High Error Rate Research Articles

Quantum encryption in phase space with displacement operators

In photonic computing, the quantum systems consist of coherent states and squeezed coherent states. Common quantum gates found in these systems are: phase shift, displacement, and squeezing gates. These gates are all unitary and reversible. Outside of quantum systems, coherent states also plays a significant role in coherent optical communications with speeds of hundreds of gigabits per second. Secure optical communications is generally implemented at the data layer with classical symmetric encryption such as Advanced Standard Encryption or AES. This inevitably allows any wiretapping to capture the transmitted data either in the plaintext mode or in the encrypted ciphertext mode in the optical infrastructure. The recent and rapid developments in Quantum computing further lift up the need for quantum secure communications in the optical infrastructure. This paper proposes a novel quantum encryption in the coherent optical domain utilizing a displacement operator and implementing with IQ-MZM optical modules, called Quantum Encryption in Phase Space or QEPS. The communication peers share a secret used to seed cryptographic pseudo random number generators to produce a synchronized random number at both the transmitter and receiver. The synchronized random numbers are used to establish displacement operators to encrypt the coherent states at the transmission and decrypt the cipher coherent states at the receiver. Therefore, malicious parties tapping along the fibre line would not extract the message in transit from optical domain due to a high Bit Error Rate or BER. The optimal displacement operator is split into a standard 16-QAM and a random phase shift operator to enhance the transmission security. We analysis the transmission security with the wiretap channel model for semantic security. We have simulated the QEPS encryption and decryption for two data modulation schemes: QPSK and 16-QAM over 80 km for transmission speeds of 56 Gbps for QPSK and 112 Gbps for 16-QAM.

Drowsiness Detection and Alertness Using Eye Motion Monitoring

The driver felt sleepy when they don’t take proper rest while driving on long routes. The restless driving careless mistake could be fatal to driver as well as others’ lives. This issue has been increased to such a level that a system required to avoid accidents and save life. The driver alertness detection can play a significant role to avoid such hazards. The system can identify the drowsiness on the face of driver and can generate an alarm for them to stop or take necessary actions. Eye state analysis is a key step for alertness detection that helps to identify the state of the eye whether it is open or close. In this paper, the method has been proposed for eye state analysis following face and eye detection to detect driver’s alertness. This system has been integrated into a four-steps includes detection of face, detection of eye, analysis of eye state, and decision regarding driver's drowsiness. A warning signal has been buzzed on drowsiness detection to alarm the driver. Simulation results validate that our proposed idea attains high accuracy and low error rate as compared to state-of-art.

Object-free backdoor attack and defense on semantic segmentation

Deep neural networks (DNNs) have proven to be applied in various fields, such as autonomous driving. However, it is compromised to a severe threat, namely a backdoor attack, which maliciously manipulates a model by poisoning a small portion of training data. Previous works mainly focused on backdoor attacks in image classification, however, it is scarcely studied in semantic segmentation widely used in computer vision. In this paper, we proposed a novel backdoor attack on semantic segmentation named object-free backdoor attack (OFBA). This method allows the free selection of object classes to be attacked during the inference, breaking the limitation of previous work only attacking object classes determined before training. In addition, to identify the security of unknown models and mitigate the threat of OFBA, we proposed a defense method called Segment Cleanse, alleviating the dilemma of insufficient computing resources and high detection error rates in Neural Cleanse. The experimental results show that our proposed methods are always effective on state-of-the-art models.

Применение нейронной сети для управления системой домашней гидропоники

Hydroponics is a method of soilless cultivation of plants. It shortens the vegetation period, reduces the risk of disease and insect infestation, and provides a year-round growing cycle. Hydroponics depends on efficient water management. It is associated with a complex design, operation, and maintenance. Neural networks can control complex technological processes in agriculture. The research objective was to use a neural network to increase the efficiency of a home hydroponics system.
 The study involved a nutrient bed hydroponics setup with ten Lactuca sativa plants. Sensors collected information about the temperature and humidity of air, illumination, and the temperature of the leaf surface. Data processing, neural network training, and microcontroller programming relied on Python 3, PyTorch, and MicroPython.
 The four-layer perceptron, which is a popular control mechanism, turned out to be the most effective neural network architecture. Fewer layers resulted in a high error rate (≥ 5%). When the number of layers was > 4, the error level remained at that of the four-layer experiment (0.2%). Further practical tests showed an increase in energy efficiency by 32.3%, compared to the classical control algorithm at close values of plant transpiration.
 Neural net technology could be integrated into energy-saving residential premises and smart home systems in order to increase the self-sufficiency of hydroponics installations.

