Reviewerly/exHarmony huggingface dataset:
Data Augmentation for Intent Classification with Off-the-shelf Large Language Models

Data augmentation is a widely employed technique to alleviate the problem of data scarcity.In this work, we propose a prompting-based approach to generate labelled training data for intent classification with off-the-shelf language models (LMs) such as GPT-3.An advantage of this method is that no task-specific LM-fine-tuning for data generation is required; hence the method requires no hyper-parameter tuning and is applicable even when the available training data is very scarce.We evaluate the proposed method in a few-shot setting on four diverse intent classification tasks.We find that GPT-generated data significantly boosts the performance of intent classifiers when intents in consideration are sufficiently distinct from each other.In tasks with semantically close intents, we observe that the generated data is less helpful.Our analysis shows that this is because GPT often generates utterances that belong to a closely-related intent instead of the desired one.We present preliminary evidence that a prompting-based GPT classifier could be helpful in filtering the generated data to enhance its quality.
Machine Learning Approaches to Predict Breast Cancer: Bangladesh Perspective

Nowadays, Breast cancer has risen to become one of the most prominent causes of death in recent years. Among all malignancies, this is the most frequent and the major cause of death for women globally. Manually diagnosing this disease requires a good amount of time and expertise. Breast cancer detection is time-consuming, and the spread of the disease can be reduced by developing machine-based breast cancer predictions. In Machine learning, the system can learn from prior instances and find hard-to-detect patterns from noisy or complicated data sets using various statistical, probabilistic, and optimization approaches. This work compares several machine learning algorithms' classification accuracy, precision, sensitivity, and specificity on a newly collected dataset. In this work Decision tree, Random Forest, Logistic Regression, Naïve Bayes, and XGBoost, these five machine learning approaches have been implemented to get the best performance on our dataset. This study focuses on finding the best algorithm that can forecast breast cancer with maximum accuracy in terms of its classes. This work evaluated the quality of each algorithm's data classification in terms of efficiency and effectiveness. And also compared with other published work on this domain. After implementing the model, this study achieved the best model accuracy, 94% on Random Forest and XGBoost.
Self-Ensembling Vision Transformer (SEViT) for Robust Medical Image Classification

Vision Transformers (ViT) are competing to replace Convolutional Neural Networks (CNN) for various computer vision tasks in medical imaging such as classification and segmentation. While the vulnerability of CNNs to adversarial attacks is a well-known problem, recent works have shown that ViTs are also susceptible to such attacks and suffer significant performance degradation under attack. The vulnerability of ViTs to carefully engineered adversarial samples raises serious concerns about their safety in clinical settings. In this paper, we propose a novel self-ensembling method to enhance the robustness of ViT in the presence of adversarial attacks. The proposed Self-Ensembling Vision Transformer (SEViT) leverages the fact that feature representations learned by initial blocks of a ViT are relatively unaffected by adversarial perturbations. Learning multiple classifiers based on these intermediate feature representations and combining these predictions with that of the final ViT classifier can provide robustness against adversarial attacks. Measuring the consistency between the various predictions can also help detect adversarial samples. Experiments on two modalities (chest X-ray and fundoscopy) demonstrate the efficacy of SEViT architecture to defend against various adversarial attacks in the gray-box (attacker has full knowledge of the target model, but not the defense mechanism) setting. Code: https://github.com/faresmalik/SEViT
ICARUS

The practical deployment of Neural Radiance Fields (NeRF) in rendering applications faces several challenges, with the most critical one being low rendering speed on even high-end graphic processing units (GPUs). In this paper, we present ICARUS, a specialized accelerator architecture tailored for NeRF rendering. Unlike GPUs using general purpose computing and memory architectures for NeRF, ICARUS executes the complete NeRF pipeline using dedicated plenoptic cores (PLCore) consisting of a positional encoding unit (PEU), a multi-layer perceptron (MLP) engine, and a volume rendering unit (VRU). A PLCore takes in positions &amp; directions and renders the corresponding pixel colors without any intermediate data going off-chip for temporary storage and exchange, which can be time and power consuming. To implement the most expensive component of NeRF, i.e., the MLP, we transform the fully connected operations to approximated reconfigurable multiple constant multiplications (MCMs), where common subexpressions are shared across different multiplications to improve the computation efficiency. We build a prototype ICARUS using Synopsys HAPS-80 S104, a field programmable gate array (FPGA)-based prototyping system for large-scale integrated circuits and systems design. We evaluate the power-performancearea (PPA) of a PLCore using 40nm LP CMOS technology. Working at 400 MHz, a single PLCore occupies 16.5 mm 2 and consumes 282.8 mW, translating to 0.105 uJ/sample. The results are compared with those of GPU and tensor processing unit (TPU) implementations.
What's in your hands? 3D Reconstruction of Generic Objects in Hands

