Reviewerly/exHarmony huggingface dataset:
Interpolated Joint Space Adversarial Training for Robust and Generalizable Defenses

Adversarial training (AT) is considered to be one of the most reliable defenses against adversarial attacks. However, models trained with AT sacrifice standard accuracy and do not generalize well to novel attacks. Recent works show generalization improvement with adversarial samples under novel threat models such as on-manifold threat model or neural perceptual threat model. However, the former requires exact manifold information while the latter requires algorithm relaxation. Motivated by these considerations, we exploit the underlying manifold information with Normalizing Flow, ensuring that exact manifold assumption holds. Moreover, we propose a novel threat model called Joint Space Threat Model (JSTM), which can serve as a special case of the neural perceptual threat model that does not require additional relaxation to craft the corresponding adversarial attacks. Under JSTM, we develop novel adversarial attacks and defenses. The mixup strategy improves the standard accuracy of neural networks but sacrifices robustness when combined with AT. To tackle this issue, we propose the Robust Mixup strategy in which we maximize the adversity of the interpolated images and gain robustness and prevent overfitting. Our experiments show that Interpolated Joint Space Adversarial Training (IJSAT) achieves good performance in standard accuracy, robustness, and generalization in CIFAR-10/100, OM-ImageNet, and CIFAR-10-C datasets. IJSAT is also flexible and can be used as a data augmentation method to improve standard accuracy and combine with many existing AT approaches to improve robustness.
IPAssess: A Protocol-Based Fingerprinting Model for Device Identification in the IoT

Internet of Things (IoT) are the most commonly used devices today, that provide services that have become widely prevalent. With their success and growing need, the number of threats and attacks against IoT devices and services have been increasing exponentially. With the increase in knowledge of IoT related threats and adequate monitoring technologies, the potential to detect these threats is becoming a reality. There have been various studies consisting of fingerprinting based approaches on device identification but none have taken into account the full protocol spectrum. IPAssess is a novel fingerprinting based model which takes a feature set based on the correlation between the device characteristics and the protocols and then applies various machine learning models to perform device identification and classification. We have also used aggregation and augmentation to enhance the algorithm. In our experimental study, IPAssess performs IoT device identification with a 99.6\% classification accuracy.
Adaptive Deep Learning based Cryptocurrency Price Fluctuation Classification

This paper proposes a deep learning based predictive model for forecasting and classifying the price of cryptocurrency and the direction of its movement.These two tasks are challenging to address since cryptocurrencies prices fluctuate with extremely high volatile behavior.However, it has been proven that cryptocurrency trading market doesn't show a perfect market property, i.e., price is not totally a random walk phenomenon.Based upon this, this study proves that the price value forecast and price movement direction classification is both predictable.A recurrent neural networks based predictive model is built to regress and classify prices.With adaptive dynamic features selection and the use of external dependable factors with a potential degree of predictability, the proposed model achieves unprecedented performance in terms of movement classification.A naïve simulation of a trading scenario is developed and it shows a 69% profitability score a cross a six months trading period for bitcoin.
Design and Implementation of Procedural Self-Instructional Contents and Application on Smart Glasses

Instructional contents are used to demonstrate a technical process to teach and walkthrough certain procedures to carry out a task.This type of informational content is widely used for teaching and lectures in form of tutorial videos and training videos.Since there are questions and uncertainties for what could be the killer application for the novel wearables, we propose a self-instruction training application on a smart glass to utilize already-available instruction videos as well as public open data in creative ways.We design and implement a prototype application to help users train by wearing smart glasses specifically designed for two concrete and hand-constrained use cases where the user's hands need to be free to operate.To increase the efficiency and feasibility of the self-instruction training, we contribute to the development of a wearable killer application by integrating a voice-based user interface using speech recognizer, public open data APIs, and timestamp-based procedural content navigation structure into our proof-of-concept application.
Fourier Image Watermarking: Print-Cam Application

Digital image watermarking is an active research field since it provides protection, security, and authenticity of data. This paper presents development and implementation of a blind and robust watermarking application for ID images under a print-cam system. In the present case, the images are watermarked and printed on ID cards and then detected freehandedly with a smartphone camera. In order to design an efficient and robust image watermarking application, the attacks produced in print-cam processes, such as geometric distortions, must be resolved. Accordingly, the proposed watermarking approach is applied in the Fourier domain. Then, a frame-based projective rectification is integrated to deal with geometric distortions by using detection of Hough lines. Moreover, better robustness against print-cam watermarking attacks was achieved compared with the existing methods, and an Android application was designed and implemented based on the proposed scheme.
Long Short Term Memory Networks for Stroke Activity Recognition base on Smartphone

