Reviewerly/exHarmony huggingface dataset:
Reliable of traditional cloth pattern Classification Using Convolutional Neural Network

Basically the traditional cloth pattern varieties increase every year so that they become more difficult to identify. Based on that fact, automatic traditional cloth patterns has become more important to help people recognize their patterns. This research can improve reliability in recognizing and understanding traditional cloth patterns originating from several regions in Indonesia by using a Deep Convolution neural network. In this study, several conditions were carried out, namely, the amount of training image data was more than the number of test images (3 conditions) and vice versa where the number of training images was much less but could recognize more test images (2 conditions). To support classification reliability, data processing is carried out starting from preprocessing with grayscale, resizing, noise reduction, and continued with image segmentation, and feature extraction and classification using the CNN Algorithm. After We do experiment with 42 classes of traditional cloth patterns from 22 provinces on collecting images that can be found in various scales and degrees than the average classification accuracy using Inception V3 3.0 is 83.9% with condition 1, but the highest is between 98.1 %–99.7%. Meanwhile, if using resnetV2 50 the average classification accuracy is between 78.3% for the highest between 93.7%–94.3%. This Research can be continued using antoher Deep CNN algorithm to get the optimal of classification accuracy.
Roza: a new and comprehensive metric for evaluating classification systems

Many metrics such as accuracy rate (ACC), area under curve (AUC), Jaccard index (JI), and Cohen's kappa coefficient are available to measure the success of the system in pattern recognition and machine/deep learning systems. However, the superiority of one system to one other cannot be determined based on the mentioned metrics. This is because such a system can be successful using one metric, but not the other ones. Moreover, such metrics are insufficient when the number of samples in the classes is unequal (imbalanced data). In this case, naturally, by using these metrics, a sensible comparison cannot be made between two given systems. In the present study, the comprehensive, fair, and accurate Roza (Roza means rose in Persian. When different permutations of the metrics used are superimposed in a polygon format, it looks like a flower, so we named it Roza.) metric is introduced for evaluating classification systems. This metric, which facilitates the comparison of systems, expresses the summary of many metrics with a single value. To verify the stability and validity of the metric and to conduct a comprehensive, fair, and accurate comparison between the systems, the Roza metric of the systems tested under the same conditions are calculated and comparisons are made. For this, systems tested with three different strategies on three different datasets are considered. The results show that the performance of the system can be summarized by a single value and the Roza metric can be used in all systems that include classification processes, as a powerful metric.
Research on Spoken Language Understanding Based on Deep Learning

Aiming at solving the problem that the recognition effect of rare slot values in spoken language is poor, which affects the accuracy of oral understanding task, a spoken language understanding method is designed based on deep learning. The local features of semantic text are extracted and classified to make the classification results match the dialogue task. An intention recognition algorithm is designed for the classification results. Each datum has a corresponding intention label to complete the task of semantic slot filling. The attention mechanism is applied to the recognition of rare slot value information, the weight of hidden state and corresponding slot characteristics are obtained, and the updated slot value is used to represent the tracking state. An auxiliary gate unit is constructed between the upper and lower slots of historical dialogue, and the word vector is trained based on deep learning to complete the task of spoken language understanding. The simulation results show that the proposed method can realize multiple rounds of man-machine spoken language. Compared with the spoken language understanding methods based on cyclic network, context information, and label decomposition, it has higher accuracy and F1 value and has higher practical application value.
An Investigation on Sparsity of CapsNets for Adversarial Robustness

The routing-by-agreement mechanism in capsule networks (CapsNets) is used to build visual hierarchical relationships with a characteristic of assigning parts to wholes. The connections between capsules of different layers become sparser with more iterations of routing. This paper proposes techniques in measuring, controlling, and visualizing the sparsity of CapsNets. One essential observation in this paper is that the sparser CapsNets are possibly more robust to the adversarial attacks. We believe this observation will provide insights into designing more robust models.
Ensuring reference independence and cautious monotony in abstract argumentation

In the symbolic artificial intelligence community, abstract argumentation with its semantics, i.e. approaches for defining sets of valid conclusions (extensions) that can be derived from argumentation graphs, is considered a promising method for non-monotonic reasoning. However, from a sequential perspective, abstract argumentation-based decision-making processes typically do not guarantee an alignment with common formal notions to assess consistency; in particular, abstract argumentation can, in itself, not enforce the satisfaction of relational principles such as reference independence (based on a key principle of microeconomic theory) and cautious monotony. In this paper, we address this issue by introducing different approaches to ensuring reference independence and cautious monotony in sequential argumentation: a reductionist, an expansionist, and an extension-selecting approach. The first two approaches are generically applicable, but may require comprehensive changes to the corresponding argumentation framework. In contrast, the latter approach guarantees that an extension of the corresponding argumentation framework can be selected to satisfy the relational principle by requiring that the used argumentation semantics is weakly reference independent or weakly cautiously monotonous, respectively, and also satisfies some additional straightforward principles. To highlight the relevance of the approach, we illustrate how the extension-selecting approach to reference independent argumentation can be applied to model (boundedly) rational economic decision-making.
Echo-Guard: Acoustic-Based Anomaly Detection System for Smart Manufacturing Environments