Our work aims to reconstruct hand-held objects given a single RGB image. In contrast to prior works that typically assume known 3D templates and reduce the problem to 3D pose estimation, our work reconstructs generic hand-held object without knowing their 3D templates. Our key insight is that hand articulation is highly predictive of the object shape, and we propose an approach that conditionally reconstructs the object based on the articulation and the visual input. Given an image depicting a hand-held object, we first use off-the-shelf systems to estimate the underlying hand pose and then infer the object shape in a normalized hand-centric coordinate frame. We parameterized the object by signed distance which are inferred by an implicit network which leverages the information from both visual feature and articulation-aware coordinates to process a query point. We perform experiments across three datasets and show that our method consistently outperforms baselines and is able to reconstruct a diverse set of objects. We analyze the benefits and robustness of explicit articulation conditioning and also show that this allows the hand pose estimation to further improve in test-time optimization.
Distributed Reduced Convolution Neural Networks

A Convolution Neural Network (CNN) is a popular tool in the domains of pattern recognition and machine learning. The performance of KCNN (kernel-based convolutional neural networks) is better than that of regular CNN. Although the KCNN can solve challenging nonlinear problems, doing so when dealing with a large-size kernel matrix is time-consuming and memory-intensive. The computational load and memory usage could be drastically decreased by adopting a reduced kernel strategy. But as the total amount of training data grows at an exponential pace, it becomes hard for a single worker to efficiently store the kernel matrix. Because of this, there can be no effective centralised data mining. In this research, we suggest the use of a distributed reduced kernel, or DRCNN, to train CNN using data that is stored in several locations. The data in the DRCNN will be spread out amongst the nodes at random. Static communication between nodes is defined by the network's architecture rather than the quantity of training data kept on each node. The DRCNN is an alternate direction multiplier (ADMM)-based distributed training technique, in contrast to the standard reduced kernel CNN. Experiments on the huge data set show that the distributed method may yield nearly the same results as the centralised algorithm, and it takes significantly less time. As a result, the amount of time spent computing is drastically reduced.
Trapped-ion quantum simulation of collective neutrino oscillations

It is well known that the neutrino flavor in extreme astrophysical environments changes under the effect of three contributions: the vacuum oscillation, the interaction with the surrounding matter, and the collective oscillations due to interactions between different neutrinos. The latter adds a nonlinear contribution to the equations of motion, making the description of their dynamics complex. In this work we study various strategies to simulate the coherent collective oscillations of a system of $N$ neutrinos in the two-flavor approximation using quantum computation. This was achieved by using a pair-neutrino decomposition designed to account for the fact that the flavor Hamiltonian, in the presence of the neutrino-neutrino term, presents an all-to-all interaction that makes the implementation of the evolution dependent on the qubit topology. We analyze the Trotter error caused by the decomposition demonstrating that the complexity of the implementation of time evolution scales polynomially with the number of neutrinos and that the noise from near-term quantum device simulation can be reduced by optimizing the quantum circuit decomposition and exploiting a full-qubit connectivity. We find that the gate complexity using second order Trotter-Suzuki formulas scales better with system size than with other decomposition methods such as quantum signal processing. We finally present the application and the results of our algorithm on a real quantum device based on trapped-ion qubits.
Measures for explainable AI: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-AI performance

If a user is presented an AI system that portends to explain how it works, how do we know whether the explanation works and the user has achieved a pragmatic understanding of the AI? This question entails some key concepts of measurement such as explanation goodness and trust. We present methods for enabling developers and researchers to: (1) Assess the a priori goodness of explanations, (2) Assess users' satisfaction with explanations, (3) Reveal user's mental model of an AI system, (4) Assess user's curiosity or need for explanations, (5) Assess whether the user's trust and reliance on the AI are appropriate, and finally, (6) Assess how the human-XAI work system performs. The methods we present derive from our integration of extensive research literatures and our own psychometric evaluations. We point to the previous research that led to the measurement scales which we aggregated and tailored specifically for the XAI context. Scales are presented in sufficient detail to enable their use by XAI researchers. For Mental Model assessment and Work System Performance, XAI researchers have choices. We point to a number of methods, expressed in terms of methods' strengths and weaknesses, and pertinent measurement issues.
Comovements and hedging effectiveness between conventional and Islamic cryptocurrencies: evidence from the COVID-19 pandemic

