Reviewerly/exHarmony huggingface dataset:
Robotic Applications of Pre-Trained Vision-Language Models to Various Recognition Behaviors

A number of models that learn the relations between vision and language from large datasets have been released. These models perform a variety of tasks, such as answering questions about images, retrieving sentences that best correspond to images, and finding regions in images that correspond to phrases. Although there are some examples, the connection between these pre-trained vision-language models and robotics is still weak. If they are directly connected to robot motions, they lose their versatility due to the embodiment of the robot and the difficulty of data collection, and become inapplicable to a wide range of bodies and situations. Therefore, in this study, we categorize and summarize the methods to utilize the pre-trained vision-language models flexibly and easily in a way that the robot can understand, without directly connecting them to robot motions. We discuss how to use these models for robot motion selection and motion planning without re-training the models. We consider five types of methods to extract information understandable for robots, and show the examples of state recognition, object recognition, affordance recognition, relation recognition, and anomaly detection based on the combination of these five methods. We expect that this study will add flexibility and ease-of-use, as well as new applications, to the recognition behavior of existing robots.
OSVAE-GAN: Orthogonal Self-Attention Variational Autoencoder Generative Adversarial Networks for Time Series Anomaly Detection

Time series anomaly detection is a binary classification problem with unbalanced data, which aims to identify data that fall outside of the normal behaviors. Since the proportion of the abnormal data is very small, the cost of labeling all data is prohibitively high. Therefore, unsupervised methods are more suitable than supervised methods. With the rapid development of deep learning, various multivariate time series anomaly detection methods based on deep learning have been proposed. However, existing methods do not fully capture the spatial-temporal correlations and are not robust to noise. To address these issues, we propose an unsupervised anomaly detection method called Orthogonal Self-Attention Variational Autoencoder Generative Adversarial Networks (OSVAE-GAN). To fully extract the spatial-temporal correlations, we use an orthogonal self-attention (OS) mechanism. Moreover, to increase the capability to deal with complex multivariate data, we integrate two generative adversarial networks (GANs) with the variational autoencoder (VAE). Finally, to reduce the influence of noise, we introduce the maximum mean discrepancy (MMD) loss. Experiments are conducted on five public datasets, which show that the proposed method is superior to the existing methods.
Enhancement Strategies For Copy-Paste Generation &amp; Localization in RGB Satellite Imagery

Satellite imagery is widely used for various applications, such as land-cover classification, field delineation, and environmental monitoring. However, satellite images can also be subject to malicious manipulation, such as copy-paste attacks, where a region from one image is pasted onto another to create a fake scene. Due to the different processing chains that characterize their lifecycle, the multimedia forensics community developed specific tools for analyzing RGB satellite images. Among the characteristics that differentiate satellite images from standard digital pictures, their dynamic characteristics have still not been investigated. In this paper, we study the effect of different data normalization techniques for the generation and analysis of copy-paste attacks on RGB satellite imagery. We show that these techniques boost deep learning tools developed for copy-paste localization, as they promote the extraction of satellite attribution traces. However, they could also help hide these clues, making the detection task more challenging. Our results suggest that careful attention and precise consideration of the data lifecycle should be given when analyzing data modalities different from standard digital pictures.
Facial Shape-Based Eyeglass Recommendation Using Convolutional Neural Networks

Eyeglasses are not only used to protect our vision and prevent dust from getting into our eyes. Additionally, glass that fits properly can give a person an elegant appearance. However, people often find it difficult to choose eyeglasses that fit their face shape; to address this issue, we have proposed a novel architecture in this paper. In order to do this, we created a pipeline that can recommend eyeglasses based on the form of the eyes using multiple transfer learning architecture to predict the face shape from a given image. We utilized InceptionV4 [17], InceptionV3[18], Vit Small [12], DenseNet121 [10], ResNet50 [9], and VGG16 [16] to predict the facial shape from the image and achieve a test accuracy of 75%. We used 5500 photos with five different face shapes (Heart, Oblong, Oval, Round, Square) for this experiment, and two distinct datasets were gathered from Kaggle [2] and GitHub [1]. By simply uploading the photograph to our recommendation system, our proposed solution can assist users in selecting the appropriate eyewear.
Fake Product Detection Using Image Processing in Blockchain