Towards Universally Designed Communication: Opportunities and Challenges in the Use of Automatic Speech Recognition Systems to Support Access, Understanding and Use of Information in Communicative Settings.

Unlike physical barriers, communication barriers do not have an easy solution: people speak or sign in different languages and may have wide-ranging proficiency levels in the languages they understand and produce. Universal Design (UD) principles in the domain of language and communication have guided the production of multimodal (audio, visual, written) information. For example, UD guidelines encourage websites to provide information in alternative formats (for example, a video with captions; a sign language version). The same UD for Learning principles apply in the classroom, and instructors are encouraged to prepare content to be presented multimodally, making use of increasingly available technology. In this chapter, I will address some of the opportunities and challenges offered by automatic speech recognition (ASR) systems. These systems have many strengths, and the most evident is the time they employ to convert speech sounds into a written form, faster than the time human transcribers need to perform the same process. These systems also present weaknesses, for example, a higher rate of errors when compared to human-generated transcriptions. It is essential to weigh the strengths and weaknesses of technology when choosing which device(s) to use in a universally designed environment to enhance access to information and communication. It is equally imperative to understand which tools are most appropriate for diverse populations. Therefore, researchers should continue investigating how people process information in a multimodal format, and how technology can be improved based on this knowledge and users' needs and feedback.

Short-Circuit Performance Analysis of a Distribution Transformer Using Coupled Field-Circuit Approach

The inability of power transformers to resist a short-circuit (SC) fault significantly impacts the safety of power systems. During SC faults, the leakage field undergoes significant distortion, resulting in large SC electromagnetic forces (EFs), which results in critical mechanical stresses. The finite element method (FEM) might be one of the best non-destructive numerical methods that enable designers to assess the transformer SC performance. Using the FEM, most SC performance analysis studies assume either the three-phase (LLL) or the single-phase-to-ground (LG) SC fault. Moreover, the existing studies use an excitation method that provides a high error rate due to the sensitivity of transient solvers. Furthermore, no considerable attention has been given to analyzing the influence of grounding the wye-connected windings on the SC performance. In this work, a non-linear-transient field-circuit coupled 3-D finite element model is created to analyze the SC performance of a delta-wye connected distribution transformer under various SC fault conditions. Results show that grounding the secondary windings significantly impacts the performance when a LG fault is applied. Except for the LG fault with wye configuration of the secondary windings, the windings SC current and EFs decay/rise over time with an exponential trend till reaching steady state.

Integration of hybrid and self-correction method improves the quality of long-read sequencing data.

Third-generation sequencing (TGS) technologies have revolutionized genome science in the past decade. However, the long-read data produced by TGS platforms suffer from a much higher error rate than that of the previous technologies, thus complicating the downstream analysis. Several error correction tools for long-read data have been developed; these tools can be categorized into hybrid and self-correction tools. So far, these two types of tools are separately investigated, and their interplay remains understudied. Here, we integrate hybrid and self-correction methods for high-quality error correction. Our procedure leverages the inter-similarity between long-read data and high-accuracy information from short reads. We compare the performance of our method and state-of-the-art error correction tools on Escherichia coli and Arabidopsis thaliana datasets. The result shows that the integration approach outperformed the existing error correction methods and holds promise for improving the quality of downstream analyses in genomic research.