The Industrial Internet of Things (IIoT) provides intelligence to industrial systems by linking sensors and devices with computer systems and software. However, it also increases the attack surface and exposes industrial systems to various types of IIoT threats. Smart manufacturing environments, built based on IIoT, are also automated and unattended and must respond to physical threats (e.g., vandalism, destruction, theft, etc.) and cybersecurity threats (e.g., DoS, DDOS, backdoor, etc.). In this paper, we propose Echo-Guard, an acoustic-based anomaly detection system to protect smart manufacturing environments. The Echo-Guard records acoustic signals coming from machines in the smart manufacturing environment and converts them into spectrogram images. The spectrogram images are further classified using CNN to detect anomalies in machine motion sounds. Our evaluation, conducted in a smart factory environment, shows that Echo-Guard is effective, achieving 99.44% accuracy, confirming the possibility that machine motion sounds can be utilized to detect anomalies.
Demo of marius

Graph embeddings have emerged as the de facto representation for modern machine learning over graph data structures. The goal of graph embedding models is to convert high-dimensional sparse graphs into low-dimensional, dense and continuous vector spaces that preserve the graph structure properties. However, learning a graph embedding model is a resource intensive process, and existing solutions rely on expensive distributed computation to scale training to instances that do not fit in GPU memory. This demonstration showcases Marius: a new open-source engine for learning graph embedding models over billion-edge graphs on a single machine. Marius is built around a recently-introduced architecture for machine learning over graphs that utilizes pipelining and a novel data replacement policy to maximize GPU utilization and exploit the entire memory hierarchy (including disk, CPU, and GPU memory) to scale to large instances. The audience will experience how to develop, train, and deploy graph embedding models using Marius' configuration-driven programming model. Moreover, the audience will have the opportunity to explore Marius' deployments on applications including link-prediction on WikiKG90M and reasoning queries on a paleobiology knowledge graph. Marius is available as open source software at https://marius-project.org.
Evaluation of High-Resolution Satellite-Derived Solar Radiation Data for PV Performance Simulation in East Africa

Access to reliable, clean, modern cooking enhances life chances. One option is photovoltaic cooking systems. Accurate solar data are needed to ascertain to what extent these can satisfy the needs of local people. In this paper, we investigate how to choose the most accurate satellite-derived solar irradiance database for use in Africa. This is necessary because there is a general shortage of ground measurements for Africa. The solar data are needed to model the output of solar cooking systems, such as a solar panel, battery and electric pressure cooker. Four easily accessible global horizontal irradiation (GHI) satellite databases are validated against ground measurements using a range of statistical tests. The results demonstrate the impact of the mathematical measure used and the phenomenon of balancing errors. Fitting of the satellite model to the appropriate climate zone and/or nearby measurements improves accuracy, as does higher spatial and temporal resolution of input parameters. That said, all the four databases reviewed were found to be suitable for simulating PV yield in East Africa.
A Frequency Domain Constraint for Synthetic and Real X-ray Image Super Resolution

Synthetic X-ray images are simulated X-ray images projected from CT data. High-quality synthetic X-ray images can facilitate various applications such as surgical image guidance systems and VR training simulations. However, it is difficult to produce high-quality arbitrary view synthetic X-ray images in real-time due to different CT slice thickness, high computational cost, and the complexity of algorithms. Our goal is to generate high-resolution synthetic X-ray images in real-time by upsampling low-resolution images with deep learning-based super-resolution methods. Reference-based Super Resolution (RefSR) has been well studied in recent years and has shown higher performance than traditional Single Image Super-Resolution (SISR). It can produce fine details by utilizing the reference image but still inevitably generates some artifacts and noise. In this paper, we introduce frequency domain loss as a constraint to further improve the quality of the RefSR results with fine details and without obvious artifacts. To the best of our knowledge, this is the first paper utilizing the frequency domain for the loss functions in the field of super-resolution. We achieved good results in evaluating our method on both synthetic and real X-ray image datasets.
Master equation for non-Markovian quantum Brownian motion: The emergence of lateral coherences