There In recent times, the expansion of counterfeit products has become worldwide. There are many counterfeit goods in the latest supply chain. According to the record, counterfeit product affairs have increased in the last few years. It is essential to have a system for customers or clients to check the all details of the product so that client can decide that the product is counterfeit or not. In India actually, there is no such system to identify counterfeit products. So, the solution includes a simple QR code-based identification that can help the end-user or customers to scan and detect the genuineness of the product by using a smartphone. To design and developed a system for implementing system to detect fake products using blockchain technology, in this work system carried out three different modules like Manufacturer, Retailer and Consumers as well. Each transaction has stored into the blockchain which eliminate all network data attacks in P2P environment for product systems Index Terms – Fake Product Detection, Image Processing, Java, Blockchain.
Bridging the Gap Between Quantum Computing and Artificial Intelligence

Quantum machine learning (QML) holds the potential to transform various industries, yet its widespread adoption faces formidable challenges. This paper provides a condensed exploration of these challenges and opportunities. I delve into: • Hardware Limitations: Present quantum computers are constrained in qubit count and gate quality, posing obstacles to real-world QML implementation. We dissect the implications of these limitations on QML computations. • Error Correction: Quantum systems are prone to errors stemming from hardware noise and gate imperfections. We scrutinize the strategies to mitigate these errors and enhance QML accuracy. • Algorithm Development: The evolution of QML algorithms is examined, including computational complexity issues and adaptability to noisy quantum hardware. then pivot to recent developments in quantum hardware, which are not only addressing these challenges but also generating new prospects: • Addressing Challenges: Increased qubit counts and improved qubit quality empower quantum computers to tackle complex real-world problems, with reduced noise and enhanced scalability. • Contributing to Opportunities: Recent hardware advancements have catalyzed the creation of efficient quantum algorithms and the exploration of novel quantum applications in healthcare, finance, and materials science. • Creating Opportunities: Quantum computing, with its unique capabilities, has the potential to unlock scientific and engineering discoveries unattainable through classical computing, ushering in a new era of innovation across diverse industries. As quantum hardware continues to advance, these developments are poised to shape the future of quantum machine learning and its profound impact on society.
Information Literacy Using the Central Sulawesi Museum on Motivation for Learning History at SMA Labschool Untad Palu

The importance of information literacy learning among teachers and students has enhanced comprehension and digital media skills. Learning activity and motivation are the factors that spark a particular behavior and give it a resistant orientation that will result in tenacity and not easily cracks in reaching achievement despite many obstacles, basically having a variety of learning-related motivations. This study introduces museums to students as a learning resource to explore the function of information literacy and the desire to study student history. It is qualitative research. People will be asked to participate in interviews and observations, contributing information, opinions, and perceptions to understand the social phenomenon from the participant's perspective. The results indicated how much cultural park administrators and museums promoted using information technologies to attract visitors to educational institutions. Offering details on museum collections and exhibitions relating to historical topics might help implementers of cultural parks and museums motivate students to learn more. This is particularly beneficial for technical implementers since it helps the general public learn about museum collections through brochures, catalogs, websites for cultural parks and museums, and social media, like Instagram.
Are you Really Alone? Detecting the use of Speech Separation Techniques on Audio Recordings

The pervasive influence of digital media has brought about new challenges in verifying the authenticity and integrity of audio recordings. The ease of editing and altering audio has raised concerns regarding the potential malicious use of speech separation techniques, where multiple speakers' voices can be extracted from a mixed recording. In light of these emerging threats, the need for robust forensic detectors that can identify the presence of speech separation forgeries becomes increasingly crucial. In this paper, we propose a novel forensic detector designed to discern between original single-speaker speech recordings and those obtained using speech separation techniques applied to audio recordings containing multiple speakers. Leveraging the power of Convolutional Neural Networks (CNNs), we explore the efficacy of different Short-Time Fourier Transform (STFT) representations in tackling the task. While many conventional approaches in the literature employ the audio spectrogram (i.e., the STFT magnitude) as input for CNNs, our study explores the use of the STFT real and imaginary parts, as well as the STFT magnitude and phase. In doing so, we ensure the preservation of all essential information embedded within the speech signal. Results show that the proposed signal representation improves over the sole use of the spectrogram. Moreover, the proposed approach is able to generalize to datasets and speech separation techniques never seen in training. Finally, our proposed detector shows promising results on preliminary experiments performed on synthetically generated audio tracks.
Comparative Study for Deep Deterministic Policy Gradient and Soft Actor Critic Using an Inverted Pendulum System