Imputation of ancient human genomes

Due to postmortem DNA degradation and microbial colonization, most ancient genomes have low depth of coverage, hindering genotype calling. Genotype imputation can improve genotyping accuracy for low-coverage genomes. However, it is unknown how accurate ancient DNA imputation is and whether imputation introduces bias to downstream analyses. Here we re-sequence an ancient trio (mother, father, son) and downsample and impute a total of 43 ancient genomes, including 42 high-coverage (above 10x) genomes. We assess imputation accuracy across ancestries, time, depth of coverage, and sequencing technology. We find that ancient and modern DNA imputation accuracies are comparable. When downsampled at 1x, 36 of the 42 genomes are imputed with low error rates (below 5%) while African genomes have higher error rates. We validate imputation and phasing results using the ancient trio data and an orthogonal approach based on Mendel’s rules of inheritance. We further compare the downstream analysis results between imputed and high-coverage genomes, notably principal component analysis, genetic clustering, and runs of homozygosity, observing similar results starting from 0.5x coverage, except for the African genomes. These results suggest that, for most populations and depths of coverage as low as 0.5x, imputation is a reliable method that can improve ancient DNA studies.

Classification of Marble Types Using Machine Learning Techniques

Natural stones are one of the indispensable elements of people from shelter to weapons. Among these stone types, marbles and marble-derived products are among the objects that people always prefer, from bathroom to kitchen, from garden design to small decorative home decorations. While the marbles are named according to the regions where they are extracted, their types and qualities are classified based on observation by people who are qualified as experts in this field. This classification, which is made by experts based on observation, carries risks in economic terms, increases the workload and is a difficult process with a high error rate. These processes need a fast, easy and highly accurate digital transformation. In this study, feature extraction was done by using deep learning in the species classification of marbles. The extracted features were classified using machine learning techniques. As a result of the application made with the data set consisting of 3703 marble and marble-derived natural stone images belonging to 28 different species, a classification success of 99.7% was obtained with the DenseNet deep learning model and the K-Nearest Neighbor method.

Estimating Dementia Risk Using Multifactorial Prediction Models

The clinical value of current multifactorial algorithms for individualized assessment of dementia risk remains unclear. To evaluate the clinical value associated with 4 widely used dementia risk scores in estimating 10-year dementia risk. This prospective population-based UK Biobank cohort study assessed 4 dementia risk scores at baseline (2006-2010) and ascertained incident dementia during the following 10 years. Replication with a 20-year follow-up was based on the British Whitehall II study. For both analyses, participants who had no dementia at baseline, had complete data on at least 1 dementia risk score, and were linked to electronic health records from hospitalizations or mortality were included. Data analysis was conducted from July 5, 2022, to April 20, 2023. Four existing dementia risk scores: the Cardiovascular Risk Factors, Aging and Dementia (CAIDE)-Clinical score, the CAIDE-APOE-supplemented score, the Brief Dementia Screening Indicator (BDSI), and the Australian National University Alzheimer Disease Risk Index (ANU-ADRI). Dementia was ascertained from linked electronic health records. To evaluate how well each score predicted the 10-year risk of dementia, concordance (C) statistics, detection rate, false-positive rate, and the ratio of true to false positives were calculated for each risk score and for a model including age alone. Of 465 929 UK Biobank participants without dementia at baseline (mean [SD] age, 56.5 [8.1] years; range, 38-73 years; 252 778 [54.3%] female participants), 3421 were diagnosed with dementia at follow-up (7.5 per 10 000 person-years). If the threshold for a positive test result was calibrated to achieve a 5% false-positive rate, all 4 risk scores detected 9% to 16% of incident dementia and therefore missed 84% to 91% (failure rate). The corresponding failure rate was 84% for a model that included age only. For a positive test result calibrated to detect at least half of future incident dementia, the ratio of true to false positives ranged between 1 to 66 (for CAIDE-APOE-supplemented) and 1 to 116 (for ANU-ADRI). For age alone, the ratio was 1 to 43. The C statistic was 0.66 (95% CI, 0.65-0.67) for the CAIDE clinical version, 0.73 (95% CI, 0.72-0.73) for the CAIDE-APOE-supplemented, 0.68 (95% CI, 0.67-0.69) for BDSI, 0.59 (95% CI, 0.58-0.60) for ANU-ADRI, and 0.79 (95% CI, 0.79-0.80) for age alone. Similar C statistics were seen for 20-year dementia risk in the Whitehall II study cohort, which included 4865 participants (mean [SD] age, 54.9 [5.9] years; 1342 [27.6%] female participants). In a subgroup analysis of same-aged participants aged 65 (±1) years, discriminatory capacity of risk scores was low (C statistics between 0.52 and 0.60). In these cohort studies, individualized assessments of dementia risk using existing risk prediction scores had high error rates. These findings suggest that the scores were of limited value in targeting people for dementia prevention. Further research is needed to develop more accurate algorithms for estimation of dementia risk.

