Reviewerly/exHarmony huggingface dataset:
TTS-Guided Training for Accent Conversion Without Parallel Data

Accent Conversion (AC) seeks to change the accent of speech from one (source) to another (target) while preserving the speech content and speaker identity. However, many existing AC approaches rely on source-target parallel speech data during training or reference speech at run-time. We propose a novel accent conversion framework without the need for either parallel data or reference speech. Specifically, a text-to-speech (TTS) system is first pretrained with target-accented speech data. This TTS model and its hidden representations are expected to be associated only with the target accent. Then, a speech encoder is trained to convert the accent of the speech under the supervision of the pretrained TTS model. In doing so, the source-accented speech and its corresponding transcription are forwarded to the speech encoder and the pretrained TTS, respectively. The output of the speech encoder is optimized to be the same as the text embedding in the TTS system. At run-time, the speech encoder is combined with the pretrained speech decoder to convert the source-accented speech toward the target. In the experiments, we converted English with two source accents (Chinese/Indian) to the target accent (American/British/Canadian). Both objective metrics and subjective listening tests successfully validate that the proposed approach generates speech samples that are close to the target accent with high speech quality.
Decoherence and nonclassicality of photon-added and photon-subtracted multimode Gaussian states

Photon addition and subtraction render Gaussian states non-Gaussian. We provide a quantitative analysis of the change in nonclassicality produced by these processes by analyzing the Wigner negativity and quadrature coherence scale (QCS) of the resulting states. The QCS is a recently introduced measure of nonclassicality [Phys. Rev. Lett. 122, 080402 (2019); Phys. Rev. Lett. 124, 090402 (2020)], which we show to undergo a relative increase under photon addition and subtraction that can be as large as 200%. This implies that the degaussification and the concomitant increase of nonclassicality come at a cost. Indeed, the QCS is proportional to the decoherence rate of the state so that the resulting states are considerably more prone to environmental decoherence. Our results are quantitative and rely on explicit and general expressions for the characteristic and Wigner functions of photon-added and -subtracted single- and multimode Gaussian states for which we provide a simple and straightforward derivation. These expressions further allow us to certify the quantum non-Gaussianity of the photon-subtracted states with positive Wigner function.
Multi-Agent Semi-Siamese Training for Long-Tail and Shallow Face Learning

With the recent development of deep convolutional neural networks and large-scale datasets, deep face recognition has made remarkable progress and been widely used in various applications. However, unlike the existing public face datasets, in many real-world scenarios of face recognition, the depth of the training dataset is shallow, which means that only two face images are available for each ID. With the non-uniform increase of samples, such issue is converted to a more general case, known as long-tail face learning, which suffers from data imbalance and intra-class diversity dearth simultaneously. These adverse conditions damage the training and result in the decline of model performance. Based on Semi-Siamese Training, we introduce an advanced solution, named Multi-Agent Semi-Siamese Training (MASST), to address these problems. MASST includes a probe network and multiple gallery agents—the former aims to encode the probe features, and the latter constitutes a stack of networks that encode the prototypes (gallery features). For each training iteration, the gallery network, which is sequentially rotated from the stack, and the probe network form a pair of Semi-Siamese networks. We give the theoretical and empirical analysis that, given the long-tail (or shallow) data and training loss, MASST smooths the loss landscape and satisfies the Lipschitz continuity with the help of multiple agents and the updating gallery queue. The proposed method is out of extra-dependency, and thus can be easily integrated with the existing loss functions and network architectures. It is worth noting that although multiple gallery agents are employed for training, only the probe network is needed for inference, without increasing the inference cost. Extensive experiments and comparisons demonstrate the advantages of MASST for long-tail and shallow face learning.
Predictive Modeling Using Artificial Intelligence and Machine Learning Algorithms on Electronic Health Record Data

The rapid adoption of electronic health record (EHR) systems in US hospitals from 2008 to 2014 produced novel data elements for analysis. Concurrent innovations in computing architecture and machine learning (ML) algorithms have made rapid consumption of health data feasible and a powerful engine for clinical innovation. In critical care research, the net convergence of these trends has resulted in an exponential increase in outcome prediction research. In the following article, we explore the history of outcome prediction in the intensive care unit (ICU), the growing use of EHR data, and the rise of artificial intelligence and ML (AI) in critical care.
Application of Microwave Energy in Agriculture