Aims this study to analyze rehabilitation exercises using sensor data embedded in smartphones is widely used to recognize human activities regularly to gain a better understanding of human behavior. However, it is rarely used to measure accelerators of patient movement in hospital wards. This paper proposes an optimized neural network-based feature extraction approach based on the Long Short Term Memory technique to recognize human activity using data from a tri-axial accelerometer. The activity to be measured is the Range of Motion for patient rehabilitation, and rehabilitation exercises are one of the most important steps for recovery. The results of the softmax regression trained on the dataset set have 1,076,105 records and 12 class attributes (class distribution). The experimental results on the SAR dataset show that our Long Short Term Memory-based approach is practical and successful, with an accuracy of 89.74%.
CRAN package bayesvl on CSIRO

bayesvl: Visually Learning the Graphical Structure of Bayesian Networks and Performing MCMC with 'Stan'
Deep Learning Based Garbage Detection for Autonomous Garbage Collection Vehicles

With the rapid technological advances and the increasing human population, the need for more production has emerged and consumption has increased accordingly.As a result of this increased consumption, more garbage has been generated.Environmental pollution caused by these garbages emerges as a problem that people have to overcome both in Turkey and in the world.Many studies have been conducted to overcome this problem.Especially today, with the development of autonomous vehicles and artificial intelligence, the solutions using these technologies have increased.In this study, a new data set was created for autonomous garbage collection vehicles and a model was proposed in which these vehicles can be used.The data set was prepared with images of garbage with paper cups, which is one of the most polluting garbage, taken in different places, and images consisting of different garbage without paper cups.Paper cups were detected from these images with pre-trained Squenzenet, VGG-19 and GoogLeNet convolutional neural networks.The performance rate of the SquenzeNet, GoogLeNet and Vgg-19 networks used in the study was found as 97.77%, 96.44% 94.66%, respectively.
Improved Far-field Speaker Recognition Method Based Geometry Acoustic Simulation and SpecAugment

Deep learning method has made a great breakthrough in the field of speaker recognition. However, the performance of deep learning method based neural network often degrade in far-field speaker recognition task, with respect to the near-field. One of the indispensable factors is that deep learning performance has a great impact on the quality and diversity of data. Utterance in far-field scenario always mixed with variant reverberation and noise which could interfere the prediction of model. In this paper, we proposed a novel method comprised of geometry acoustic simulation (GAS) and SpecAugment, which makes the performance of deep learning method on far-field speaker recognition task more stable and excellent. Based on near-field speech dataset, by means of physical simulation, far-field speech with different types and characters can be generated to provide as many scenes as possible for speaker recognition. Simultaneously, the SpecAugment method has been applied to enhance the diversity of utterance data on mel-spectrogram. Then, ResNet34 and AM-Softmax Loss were used as the most commonly used methods for feature extraction and model training. Our proposed method has been trained on Voxceleb1 and V oxceleb2 dataset, and test on Vox celeb original test set. Evaluations show that our proposed method has 46% and 40% of EER and minDCF relatively improvement on far-field speaker recognition task.
Combined Use of Satellite Data and Machine Learning for Detecting, Measuring, and Monitoring Active Lava Flows at Etna Volcano

Earth and Space Science Open Archive PosterOpen AccessYou are viewing the latest version by default [v1]Combined Use of Satellite Data and Machine Learning for Detecting, Measuring, and Monitoring Active Lava Flows at Etna VolcanoAuthorsEleonoraAmatoiDClaudiaCorradinoiDFedericaTorrisiiDCiroDel NegroiDSee all authors Eleonora AmatoiDCorresponding Author• Submitting AuthorINGV National Institute of Geophysics and VolcanologyDipartimento di Matematica e Informatica University of Palermo ItalyiDhttps://orcid.org/0000-0002-4244-3972view email addressThe email was not providedcopy email addressClaudia CorradinoiDINGV National Institute of Geophysics and VolcanologyiDhttps://orcid.org/0000-0003-3054-6840view email addressThe email was not providedcopy email addressFederica TorrisiiDINGV National Institute of Geophysics and VolcanologyDipartimento di Ingegneria Elettrica Elettronica e Informatica University of Catania ItalyiDhttps://orcid.org/0000-0002-7066-6508view email addressThe email was not providedcopy email addressCiro Del NegroiDINGV National Institute of Geophysics and VolcanologyiDhttps://orcid.org/0000-0001-5734-9025view email addressThe email was not providedcopy email address
Research on Automation Control of University Logistics Management System Based on Wireless Communication Network

