Reviewerly/exHarmony huggingface dataset:
Lung Cancer Detection Based On CT-Scan Images With Detection Features Using Gray Level Co-Occurrence Matrix (GLCM) and Support Vector Machine (SVM) Methods

Lung cancer is all malignant diseases in the lungs, including malignancies originating from the lungs themselves (primary) or those originating from other organs (metastasis). Lung cancer is one of the leading causes of death worldwide. Lung cancer is a tumor that grows rapidly and can spread to other organs. The onset of cancer is characterized by abnormal cell growth that can damage other normal tissue cells. Computerized Tomography (CT) is an imaging technique often used to diagnose lung cancer. Lung cancer can be classified into benign and malignant cancer. It is very important to diagnose lung cancer at an early stage to speed up the treatment process and the actions that will be taken. This study aims to develop a lung cancer detection system based on CT-scan images. This detection system has 4 main stages, namely pre-processing of CT-Scan images to improve image quality, segmentation to identify and separate the desired cancer object from the background, feature extraction based on area, contrast, energy, entropy, and homogeneity. The classification of lung cancer into cancer benign and malignant cancer. From the system trial, the accuracy level based on the system decision in determining the diagnosis of lung cancer is benign or malignant was 83.33%.
Faulty Requirements Made Valuable: On the Role of Data Quality in Deep Learning

Large collections of data help evolve deep learning into the state-of-the-art in solving many artificial intelligence problems. However, the requirements engineering (RE) community has yet to adapt to such sweeping changes caused exclusively by data. One reason is that the traditional requirements quality like unambiguity becomes less applicable to data, and so do requirements fault detection techniques like inspections. In this paper, we view deep learning as a class of machines whose effects must be evaluated with direct consideration of inherent data quality attributes: accuracy, consistency, currentness, etc. We substantiate this view by altering stationarity of the multivariate time-series data, and by further analyzing how the stationarity changes affect the behavior of a recurrent neural network in the context of predicting combined sewer overflow. Our work sheds light on the active role RE plays in deep learning.
Time-aware Concept Drift Detection Using the Earth Mover’s Distance

Modern business processes are embedded in a complex environment and, thus, subjected to continuous changes. While current approaches focus on the control flow only, additional perspectives, such as time, are neglected. In this paper, we investigate a more general concept drift detection framework that is based on the Earth Mover's Distance. Our approach is flexible in terms of incorporating additional perspectives thanks to the capability of defining custom feature representations, as well as expressive feature similarity measures. We demonstrate the former by incorporating the time perspective using both a time-binning-based trace descriptor and a suitable similarity measure that considers time and control flow. We evaluate the resulting sliding window detector on different types of control-flow and time drifts, and holistic drifts involving multiple perspectives.
Domain-specific meta-embedding with latent semantic structures

Meta-embedding aims at assembling pre-trained embeddings from various sources and producing more expressively powerful word representations. Many natural language processing (NLP) tasks in a specific domain benefit from meta-embedding, especially when the task suffers from low resources. This paper proposes an unsupervised meta-embedding method that jointly models background knowledge from the source embeddings and domain-specific knowledge from the task domain. Specifically, embeddings from multiple sources for a word are dynamically aggregated to a single meta-embedding by a differentiable attention module. The embeddings derived from pre-training on a large-scale corpus provide complete background knowledge of word usage. Then, the meta-embedding is further enriched by exploring domain-specific knowledge from each task domain in two ways. First, contextual information in the raw corpus is considered to capture the semantics of words. Second, a graph representing domain-specific semantic structures is extracted from the raw corpus to highlight the relationships between salient words, then the graph is modeled by a powerful graph convolution network to effectively capture rich semantic structures among words in the task domain. Experiments conducted on two tasks, i.e., text classification and relation extraction, show that our model outputs more accurate word meta-embeddings for the task domain, compared to other state-of-the-art competitors. .
Artificial Bee Colony Optimization Algorithm Incorporated With Fuzzy Theory for Real-Time Machine Learning Control of Articulated Robotic Manipulators

