Reviewerly/exHarmony huggingface dataset:
Particle swarm optimization with average-fitness based selection

The search trajectories of particles in Particle Swarm Optimization are influenced by attractions towards personal best positions. These personal best positions are meant to represent promising areas of the search space for further exploration and exploitation. However, the best position that has been visited by a particle may not be the best area for further exploration. Traditional fitness-based selection is suitable for identifying areas of the search space for further exploitation. Average-Fitness Based Selection is introduced as an alternative to identify the best areas for further exploration. Initial experiments demonstrate that this alternate form of selection can improve the performance of Particle Swarm Optimization in a multi-modal search space.
Generating failing test suites for quantum programs with search (hot off the press track at GECCO 2022)

The inherent complexity of quantum programs, due to features such as superposition and entanglement, makes their testing particularly challenging. To tackle these challenges, we present a search-based approach, called Quantum Search-Based Testing (QuSBT), for automatically generating test suites of a given size that possibly expose failures of the quantum program under test. QuSBT encodes a test suite as a search individual, and tries to maximize the objective function that counts the number of failing tests in the test suite. Due to non-deterministic nature of quantum programs, the approach repeats the execution of each test multiple times, and uses suitable statistical tests to assess if a test passes or fails. QuSBT employs a genetic algorithm to perform the search. Experiments on 30 faulty quantum programs show that QuSBT is statistically better than random search, and is able to efficiently generate maximal failing test suites.
Developing Real-time Recognition Algorithms on Jetson Nano Hardware

Today, the field of artificial intelligence (AI) is increasingly developing with the development of the 4.0 revolution. The applicability for the social life of AI and deep learning is indeed enormous. Convolutional neural network (CNN) models have many advantages for object detection and classification problems. In the paper, we present the results of real-time object recognition algorithms on Jetson Nano hardware. We perform the algorithm to recognize and deploy on GPU with largest optimal rate as 76.26%. The results show that Mobilenetv2 and YOLOv3 models are the most optimal for object recognition with the processing time of 50 and 51 milliseconds, respectively.
Absolute fully entangled fraction from spectrum

Fully entangled fraction (FEF) is a significant figure of merit for density matrices. In bipartite $ d \otimes d $ quantum systems, the threshold value FEF $ > 1/d $, carries significant implications for quantum information processing tasks. Like separability, the value of FEF is also related to the choice of global basis of the underlying Hilbert space. A state having its FEF $ \le 1/d $, might give a value $ > 1/d $ in another global basis. A change in the global basis corresponds to a global unitary action on the quantum state. In the present work, we find that there are quantum states whose FEF remains less than $ 1/d $, under the action of any global unitary i.e., any choice of global basis. We invoke the hyperplane separation theorem to demarcate the set from states whose FEF can be increased beyond $ 1/d $ through global unitary action. Consequent to this, we probe the marginals of a pure three party system in qubits. We observe that under some restrictions on the parameters, even if two parties collaborate (through unitary action on their combined system) they will not be able to breach the FEF threshold. The study is further extended to include some classes of mixed three qubit and three qutrit systems. Furthermore, the implications of our work pertaining to $ k- $copy nonlocality and teleportation are also investigated.
Control of a Wheelchair-Mounted 6DOF Assistive Robot With Chin and Finger Joysticks

Throughout the last decade, many assistive robots for people with disabilities have been developed; however, researchers have not fully utilized these robotic technologies to entirely create independent living conditions for people with disabilities, particularly in relation to activities of daily living (ADLs). An assistive system can help satisfy the demands of regular ADLs for people with disabilities. With an increasing shortage of caregivers and a growing number of individuals with impairments and the elderly, assistive robots can help meet future healthcare demands. One of the critical aspects of designing these assistive devices is to improve functional independence while providing an excellent human–machine interface. People with limited upper limb function due to stroke, spinal cord injury, cerebral palsy, amyotrophic lateral sclerosis, and other conditions find the controls of assistive devices such as power wheelchairs difficult to use. Thus, the objective of this research was to design a multimodal control method for robotic self-assistance that could assist individuals with disabilities in performing self-care tasks on a daily basis. In this research, a control framework for two interchangeable operating modes with a finger joystick and a chin joystick is developed where joysticks seamlessly control a wheelchair and a wheelchair-mounted robotic arm. Custom circuitry was developed to complete the control architecture. A user study was conducted to test the robotic system. Ten healthy individuals agreed to perform three tasks using both (chin and finger) joysticks for a total of six tasks with 10 repetitions each. The control method has been tested rigorously, maneuvering the robot at different velocities and under varying payload (1–3.5 lb) conditions. The absolute position accuracy was experimentally found to be approximately 5 mm. The round-trip delay we observed between the commands while controlling the xArm was 4 ms. Tests performed showed that the proposed control system allowed individuals to perform some ADLs such as picking up and placing items with a completion time of less than 1 min for each task and 100% success.
Automatic Generation System of Frequently Asked Questions Based on the T5 Model

