Reviewerly/exHarmony huggingface dataset:
Ultrasound classification of breast masses using a comprehensive Nakagami imaging and machine learning framework

In this study we investigate the potential of parametric images formed from ultrasound B-mode scans using the Nakagami distribution for non-invasive classification of breast lesions and characterization of breast tissue. Through a sliding window technique, we generated seven types of Nakagami images for each patient scan in our dataset using basic and as well as derived parameters of the Nakagami distribution. To determine the suitable window size for image generation, we conducted an empirical analysis using 4 windows, which includes 3 column windows of lengths 0.1875 mm, 0.45 mm and 0.75 mm and widths of 0.002 mm, along with the standard square window with sides equal to three times the pulse length of incident ultrasound. From the parametric image sets generated using each window, we extracted a total of 72 features that consisted of morphometric, elemental and hybrid features. To our knowledge no other literature has conducted such a comprehensive analysis of Nakagami parametric images for the classification of breast lesions. Feature selection was performed to find the most useful subset of features from each of the parametric image sets for the classification of breast cancer. Analyzing the classification accuracy and Area under the Receiver Operating Characteristic (ROC) Curve (AUC) of the selected feature subsets, we determined that the selected features acquired from Nakagami parametric images generated using a column window of length 0.75 mm provides the best results for characterization of breast lesions. This optimal feature set provided a classification accuracy of 93.08%, an AUC of 0.9712, a False Negative Rate (FNR) of 0%, and a very low False Positive Rate (FPR) of 8.65%. Our results indicate that the high accuracy of such a procedure may assist in the diagnosis of breast cancer by helping to reduce false positive diagnoses.
Zero Trust Federation: Sharing Context under User Control towards Zero Trust in Identity Federation

Perimeter models, which provide access control for protecting resources on networks, make authorization decisions using the source network of access requests as one of critical factors. However, such models are problematic because once a network is intruded, the attacker gains access to all of its resources. To overcome the above problem, a Zero Trust Network (ZTN) is proposed as a new security model in which access control is performed by authenticating users who request access and then authorizing such requests using various information about users and devices called contexts. To correctly make authorization decisions, this model must take a large amount of various contexts into account. However, in some cases, an access control mechanism cannot collect enough context to make decisions, e.g., when an organization that enforces access control joins the identity federation and uses systems operated by other organizations. This is because the contexts collected using the systems are stored in individual systems and no federation exists for sharing contexts. In this study, we propose the concept of a Zero Trust Federation (ZTF), which applies the concept of ZTN under the identity federation, and a method for sharing context among systems of organizations. Since context is sensitive to user privacy, we also propose a mechanism for sharing contexts under user control. We also verify context sharing by implementing a ZTF prototype.
A Robust Zero-Watermarkinging Algorithm Based on PHTs-DCT for Medical Images in the Encrypted Domain

In the process of network transmission, medical images are maliciously attacked to cause the loss of diagnostic data or the leakage of patient privacy. To solve this problem, this paper proposes a robust watermarking algorithm for encrypted medical images based on PHTs-DCT. First, use DWT-DCT and tent chaotic sequence to encrypt the medical image, which increases the security of the carrier medical image information. Then, PHTs-DCT transform is performed on the encrypted medical image to extract the feature vector. Finally, using the homomorphism of the encryption algorithm, combined with the concept of a third party, the zero-watermarking technology is used to embed and extract the watermark of the encrypted medical image. Experiments based on the MATLAB simulation platform, a large number of experimental results show that the proposed algorithm has good robustness to conventional attacks, and geometric attacks, especially in scaling, compression and translation attacks.
Public key image encryption algorithm based on pixel information and random number insertion

In this study, a public key image encryption algorithm is proposed based on pixel information and random number insertion. In the first step, two large prime numbers and the public key are selected randomly to produce the private key. According to the pixel information of the plain image and utilizing the Rivest-Shamir-Adleman (RSA) algorithm, the corresponding cipher information can be obtained by using the public key to encrypt the plain information extracted from the plain image. Both the plain and cipher information are then converted into the initial keys of the chaotic system based on a key transformation mapping. Consequently, the keystreams are generated based on iterative calculations. In the second step, different keystreams are selected for pixel arrangement and modulo operation preprocessing for the plain image. Subsequently, random numbers are inserted in the preprocessed image to obtain an extended image. The final cipher image can be got by selecting different keystreams to encrypt the extended image using XOR diffusion and add modulus operations. Combined with public key cryptosystem RSA, the proposed algorithm can achieve the effect of a one-time pad. Finally, experimental test results show that the cipher image obtained using our algorithm is highly random and robust, and can effectively resist violent, statistical, and differential attacks. A series of security analyses are implemented to validate the proposed asymmetric image encryption algorithm.
Multi-Step Reasoning Over Unstructured Text with Beam Dense Retrieval