Investigation of carbon emission in slurry shield tunnel construction based on modified process analysis method

Underground engineering, including shield tunnel construction, is a significant contributor to carbon dioxide emissions in infrastructure engineering projects. To better predict and control the carbon emissions associated with shield tunnel construction, this paper presents a novel calculation method: the modified process analysis method based on input-output and process analysis methods. To evaluate the effectiveness of the proposed method, a specific shield tunnel construction project was selected as a case study. The modified process analysis method was used to analyze the various factors that influence carbon emissions during the project’s construction phase. In addition, a neural network approach was applied to validate the accuracy of the calculation using the LSTM and BP neural network. The results demonstrate that the proposed method not only combines the strengths of traditional methods but also offers high accuracy and acceptable error rates. Based on these findings, several measures to reduce carbon emissions during shield tunnel construction are suggested, providing valuable insights for reducing CO2 emissions associated with infrastructure engineering projects. This study highlights the importance of adopting innovative approaches to reduce carbon emissions and promotes the implementation of sustainable practices in the construction industry. Through the use of advanced analytical methods, such as the proposed modified process analysis method, we can effectively mitigate the environmental impact of construction activities and make significant contributions to the global effort to combat climate change.

EVALUASI DAN STRATEGI MENINGKATKAN KINERJA ORDER PICKING DI GUDANG RITEL MENGGUNAKAN SIMULASI FLEXSIM

Economic growth based on economic sharing encourages e-commerce transactions to increase. Along with this, the need for an effective distribution process is also increasing, in order to fulfill orders quickly and accurately. This study was conducted on the logistics department at one of the retail companies providing electronic equipments and accessories. Preliminary observations indicate the high level of overtime experienced by employees in the logistics department, as well as the high rate of errors in delivery of ordered products, which results in high complaints from customers. The aim of the study is to evaluate the performance of the order picking process that is currently being carried out by the company, and to formulate a suitable approach to be applied by the company. The research was conducted with a discrete simulation approach, using Flexsim v2022. Modeling begins with creating entities according to the actual warehouse specifications. After that, the model is developed by determining the process flow or simulation flow, by entering parameters such as the number of orders, time, and shelf location. The simulation results show that the current wave picking approach is not optimal. The simulation shows that the sequential zone picking strategy is estimated to result in a decrease in the time of order pickings and the distance experienced by the officers, so that it can be a strategy that can reduce the rate of overtime and errors in order pickings.

The impact of depressive symptomology, rumination and objective memory performance on subjective cognitive complaints

IntroductionCognitive impairment in depression can present as subjective or objective, but the intensity of the former is higher and is not correlated with the deficits measured in neuropsychological tests. We hypothesised that rumination would be associated with subjective cognitive impairment. MethodsThe study was performed through the online PsyToolkit platform. It included 168 healthy people and 93 people with depression. To examine memory, a recognition-type task was used with emotionally charged words as the stimulus. Depression symptoms were measured using the Beck Depression Inventory-II, subjective cognitive impairment with the Perceived Deficits Questionnaire-20, and intensity of rumination with the Polish Questionnaire of Rumination. ResultsMDD patients had significantly higher levels of depression symptoms, rumination, and subjective cognitive deficits than the control group. In the memory task, the MDD group had a higher error rate than the control group. In hierarchical regression analysis, depression and rumination were found to be significant predictors of subjective cognitive impairment, whereas objective memory performance was not. Exploratory analyses revealed that rumination mediates the association between depression and subjective cognitive complaints. ConclusionCognitive problems are common in depression and affect the quality of life. The results suggest that patients with depression have higher levels of rumination and subjective memory impairment, and that there is no direct link between subjective and objective cognitive decline. The findings may have implications for the development of effective treatment strategies for depression and cognitive impairment.

Assessing the Resilience of Machine Learning Classification Algorithms on SARS-CoV-2 Genome Sequences Generated with Long-Read Specific Errors.

