empirical comparison of the proposed approach against the state-of-the-art approaches using 14 real-life event logs in terms of data utility loss and computational efficiency.
Domain Expansion for End-to-End Speech Recognition: Applications for Accent/Dialect Speech

Training Automatic Speech Recognition (ASR) systems with sequentially incoming data from alternate domains is an essential milestone in order to reach human intelligibility level in speech recognition. The main challenge of sequential learning is that current adaptation techniques result in significant performance degradation for previously-seen domains. To mitigate the catastrophic forgetting problem, this study proposes effective domain expansion techniques for two scenarios: 1) where only new domain data is available, and 2) where both prior and new domain data are available. We examine the efficacy of the approaches through experiments on adapting a model trained with native English to different English accents. For the first scenario, we study several existing and proposed regularization-based approaches to mitigate performance loss of initial data. The experiments demonstrate the superior performance of our proposed Soft KL-Divergence (SKLD)-Model Averaging (MA) approach. In this approach, SKLD first alleviates the forgetting problem during adaptation; next, MA makes the final efficient compromise between the two domains by averaging parameters of the initial and adapted models. For the second scenario, we explore several rehearsal-based approaches, which leverage initial data to maintain the original model performance. We propose Gradient Averaging (GA) as well as an approach which operates by averaging gradients computed for both initial and new domains. Experiments demonstrate that GA outperforms retraining and specifically designed continual learning approaches, such as Averaged Gradient Episodic Memory (AGEM). Moreover, GA significantly improves computational costs over the complete retraining approach.
Memory Efficient Local Features Descriptor for Identity Document Detection on Mobile and Embedded Devices

In this paper, we propose a data-driven approach to training a memory-efficient local feature descriptor for identity documents location and classification on mobile and embedded devices. The proposed algorithm for retrieving a dataset of patches is based on the specifics of document detection in smartphone camera-captured images with a template matching approach. The retrieved dataset of patches relevant to the domain, which includes splits for features training, features selection, and testing, is made public. We train a binary descriptor using the retrieved dataset of patches, each bit of the descriptor relies on a single computationally-efficient feature. To estimate the influence of different feature spaces on the descriptor performance, we perform descriptor training experiments using gradient-based and intensity-based features. Extensive experiments in identity document location and classification benchmarks showed that the resulting 128 and 192-bit descriptors which use gradient-based features outperformed a state-of-the-art 512-bit BEBLID descriptor for arbitrary keypoints matching in all cases except the cases of extreme projective distortions, being significantly more efficient in cases of low lighting. The 64-bit gradient-based descriptor obtained within the approach showed better quality than 128 and 256-bit BinBoost descriptors in scanned document images. To evaluate the influence of the descriptor size on the matching speed, we propose a model based on the required number of processor instructions for computing the Hamming distance between a pair of descriptors on various energy-efficient processor architectures.
Fronesis: Digital Forensics-Based Early Detection of Ongoing Cyber-Attacks

Traditional attack detection approaches utilize predefined databases of known signatures about already-seen tools and malicious activities observed in past cyber-attacks to detect future attacks. More sophisticated approaches apply machine learning to detect abnormal behavior. Nevertheless, a growing number of successful attacks and the increasing ingenuity of attackers prove that these approaches are insufficient. This paper introduces an approach for digital forensics-based early detection of ongoing cyber-attacks called Fronesis. The approach combines ontological reasoning with the MITRE ATT&CK framework, the Cyber Kill Chain model, and the digital artifacts acquired continuously from the monitored computer system. Fronesis examines the collected digital artifacts by applying rule-based reasoning on the Fronesis cyber-attack detection ontology to identify traces of adversarial techniques. The identified techniques are correlated to tactics, which are then mapped to corresponding phases of the Cyber Kill Chain model, resulting in the detection of an ongoing cyber-attack. Finally, the proposed approach is demonstrated through an email phishing attack scenario.
Cost-Efficient Scheduling of Streaming Applications in Apache Flink on Cloud