Complex question answering often requires finding a reasoning chain that consists of multiple evidence pieces.Current approaches incorporate the strengths of structured knowledge and unstructured text, assuming text corpora is semi-structured.Building on dense retrieval methods, we propose a new multi-step retrieval approach (BEAMDR) that iteratively forms an evidence chain through beam search in dense representations.When evaluated on multi-hop question answering, BEAMDR is competitive to state-of-the-art systems, without using any semi-structured information.Through query composition in dense space, BEAMDR captures the implicit relationships between evidence in the reasoning chain.
RAPT: Pre-training of Time-Aware Transformer for Learning Robust Healthcare Representation

With the development of electronic health records (EHRs), prenatal care examination records have become available for developing automatic prediction or diagnosis approaches with machine learning methods. In this paper, we study how to effectively learn representations applied to various downstream tasks for EHR data. Although several methods have been proposed in this direction, they usually adapt classic sequential models to solve one specific diagnosis task or address unique EHR data issues. This makes it difficult to reuse these existing methods for the early diagnosis of pregnancy complications or provide a general solution to address the series of health problems caused by pregnancy complications. In this paper, we propose a novel model RAPT, which stands for RepresentAtion by Pre-training time-aware Transformer. To associate pre-training and EHR data, we design an architecture that is suitable for both modeling EHR data and pre-training, namely time-aware Transformer. To handle various characteristics in EHR data, such as insufficiency, we carefully devise three pre-training tasks to handle data insufficiency, data incompleteness and short sequence problems, namely similarity prediction, masked prediction and reasonability check. In this way, our representations can capture various EHR data characteristics. Extensive experimental results for four downstream tasks have shown the effectiveness of the proposed approach. We also introduce sensitivity analysis to interpret the model and design an interface to show results and interpretation for doctors. Finally, we implement a diagnosis system for pregnancy complications based on our pre-training model. Doctors and pregnant women can benefit from the diagnosis system in early diagnosis of pregnancy complications.
Mobile Learning in Pre-Service Teacher Education: Perceived Usefulness of AR Technology in Primary Education

Mobile learning is a resource that can enhance the teaching-learning process of students and improve the training of future teachers. Specifically, augmented reality (AR) technology allows for immersive and experiential learning without the need to leave the classroom. The purposes of this paper were to apply AR technology in the training of future Primary Education teachers and to analyze the perceived usefulness of AR in the classroom by future teachers. A quantitative approach was used based on a design with a control group and two experimental groups with a post-test using a sample of 171 second-year students studying an education degree. The results showed that experimentation with AR promoted a slight increase in student motivation. However, no significant differences were found between the control group and the experimental groups. Finally, the findings allowed us to establish that the implementation of resources such as AR does not differ in the opinion of future teachers about the inclination to implement AR in the classroom.
Thermal Face Generation Using StyleGAN

This article proposes the use of generative adversarial networks (GANs) via StyleGAN2 to create high-quality synthetic thermal images and obtain training data to build thermal face recognition models using deep learning. We employed different variants of StyleGAN2, incorporating the new improved version of StyleGAN that uses adaptive discriminator augmentation (ADA). In addition, three different thermal databases from the literature were employed to train a thermal face detector based on YOLOv3 and to train StyleGAN2 and its variants, evaluating different metrics. The synthetic thermal database was built using GANSpace to manipulate the intermediate latent space w of StyleGAN2 and obtain images with different characteristics, such as eyeglasses, rotation, beards, etc. We carried out the training of 6 pretrained deep learning models for face recognition to validate the use of our synthetic thermal database, obtaining 99.98% accuracy for classifying synthetic thermal face images.
CASIA-Face-Africa: A Large-Scale African Face Image Database