Question generation is a widely used technique in natural language processing, and it can be applied in many contexts. In order to provide domain-specific FAQs efficiently and comprehensively, this work designs an automated system for Frequently Asked Questions (FAQ) generation based on the T5 model. This work makes some optimizations and adjustments based on an open-source project with high review scores, mainly in terms of performance and data format, in order to be able to obtain a more comprehensive output with more direct input, which ultimately meets the expectations. This work also designed a Django-based web page as an initial user interface. This work is a practical application project for question generation with some practicality and can be used to build or extend a domain-specific FAQ knowledge database.
Multi-attribute balanced sampling for disentangled GAN controls

Various controls over the generated data can be extracted from the latent space of a pre-trained GAN, as it implicitly encodes the semantics of the training data. The discovered controls allow to vary semantic attributes in the generated images but usually lead to entangled edits that affect multiple attributes at the same time. Supervised approaches typically sample and annotate a collection of latent codes, then train classifiers in the latent space to identify the controls. Since the data generated by GANs reflects the biases of the original dataset, so do the resulting semantic controls. We propose to address disentanglement by balancing the semantics of the dataset before training the classifiers. We demonstrate the effectiveness of this approach by extracting disentangled linear directions for face manipulation on state-of-the-art GAN architectures (including StyleGAN2 and StyleGAN3) and two datasets, CelebAHQ and FFHQ. We show that this simple and general approach outperforms state-of-the-art classifier-based methods while avoiding the need for disentanglement-enforcing post-processing.
Deep Learning for Predictive Analytics in Reversible Steganography

Deep learning is regarded as a promising solution for reversible steganography.There is an accelerating trend of representing a reversible steo-system by monolithic neural networks, which bypass intermediate operations in traditional pipelines of reversible steganography.This end-to-end paradigm, however, suffers from imperfect reversibility.By contrast, the modular paradigm that incorporates neural networks into modules of traditional pipelines can stably guarantee reversibility with mathematical explainability.Prediction-error modulation is a well-established reversible steganography pipeline for digital images.It consists of a predictive analytics module and a reversible coding module.Given that reversibility is governed independently by the coding module, we narrow our focus to the incorporation of neural networks into the analytics module, which serves the purpose of predicting pixel intensities and a pivotal role in determining capacity and imperceptibility.The objective of this study is to evaluate the impacts of different training configurations upon predictive accuracy of neural networks and provide practical insights.In particular, we investigate how different initialisation strategies for input images may affect the learning process and how different training strategies for dual-layer prediction respond to the problem of distributional shift.Furthermore, we compare steganographic performance of various model architectures with different loss functions.
Domain Adaptive Semantic Segmentation with Self-Supervised Depth  Estimation

Domain adaptation for semantic segmentation aims to improve the model performance in the presence of a distribution shift between source and target domain. Leveraging the supervision from auxiliary tasks~(such as depth estimation) has the potential to heal this shift because many visual tasks are closely related to each other. However, such a supervision is not always available. In this work, we leverage the guidance from self-supervised depth estimation, which is available on both domains, to bridge the domain gap. On the one hand, we propose to explicitly learn the task feature correlation to strengthen the target semantic predictions with the help of target depth estimation. On the other hand, we use the depth prediction discrepancy from source and target depth decoders to approximate the pixel-wise adaptation difficulty. The adaptation difficulty, inferred from depth, is then used to refine the target semantic segmentation pseudo-labels. The proposed method can be easily implemented into existing segmentation frameworks. We demonstrate the effectiveness of our approach on the benchmark tasks SYNTHIA-to-Cityscapes and GTA-to-Cityscapes, on which we achieve the new state-of-the-art performance of $55.0\%$ and $56.6\%$, respectively. Our code is available at \url{https://qin.ee/corda}.
Leveraging Randomized Compiling for the QITE Algorithm