Nowadays, individuals with and without technical knowledge have started utilizing the technology to a broader extent. The utilization of technology has gone deeper concerning gadgets that aid in wireless communication with anyone or anything. This advancement has paved the way for a trending technology named wireless communication network (WCN) in recent years. In addition to this, there has been a significant change and development in the field of trading goods. Manual ordering of goods has changed to online ordering, and hence, supply chain management. This research focuses on applying WCN to a logistic tracking information system (LTIS) for a university with automatic control of the system. A novel algorithm named intelligent logistics system construction algorithm is implemented to evaluate the efficiency and performance of data. This model aids in the tracking goods and automatic control of university logistics.
Pengaruh Model Pembelajaran Example Non Example dan Motivasi Belajar Siswa terhadap Keterampilan Menulis Teks Prosedur

This experimental research was motivated by a problem in learning Indonesian: the low score of students' learning outcomes in writing procedural texts. Based on this, this research was conducted to know the effect of the Example Non Example learning model and learning motivation on the skills of writing procedure text. The samples of this study were students in grade 7-I (experimental class) and grade 7-H (control class) at MTs Negeri 4 Pasaman Barat for the academic year 2020/2021. The data of this research are the score of the learning motivation questionnaire and the test scores for writing procedural text writing skills in the experimental and control classes. The final results of this study indicate that the Example Non Example learning model has a good effect on the writing skills of the seventh-grade students of MTs Negeri 4 Pasaman Barat. It can be seen from the scores of students' procedural text writing skills taught using the Example Non Example cooperative learning model better than students taught using conventional learning models. Furthermore, there is also an interaction between the Example Non-Example learning model and learning motivation so that students can write procedure texts better.
How Blockchain Can Reinvent the Field of Charity - A Case Study

In the modern world, financial aid helps in economic process and also as social capital. The charity organizations with primary aim of financial aid play very important role during this sector. On the other hand, a series of scandals have rocked the manner that the general public perceives the standard charity, and trust in charitable organizations. It's no coincidence that headline-grabbing scandals within the world of financial aid coincide with the decline in religion toward these organizations. Blockchain technology because the integral a part of the system. Blockchain technology may facilitate resurrect the pictures of charities willing to adopt its services. By minimizing body costs through automation, providing additional responsibility through traceable giving milestones, and permitting donors to see additional clearly wherever their funds area unit going, blockchain might facilitate restore a number of the lost believability to charities that prove warrant the public’s trust. The Blockchain build trust with donors, recipients, and alternative stakeholders reach the proper individuals and improve administration prices and efficaciousness.
A practical perspective on connective generation

In data-driven natural language generation, we typically know what relation should be expressed and need to select a connective to lexicalize it.In the current contribution, we analyse whether a sophisticated connective generation module is necessary to select a connective, or whether this can be solved with simple methods, such as random choice between connectives that are known to express a given relation, or usage of a generic language model.Comparing these methods to the distributions of connective choices from a human connective insertion task, we find mixed results: for some relations, it is acceptable to lexicalize them using any of the connectives that mark this relation.However, for other relations (temporals, concessives) either a more detailed relation distinction needs to be introduced, or a more sophisticated connective choice module would be necessary.
Problematika Pembelajaran Daring di Masa Pandemi Covid-19 Ditinjau dari Sudut Pandang Mahasiswa

This study aims to discover online learning problems amid the covid-19 pandemic. The method used is descriptive qualitative with a phenomenological approach. The research subjects were Billfath University Indonesian Language Education study program students. The research data is in survey results and interviews with Indonesian Language Education students. The results of surveys and interviews found that the biggest problems in online learning, namely: unstable internet networks and data packages used by students. These two problems ultimately impact the level of understanding of the material by students. In addition, online learning also impacts the ability of mobile phones to respond to data. The memory capacity of mobile phones certainly causes this has exceeded, thus hampering students in learning. Therefore, mutual agreement is needed between students and lecturers so that lecture activities can run smoothly and achieve the lecture's objectives.
Psychological Barriers of Students in the Study of Legal English

The article examines the main factors of the emergence of psychological barriers among law students in the study of English. Based on the works of Russian and foreign psychological and pedagogical researchers, the essence of the concept of “psychological barrier” is determined, and the difficulties arising in the process of professionally oriented teaching of English in higher education are described. The survey conducted among students of Kutafin Moscow State Law University (MSAL) identified the main reasons for the emergence of psychological barriers in the study of Legal English and their subcategories. Particular attention is paid to strategies for effective work of teachers to overcome the barriers and create an optimal educational space for the successful mastering of Legal English, namely, the timely identification of factors affecting the emergence of psychological barriers; modeling of communicative situations that motivate each student to express their opinion in a foreign language.
Harmonica: A Framework for Semi‐automated Design and Implementation of Blockchain Applications