Understanding the behavior of a quantum system coupled to its environment is of fundamental interest in the general field of quantum technologies. It also has important repercussions on foundational problems in physics, such as the process of decoherence and the so-called quantum measurement problem. There have been many approaches to explore Markovian and non-Markovian dynamics within the framework of open quantum systems, but the richness of the ensuing dynamics is still not fully understood. In this paper we develop a non-Markovian extension of the standard Caldeira-Leggett model, based on expanding the dynamics of the reduced system at high temperature in inverse powers of the high-frequency cutoff of the Ohmic spectral density of the environment and derive a non-Markovian master equation for the reduced density matrix for the case of a general potential. We also obtain a fully analytical solution in the free particle case. While the short-time behavior of this solution does not diverge substantially from the Markovian behavior, at intermediate times we find a resurgence of coherence, which we name lateral coherence. We identify this with a corresponding transient negative entropy production rate, which is understood to be characteristic of non-Markovian dynamics. We also analyze the positivity of the reduced density matrix and derive the corresponding Fokker-Planck equation in the classical limit.
Machine Learning and Survey-based Predictors of InfoSec Non-Compliance

Survey items developed in behavioral Information Security (InfoSec) research should be practically useful in identifying individuals who are likely to create risk by failing to comply with InfoSec guidance. The literature shows that attitudes, beliefs, and perceptions drive compliance behavior and has influenced the creation of a multitude of training programs focused on improving ones’ InfoSec behaviors. While automated controls and directly observable technical indicators are generally preferred by InfoSec practitioners, difficult-to-monitor user actions can still compromise the effectiveness of automatic controls. For example, despite prohibition, doubtful or skeptical employees often increase organizational risk by using the same password to authenticate corporate and external services. Analysis of network traffic or device configurations is unlikely to provide evidence of these vulnerabilities but responses to well-designed surveys might. Guided by the relatively new IPAM model, this study administered 96 survey items from the Behavioral InfoSec literature, across three separate points in time, to 217 respondents. Using systematic feature selection techniques, manageable subsets of 29, 20, and 15 items were identified and tested as predictors of non-compliance with security policy. The feature selection process validates IPAM's innovation in using nuanced self-efficacy and planning items across multiple time frames. Prediction models were trained using several ML algorithms. Practically useful levels of prediction accuracy were achieved with, for example, ensemble tree models identifying 69% of the riskiest individuals within the top 25% of the sample. The findings indicate the usefulness of psychometric items from the behavioral InfoSec in guiding training programs and other cybersecurity control activities and demonstrate that they are promising as additional inputs to AI models that monitor networks for security events.
Breast Cancer Diagnosis Using Optimized Machine Learning Algorithms

This paper presents an investigation study of seven Machine Learning Algorithms (MLAs) for Breast Cancer (BC) diagnosis. These algorithms are: Decision Tree (DT), Discriminated Analysis (DA), Naive Bayes (NB), Support Vector Machine (SVM), K Nearest Neighbor (KNN), Ensemble Methods (EMs) and Multi-Layer Perceptron (MLP) classifier. All of these algorithms are applied to the Wisconsin Diagnostic Breast Cancer (Diagnostic) (WDBC) dataset.The main objective of the study is to optimize the hyperparameters of each MLA in order to achieve the best BC classification. This process can also help to reduce the effort and time required for classification. For this reason, Bayesian optimization method is used in MATLAB software to select the hyperparameters values of the six first algorithms. In Python language, Grid search method is used to optimize the MLP hyperparameters. To demonstrate the effect of the optimization process, several predefined models with a corresponding optimized model are evaluated for each algorithm to diagnose the category of BC, whether benign or malignant. The maximum accuracy reported in this study is 96.52%, offered by SVM and MLP algorithms.
Fully Abstract and Robust Compilation

The most prominent formal criterion for secure compilation is full abstraction, the preservation and reflection of contextual equivalence. Recent work introduced robust compilation, defined as the preservation of robust satisfaction of hyperproperties, i.e., their satisfaction against arbitrary attackers. In this paper, we initially set out to compare these two approaches to secure compilation. To that end, we provide an exact description of the hyperproperties that are robustly satisfied by programs compiled with a fully abstract compiler, and show that they can be meaningless or trivial. We then propose a novel criterion for secure compilation formulated in the framework of Mathematical Operational Semantics (MOS), guaranteeing both full abstraction and the preservation of robust satisfaction of hyperproperties in a more sensible manner.
<scp>IoT Notary</scp> : Attestable Sensor Data Capture in IoT Environments

Contemporary IoT environments, such as smart buildings, require end-users to trust data-capturing rules published by the systems. There are several reasons why such a trust is misplaced—IoT systems may violate the rules deliberately or IoT devices may transfer user data to a malicious third-party due to cyberattacks, leading to the loss of individuals’ privacy or service integrity. To address such concerns, we propose IoT Notary , a framework to ensure trust in IoT systems and applications. IoT Notary provides secure log sealing on live sensor data to produce a verifiable “proof-of-integrity,” based on which a verifier can attest that captured sensor data adhere to the published data-capturing rules. IoT Notary is an integral part of TIPPERS, a smart space system that has been deployed at the University of California, Irvine to provide various real-time location-based services on the campus. We present extensive experiments over real-time WiFi connectivity data to evaluate IoT Notary , and the results show that IoT Notary imposes nominal overheads. The secure logs only take 21% more storage, while users can verify their one day’s data in less than 2 s even using a resource-limited device.
Edge Trusted Sharing: Task-Driven Decentralized Resources Collaborate in IoT