The success of the current generation of Noisy Intermediate-Scale Quantum (NISQ) hardware shows that quantum hardware may be able to tackle complex problems even without error correction. One outstanding issue is that of coherent errors arising from the increased complexity of these devices. These errors can accumulate through a circuit, making their impact on algorithms hard to predict and mitigate. Iterative algorithms like Quantum Imaginary Time Evolution are susceptible to these errors. This article presents the combination of both noise tailoring using Randomized Compiling and error mitigation with a purification. We also show that Cycle Benchmarking gives an estimate of the reliability of the purification. We apply this method to the Quantum Imaginary Time Evolution of a Transverse Field Ising Model and report an energy estimation and a ground state infidelity both below 1\%. Our methodology is general and can be used for other algorithms and platforms. We show how combining noise tailoring and error mitigation will push forward the performance of NISQ devices.
Classification of activity on the human activity recognition dataset using logistic regression

Smartphones and smartwatches have become an inevitably necessary device in everyday life for the past few years. Smartphones proliferated in the community are equipped with various sensors such as Accelerometer and Gyroscope which collect raw data. In previous studies, these sensors were placed in various positions on the human body to perform Human Activity Recognition (HAR). HAR has been widely applied to our daily lives such as for detecting health, human behavior and location tracking of health actions. The dataset used in this study utilizes data from UCI Machine Learning with 30 subjects. This study proposes the Logistic Regression method with the addition of Hyperparameters to achieve better accuracy results.
Training Text-to-Text Transformers with Privacy Guarantees

Recent advances in NLP often stem from large transformer-based pre-trained models, which rapidly grow in size and use more and more training data. Such models are often released to the public so that end users can fine-tune them on a task dataset. While it is common to treat pre-training data as public, it may still contain personally identifiable information (PII), such as names, phone numbers, and copyrighted material. Recent findings show that the capacity of these models allows them to memorize parts of the training data, and suggest differentially private (DP) training as a potential mitigation. While there is recent work on DP fine-tuning of NLP models, the effects of DP pre-training are less well understood it is not clear how downstream performance is affected by DP pre-training, and whether DP pre-training mitigates some of the memorization concerns. We focus on T5 and show that by using recent advances in JAX and XLA we can train models with DP that do not suffer a large drop in pre-training utility, nor in training speed, and can still be fine-tuned to high accuracy on downstream tasks (e.g. GLUE). Moreover, we show that T5s span corruption is a good defense against data memorization.
CS/NLP at SemEval-2022 Task 4: Effective Data Augmentation Methods for Patronizing Language Detection and Multi-label Classification with RoBERTa and GPT3

This paper presents a combination of data augmentation methods to boost the performance of state-of-the-art transformer-based language models for Patronizing and Condescending Language (PCL) detection and multi-label PCL classification tasks.These tasks are inherently different from sentiment analysis because positive/negative hidden attitudes in the context will not necessarily be considered positive/negative for PCL tasks.The oblation study observes that the imbalance degree of PCL dataset is in the extreme range.This paper presents a modified version of sentence paraphrasing deep learning model (PEGASUS) to tackle the limitation of maximum sequence length.The proposed algorithm has no specific maximum input length to paraphrase sequences.Our augmented underrepresented class of annotated data achieved competitive results among top-16 SemEval-2022 participants.This paper's approaches rely on fine-tuning pretrained RoBERTa and GPT3 models such as Davinci and Curie engines with extra-enriched PCL dataset.Furthermore, we discuss Few-Shot learning technique to overcome the limitation of low-resource NLP problems.
Developmental History of the Vocal Tract Organ