The emergence of third-generation single-molecule sequencing (TGS) technology has revolutionized the generation of long reads, which are essential for genome assembly and have been widely employed in sequencing the SARS-CoV-2 virus during the COVID-19 pandemic. Although long-read sequencing has been crucial in understanding the evolution and transmission of the virus, the high error rate associated with these reads can lead to inadequate genome assembly and downstream biological interpretation. In this study, we evaluate the accuracy and robustness of machine learning (ML) models using six different embedding techniques on SARS-CoV-2 error-incorporated genome sequences. Our analysis includes two types of error-incorporated genome sequences: those generated using simulation tools to emulate error profiles of long-read sequencing platforms and those generated by introducing random errors. We show that the spaced k-mers embedding method achieves high accuracy in classifying error-free SARS-CoV-2 genome sequences, and the spaced k-mers and weighted k-mers embedding methods are highly accurate in predicting error-incorporated sequences. The fixed-length vectors generated by these methods contribute to the high accuracy achieved. Our study provides valuable insights for researchers to effectively evaluate ML models and gain a better understanding of the approach for accurate identification of critical SARS-CoV-2 genome sequences.

A novel hybrid feature extraction and ensemble C3D classification for anomaly detection in surveillance videos

Anomaly detection in several deep learning frameworks are recently presented on real-time video databases as a challenging task. However, these frameworks have high false positive rate (FPR) and error rate due to various backgrounds, motion appearance and semantic high-level and low-level features for anomaly detection through action classification. Also, extraction of features and classification are the major problems in traditional convolution neural network (CNN) on real-time video databases. The proposed work is a novel action classification framework which is designed and implemented on large video databases with high true positive rate (TPR) and error rate. In this framework, Kalman based incremental principal component analysis (IPCA) feature extraction method; C3D and non-linear support vector machine (SVM) classifier are used to improve the action prediction (anomaly detection) on the large real-time video databases. The proposed frame work shown new results of high computation performance than the traditional deep learning frameworks for action classification.

Cognitive screening among older afro‐caribbean adults in rural areas of South Florida

AbstractBackgroundAlzheimer’s disease and related dementias (ADRD) disproportionately affects communities of color with older black Americans performing worse than Whites on cognitive tests and facing higher ADRD risks.1,2 Contributors to low diagnostic rates include inadequate knowledge, cultural norms, low health literacy, and an unwillingness to discuss early signs of disease. 1,3‐5 Older Afro‐Caribbeans are a growing subset of the population, particularly in rural South Florida, but best practices to screen for ADRD are unknown.6 Many instruments, including the Montreal Cognitive Assessment (MoCA) have not been assessed within this population. We report on the use of the MoCA 5‐minute test/telephone (Mini‐MoCA) as a screening tool in older, rural‐dwelling Afro‐Caribbeans.7 MethodWe conducted survey interviews with 53 Afro‐Caribbean participants. Participants were assessed with the 5‐minute Mini‐MoCA, which included tests of language fluency, orientation and recall. Linear regression models were used to investigate the association between different components of the Mini‐MoCA its effects on age, sex, and education.ResultA total of 53 Afro‐Caribbean participants (67.2 +10.8y (Mean ± SD), 68% with 10y or less of education) residing in rural South Florida within the last 20 years were included. Participants were largely from Haiti (66%) with 34% from other Caribbean countries including Cuba, Jamaica, Puerto Rico, and Barbados. About 80% of participants reported not having their memory tested prior to the study. The Mini‐MoCA demonstrated good reliability in this sample (α = 0.734). We found that 53% had a total score of 11 or lower on the Mini‐MoCA. Linear regression suggested a significant association between Mini‐MoCA score and education (p = .001).ConclusionWhile the Mini‐MoCA showed good reliability in low‐educated older Afro‐Caribbeans, scores were strongly dependent on years of education. Disparities in educational attainment may contribute to disparities in performance and affect true detection on cognitive impairment in these communities. Social and cultural factors must be considered when interpreting the Mini‐MoCA, given the high error rates on items that depend on the ability to read and write. Study results highlight the need to use a language‐neutral test to accurately measures cognitive impairment among this specific population.

DeepITEH: a deep learning framework for identifying tissue-specific eRNAs from the human genome.

