Reviewerly/exHarmony huggingface dataset:
The Representative Points of Nonlinear Transformation of Random Variable

In this paper, we study the RPs of the nonlinear transformation of random variable under the mean-squared errors criterion. We explore the RPs of exponential distribution and uniform distribution, lognormal distribution and standard normal distribution, standard normal distribution and Chi-square distribution. We find that the RPs following the similar transformation of random variable have almost certain ralations. When the number of RPs is large, this relationship is consistent with that between variables, and some parameters may be slightly different. The existing methods to calculate the RPs of distributions have the defect of huge amount of calculation. By exploring the relationship between RPs of distributions, we can find a new method to calculate the RPs of distributions and avoid the defects of existing methods.
Post-selection Inference with Application to Autoregressive Processes

Post-selection inference is recently proposed approach to inference after model selection. Unlike the traditional statistical inference framework, the post-selection inference aims to building a valid hypothesis test that can be jointly tuned to maximize the inference performance. The models proposed recently for inference after model selection often belong to a family of regressions confirmatory framework, and collect a design matrix into a fixed-candidate features. In this paper, we conjecture that the use of a fix-candidate features is a bottleneck in improving the performance of this basic confirmatory data analysis, and propose to extend this by allowing a model to adaptively (soft-)search for parts of a source observations after model selection that are relevant to predicting a target observation, without having to form these data as a hard segment explicitly. With this new approach, we achieve a prediction performance comparable to the traditional state-of-the-art confirmatory-based methods on the task of p-valve computation. Furthermore, prediction analysis reveals that the (soft-)alignments found by the model after model selection agree well with our intuition.
The Translation of Indonesian Passive Voice in Research Articles’ Abstracts into English: Human Vs Machine Translation

The existence of abstracts within research articles (RA) is one of the requirements of a published article. When the research is important internationally rather than locally, the abstract should be translated into English. Since lots of passive voice construction was found In Indonesian research articles, this study aims to explore the translation of passive voice in Indonesian RA abstracts into English conducted by human translation and machine translation (google translate). This present study is translational research by applying interpretative qualitative-quantitative analysis. The data in this study were RA abstracts obtained from Journal “Linguistics Indonesia” which was published in 2015- 2021, there are 60 research articles in total. After analyzing those articles with AntConc (a corpus-based analyzing tool), it was found that there are 341 passive voice occurrences in Indonesian research articles. However, in the translated abstract corpus, TERA-HT there are 308 occurrences and in TERA MT with 321 occurrences. Besides the differences in the frequency, there are also some differences in linguistics feature of passive in the translated abstracts, such as 1) The difference in using tenses; 2) The differences in using “Be (Singular/Plural); 3) the difference in using the passive voice construction and 4) Modulation (the change of Passive voice into Active voice). This research also reports that human translation is better than machine translation.
A Comparative Analysis of Clustering Techniques with Feature Selection for Predicting Breast Cancer Recurrence

After recovery from breast cancer, chances are there for the recurrence of cancer where it comes back after initial treatments. Conventional methods to detect breast cancer(BC) recurrence such as watch for recurrent cancer during the followup examinations are done by oncologists take a longer time to reveal the threat. Thereby, early prediction and detection of recurrence cancer is the vital need of every patient since it is their greatest fear. Since prediction has to be fast and efficient, highly influenced features for the recurrence should be identified. This research aims to comparatively analyze the advantages of using feature selection on clustering algorithms in predicting BC recurrence. In this study, we have performed breast cancer recurrence prediction using clustering techniques namely, Kmeans algorithm, Complete-Linkage(CL) algorithm, Expectation-Maximization(EM) algorithm, and Hierarchical Kmeans(HK) algorithm and Information GainfIG) as the feature selection. We studied the outputs from the clustering algorithms with IG feature selection and compared them with outputs from clustering algorithms without using feature selection. Finally, the results exhibited that the EM algorithm with IG feature selection outperformed the other clustering techniques.
XipCOIN: A Proposal for Decentralized Offline Payments using Ethereum

Offline digital payment systems are currently limited in their scope. Extending them using new technologies like blockchain can expand the offline payment landscape and create new directions for payment systems. This paper proposes XipCOIN as a construction of such a system, based on the Ethereum blockchain. It exploits the recently defined Layer 2 Rollups model of significantly increasing performance and creating desirable transaction characteristics by moving the transactions off the primary Layer 1 blockchain. XipCOIN builds on the approach, established in previous studies by Dmitrienko et al. and Takahashi et al., of using a Trusted Execution Environment to validate the transaction offline by the payee with the payer trusting the payee signature on the confirmation.
A rule based approach to minimize false-positive declines in Electronic Card Not Present financial transactions using feature engineering techniques