The purpose of this research is to modernize microwave dryers for drying agricultural products, where it is simultaneously possible to carry out thermal and non-thermal microwave modifications of objects.The paper considers microwave dryers for processing of both agricultural products (grain processing, pre-sowing seed treatment) and building materials (wood, polymer threads).The possibility of forming a new field based on the use of microwave electrotechnological installations on hybrid-type working chambers is shown, which will allow an agricultural producer to enter the commodity market not only with agricultural produce, but also with polymer materials with new properties obtained with non-thermal microwave modification in microwave electrotechnological installations with a hybrid-type chamber.As a result of the modernization of existing microwave electrotechnological installations, in which only thermal or non-thermal microwave modifications are carried out, and combining them into one microwave electrotechnological installation with a hybrid type chamber, in which thermal microwave processing of agricultural products, wood and non-thermal microwave processing of polymer materials will be carried out simultaneously, will significantly expand the possibilities of using microwave electrotechnology in agriculture.
Age-related differences in upper limb motor performance and intrinsic motivation during a virtual reality task

Abstract Background In recent years, virtual reality (VR) has evolved from an alternative to a necessity in older adults for health, medical care, and social interaction. Upper limb (UL) motor skill, is an important ability in manipulating VR systems and represents the brain’s regulation of movements using the UL muscles. In this study, we used a haptic-feedback Virtual Box and Block Test (VBBT) system and an Intrinsic Motivation Inventory (IMI) to examine age-related differences in UL motor performance and intrinsic motivation in VR use. The findings will be helpful for the development of VR applications for older adults. Methods In total, 48 young and 47 older volunteers participated in our study. The parameters including VBBT score, number of velocity peaks, velocity, grasping force and trajectory length were calculated to represent the task performance, manual dexterity, coordination, perceptive ability and cognitive ability in this study. Results Age-related differences could be found in all the parameters (all p &lt; 0.05) in VR use. Regression analysis revealed that the task performance of young adults was predicted by the velocity and trajectory length (R 2 = 64.0%), while that of older adults was predicted by the number of velocity peaks (R 2 = 65.6%). Additionally, the scores of understandability, relaxation and tiredness were significantly different between the two groups (all p &lt; 0.05). In older adults, the understandability score showed large correlation with the IMI score (|r| = 0.576, p &lt; 0.001). In young adults, the correlation was medium (|r| = 0.342, p = 0.017). No significant correlation was found between the IMI score and VBBT score (| r | = 0.142, p = 0.342) in older adults, while a medium correlation (| r | = 0.342, p = 0.017) was found in young adults. Conclusions The findings demonstrated that decreased smoothness in motor skills dominated the poor VR manipulation in older adults. The experience of understandability is important for older adults’ intrinsic motivation in VR use.
Detecting the steerability through an optimized steering criterion in two-photon systems

The steerability of a quantum state can be detected by steering inequalities. The linear steering inequalities show that more steerable states can be discovered with the increase of measurements. In order to detect more steerable states in two-photon systems, we first theoretically derive an optimized steering criterion based on infinity measurements for an arbitrary two-qubit state. The steering criterion is only determined by the spin correlation matrix of the state, and do not require infinity measurements. Then, we prepared the Werner-like states in two-photon systems, and measure their spin correlation matrices. Finally, we apply three steering criteria, which include our steering criterion, the three-measurement steering criterion and the geometric Bell-like inequality, to distinguish the steerability of these states. The results show that our steering criterion can detect the most steerable states under the same experimental conditions. Thus, our work provides a valuable reference for detecting the steerability of quantum states.
Software Engineering Practices in Academia: Promoting the 3Rs—Readability, Resilience, and Reuse

Over the past decade as data science has become integral to the research workflow, we, like many others, have learned that good data science requires high-quality software engineering.Unfortunately, our experience is that many data science projects can be limited by the absence of software engineering processes.We advocate that data science projects should incorporate what we call the 3Rs of software engineering: readability (human understandable codes), resilience (fails rarely/gracefully), and reuse (can easily be used by others and can be embedded in other software).This article discusses engineering practices that promote 3R software in academia.We emphasize that best practices in academia may differ from those in industry because of substantial differences in project scope (most academic projects have a single developer who is the sole user) and the reward systems in place in academia.We provide a framework for selecting a level of software engineering rigor that aligns well with the project scope, something that may change over time.We further discuss how to improve training in software engineering skills in an academic environment and how to build communities of practice that span across disciplines.
Analysis of Load Balancing Least Connection and Shortest Expected Delay Algorithm for Web Server Using Kube-Proxy on Kubernetes