This paper presents a real-time machine learning control (MLC) of articulated robotic manipulators using artificial bee colony optimization (ABC) algorithm incorporated with fuzzy theory. The modified ABC with dynamic weight is used to optimize the fuzzy structure and fractional order. The fractional parameters, fuzzy membership functions and rule base are determined by means of the ABC computation. This ABC-fuzzy hybrid learning algorithm is applied to real-time MLC of robotic manipulators by including fractional order proportional-integral-derivative (FOPID) control strategy. The MLC's control gain parameters are online tuned via the ABC-fuzzy optimization. With the kinematics analysis of a six-degree-of-freedom (DOF) articulated arm via reverse coordinates approach, an ABC-fuzzy MLC is developed to achieve motion control. A real-time operating system (RTOS) on a microprocessor collaborates with the ABC-fuzzy MLC to meet critical timing constraint by considering the dynamics of actuators. Finally, the mechatronic design and experimental setup of a six-DOF articulated robotic manipulator are constructed. Experimental results and comparative works are provided to demonstrate the merit of the proposed methods. Compared with the conventional control schemes, the proposed ABC-fuzzy MLC has theoretical and practice significance in term of real-time capability, online parameter tuning, convergent behavior and hybrid MLC. The proposed MLC methodologies are applicable to designing real-time modern controllers in both industry and academia.
Localization Integrity for Intelligent Vehicles Through Fault Detection and Position Error Characterization

Localization integrity consists in providing a real-time measure of the level of trust to be placed in the localization estimates as vehicles operate. It provides a means of knowing whether position estimates are usable for navigation purposes. This paper formalizes the integrity concept and its underlying principles. Vehicles operate in different navigation environments, and so multiple sensors are used to ensure the required performance. Different sources of error exist. They must be bounded according to the acceptable level of risk for the application. This paper presents a generic approach for addressing integrity. It combines measurement rejection (for measurements considered to be faults) and position error characterization. For this purpose, a multi-sensor data fusion with a Fault Detection and Exclusion algorithm is constituted using a bank of information filters. These filters allow detected faults to be isolated without any prior assumption regarding the number of simultaneous errors. In addition, external integrity is expressed as a Protection Level of the localization solution. It uses a Student’s <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula> -distribution in order to bound the distribution of the position error applicable to small integrity risks after a learning step. The approach is tested on data acquired on public roads using an experimental vehicle equipped with off-the-shelf proprioceptive and exteroceptive sensors together with an HD map. The results obtained validate the proposed approach.
Presence of New Forms of Intercultural Communication in Higher Education: Emojis and Social Interactions through WhatsApp among Graduate Students

Communication processes are part of human life. Information and communication technologies (ICTs) and, especially, the use of smartphones have generated new forms of interaction and communication, both synchronous and asynchronous, in formal and informal spaces. One of the most used applications is WhatsApp, which allows the sending of text, documents, images, and emojis. It is important to study these new forms and languages of communication in higher education because any educational or training process is essentially communicative. This research was carried out using a mixed approach, with a nonexperimental, descriptive, and transversal design, using a technique of a survey made up of 30 items. It was applied to postgraduate university students to determine their use and value among people with a consolidated university academic career, which would allow us to determine the presence of these new languages in this context. The basic objectives were to identify the frequency of use of emojis through WhatsApp amongst Mexican graduate students and to analyze the advantages and disadvantages of their use. The results confirmed the increase of emojis in conversations conducted by WhatsApp among fellow students. As positive elements, it was determined that they favor the development of a pleasant environment, confidence, and empathy, in addition to expressing emotions, feelings, and reactions. However, emojis also have drawbacks and disadvantages. Among them is the risk that they are misunderstood since their meaning is not shared, that they divert attention from a central idea, and, in general, that they sometimes have an addictive character that implies a waste of time in a study context, such as at the university. In conclusion, it can be argued that ICTs are profoundly transforming the processes of written communication in today’s society, including the university world. New languages that present intercultural, dynamic, dialectic, and ecosystemic alternatives to speech or writing have appeared. Emojis, in the context of higher education, would be an example of this trend.
ABKS-SKGA: Attribute-based keyword search secure against keyword guessing attack

In order to guarantee security and privacy of sensitive data, attribute-based keyword search (ABKS) enables data owners to upload their encrypted data to cloud servers, and authorizes intended data users to retrieve it. Meanwhile, ABKS outsources heavy search work to cloud servers, which makes ABKS adaptive to mobile computing environment. However, as cloud servers can both generate keyword ciphertexts and run search algorithm, the existing most ABKS schemes are vulnerable to keyword guessing attack. In this paper, we show the fundamental cause that the existing ABKS schemes do not resist keyword guessing attack is any entity can generate keyword ciphertext. To solve the above problem, in the phase of keyword ciphertext generation, we use private key of data owner to sign keyword prior to generating keyword ciphertext. Therefore, any other entity does not forge keyword ciphertext, which can resist keyword guessing attack. We give the formal definition and security model of attributed-based keyword search secure against keyword guessing attack (ABKS-SKGA). Furthermore, we provide an ABKS-SKGA scheme. The ABKS-SKGA scheme is proved secure against chosen-plaintext attack (CPA). Performance analysis shows that the proposed scheme is practical.
Transfer fine-tuning of BERT with phrasal paraphrases