In this research paper, we are been proposing a rule-based approach to minimize false-positive declines ("Legitimate transactions are been declined falsely identifying as fraudulent") in electronic CNP transactions. Related to the increased popularity of digital payments FP declines are becoming a severe problem among merchants who provide digital payment solutions. It’s estimated that nearly 10% of the transactions are been declined as fraudulent transactions but only very few of them have fallen into the fraud category. To address this problem we have proposed a feature engineering technique based on behavior analysis. Our research is conducted based on a real-life CNP transactional data set from one of the largest fintech service solution providers in Sri Lanka and we have generated 130 features for each transaction and have employed an XgBoost Classifier to learn the classifier. We found out that this solution can mainly benefit the merchants who provide electronic payment solutions which involve CNP transactions to minimize false-positive declines targeting legitimate frequent customers and by the same, it minimizes the fraud losses and protects the customer’s interests.
A Novel FPGA Architecture of Commentz-Walter Algorithm using Bit-Split String-Matching Engines

This paper describes a reconfigurable hardware implementation of CommentzWalter algorithm with bit-split string matching engines to match multiple protein sequences. It is reported that multiple pattern matching using the most widely used Aho-Corasick algorithm for different applications has been carried out on graphics processing units and field programmable gate arrays to accelerate the matching process. Commentz-Walter algorithm, is a multiple pattern matching algorithm and more complex than Aho-Corasick. There are no reports of it being directly implemented in any hardware platform except as software implementations on general purpose processors. In this work, a specific architecture for our target application using CommentzWalter algorithm has been developed and tested with a simulator for hardware description languages. This architecture can match multiple patterns of proteins efficiently when implemented on a Field Programmable Gate Array. Finally, we compare a previously developed hardware architecture of bit-split Aho-Corasick with our bit-split Commentz-Walter architecture. Using the Intel Stratix IV GX EP4SGX230KF40C2 FPGA chip as the target device, the compilation results with Quartus II show that the synthesis logic utilization is 5% with 2203 total number of registers, 64896 FPGA block memory bits and 4 DSP block 18-bit elements. The simulation and practical experimental results show that the proposed architecture can effectively improve the performance of the Commentz-Walter algorithm.
Mammogram-Based Cancer Detection Using Deep Convolutional Neural Network

Breast cancer is now a common health problem among most women. Breast cancer is the world’s second-largest cause of mortality for women, and it affects mostly women over the age of 50. The major reasons are that most women do not have proper knowledge about breast diseases/conditions, and the inability to detect abnormalities in the initial stages. A mammogram is one of the best imaging modalities recommended by doctors to diagnose breast cancers. Consultant radiologists are necessary for the identification of those breast pathologies by mammogram images. For a human, it takes some time to read and have an opinion about the condition. Also, the pandemic situation makes the diagnosis processes even more difficult due to the unavailability of doctors and other medical staff. Deep learning approaches are applied for breast cancer detection, and it helps radiologists to identify breast pathologies quickly and accurately. In this work, the mammogram images are collected using MIAS, DDSM, and INbreast databases. The proposed system identifies the location of the lump within the breast, if the lump is malignant or benign, the size of the lump, and the state of the nipple (It is abnormal or not). Convolutional Neural Network (CNN) method for classifying screening mammograms obtained outstanding performance compared to the previous methods. This CNN method produces 96.5% accuracy for breast tumor classification and produces the 80% accuracy for nipple classification.
Party-based Sentiment Analysis Pipeline for the Legal Domain

Since the advent of research to automate existing manual language workflows, legal domain has risen as a key area. When it comes to making the process of court cases more efficient with the said automation, legal information extraction is vital for legal professionals. In this study, we propose a unified pipeline to annotate party-based sentiment for court cases which reduces manual work. To achieve this end, we combined two state-of-the-art models into a single workflow. The first model extracts the entities which represent each party in a court case, while the second model analyzes the sentiment with respect to each party in a given sentence of a court case. In this study we propose two approaches for defining the pipeline which maps the output of the party extraction system into the party-based sentiment analysis system. Further, we propose improvements to the existing party extraction system.
Hate Speech Detection in Sinhala-English Code-Mixed Language