Stream processing has been gaining extensive attention in the past few years. Apache Flink is a new generation of distributed stream processing engines that can process a great deal of data in real-time with low latency. But the default scheduler of Flink adopts a random task scheduling strategy, which does not consider the cost and load balancing in the cloud environment. In this article, a cost-efficient task scheduling algorithm (CETSA) and a cost-efficient load balancing algorithm (LBA-CE) for Flink are proposed to reduce the job execution cost while optimizing load balancing. First, a cost-efficient model and a load balancing model based on Flink are constructed. Then, the core mechanism of Flink task scheduling is improved based on the cost-efficient model and the improved task scheduler is implemented. In addition, the concept of node adaptation is introduced into cost-efficient scheduling according to the load balancing model, ensuring that the cluster load is balanced as much as possible while reducing the cost in a heterogeneous cluster. Extensive experiments have been performed with Hibench's Wordcount and Fixwindow workloads in the cloud environment. The experimental results indicate that compared to the baseline scheduling algorithm, the proposed algorithms reduce the cost by about 37.9% and 20.2% on average, and the load deviation of the cluster is reduced by about 23.1% and 24.6% on average, respectively. In summary, the proposed algorithms in this paper can significantly reduce the cost of executing jobs and optimize the load balancing of the cluster in Flink.
Mobile Augmented Reality for Adding Detailed Multimedia Content to Historical Physicalizations

Combining augmented reality (AR) and physicalization offers both opportunities and challenges when representing detailed historical data. In this article, we describe a framework where mobile AR supplements views of 3-D prints of historical locations with interactive functionality and small visual details that the prints alone cannot display. Since seeing certain details requires bringing the camera close to the physical objects, the resulting camera frames may lack the visual information necessary to determine objects' positions and accurately superimpose the overlay. We address this by enhancing tracking of 3-D prints at close distances and employing visualization techniques that allow viewing small details in ways that do not interfere with tracking. To demonstrate these techniques, we apply our framework to the preservation of two heritage sites that represent large real-life areas containing smaller details of interest.
n<sup>2</sup>MFn<sup>2</sup>: Non-negative Matrix Factorization in A Single Deconstruction Single Reconstruction Neural Network Framework for Dimensionality Reduction

One of the most commonly used approaches for handling complex datasets with high dimensions is dimension-ality reduction. In this scenario, a single deconstruction single reconstruction neural network model for non-negative matrix factorization technique under neural network framework has been developed aiming towards low rank approximation. With the help of hierarchical learning, the pervasiveness of the non-negative input data has been processed to produce a part-based, sparse, and meaningful representation. A modification of the He initialization technique to initialize weights maintaining the non-negativity criteria of the model, has also been proposed. Necessary modification of the ReLU activation function has been made for inhibiting a layer's entire population of neurons from simultaneously adjusting their weights. Regularization has been used in the design of the model's objective function to minimize the risk of overfitting. To prove the competency of the proposed model, the results have been analyzed and compared with that of six other leading dimension reduction techniques on three popular datasets for classification. The analysis of the same has justified the effectiveness and superiority of the model over some others. Additionally the computational complexity of the model has been discussed.
Distributed Multi-Sensor Control for Multi-Target Tracking

This paper proposes a new sensor control algorithm for multi-target tracking applications within distributed sensor networks. In multi-target tracking applications, most sensor control algorithms are designed for centralized sensor networks, where there is a central processing node that is computationally inefficient. This paper first provides a conceptual and mathematical overview of the multi-sensor multi-target tracking framework, using random finite set (RFS) filters and sensor fusion. We will also provide an overview of the existing sensor control methods. We then explore coordinate descent-based sensor control and introduce a fully distributed algorithm utilizing coordinate descent and an information-theoretic objective function. This method is tested on synthetic data and compared to alternative methods. The results show that the proposed method outperforms equivalent independent multi-sensor control methods.
Proposing Vietnamese Text Recognition Algorithm Combining CRAFT and VietOCR