Sixth generation (6G) is committed to providing a fully connected world. The deployment and application of the 6G technology in the Internet of Things (IoT) can efficiently collaborate IoT resources and realize resource sharing, which mainly encourages IoT development. However, due to the lack of trust between IoT resources, security and privacy become the main challenges. As an emerging technology, blockchain can solve the trust-absence issues and provide more benefits, but the introduction of blockchain also brings problems for IoT resource collaboration and sharing. This article first proposes a blockchain-enabled edge resource-sharing (BEERS) architecture by combining blockchain and edge computing technology. Then based on a typical resource sharing and collaboration scenario, the joint optimization problem of resource scheduling and task assignment (JRSTA) is constructed. We decompose JRSTA into a primal problem and a master problem and design a greedy-based task assignment (GBTA) algorithm to solve the primal problem. Based on the GBTA algorithm, the resource scheduling and task assignment (RSTA) algorithm is developed. Next, we design a layered parallel edge RSTA (LPRSTA) mechanism to improve practicability. Finally, we analyze the security and the performance of our proposed architecture and algorithms. The results show that the proposed architecture can support the secure collaboration of IoT resources, and the proposed algorithm can achieve effective JRSTA.
Bangla Image Caption Generation Through CNN-Transformer Based Encoder-Decoder Network

Automatic Image Captioning is the never-ending effort of creating syntactically and validating the accuracy of textual descriptions of an image in natural language with context. The encoder-decoder structure used throughout existing Bengali Image Captioning (BIC) research utilized abstract image feature vectors as the encoder’s input. We propose a novel transformer-based architecture with an attention mechanism with a pre-trained ResNet-101 model image encoder for feature extraction from images. Experiments demonstrate that the language decoder in our technique captures fine-grained information in the caption and, then paired with image features, produces accurate and diverse captions on the BanglaLekhaImageCaptions dataset. Our approach outperforms all existing Bengali Image Captioning work and sets a new benchmark by scoring 0.694 on BLEU-1, 0.630 on BLEU-2, 0.582 on BLEU-3, and 0.337 on METEOR.
Motion Planning in Uncertain Environments with Rapidly-Exploring Random Markov Decision Processes

We propose rapidly-exploring random Markov decision processes (RRMDPs), a novel sampling-based motion planning approach for situations where the environment parameters are not fully known a priori, but a prior distribution over such parameters is available. Our algorithm combines ideas from established motion planning algorithms to achieve motion policies that are able to robustly drive the robot to its goal in the presence of uncertain action outcomes. We evaluate RRMDP in two domains, showing that it can synthesise motion policies that are more robust than the motion plans obtained by particle rapidly-exploring random trees (pRRT), a widely used algorithm for motion planning under uncertainty which RRMDP builds upon.
Kannada Sentiment Analysis Using Vectorization and Machine Learning

The sentiment analysis (SA) also known as opinion mining (OM) is a new arena in text mining and NLP field. We are presenting a method to analyze the IMDB movie reviews translated to Kannada using Google translator along with other reviews collected from various creditable sites like Vijayakarnataka, Gadgetloka, and filmibeats. In sentiment analysis, many research has been carried out on English text. Methods and resources of English may not produce good results for other languages. In this paper, we are analyzing around 50,034 of reviews with positive and negative labels. Our ensemble classification technique using various vectorization has achieved the accuracy of 89%.
ELEMENT: Text Extraction for the Dark Web

The increasing amount of data on the Internet has been a constant challenge when text cleaning and relevant-text extraction are of interest. One of the areas of focus on the internet is the Dark Web; the data here is much more volatile and dynamic. With more researchers looking for data and extracting information, the algorithms have always been in a state of constant improvement. The solutions currently offered, all work based on text feature extraction algorithms like TF-IDF, Bag of Words, Word2Vec. Discussion on these extraction methods is well documented but a critical evaluation among these algorithms is amiss from standard literature. This paper discusses a balanced approach for tagging extracted data; ELEMENT (Effective Lemmatization, Efficient Management of Extracting Noteworthy Tags) which is a modified form of TF-IDF. Having a balanced approach like ELEMENT will benefit from being able to perform well under any given circumstance. The paper discusses and compares the proposed approach with existing strategies of text feature extraction. This comparison spans across accuracy of feature extraction, efficiency concerning Time and Space, concluding with a simplistic view of the strengths and weaknesses of each algorithm.