With the steady increase of user-generated content on the internet, the amount of hate content on the internet is also being rapidly increased. Social media sites, review forums, microblogging sites encourage users to convey their thoughts with minimum restrictions. This leads to expressing hate towards others who do not believe their beliefs. This study focuses on identifying hate speech texts that are written in Sinhala-English code-mixed language (Singlish) which is mostly used by Sri Lankans on the internet. Due to the unavailability of Sinhala-English code-mixed datasets, the dataset was created using comments on YouTube and Facebook. In this research, eight machine learning algorithms and three ensemble approaches were evaluated to detect hate speech in Singlish. Furthermore, their accuracy, precision, recall, and f1-score were evaluated. Afterwards, based on the performance of the considered algorithms, Support Vector Machine (SVM), Multinominal Naïve Bayes (MNB), AdaBoost Classifier, and Logistic Regression classifiers were used to develop ensemble learning-based solutions. In terms of ensemble learning approaches, soft voting, hard voting, and stacking were evaluated. The hard voting approach outperformed other baseline algorithms and ensemble approaches with 84% accuracy and f1-score.
Speaker Identification in Vehicular Environment using GMM and BiLSTM

Speaker recognition in the vehicular environment is becoming increasingly significant with the advancement of smart and connected devices. Noisy conditions in the vehicular environment pose a pressing challenge to speaker identification tasks. Thus, a robust speaker recognition system is proposed that is not susceptible to a drop in performance when subjected to various SNR levels. This paper analyzes the performance of speaker identification using GMM and BiLSTM in noisy environments. The model is trained to identify speakers from the TIMIT speech corpus. The paper also explores data augmentation techniques, namely, time stretching and time rolling. On training and testing the proposed speaker identification system with TIMIT and custom dataset, results show that more data for both training and testing purposes can eventually improve the performance of the system.
Accelerating Robot Reinforcement Learning with Samples of Different Simulation Precision

Training an effective model via reinforcement learning method in a simulated environment demands considerable computational cost. To improve the efficiency of reinforcement learning, most existing efforts have been focusing on learning algorithms or sampling techniques, but little attention has been spent on better utilizing the simulators themselves. In this paper, we propose a novel method to accelerate the training process by sampling with different simulation precisions in the simulator. Specifically, a sequential training mode and a joint training mode are proposed. In the sequential training mode, a basic model is first learned by using samples from a low-precision simulation environment, and then further fine-tuned in a high-precision simulation environment. In the joint training mode, the training procedure is conducted by sampling from the low and high-precision simulation environments simultaneously. Extensive experiments in different robotic scenarios demonstrate that our method achieves almost the same performance as conventional methods, but requires much less training cost, where 35&#x0025; to 63&#x0025; of the training time is saved without loss of performance.
A Residual Fingerprint-Based Defense Against Adversarial Deepfakes

The authenticity and integrity of digital visual media have always been a crucial branch in the domain of multimedia forensics and information security. Currently, the emerging Deepfakes destroy the authenticity and integrity as well as trump up a person&#x0027;s behaviors that do not exist in reality, which pose potential threats to individual, social and even national security. Although multiple well-designed deep neural networks have achieved satisfactory performance on Deepfake detection, by adding imperceptible but purposeful adversarial perturbations to fake images or videos, the crafted adversarial Deepfakes are demonstrated to cause the malfunction of detectors. In response to such threats, little recent research attempts to take defensive measures but shows scarce applicability, and the effective conventional defenses are insufficient to protect the particular Deepfake detectors. Therefore, to defend against adversarial Deepfakes, we propose a residual fingerprint-based defense customized for Deepfake detectors. By analyzing the impacts of adversarial perturbations on detectors, we construct a reconstruction network, and propose novel strategies to degrade the adversarial efficacy as well as extract discriminative residual fingerprints. Ultimately, we transform the extracted residual fingerprints for Deepfake detection. The evaluation results indicate the performance of compromised detectors is regained by our proposed defense, which is qualified for enhancing the security and reliability of multiple Deepfake detectors.
Sprofiler: Automatic Generating System of Container-Native System Call Filtering Rules for Attack Surface Reduction

Containerized virtualization technologies are applied to many production environments. Compared with VMs, the image size of containers is relatively small and the containers launch quickly. Therefore, containers can achieve fast scale-out, high portability, and fast release cycles. Meanwhile, containerized virtualization may cause security incidents when operated in a multi-tenant environment, such as public cloud. Container runtimes may have a vulnerability of privilege escalation attack from a container to its host because containers and their host share the kernel of operating system. Even if there is no vulnerability in container runtimes, the privilege escalation attacks may be possible when the container runtimes are configured inadequately. As a countermeasure, restricting system calls using system call filter such as Seccomp-BPF could be applied. However, it is difficult for system administrators to know the system calls issued by applications. It is necessary to have a function that examines the system calls issued by the application and support the creation of filtering rules.In this study, we propose Sprofiler which generates filtering rules that are suitable for a workload of a container by combining static analysis of application executable files and dynamic analysis of system calls issued from the container. This paper describes the design and implementation of the Sprofiler. Moreover, it shows the evaluation results of the effectiveness by applying system call filtering rules generated by the Sprofiler.
ATO-EDGE: Adaptive Task Offloading for Deep Learning in Resource-Constrained Edge Computing Systems