Letter recognition based on artificial neural networks is currently achieving promising results. In this paper, we propose a method that combines character-region awareness for text detection (CRAFT) and VietOCR to recognize letters and Vietnamese texts effectively based on finding characters in words and affinity among them. The algorithm uses the tool to crop each character to put it into VietOCR to recognize letters. The results show that the algorithm achieves an accuracy of 89.84% with 10 epochs. We find that the proposed method has accuracy and execution time depending on the variety of input data based on the obtained results.
Trans2Unet: Neural fusion for Nuclei Semantic Segmentation

Nuclei segmentation, despite its fundamental role in histopathological image analysis, is still a challenge work. The main challenge of this task is the existence of overlapping areas, which makes separating independent nuclei more complicated. In this paper, we propose a new two-branch architecture by combining the Unet and TransUnet networks for nuclei segmentation task. In the proposed architecture, namely Trans2Unet, the input image is first sent into the Unet branch whose the last convolution layer is removed. This branch makes the network combine features from different spatial regions of the input image and localizes more precisely the regions of interest. The input image is also fed into the second branch. In the second branch, which is called TransUnet branch, the input image will be divided into patches of images. With Vision transformer (ViT) in architecture, TransUnet can serve as a powerful encoder for medical image segmentation tasks and enhance image details by recovering localized spatial information. To boost up Trans2Unet efficiency and performance, we proposed to infuse TransUnet with a computational-efficient variation called “Waterfall” Atrous Spatial Pooling with Skip Connection (WASP-KC) module, which is inspired by the “Waterfall” Atrous Spatial Pooling (WASP) module. Experiment results on the 2018 Data Science Bowl benchmark show the effectiveness and performance of the proposed architecture while compared with previous segmentation models.
TransformNet: Self-supervised Representation Learning Through Predicting Geometric Transformations

Deep neural networks need a big amount of training data, while in the real world there is a scarcity of data available for training purposes. To resolve this issue unsupervised methods are used for training with limited data. In this report, we describe the unsupervised semantic feature learning approach for recognition of the geometric transformation applied to the input data. The basic concept of our approach is that if someone is unaware of the objects in the images, he/she would not be able to quantitatively predict the geometric transformation that was applied to them. This self supervised scheme is based on pretext task and the downstream task. The pretext classification task to quantify the geometric transformations should force the CNN to learn high-level salient features of objects useful for image classification. In our baseline model, we define image rotations by multiples of 90 degrees. The CNN trained on this pretext task will be used for the classification of images in the CIFAR-10 dataset as a downstream task. we run the baseline method using various models, including ResNet, DenseNet, VGG-16, and NIN with a varied number of rotations in feature extracting and fine-tuning settings. In extension of this baseline model we experiment with transformations other than rotation in pretext task. We compare performance of selected models in various settings with different transformations applied to images, various data augmentation techniques as well as using different optimizers. This series of different type of experiments will help us demonstrate the recognition accuracy of our self-supervised model when applied to a downstream task of classification.Code: https://github.com/hashimsayed0/TransformNet
Implementation of QKD BB84 Protocol in Qiskit

Quantum cryptography is a practical method of secret communication that guarantees the foolproof secrecy. This paper presents the simulation for implementation of a Quantum Key Distribution (QKD) protocol BB84, the pioneering, and most basic and fundamental protocol. We use the quantum computing platform developed by IBM named as Qiskit for the simulation of the BB84 protocol, implementing it with and without an Eve eavesdropper for studying the relative outcomes. Recently, Qiskit is globally very popular for quantum simulations because it provide us the opportunity to simulate quantum logic circuit on classical computer as well as real quantum computers owned by IBM. As per our best knowledge, there is no any proposal published for quantum circuit by anyone for BB84 QKD. We performed experimentation on local as well as cloud simulators: 1) The "qasm_simulator" was used as local machine simulator, and 2) The "ibmqx2" simulator was used as IBM cloud quantum simulator. The outcome of local as well as IBM simulator with and without an eavesdropper, between the two parties involved in key exchange were recorded and presented in the results section.
Object detection algorithm based on cosine similarity IoU