Sentence pair modelling is defined as the task of identifying the semantic interaction between a sentence pair, i.e., paraphrase and textual entailment identification and semantic similarity measurement. It constitutes a set of crucial tasks for research in the area of natural language understanding. Sentence representation learning is a fundamental technology for sentence pair modelling, where the development of the BERT model realised a breakthrough. We have recently proposed transfer fine-tuning using phrasal paraphrases to allow BERT’s representations to be suitable for semantic equivalence assessment between sentences while maintaining the model size. Herein, we reveal that transfer fine-tuning with simplified feature generation allows us to generate representations that are widely effective across different types of sentence pair modelling tasks. Detailed analysis confirms that our transfer fine-tuning helps the BERT model converge more quickly with a smaller corpus for fine-tuning.
A Blockchain-Based Secure Data Aggregation Strategy Using Sixth Generation Enabled Network-in-Box for Industrial Applications

Sixth generation (6G) network is a revolutionary technology to satisfy the ever-growing demands from the sustainable development of emerging industrial applications and services. Due to its high flexibility, convenient and rapid deployment, self-organization capability, and outstanding expansibility, network-in-box (NIB) represents a promising approach for future networks. The integration of NIB with 6G can lead to many new applications in geoscience, robotics, and industrial automation. For 6G-enabled NIB, services are deployed directly on the NIB, which increases the fault tolerance and reduces the traffic volume on the backhaul link. As more and more data are processed and shared in industrial applications and services, the security of data aggregation becomes a key challenge for 6G-enabled NIB. To address this challenge, in this article, we propose a blockchain based privacy-aware distributed collection (BPDC) oriented strategy for data aggregation. In BPDC, an improved blockchain with a new block header structure and two different block generation rules are designed and introduced, which restricts the task receivers to search and receive the tasks beyond their levels of security permission. While guaranteeing the data aggregation performance, BPDC can also achieve privacy protection by decomposing sensitive tasks and task receivers into multiple groups. Validation experiments show that the BPDC accomplishes low overhead, high throughput, and privacy preservation in various industrial applications.
Disabling Backdoor and Identifying Poison Data by using Knowledge Distillation in Backdoor Attacks on Deep Neural Networks

Backdoor attacks are poisoning attacks and serious threats to deep neural networks. When an adversary mixes poison data into a training dataset, the training dataset is called a poison training dataset. A model trained with the poison training dataset becomes a backdoor model and it achieves high stealthiness and attack-feasibility. The backdoor model classifies only a poison image into an adversarial target class and other images into the correct classes. We propose an additional procedure to our previously proposed countermeasure against backdoor attacks by using knowledge distillation. Our procedure removes poison data from a poison training dataset and recovers the accuracy of the distillation model. Our countermeasure differs from previous ones in that it does not require detecting and identifying backdoor models, backdoor neurons, and poison data. A characteristic assumption in our defense scenario is that the defender can collect clean images without labels. A defender distills clean knowledge from a backdoor model (teacher model) to a distillation model (student model) with knowledge distillation. Subsequently, the defender removes poison-data candidates from the poison training dataset by comparing the predictions of the backdoor and distillation models. The defender fine-tunes the distillation model with the detoxified training dataset to improve classification accuracy. We evaluated our countermeasure by using two datasets. The backdoor is disabled by distillation and fine-tuning further improves the classification accuracy of the distillation model. The fine-tuning model achieved comparable accuracy to a baseline model when the number of clean images for a distillation dataset was more than 13% of the training data. Our results indicate that our countermeasure can be applied for general image-classification tasks and that it works well whether the defender's received training dataset is a poison dataset or not.
Email Spam Detection using Bidirectional Long Short Term Memory with Convolutional Neural Network

Communication over email in this era of Internet has become very popular on account of its being cheap and easy to use for messaging and sharing important information to others. But spam messages often times make large volume of unwanted messages in the users inbox and it also wastes the resources as well as valuable time of the users. Therefore, in order to identify the message whether it is spam or ham, an efficient and accurate technique is required. In this paper, we propose a new model for detecting spam messages based on the sentiment analysis of the textual data of the email body. We incorporate Word-Embeddings and Bidirectional LSTM network to analyze the sentimental and sequential properties of texts. Furthermore, we speed up the training time and extract higher level text features for Bi-LSTM network using Convolution Neural Network. We involve two datasets namely lingspam dataset and spam text message classification dataset and adopt recall, precision and f-score for comparing and evaluating the performance of our proposed approach. Our model achieves improved performance of accuracy about 98-99%. Apart from this, we demonstrate our model outperforms not only to some popular machine learning classifiers but also to state of the art approaches for detecting spam messages and hence, proves the superiority by itself.
Particle