ABSTRACT Designing blockchain‐based applications is a tedious task. Compared to traditional software engineering, software architects cannot rely on previous experiences or proven practices, often formalized as software patterns. Also, the selection of an adequate blockchain technology is difficult without deep knowledge of the technology. This paper introduces Harmonica, a framework for the design and implementation of a blockchain‐based application. This framework is divided in three parts: a decision‐making engine to recommend a blockchain technology and blockchain‐based software patterns relying on requirements, a configurator to generate code stubs and configuration files, and a knowledge base to support those tools.
Mechanical Oars to Promote the Lancang Kuning Boat in the Riau Archipelago

The Lancang Kuning boat is one of 10 traditional boats in Indonesia.,In some areas, the traditional boat shape still exists today but the Lancang Kuning boat is no longerfound in the Riau Archipelago Malay community.For this reason, it is necessary to trace back the history of theLancang Kuning boat.The design of the propulsion system on the traditional Lancang Kuning boat usesa combination of non-mechanical and mechanical propulsion systems.The mechanical rowing of the traditional Lancang Kuning boat has 18 oars, consisting of 9 pairs of oars.from the left side and 9 oars from the right side.The mechanical paddle of the traditional lancang kuning boat is driven by human power.replacedby an electric motor forthe propulsion of this boat.The resultingboat still looks traditional but can reach a faster speed.This updated design of the Lancang Kuning would make the boat suitable for tourism activities and competition activities in the Riau Islands Province.
The Advertising Approach Differentiation of Indonesia’s Halal Toothpaste Products

Abstract This study aims to understand how the Halal Toothpaste Products in Indonesia differentiate themselves from one another through Advertising. This study was conducted to provide strategic Advertising recommendations. The study used the framework of the Elaboration Likelihood Theory Model and the Advertising concepting approaches to analyze the applied techniques on the executed Advertising materials. This qualitative descriptive try to describe narratively and visually the TV Advertising material that was collected from the official YouTube account of each brand. Data were collected from relevant literature, documents, and online publications. The result of the study shows that all the Halal Toothpaste products utilized the central route and approaches whereas each of the product Advertising materials elaborates their respective product ingredients and their benefits. Each of the Advertising materials communicates Halal and its nuances both verbally and visually. The Halal label of the Toothpaste is mainly influenced by the ingredient within the product that is perceived to be correlated with the Islamic culture by the Indonesian consumers, Siwak. To create differentiation, each of the Advertising materials applied the combination of several Advertising concepting approaches. In practice, the result of the study can be a reference for Strategic Planners and Creative Professionals in Advertising Agencies on ‘What to Say’ and How to Say’ in the Halal Product Category. Academically, the study will be a reference for more research ideas in the future on Halal Advertising Strategy and Halal Product Campaign.
Dual Branch Relation Network with Feature Weighting for Few-Shot Learning

Few-shot learning is proposed to enable classifier to rapidly generalize to novel classes with limited exemplars on the basis of prior knowledge. Nevertheless, the majority of models which base on the metric learning still have mediocre performance because of the neglect of the critical part misalignment in spatial position between images. In order to address this issue, We design a metric learning based model with two branches called dual branch relation network (DBRN). One branch measure the similarity between support image and query image directly by using relation network as the measurement network. Another branch measures the similarity of image pairs in consideration of the relationship between every local region of query image and support image. The final matching score is the fusion of results from two branches. Before the two branches measuring similarity, we introduce a spatial-wise feature weighting method to enhance the similar local features between images and a channel-wise feature weighting method to weight the channels of features by using class attentive weighting vector. In experiments, our model achieve the state-of-the-art performance on two major benchmark datasets in few-shot image recognition.
COVID-19 Contact Tracing and Data Protection Can Go Together (Preprint)

<sec> <title>UNSTRUCTURED</title> We discuss the implementation of app-based contact tracing to control the coronavirus disease (COVID-19) pandemic and discuss its data protection and user acceptability aspects. </sec>
Obstacle Avoidance and Environmental Adaptability Analysis of Snake-like Robot Based on Deep Learning

Abstract Aiming at the problems of high complexity and low accuracy of visual depth map feature recognition, a graph recognition algorithm based on principal component direction depth gradient histogram (pca-hodg) is designed in this study. In order to obtain high-quality depth map, it is necessary to calculate the parallax of the visual image. At the same time, in order to obtain the quantized regional shape histogram, it is necessary to carry out edge detection and gradient calculation on the depth map, then reduce the dimension of the depth map combined with the principal component, and use the sliding window detection method to reduce the dimension again to realize the feature extraction of the depth map. The results show that compared with other algorithms, the pca-hodg algorithm designed in this study improves the average classification accuracy and significantly reduces the average running time. This shows that the algorithm can reduce the running time by reducing the dimension, extract the depth map features more accurately, and has good robustness.