Designing optimum controllers using classical approaches is difficult due to the rising need for complicated nonlinear control systems. Reinforcement learning is the best technique to use when trying to regulate a complicated nonlinear system since it allows a system to learn via experimentation and adapt to a changing environment. Machine learning's reinforcement learning subfield examines how agents should respond and behave in a given environment in order to maximize some idea of cumulative reward. Learning can be founded on a few types of evaluative criticism. Standard Regulated learning strategies require a preparation set of information comprising of information vectors and comparing wanted yield vectors, i.e., all in all, they require an administrative framework. Such strategies can't be applied to assignments for which the ideal result of the framework isn't known. These assignments can be tackled utilizing different Support learning calculations. On the off chance that the ideal result isn't accessible, the presentation of the framework should be assessed in a roundabout way by considering the impact of its result on the climate with which the framework connects. Support learning strategies can be applied when this impact is estimated by changes in an assessment sign, or support. For such systems having continuous state spaces, DDPG and SAC are two of the most widely used algorithms. An inverted pendulum is mimicked as a control task fully intent on figuring out how to offset the pendulum with no earlier information on the elements, utilizing Support learning. In the following paper we will compare the two algorithms and verify which is better. For demonstration purposes, both the algorithms are implemented on inverted pendulum problem. The results show that the potential of SAC to contribute to reinforcement learning and control is significant. It also outperforms the DDPG in complex control problems.
Benchmarking Differential Evolution on a Quantum Simulator

The use of Evolutionary Algorithms (EA) for solving Mathematical/Computational Optimization Problems is inspired by the biological processes of Evolution. Few of the primitives involved in the Evolutionary process/paradigm are selection of ’Fit’ individuals (from a population sample) for retention, cloning, mutation, discarding, breeding, crossover etc. In the Evolutionary Algorithm abstraction, the individuals are deemed to be solution candidates to an Optimization problem and additional solution(/sets) are built by applying analogies to the above primitives (cloning, mutation etc.) by means of evaluating a ’Fitness’ function/criterion. One such algorithm is Differential Evolution(DE) which can be used to compute the minima of functions such as the rastrigin function and rosenbrock function. This work is an attempt to study the result of applying the DE method on these functions with candidate individuals generated on classical Turing modeled computation and comparing the same with those on state of the art Quantum computation.The study benchmarks the convergence of these functions by varying the parameters initialized and reports timing, convergence, and resource utilization results.
MTC-TransUNet: A Multi-Scale Mixed Convolution TransUNet for Medical Image Segmentation

Medical image segmentation is an important means to assist doctors in making accurate diagnoses. However, medical images are often subject to noise interference, and the high similarity between the background and target areas also increases the difficulty of segmentation, hence there is room for improvement in segmentation accuracy. To address these challenges, this paper proposes a multi-scale mixed convolutional image segmentation network. The network consists of two main parts: an encoder and a decoder. The encoder is formed by a combination of mixed convolutional module and multi-scale Transformer module, and the mixed convolutional module uses a blend of 2D and 3D convolutions to enhance feature extraction capabilities. Additionally, we designed a multi-scale Transformer module aimed at capturing long-range correlation information at multiple scales. In the decoding phase, we introduce coordinate convolution for the first time in the U-Net architecture to replace traditional convolution, thereby enhancing the model's spatial localization capabilities. In experimental validations on multiple public datasets, MTC-TransUNet outperforms other networks.
Proposal for Generation of a Pseudo GPS Signal Used in Indoor Mode for Navigation Testing of Small Autonomous Flying Robot

In addition to the inertial navigation sensors embedded in an autonomous flying robot, Global Navigation Satellite Systems are most often used, in particular, the GPS (Global Positioning System). This offer information with an accuracy that depends on the arrangement of a constellation of satellites. It is very often that a measurement with errors of several meters is delivered by this system. With the need for agile decisions of the autonomous flying robot during its mission, especially in the case of rescuing people during disasters, it is not allowed for uncertainty to reign over the decision of the flying robot. In order to improve the estimators implemented experimentally, a secure laboratory test environment is more preferable. Here, we propose a way that helps researchers to conduct indoor experiments with a pseudo GPS that offers circumstances similar to outdoor navigation. The results that will be discussed are obtained with the Vicon motion tracker laboratory at Aalborg University, Denmark.
Pelaksanaan Program Literasi Sekolah Dasar di Kampar

This article describes the literacy program for elementary school students through observation visits. Elementary school was chosen as a means of instilling literacy because it is hoped that by habituating literacy from an early age, students can acquire literacy habits as a provision for the future. The purpose of this study is to find out the process of literacy programs in elementary schools. Research is a type of descriptive research using a qualitative approach. The subjects involved in this study were school principals, teachers, students and the community at SDKampar. Collecting data in the study was carried out by interview, observation, and documentation methods. The results of the research analysis show that literacy activities are carried out by inviting students to take part in the school literacy program where all of these activities have been well attended by students even though there are some obstacles in them. There are many government policies implemented in elementary schools regarding literacy. The government is aware of the importance of literacy for elementary school students. Literacy isconsidered important because literacy will open wider insights and knowledge.
Face Recognition Based Smart Door Lock System using Convolution Neural Network