Burst-parallel serverless applications invoke thousands of short-lived distributed functions to complete complex jobs such as data analytics, video encoding, or compilation. While these tasks execute in seconds, starting and configuring the virtual network they rely on is a major bottleneck that can consume up to 84% of total startup time. In this paper we characterize the magnitude of this network cold start problem in three popular overlay networks, Docker Swarm, Weave, and Linux Overlay. We focus on end-to-end startup time that encompasses both the time to boot a group of containers as well as interconnecting them. Our primary observation is that existing overlay approaches for serverless networking scale poorly in short-lived serverless environments. Based on our findings we develop Particle, a network stack tailored for multi-node serverless overlay networks that optimizes network creation without sacrificing multi-tenancy, generality, or throughput. When integrated into a serverless burst-parallel video processing pipeline, Particle improves application runtime by 2.4--3X over existing overlays.
COM-PACE: Compliance-Aware Cloud Application Engineering Using Blockchain

The COVID19 Pandemic has highlighted our dependence on online services (from government, e-commerce/retail, and entertainment), often hosted over external cloud computing infrastructure. The users of these services interact with a web interface rather than the larger distributed service provisioning chain that can involve an interlinked group of providers. The data and identity of users are often provided to service provider who may share it (or have automatic sharing agreement) with backend services (such as advertising and analytics). We propose the development of compliance-aware cloud application engineering, which is able to improve transparency of personal data use -- particularly with reference to the European GDPR regulation. Key compliance operations and the perceived implementation challenges for the realization of these operations in current cloud infrastructure are outlined.
Active Subspace of Neural Networks: Structural Analysis and Universal Attacks

Active subspace is a model reduction method widely used in the uncertainty quantification community. In this paper, we propose analyzing the internal structure and vulnerability of deep neural networks using active subspace. Firstly, we employ the active subspace to measure the number of "active neurons" at each intermediate layer, which indicates that the number of neurons can be reduced from several thousands to several dozens. This motivates us to change the network structure and to develop a new and more compact network, referred to as ASNet, that has significantly fewer model parameters. Secondly, we propose analyzing the vulnerability of a neural network using active subspace by finding an additive universal adversarial attack vector that can misclassify a dataset with a high probability. Our experiments on CIFAR-10 show that ASNet can achieve 23.98x parameter and 7.30x flops reduction. The universal active subspace attack vector can achieve around 20% higher attack ratio compared with the existing approaches in our numerical experiments. The PyTorch codes for this paper are available online.
Catch Me If You Can

Industrial Control Systems (ICS) are central to the operation of critical national infrastructure (CNI) such as oil and gas, water treatment, power generation and transport systems. Effective risk management to mitigate large-scale disruption to societies and economies depends on both timely information about vulnerabilities and the consistency of this information. The longer the vulnerabilities remain "in the wild" or a lack of consistency in vulnerability reporting, the greater the impact on CNI operators' ability to systematically understand and mitigate the risks. In this paper, we focus on vulnerabilities identified and reported in Siemens ICS devices, which hold the largest share of the market. We undertake an in-depth analysis of 207 CVEs, identifying the time over which vulnerabilities were 'in the wild' before being discovered and advisories issued, and examine issues with the correctness of CVE information. We find that, on average, a vulnerability is 'in the wild' for 5.3 years, and that many CVEs do not correctly reflect and state the affected devices as Common Platform Enumerations (CPEs). Based on our findings, we propose a set of guidelines to improve the reporting and consistency of ICS CVE information.
QR code based color image stego-crypto technique using dynamic bit replacement and logistic map

This paper uses a novel color image stego-crypto technique to enhance the capacity, security and robustness of data embedding process. The proposed scheme uses a base64 encoding technique and along with QR code generator to transform the secret data, i.e., watermark to a 2D binary matrix, i.e., QR code that considered as a first-level encryption process. Followed by, embedding of individual QR code into the color image components, namely red, green and blue using a dynamic bit replacement technique that guarantees a second level of security. Subsequently, scrambling of random block-pixels and the encryption of logistic chaos are performed. The experimental outcome of three levels of security scheme has proved to be with a much larger key space and a strong resistance to brute force, statistical and differential attacks. The correlation between adjacent pixels is observed to be uniformly distributed in the cipher image and the information entropy value is close to an ideal value of 8. From the simulation results, it is observed that the proposed security scheme has outperformed the existing standard encryption techniques within a single round of iteration in terms of security and robustness.