ObjectivesThe Vocal Tract Organ has had a number of iterations resulting from advances in available technology as well as requirements of perceptual experiments and performance paradigms. The objective of this paper is to relate the development history of the Vocal Tract Organ from the original vision to what it is today as a modern version of the Vox Humana pipe organ stop for application in voice production and perception research.Study DesignDescriptiveMethods/designThe latest Vocal Tract Organ is a polyphonic eight-channel eight-stop one manual Vocal Tract Organ that enables tab stop selected three-D printed vocal tracts to be used to create sound. This version includes eight stops (four for female vowel oral tracts and four for male vowel oral tracts). The stops are implemented using conventionally engraved pipe organ stop tabs labeled “Vox Humana Female” or “Vox Humana Male” followed by the three-D printed vowel: “EE”, “AH”, “ER” or “UU.” This is described alongside the development stages from which it emerged and covers all previous versions of the Vocal Tract Organ.At the heart of the latest instrument is a Bela BeagleBone Black with a Bela cape audio expander board which incorporates eight 16-bit audio outputs at 44.1 kHz sampling rate (earlier versions based on the Arduino Mega board were limited to 8-bit audio at an audio sampling rate of 16.384 kHz which limited the overall output spectrum). The latest Vocal Tract Organ is programmed using the audio graphical programming language Pure Data which is directly compatible with the Bela system. The Pure Data patch creates eight larynx outputs at the pitches set by the keys depressed on the keyboard and these are routed to Vocal Tract Organ loudspeakers with three-D printed vocal tracts attached.ResultsThe Bela system has enabled real-time synthesis of eight-note polyphonic sounds to eight separate three-D printed vocal tracts, each being selectable via an organ tab stop switch. The instrument has been cased in a purpose-designed and built prototype laser-cut enclosure that incorporates the eight tab stops, a MIDI keyboard input, a pipe organ style swell (volume) pedal connection, four stereo (eight channels) audio amplifiers and terminal connections for the eight loudspeakers.ConclusionsThe Vocal Tract Organ functions as a musical instrument for performance and as an instrument for vowel and pitch perception research. Implementing it with the Bela family of processors allows for low audio latency of 1 ms and rapid prototyping due to being able to program directly with the high-level graphical audio programming language, Pure data (Pd).
Measuring and Mitigating the Risk of IP Reuse on Public Clouds

Public clouds provide scalable and cost-efficient computing through resource sharing. However, moving from traditional on-premises service management to clouds introduces new challenges; failure to correctly provision, maintain, or decommission elastic services can lead to functional failure and vulnerability to attack. In this paper, we explore a broad class of attacks on clouds which we refer to as cloud squatting. In a cloud squatting attack, an adversary allocates resources in the cloud (e.g., IP addresses) and thereafter leverages latent configuration to exploit prior tenants. To measure and categorize cloud squatting we deployed a custom Internet telescope within the Amazon Web Services us-east-1 region. Using this apparatus, we deployed over 3 million servers receiving 1.5 million unique IP addresses ($\approx$ 56% of the available pool) over 101 days beginning in March of 2021. We identified 4 classes of cloud services, 7 classes of third-party services, and DNS as sources of exploitable latent configurations. We discovered that exploitable configurations were both common and in many cases extremely dangerous; we received over 5 million cloud messages, many containing sensitive data such as financial transactions, GPS location, and PII. Within the 7 classes of third-party services, we identified dozens of exploitable software systems spanning hundreds of servers (e.g., databases, caches, mobile applications, and web services). Lastly, we identified 5446 exploitable domains panning 231 eTLDs—including 105 in the top 10000 and 23 in the top 1000 popular domains. Through tenant disclosures we have identified several root causes, including (a) a lack of organizational controls, (b) poor service hygiene, and (c) failure to follow best practices. We conclude with a discussion of the space of possible mitigations and describe the mitigations to be deployed by Amazon in response to this study.
Information Geometrically Generalized Covariate Shift Adaptation

Abstract Many machine learning methods assume that the training and test data follow the same distribution. However, in the real world, this assumption is often violated. In particular, the marginal distribution of the data changes, called covariate shift, is one of the most important research topics in machine learning. We show that the well-known family of covariate shift adaptation methods is unified in the framework of information geometry. Furthermore, we show that parameter search for a geometrically generalized covariate shift adaptation method can be achieved efficiently. Numerical experiments show that our generalization can achieve better performance than the existing methods it encompasses.
Index-Based Improved High Capacity Data Hiding Technique

Mandal, Pratap Chandra Mukherjee, ImonSteganography is the art of secret communication to avoid detection by an unauthorized person. The pixel value differencing (PVD) technique hides the confidential message into the difference between two adjacent pixels. The PVD-based techniques have less embedding capacity due to unidirectional embedding. An index-based data hiding is one of the solutions to enhance the embedding capacity. It has been noticed that index-based data hiding fails to recover accurate data for some cases. This identified problem has been resolved in the proposed technique. It has achieved an average embedding capacity (AEC) of 2.49 bpp which is better than the index-based data hiding technique. The proposed technique withstands steganalysis attacks.