Facial recognition technology has gained significant popularity and widespread application in recent years. It has been used for various purposes, including security and surveillance, access control, identity verification, and even social media tagging. Its ability to quickly and accurately identify individuals based on their unique facial characteristics has made it a valuable tool in many industries. Facial recognition is often used in security and surveillance systems to identify and track individuals of interest. The objective here is to enhance public safety, prevent crimes, and assist in investigations by providing real-time identification and monitoring capabilities. In the last twenty years, the computer-based facial recognition field has expanded rapidly. In terms of facial recognition accuracy, a number of algorithms have been developed and enhanced to the point that computers can now compete with humans. In order to create a highly dependable door lock system, facial recognition has been taken into consideration in this study. The Raspberry Pi platform was used to create a smart door lock system based on CNN. The Pi camera is used in the suggested method to take the person’s picture. If a match is not detected, the person is reported; otherwise, power is sent to the relay board, which triggers the relay and unlocks the solenoid lock. This is a cutting- edge method for a door lock system that uses a Raspberry Pi and face recognition. This type of lock is used in place of biometric locks in the growing COVID-19 pandemic.
Secure Key Exchange for Protecting Health Data Diffie-Hellman Based Approach

In today's interconnected communication landscape, ensuring the protection of sensitive health data has become a top priority. This research delves into fortifying the security of HL7 health data using a Diffie-Hellman-centric approach, with a particular focus on bolstering the key exchange mechanism. Our methodology encompasses a sequence of steps: first, converting HL7 files into Base64 format to ensure secure transmission; next, employing the Diffie-Hellman protocol to facilitate a robust key exchange between the sender and receiver; then, using the shared secret keys for the encryption at the sender's end and decryption at the receiver's. By refining the key exchange process, this research aims to mitigate vulnerabilities inherent in transmitting crucial healthcare information. The practical implementation is carried out through a Node.js application, which incorporates the crypto module for various cryptographic tasks. Effectiveness is assessed by examining various performance metrics, such as key exchange and encryption/decryption times. Conclusively, this work offers a significant enhancement to HL7 health data security, presenting invaluable insights for healthcare systems determined to protect their sensitive data in our digital age.
Pengaruh Greed, Opportunity Dan Locus Of Control Terhadap Perilaku Kecurangan Akademik Mahasiswa Akuntansi (Studi Empirispada Mahasiswa S1 Akuntansi Universitas Muhammadiyah Metro)

This study aims to determine: (1) the influence of greed on the academic cheating behaviour of accounting students, (2 the influence of opportunity on the academic cheating behaviour of accounting students, and (3) the influence of Locus of Control on the academic cheating behaviour of accounting students, (4) the influence of Greed, Opportunity, and Locus of Control together on the academic cheating behaviour of accounting students. A questionnaire sampling of 118 samples with research data collection techniques was carried out. The research data were obtained using SPSS version 26 software. The data analysis technique used multiple linear regression analysis techniques and hypothesis testing. The results of this study indicate that: (1) Greed affects academic cheating behaviour, (2) Opportunity has an effect on academic cheating behaviour, (3) Locus of Control has no effect on academic cheating behaviour, (4) Greed, Opportunity, and Locus of Control jointly affect the academic cheating behaviour of accounting students at Muhammadiyah Metro University.
An Investigative Analysis of Volatility in the Cryptocurrency Market

The growing global fascination with cryptocurrencies has sparked heightened interest, driven by their pronounced market volatility. This particular study endeavors to assess the risk and rewards associated with four prominent cryptocurrencies, while also delving into an examination of their interrelationships and fluctuation patterns. The investigation is based on daily closing prices spanning from January 1, 2017, to June 30, 2022. To unravel the spillover and asymmetrical repercussions of volatility, we employ various models from the GARCH family, most notably the DCC GARCH and EGARCH models. In addition, Granger causality is harnessed to uncover any causal connections among these digital assets. The findings underscore a noteworthy spillover phenomenon between Bitcoin and Ethereum, the two foremost cryptocurrencies boasting the maximum market capitalization. This spillover effect manifests as symmetric volatility impacts, setting them apart from Litecoin and RIPPLE.