On-device deep learning enables mobile devices to perform complex tasks, such as object detection and voice translation, regardless of the network condition. The advanced deep learning model gives an excellent performance, also leads to a heavy burden on resource-limited devices (i.e., mobile devices). To speed up the on-device deep learning. Prior studies focus on developing lightweight network architecture for real-time inference by sacrificing model accuracy. This paper presents ATO-EDGE: adaptive task offloading for deep learning based on edge computing. Considering three optimization goals, energy consumption, accuracy, and latency, ATO-EDGE leverages an offline pre-trained model to select a suitable deep learning model on a specific device to process the given task. We apply our approach to object detection and evaluate it on Jetson TX2, Xilinx ZYNQ 7020, and Raspberry 3B+. The deep learning model candidates contain ten typical object detection models trained on Microsoft COCO 2017 dataset. We obtain, on average, 28.25%, 35.44%, and 0.9 improvements respectively for latency, energy consumption, and mAP (mean average precision) when compared to the SOTA DETR model on the Raspberry Pi.
Visual Sensitivity Aware Rate Adaptation for Video Streaming via Deep Reinforcement Learning

Adaptive bitrate (ABR) technology is widely adopted in video streaming to improve quality of experience (QoE). However, existing methods ignore the inherent characteristic of Human Visual System (HVS), i.e., HVS has different sensitivity to the quality distortion of different video content. Therefore, video contents with high visual sensitivity have higher visual importance and need to be allocated more bitrate resources. In this paper, we propose a novel rate adaptive algorithm by incorporating newly-defined visual sensitivity model. More specifically, a set of relevant features are extracted from video content to establish a deep neural network model that accurately reflects the visual sensitivity. The network state, buffer occupancy and visual sensitivity are comprehensively considered under deep reinforcement learning framework to select appropriate bitrate for maximizing QoE. Experimental results show that the proposed algorithm can generate an ABR strategy consistent with visual sensitivity, and improves the perceptual video quality and user QoE by 14.2&#x0025; and 16.3&#x0025;, respectively.
Detection of Behavior Aging from Keystroke Dynamics

Keystroke dynamics-based authentication (KDA) is one of human behavioral-based authentication methods based on the unique typing rhythm of an individual. Nevertheless, the typing characteristics gradually change over time. Various solutions have been suggested to remedy the concept drift problem, including multimodal and unimodal adaptive methods. However, these solutions don't consider that temporal concept drift has a negative impact on performance and update frequency increases computation cost. The paper proposes weighted EDDM to detect concept drift and capture permanent concept drift (behavioral natural aging). Experimental results show that our method can accurately capture behavioral natural aging and filter temporal concept drift. Our proposed method has better performance and less computation.
Determining Performance of Computer Vision Models using Generalizability, Robustness &amp; Elasticity Score

Performance measurement of computer vision models provides information about their ability to classify objects. However, their performance gets affected in the real-world environment. We propose a modification for the metric called Generalizability, Robustness, and Elasticity score (GRE), which is used to determine the efficiency of the computer vision models. Specifically, we use unaltered Visual Question Answering (VQA) datasets and develop three new datasets for each attribute of the GRE score. The new datasets pass through three novel serial processes designed to enhance the quality of the datasets. The new datasets have a better distribution of feature information of the objects in the original dataset. Their performance is measured by running the datasets on three models specifically modified for our experiment. Two out of three models perform better on our new datasets and provide a better GRE score. We prove that our system works and can provide better results than the conventional method of measuring the performance of computer vision models.
Analysis of Evolutionary Model of DIKW Based on Cloud Resource Allocation Management

With the advent of the era of big data, the volume of business that people demand to deal with is growing promptly. An eruption of information and the availability of mass data furnish opportunities for cloud computing. There is no doubt that the overwhelming computing power of cloud computing can expeditiously manipulate large-scale data and information. Nevertheless, the traditional DIKW pyramid is appropriate for a small amount of data, and it is used to depict the process of resource allocation in a cloud environment will be extremely intricate. We lack an evolutionary model that can be applied to resource allocation in the cloud environment. This paper proposes a new model - DWA (Data-Wisdom-Action) to solve problem. By dissecting DIKW and its evolutionary model, we discover that the DWA model can illustrate the procedure of resource allocation and management of parallel mode in the cloud environment. This work not only gives rise to new inspirations for research on resource allocation but also enriches the correlative research of DIKW.