ABSTRAK Penelitian ini membandingkan dan menganalisis kinerja algoritma load balancing shortest expected delay dan Least Connection untuk Web Server menggunakan Kube-Proxy di Kubernetes. Peningkatan jumlah pengguna dan perubahan jumlah node pekerja yang mempengaruhi kinerja system menjadi focus penelitian ini. Hasil penelitian menunjukkan bahwa pada parameter elapsed time, shortest expected delay dengan memiliki waktu yaitu 216.295 ms. Dalam waktu pemrosesan server, shortest expected delay lebih baik dengan menghasilkan 214.257ms. Throughput saat menggunakan algoritma shortest expected delay lebih besar, rata-rata 560.256 paket/detik. Algoritma Least Connection lebih baik dengan memiliki efisiensi 35,24% dalam hal penggunaan CPU, dibandingkan shortest expected delay. Meningkatkan klaster dari dua node pekerja menjadi empat node pekerja menghasilkan pengurangan waktu pemrosesan server yang signifikan, yang berarti penyeimbangan beban menggunakan 4 node pekerja lebih efektif. Kata kunci: komputasi awan, load balancing, Kubernetes, Web Server. ABSTRACT This research compares and analyzes the performance of the load balancing algorithm Shortest Expected Delay and Least Connection for web server using Kube-Proxy on Kubernetes. The increasing number of users and changes in the number of worker nodes that affect system performance are the focus of this research. The results show that in elapsed time parameter, shortest expected delay has the time, which is 216.295ms. In server processing time, the shortest expected delay is better by producing 214.257ms. Throughput when using the shortest expected delay algorithm is greater, an average of 560,256 packets/second. The Least Connection algorithm is better with an efficiency of 35.24% in terms of CPU usage, compared to the shortest expected delay. Upgrading the cluster from two worker nodes to four worker nodes resulted in a significant reduction in server processing time, which meant more effective load balancing using 4 worker nodes. Keywords: cloud computing, load balancing, Kubernetes, Web Server.
Digital Companion for Elders in Tracking Health and Intelligent Recommendation Support using Deep Learning

Ambient assisted living (AAL) facilitates the daily routines of elderly people, particularly those who have clinical difficulties or physical limitations. The latest technologies like distributed compuring,internet of things (IoT) and machine learning pave the ground for the creation of an effective automated tracker which aids elder citizens to live independently. The suggested system is attempted to design a wearable that monitors the blood glucose level through sweat. To achieve high accuracy, the proposed system uses ambient sensing and deep learning based techniques. It places a strong emphasis on calculating the health index by taking into account numerous disease-related characteristics or vitals such as heart rate, blood pressure, SpO2, blood glucose level, respiration rate, sweat rate, uric acid, and temperature. From the wearable device designed the vital signs are gathered, further environmental sensors and camera fixed around the person continually monitors the behavioral pattern along with physiological signals. This ensures the improved accuracy of health state prediction from its conventional models in place. The key advantage of this device is that it may be held and operated anyplace without interrupting their day-to-day tasks because the device is to be cheap, reliable and speedy.
A study on improving realism of synthetic data for machine learning

Synthetic-to-real data translation using generative adversarial learning has achieved significant success in improving synthetic data. Yet, limited studies focus on deep evaluation and comparison of adversarial training on general-purpose synthetic data for machine learning. This work aims to train and evaluate a synthetic-to-real generative model that transforms the synthetic renderings into more realistic styles on general-purpose datasets conditioned with unlabeled real-world data. Extensive performance evaluation and comparison have been conducted through qualitative and quantitative metrics and a defined downstream perception task.
Applications of Augmented Reality in Orthopaedic Spine Surgery

The application of augmented reality (AR) in surgical settings has primarily been as a navigation tool in the operating room because of its ease of use and minimal effect on surgical procedures. The surgeon can directly face the surgical field while viewing 3D anatomy virtually, thus reducing the need to look at an external display, such as a navigation system. Applications of AR are being explored in spine surgery. The basic principles of AR include data preparation, registration, tracking, and visualization. Current literature provides sufficient preclinical and clinical data evidence for the use of AR technology in spine surgery. AR systems are efficient assistive devices, providing greater accuracy for insertion points, more comfort for surgeons, and reduced operating time. AR technology also has beneficial applications in surgical training, education, and telementorship for spine surgery. However, costs associated with specially designed imaging equipment and physicians' comfort in using this technology continue to remain barriers to its adoption. As this technology evolves to a more widespread use, future applications will be directed by the cost-effectiveness of AR-assisted surgeries.
KC Two-Way Clustering Algorithms For Multi-Child Semantic Maps In Image Mining

Image mining is now a thriving and expanding field of computer science research. Image mining is linked to the advancement of data mining in image preparation. Image mining is used to extract hidden information and in other situations where the photos do not clearly describe the situation. Image mining combines machine learning, data handling, application autonomy, and image preparation concepts. Semantic maps are used to visualize image data stored in image databases. We recommend using Multi-Child Semantic Maps to build semantic maps which fully display the image. In this study, we propose two path clustering on Multi-Child Semantic Maps (MCSM) using the K-C Means Clustering Algorithm, also known as the MCSMK-C algorithm. This algorithm causes image clustering and instructs the mining system to look at the image's top area. When mining, the MCSMK-C algorithm considers the X and Y coordinates. The system looks for groups by examining each object's territory in the database, and it saves a region if it contains more objects than the required number.
Exploring the Role of Machine Learning in Contact Tracing for Public Health: Benefits, Challenges, and Ethical Considerations