Purpose This study examines the dynamics of the comovement and causal relationship between conventional (Bitcoin, Ethereum and Binance coin) and Islamic (OneGram, X8X token and HelloGold) cryptocurrencies. Design/methodology/approach This study uses wavelet coherence approach to examine the time-varying lead-lag relationship between conventional and Islamic cryptocurrencies. Furthermore, the authors use BEKK-GARCH model to estimate the optimal weights, hedge ratio and hedging effectiveness in pre-COVID-19 and during the COVID-19 period. Findings The authors find no significant comovement in pre-COVID-19. However, the authors find significant positive comovement in conventional and Islamic cryptocurrencies at the beginning of the pandemic, and in most cases, conventional cryptocurrencies are leading. X8X and HelloGold have no/weak correlation with conventional cryptocurrencies, implying that investors can diversify the risk by making an Islamic and conventional cryptocurrencies portfolio. The authors also calculate the optimal weights, hedge ratio and hedging effectiveness using the BEKK-GARCH model. Based on the optimal weights, for the portfolios of conventional–Islamic cryptocurrencies, investors are suggested to increase their investment in Islamic cryptocurrencies during the COVID-19 than normal period. The results of hedge ratios show that hedging costs are higher during COVID-19 than before. Practical implications The findings of the paper offer several practical policy implications for investors, portfolio manager, Shariah advisors and policymakers pertaining to asset allocation, risk management, forecasting and diversification. Specifically, investors can maximize the risk adjusted returns of their conventional cryptocurrencies portfolio by adding some portions of Islamic cryptocurrencies. Considering the comovement is time-varying, investors/manager should adjust their investment strategies frequently. For the entrepreneurs in crypto-industry, it is advised to introduce new Islamic cryptocurrencies, as it has a huge growth potential because of their distinct features and performance. Originality/value This is the first study that explores the linkages between conventional and Islamic cryptocurrencies, therefore this study extends the literature of Islamic finance, stablecoins and cryptocurrencies in pre-COVID-19 and during COVID-19 period. The study results provide insights to conventional crypto investor on how to manage their portfolio during normal and turbulent period.
Data‐Class‐Specific All‐Optical Transformations and Encryption

Abstract Diffractive optical networks provide rich opportunities for visual computing tasks. Here, data‐class‐specific transformations that are all‐optically performed between the input and output fields‐of‐view (FOVs) of a diffractive network are presented. The visual information of the objects is encoded into the amplitude ( A ), phase ( P ), or intensity ( I ) of the optical field at the input, which is all‐optically processed by a data‐class‐specific diffractive network. At the output, an image sensor‐array directly measures the transformed patterns, all‐optically encrypted using the transformation matrices preassigned to different data classes, i.e., a separate matrix for each data class. The original input images can be recovered by applying the correct decryption key (the inverse transformation) corresponding to the matching data class, while applying any other key will lead to loss of information. All‐optical class‐specific transformations covering A → A , I → I , and P → I transformations using various image datasets are numerically demonstrated. The feasibility of this framework is also experimentally validated by fabricating class‐specific I → I transformation diffractive networks and is successfully tested at different parts of the electromagnetic spectrum, i.e., 1550 nm and 0.75 mm wavelengths. Data‐class‐specific all‐optical transformations provide a fast and energy‐efficient method for image and data encryption, enhancing data security and privacy.
20-Mode Universal Quantum Photonic Processor

Integrated photonics is an essential technology for optical quantum computing. Universal, phase-stable, reconfigurable multimode interferometers (quantum photonic processors) enable manipulation of photonic quantum states and are one of the main components of photonic quantum computers in various architectures. In this paper, we report the realization of the largest quantum photonic processor to date. The processor enables arbitrary unitary transformations on its 20 input modes with an amplitude fidelity of $F_{\text{Haar}} = 97.4\%$ and $F_{\text{Perm}} = 99.5\%$ for Haar-random and permutation matrices, respectively, an optical loss of 2.9 dB averaged over all modes, and high-visibility quantum interference with $V_{\text{HOM}}=98\%$. The processor is realized in $\mathrm{Si_3N_4}$ waveguides and is actively cooled by a Peltier element.
Didactic strategies in educational platforms: experience of Bachelor's Degree in Administration teachers at a public university in Paraguay

The COVID-19 pandemic induced significant shifts across all sectors, including education. This study aims to detail the didactic strategies employed by professors of the Bachelor's in Administration program at a public university in Encarnación. A theoretical review was the foundation for the field study, which involved questionnaires directed at educators. The results were examined through a mixed, non-experimental, and descriptive lens. It was discovered that measures to sustain virtual classes were rooted in previously known information and communication technologies (ICT) and the integration of new, user-friendly, and free tools. The didactic strategies deployed were varied and similarly used by all educators, mirroring those employed in face-to-face instruction. The sole difference lay in the mediums used to execute these strategies, owing to the adaptation to the online learning mode. Educators acquired technological skills through training, and Google products proved especially beneficial. The pandemic situation spurred the learning of new strategies and tools, enhancing the performance in virtual teaching.
Models and Guidelines for Integrating Expert Systems and Neural Networks