Face recognition is a popular and well-studied area with wide applications in our society. However, racial bias had been proven to be inherent in most State Of The Art (SOTA) face recognition systems. Many investigative studies on face recognition algorithms have reported higher false positive rates of African subjects cohorts than the other cohorts. Lack of large-scale African face image databases in public domain is one of the main restrictions in studying the racial bias problem of face recognition. To this end, we collect a face image database namely CASIA-Face-Africa which contains 38,546 images of 1,183 African subjects. Multi-spectral cameras are utilized to capture the face images under various illumination settings. Demographic attributes and facial expressions of the subjects are also carefully recorded. For landmark detection, each face image in the database is manually labeled with 68 facial keypoints. A group of evaluation protocols are constructed according to different applications, tasks, partitions and scenarios. The performances of SOTA face recognition algorithms without re-training are reported as baselines. The proposed database along with its face landmark annotations, evaluation protocols and preliminary results form a good benchmark to study the essential aspects of face biometrics for African subjects, especially face image preprocessing, face feature analysis and matching, facial expression recognition, sex/age estimation, ethnic classification, face image generation, etc. The database can be downloaded from our website.
GMOT-40: A Benchmark for Generic Multiple Object Tracking

Multiple Object Tracking (MOT) has witnessed remarkable advances in recent years. However, existing studies dominantly request prior knowledge of the tracking target (eg, pedestrians), and hence may not generalize well to unseen categories. In contrast, Generic Multiple Object Tracking (GMOT), which requires little prior information about the target, is largely under-explored. In this paper, we make contributions to boost the study of GMOT in three aspects. First, we construct the first publicly available dense GMOT dataset, dubbed GMOT-40, which contains 40 carefully annotated sequences evenly distributed among 10 object categories. In addition, two tracking protocols are adopted to evaluate different characteristics of tracking algorithms. Second, by noting the lack of devoted tracking algorithms, we have designed a series of baseline GMOT algorithms. Third, we perform a thorough evaluations on GMOT-40, involving popular MOT algorithms (with necessary modifications) and the proposed baselines. The GMOT-40 benchmark is publicly available at https://github.com/Spritea/GMOT40.
Structured Encryption and Dynamic Leakage Suppression

Structured encryption (STE) schemes encrypt data structures in such a way that they can be privately queried. Special cases of STE include searchable symmetric encryption (SSE) and graph encryption. Like all sub-linear encrypted search solutions, STE leaks information about queries against persistent adversaries. To address this, a line of work on leakage suppression was recently initiated that focuses on techniques to mitigate the leakage of STE schemes. A notable example is the query equality suppression framework (Kamara et al. CRYPTO'18) which transforms dynamic STE schemes that leak the query equality into new schemes that do not. Unfortunately, this framework can only produce static schemes and it was left as an open problem to design a solution that could yield dynamic constructions. In this work, we propose a dynamic query equality suppression framework that transforms volume-hiding semi-dynamic or mutable STE schemes that leak the query equality into new fully-dynamic constructions that do not. We then use our framework to design three new fully-dynamic STE schemes that are "almost" and fully zero-leakage which, under natural assumptions on the data and query distributions, are asymptotically more efficient than using black-box ORAM simulation. These are the first constructions of their kind.
Chinese Word Segmentation Based on Self‐Learning Model and Geological Knowledge for the Geoscience Domain

Abstract Chinese word segmentation (CWS) is the foundational work of geological report text mining and has an important influence on various tasks, such as named entity recognition and relation extraction. In recent years, the accuracy of the domain‐general CWS model has been limited by the domain and large scale of the training corpus, especially data on Chinese geological texts. Training these CWS models also requires much manually annotated data, which takes a large amount of time and effort. When applying these existing models/methods directly to the geoscience domain, the segmentation accuracy and performance will drop dramatically. To address this problem, we pretrain the Bidirectional Encoder Representations from Transformer (BERT), which can leverage unlabeled domain‐specific knowledge, on unlabeled Chinese geological text and then input them into a Bidirectional long short‐term memory and Conditional random field (BiLSTM‐CRF) model for extracting text features. Finally, the predicted tags are decoded by the CRF. The experimental results show that the F1 score of the proposed model reaches 96.2% on the constructed test set of geological texts. Additionally, experiments illustrate that our proposed model achieves comparable performance to that of other state‐of‐the‐art models, and the proposed cyclic self‐learning strategy can be further extended to other domains.
Throughput Optimal Routing in Blockchain-Based Payment Systems

Cryptocurrency networks, such as Bitcoin, have emerged as a distributed alternative to traditional centralized financial transaction networks. However, there are major challenges in scaling up the throughput of such networks. Lightning network and Spider network are alternates that build bidirectional payment channels on top of cryptocurrency networks using smart contracts, to enable fast transactions that bypass the Blockchain. In this article, we study the problem of routing transactions in such a payment processing network. We first propose a Stochastic model to study such a system, as opposed to a fluid model that is studied in the literature. Each link in such a model is a two-sided queue, and unlike classical queues, such queues are not stable unless there is an external control. We propose a notion of stability for the payment processing network consisting of such two-sided queues using the notion of on-chain rebalancing. We then characterize the capacity region and propose a throughput optimal algorithm that stabilizes the system under any load within the capacity region. The stochastic model enables us to study closed-loop policies, which typically have better queuing/delay performance than the open-loop policies (or static split rules) studied in the literature. We investigate this through simulations.
Beyond Class-Level Privacy Leakage: Breaking Record-Level Privacy in Federated Learning