This article discusses the role of machine learning in contact tracing for public health, particularly in controlling the spread of infectious diseases like COVID-19. The traditional methods of contact tracing have proven to be insufficient in dealing with the scale and complexity of the pandemic, leading to the exploration of new technologies such as machine learning. The article reviews various machine learning models and techniques that have been developed for contact tracing and highlights the potential benefits of using machine learning, including improved accuracy, efficiency, and personalized risk assessments. One of the major challenges in contact tracing using machine learning is data collection and integration. The article discusses the importance of data quality and integration in developing effective machine learning models. It also highlights the need for privacy and security protocols to protect sensitive data and ensure ethical use of machine learning in contact tracing. The article also discusses various evaluation metrics and techniques that can be used to assess the performance of machine learning models in contact tracing. The ethical considerations and challenges of using machine learning in contact tracing are also discussed in detail, highlighting the importance of developing ethical frameworks that can guide the use of machine learning in contact tracing, ensuring that it is both effective and socially responsible. The article provides case studies of the use of machine learning in contact tracing, including lessons learned and best practices. The article discusses future directions and opportunities for the use of machine learning in contact tracing, highlighting the need for ongoing research and development in this area to improve the accuracy, efficiency, and accessibility of machine learning models for contact tracing. This article provides a comprehensive overview of the potential benefits of using machine learning in contact tracing and the challenges and ethical considerations that must be addressed.
Unsupervised synthetic image refinement via contrastive learning and consistent semantic-structural constraints

Ensuring the realism of computer-generated synthetic images is crucial to deep neural network (DNN) training. Due to different semantic distributions between synthetic and real-world captured datasets, there exists semantic mismatch between synthetic and refined images, which in turn results in the semantic distortion. Recently, contrastive learning (CL) has been successfully used to pull correlated patches together and push uncorrelated ones apart. In this work, we exploit semantic and structural consistency between synthetic and refined images and adopt CL to reduce the semantic distortion. Besides, we incorporate hard negative mining to improve the performance furthermore. We compare the performance of our method with several other benchmarking methods using qualitative and quantitative measures and show that our method offers the state-of-the-art performance.
Optimizing Artificial Neural Networks Using Levy- Chaotic Mapping on Wolf Pack Optimization Algorithm for Detect Driving Sleepiness

Artificial Neural Networks (ANNs) are utilized to solve a variety of problems in many domains. In this type of network, training and selecting parameters that define networks architecture play an important role in enhancing the accuracy of the network's output; Therefore, Prior to training, those parameters must be optimized. Grey Wolf Optimizer (GWO) has been considered one of the efficient developed approaches in the Swarm Intelligence area that is used to solve real-world optimization problems. However, GWO still faces a problem of the slump in local optimums in some places due to insufficient diversity. This paper proposes a novel algorithm Levy Flight- Chaotic Chen mapping on Wolf Pack Algorithm in Neural Network. It efficiently exploits the search regions to detect driving sleepiness and balance the exploration and exploitation operators, which are considered implied features of any stochastic search algorithm. Due to the lack of dataset availability, a dataset of 15 participants has been collected from scratch to evaluate the proposed algorithm's performance. The results show that the proposed algorithm achieves an accuracy of 99.3%. Index Terms— Electrooculography, drowsiness, neural network (NN), grey wolf optimizer (GWO), levy flight distribution, chaotic Chen map.
Quantum programming: Working with IBM’S qiskit tool

One of the greatest technological advancements in the last century lies in digital computer science. The idea of storing information and performing complicated calculations with the help of bits, i.e., 0 and 1. But due to a sudden surge in data, the classical computer system has been becoming weak in data processing. Quantum computers offer promising substantial speedup over classical computers for many applications. Quantum chip fabrication has made remarkable gains in recent years, with the number of qubits and fidelity growing. In general computing, a binary digit is the smallest unit of information or a bit. “In quantum computing, the term Qubit (Quantum Bit) serves the exact function of the term bit.” IBM Research released the IBMQ Experience in 2018, the first quantum computer that anyone can use and make accessible to a huge audience of different countries through cloud access. IBM also introduced the tool QISKIT (Quantum information software kit), which enables teachers, researchers, and developers to write coding and run their coding on quantum machines. It also includes different packages of quantum computing. In this paper, the author has discussed different steps to install qiskit. Mainly this paper focused on the “programming and application side of quantum computing.” Qiskit tools used in the python programming language. The “quantum circuits” are fabricated with the use of quantum gates and favorable algorithms with less execution time