A synergism is rapidly developing in the fields of expert systems and neural networks, and an understanding is starting to develop about the theoretical basis and methodology for integrating these two technologies. This chapter presents models and guidelines for integration for practical applications of hybrid intelligent systems. Expert systems and neural networks are each summarized to highlight their separate strengths and weaknesses. Then, different ways to combine the technologies are explained and illustrated with examples. Cases of actual systems are described to illustrate the guidelines for using the different models appropriately for practical applications.
Agile Software Development

The bases of Agile software development is on the need to satisfy the customer through early and continuous delivery of the valuable software. It’s a set of best practice that allows rapid delivery of high quality software to meet customer needs and also accommodate changes in the requirements. Agile development is the ability to develop software quickly keeping pace with the rapidly changing requirements. In order to achieve this agility the practice with necessary discipline and feedback are required and the design principles should keep the software flexible and maintainable. Agile methods are a family of development process which can be chosen based on the project needs. They are more adaptive methods whose focus is on adapting quickly. Some of the Agile methods are Agile Modeling, Extreme programming, Pair Programming, Test Driven Development, Feature Driven Development, Open Unified process, Re-factoring etc. Agile practices benefit in terms of increased project success rate and user acceptance, better risk management, delivery of quality content on time and most important adjust to changing requirements. Today in the field of military embedded systems, developers face the pressure to shorten the life cycle and adapt to the changing requirements without forgoing the quality and reliability required for the safety critical applications. This requirement has led the thinking in the direction of Agile approaches. Agile methods chosen with special care for some of the concepts like change management, traceability, test driven development and automated documentation could form an adaptable approach for the embedded systems. This paper describes Agile Software Development methodologies and practices, and about how Agile can we get with the military embedded systems. Keyworks: Agile software, pair programming, software development, software
Enhanced Quantum Synchronization via Quantum Machine Learning

We study the quantum synchronization between a pair of two-level systems inside two coupled cavities. By using a digital-analog decomposition of the master equation that rules the system dynamics, we show that this approach leads to quantum synchronization between both two-level systems. Moreover, we can identify in this digital-analog block decomposition the fundamental elements of a quantum machine learning protocol, in which the agent and the environment (learning units) interact through a mediating system, namely, the register. If we can additionally equip this algorithm with a classical feedback mechanism, which consists of projective measurements in the register, reinitialization of the register state and local conditional operations on the agent and environment subspace, a powerful and flexible quantum machine learning protocol emerges. Indeed, numerical simulations show that this protocol enhances the synchronization process, even when every subsystem experience different loss/decoherence mechanisms, and give us the flexibility to choose the synchronization state. Finally, we propose an implementation based on current technologies in superconducting circuits.
Disambiguating Visual Verbs

In this article, we introduce a new task, visual sense disambiguation for verbs: given an image and a verb, assign the correct sense of the verb, i.e., the one that describes the action depicted in the image. Just as textual word sense disambiguation is useful for a wide range of NLP tasks, visual sense disambiguation can be useful for multimodal tasks such as image retrieval, image description, and text illustration. We introduce a new dataset, which we call VerSe (short for Verb Sense) that augments existing multimodal datasets (COCO and TUHOI) with verb and sense labels. We explore supervised and unsupervised models for the sense disambiguation task using textual, visual, and multimodal embeddings. We also consider a scenario in which we must detect the verb depicted in an image prior to predicting its sense (i.e., there is no verbal information associated with the image). We find that textual embeddings perform well when gold-standard annotations (object labels and image descriptions) are available, while multimodal embeddings perform well on unannotated images. VerSe is publicly available at https://github.com/spandanagella/verse.
MNIST Dataset Classification Utilizing k-NN Classifier with Modified Sliding-Window Metric

The MNIST dataset of the handwritten digits is known as one of the commonly used datasets for machine learning and computer vision research. We aim to study a widely applicable classification problem and apply a simple yet efficient K-nearest neighbor classifier with an enhanced heuristic. We evaluate the performance of the K-nearest neighbor classification algorithm on the MNIST dataset where the L2 Euclidean distance metric is compared to a modified distance metric which utilizes the sliding window technique in order to avoid performance degradation due to slight spatial misalignments. The accuracy metric and confusion matrices are used as the performance indicators to compare the performance of the baseline algorithm versus the enhanced sliding window method and results show significant improvement using this proposed method.