Federated learning (FL) enables multiple clients to collaboratively build a global learning model without sharing their own raw data for privacy protection. Unfortunately, recent research still found privacy leakage in FL, especially on image classification tasks, such as the reconstruction of class representatives. Nevertheless, such analysis on image classification tasks is not applicable to uncover the privacy threats against natural language processing (NLP) tasks, whose records composed of sequential texts cannot be grouped as class representatives. The finer (record-level) granularity in NLP tasks not only makes it more challenging to extract individual text records, but also exposes more serious threats. This article presents the first attempt to explore the record-level privacy leakage against NLP tasks in FL. We propose a framework to investigate the exposure of the records of interest in federated aggregations by leveraging the perplexity of language modeling. Through monitoring the exposure patterns, we propose two correlation attacks to identify the corresponding clients when extracting their specific records. Extensive experimental results demonstrate the effectiveness of the proposed attacks. We have also examined several countermeasures and shown that they are ineffective to mitigate such attacks, and hence further research is expected.
Poster: Fuzz Testing of Quantum Program

Nowadays, quantum program is widely used and quickly developed. However, the absence of testing methodology restricts their quality. Different input format and operator from traditional program make this issue hard to resolve. In this paper, we present QuanFuzz, a search-based test input generator for quantum program. We define the quantum sensitive information to evaluate test input for quantum program and use matrix generator to generate test cases with higher coverage. Because of the impossibility of copying qubit, we record the operations which lead initial seeds to test inputs instead of recording qubits themselves. First, we extract quantum sensitive information - measurement operations on those quantum registers and the sensitive branches associated with those measurement results, from the quantum source code. Then, we use the sensitive information guided algorithm to mutate the initial input matrix and select those matrices which improve the probability weight for a value of the quantum register to trigger the sensitive branch. QuanFuzz on benchmarks and acquired 20% - 60% more coverage compared to traditional testing methods.
EngraveChain: A Blockchain-Based Tamper-Proof Distributed Log System

A reliable log system is a prerequisite for many applications. Financial systems need to have transactions logged in a precise manner, medical systems rely on having trusted medical records and security logs record system access requests in order to trace malicious attempts. Keeping multiple copies helps to achieve availability and reliability against such hackers. Unfortunately, maintaining redundant copies in a distributed manner in a byzantine setting has always been a challenging task, however it has recently become simpler given advances in blockchain technologies. In this work, we present a tamper-resistant log system through the use of a blockchain. We leverage the immutable write action and distributed storage provided by the blockchain as a basis to develop a secure log system, but we also add a privacy preserving layer that is essential for many applications. We detail the security and privacy aspects of our solution, as well as how they relate to performance needs in relevant settings. Finally, we implement our system over Hyperledger Fabric and demonstrate the system’s value for several use cases. In addition, we provide a scalability analysis for applying our solution in a large-scale system.
Effects of the meteorological data resolution and aggregation on the optimal design of photovoltaic power plants

The accuracy of the simulation and optimization of ground-mounted photovoltaic plants depends on the reliability of the meteorological datasets, which is affected by their source, length, and resolution. Quantifying the effect of the irradiance data temporal resolution on the optimal design parameters and the expected profitability is important to improve the credibility of photovoltaic design simulations. The optimal values of nine important design parameters, the annual energy yield, and the levelized cost of electricity are calculated for 50 Baseline Surface Radiation Network stations by an automatic photovoltaic optimization method based on datasets with six different resolutions created by two aggregation methods. The aggregation by averaging suppresses the high irradiance values resulting from the transient cloud enhancement effect, while the aggregation by sampling a single value from the middle of each aggregation interval can retain the original distribution of minute-resolution data. The optimization based on averaged hourly data underestimates the levelized cost of electricity by up to 3%, overestimates the inverter sizing ratio by 0.05 on average, and the tilt angle by up to 5° compared to the high-resolution datasets. The datasets aggregated by sampling provide more reliable results even at lower resolutions; therefore, it can be an effective technique for accurate photovoltaic optimization without the long calculation time of the minute-resolution simulation.