Aiming at the problem that the traditional IoU-NMS algorithm has poor filtering of redundant boxes with high confidence scores in RetinaNet, an object detection algorithm based on cosine similarity IoU is proposed. Based on the original IoU calculation method, the cosine similarity between the detection boxes is calculated by the vector to better evaluate the similarity between them, which removes redundant boxes with high confidence scores and retain more accurate detection boxes. Meanwhile, CBAM is added to the ResNet-50 network to extract richer semantic information. the detection accuracy of the PASCAL VOC dataset reaches 80.6%, which is 2.1% higher than the benchmark algorithm.
Analysis of Multimodal Representation Learning Across Medical Images and Reports Using Multiple Vision and Language Pre-Trained Models

Medical images are the visual representation of the organs and their functions. The medical images are used for finding the diseases and highly trained professionals interpret these images into reports. The interpretation of these images requires a lot of time in a day for the reports summarization and highlighting the findings for the naive person. Enhancing the medical diagnosis of diseases through the generation of summarized reports along with findings has immense potential. The sensitivity of the findings of the chest x-rays provided by the existing techniques are not so accurate and the limitation of data also affects the training of the models. Therefore, a joint image-text embedding extraction from medical images and reports, in radiology reports summarization along with correct findings will significantly assist the doctors and reduce their burden. In this approach, learning from multimodal representation using radiographs and reports through pre-trained vision-and-language models. The pre-trained model classified the diseases or findings in the chest x-rays and used Gated Recurrent Units to generate a summarized report. The image-text joint embedding using the pre-trained models helps in more accurate report summarization and improves performance in thoracic findings tasks.
Substitution Layer Analysis of NIST Lightweight Cryptography Competition Finalists

From the start of the 21st century, resource-constrained devices like smart cards, RFID tags and SCADA systems have been utilized commonly in security-critical systems. This vast adoption raised many security concerns like privacy and integrity of the data for stakeholders. Secure cryptographic mechanisms are a vital tool to resolve these issues. The implementation of conventional symmetric and asymmetric ciphers is not feasible because of the limited resources. In March 2017, NIST requested the research community to submit lightweight cryptographic algorithms for future standardization. After submissions, NIST finalized ten algorithms after two rounds of scrutiny. The confusion layer in these ciphers is the essential component that resists maximum security attacks. This study aims to carry out a comparative analysis of the cryptographic properties of the confusion layer of these finalists. The outcome of this research work will assist stakeholders in selecting the right choice for preserving the privacy and integrity of their data in such systems.
A Queueing-theoretic Analysis of the Performance of a Cloud Computing Infrastructure: Accounting for Task Reneging or Dropping

Cloud computing has revolutionized the information technology era. It offers high-speed computing, storage, and ICT resources on demand. One of the significant challenges in cloud computing is the impact of impatient users or request (task) reneging. When a request has been compromised, missed its execution deadline, or depends on other rejected ones, it must be removed from the queue without being processed. The reneging or removal of tasks from queues may trigger the reneging or removal of other tasks that depend of them. We refer to this reneging of requests from the load balancing or computing queues as correlated reneging. We presented the performance analysis of a network of queues that constitute a queueing model of a simplified cloud computing infrastructure. We show the relationship between the load, delay, and probability of buffer saturation (blocking probability). It is seen that at about 80% utilization, a small increase in utilization (due to a small increase in the arrival rate <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\Delta\lambda$</tex> for a fixed service rate <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mu)$</tex> results in a sharp increase in the delay experienced by the tasks, and on the probability of task rejection or blocking.