Enhancers are vital cis-regulatory elements that regulate gene expression. Enhancer RNAs (eRNAs), a type of long noncoding RNAs, are transcribed from enhancer regions in the genome. The tissue-specific expression of eRNAs is crucial in the regulation of gene expression and cancer development. The methods that identify eRNAs based solely on genomic sequence data have high error rates because they do not account for tissue specificity. Specific histone modifications associated with eRNAs offer valuable information for their identification. However, identification of eRNAs using histone modification data requires the use of both RNA-seq and histone modification data. Unfortunately, many public datasets contain only one of these components, which impedes the accurate identification of eRNAs. We introduce DeepITEH, a deep learning framework that leverages RNA-seq data and histone modification data from multiple samples of the same tissue to enhance the accuracy of identifying eRNAs. Specifically, deepITEH initially categorizes eRNAs into two classes, namely, regularly expressed eRNAs and accidental eRNAs, using histone modification data from multiple samples of the same tissue. Thereafter, it integrates both sequence and histone modification features to identify eRNAs in specific tissues. To evaluate the performance of DeepITEH, we compared it with four existing state-of-the-art enhancer prediction methods, SeqPose, iEnhancer-RD, LSTMAtt, and FRL, on four normal tissues and four cancer tissues. Remarkably, seven of these tissues demonstrated a substantially improved specific eRNA prediction performance with DeepITEH, when compared with other methods. Our findings suggest that DeepITEH can effectively predict potential eRNAs on the human genome, providing insights for studying the eRNA function in cancer. The source code and dataset of DeepITEH have been uploaded to https://github.com/lyli1013/DeepITEH.

High‐throughput genotyping in estimating genetic resources and detecting pathogens in aquaculture

High‐throughput genotyping in estimating genetic resources and detecting pathogens in aquaculture

A study on the classification of Chinese folk instruments based on deep learning

Due to the Internet's tremendous expansion in recent years, digital music has seen explosive growth. It is becoming increasingly important to provide fast and accurate music retrieval for users, and music information retrieval has gradually become a research hotspot. Among them, the classification of musical instruments is one of the hot research directions. As an important part of the world's musical instruments, the Chinese folk musical instrument tradition has great research value, but there is less research in this direction. Traditional music classification methods can be roughly divided into two steps: manual feature extraction and traditional machine learning. But traditional music classification has several shortcomings, as follows. Firstly, manual extraction of music features has a high error rate and it is very difficult to ensure the accuracy of the features, and the labor cost is high; secondly, traditional machine learning still has problems with multiple classifications and training on large scale data. Therefore, in this paper, based on previous research, the original data features are extracted from the Mel Frequency Cepstrum Coefficient (MFCC), which is a feature that can represent musical timbre, and deep learning is proposed to be used to classify traditional Chinese folk instruments. This paper finds that the results of identifying and classifying Chinese folk musical instruments using different classical classification methods are compared when the training set and test set data are the same. According to the experimental results, the use of KNN has the best performance among these classification methods.

Reducing CNOT count in quantum Fourier transform for the linear nearest-neighbor architecture

Physical limitations of quantum hardware often necessitate nearest-neighbor (NN) architecture. When synthesizing quantum circuits using the basic gate library, which consists of CNOT and single-qubit gates, CNOT gates are required to convert a quantum circuit into one suitable for an NN architecture. In the basic gate library, CNOT gates are considered the primary cost of quantum circuits due to their higher error rates and longer execution times compared to single-qubit gates. In this paper, we propose a new linear NN (LNN) circuit design for quantum Fourier transform (QFT), one of the most versatile subroutines in quantum algorithms. Our LNN QFT circuit has only about 40% of the number of CNOT gates compared to previously known LNN QFT circuits. Subsequently, we input both our QFT circuits and conventional QFT circuits into the Qiskit transpiler to construct QFTs on IBM quantum computers, which necessitate NN architectures. Consequently, our QFT circuits demonstrate a substantial advantage over conventional QFT circuits in terms of the number of CNOT gates. This outcome implies that the proposed LNN QFT circuit design could serve as a novel foundation for developing QFT circuits implemented in quantum hardware that demands NN architecture.