Reviewerly/exHarmony huggingface dataset:
Embedded System and Smart Phone based Object Recognition Technique for Visually Impaired

The goal of this work is to help the blind person to recognize an object clearly. An integrated sensor and Artificial Intelligence based approach has been used in this work. The system consists of two modules, one module contains all sensors interfaced with microcontroller unit and another module contains android applications. The proposed system uses Ultrasonic Sensor, Water Sensor, Fire Sensor and Arduino UNO ATmega328 microcontroller in integrated module. These sensors are used here to detect the presence of object, water and fire respectively, near by the visually disabled person. Here, the Talkback and select to speak feature are used to connect an internet in smart phone. It will also send the audio, video call, multimedia message and GPS location on the android mobile using Google assistant application. The object detection application in android mobile will provide the exact object recognition facility and output of ODA will be heard in wireless headphone.
Study of Object Detection with Faster RCNN

Numerous studies in the field of object detection have been conducted over the past few decades. Several effective methods have been developed. Among various object detection algorithms, Faster RCNN offers excellent results in both detection speed and accuracy. It is a combination of Fast RCNN and RPN layers. This paper conducts a comparative study of object detection using Faster RCNN. The study shows that use of smaller convolutional network called Region Proposal Network improves performance of the system. It shows that object detection using Faster RCNN can give high accuracy and faster performance as compared to other methods and algorithms. It takes only 0.2 seconds to predict a single image. Also, it gives 70% Mean Accuracy Precision (mAP) on the PASCAL VOC 2007 and PASCAL VOC 2012 datasets.
A Comparative Study on Effect of Temporal Phase for Speaker Verification

In this paper, the temporal phase influence on speech signal is demonstrated through different experimental models, notably for speaker verification. Feature extraction is a fundamental block in a speaker recognition system responsible for obtaining speaker characteristics from speech signal. The commonly used short-term spectral features accentuate the magnitude spectrum while totally removing the phase spectrum. In this paper, the phase spectrum knowledge is extensively extracted and studied along with the magnitude information for speaker verification. The Linear Prediction Cepstral Coefficients (LPCC) are extracted from speech signal temporal phase and its scores are fused with Mel-Frequency Cepstral Coefficients (MFCC) scores. The trained data are modeled using the state-of-art speaker specific Gaussian mixture model (GMM) and GMM-Universal Background Model (GMM-UBM) for both LPCC and MFCC features. The scores are matched using dynamic time warping (DTW). The proposed method is tested on a fixed-pass phrase with a duration of <5 s in a speech signal. The score level fusion technique helps in the reduction of equal error rate (EER) and improves recognition rate.
Supervised Classification and Finding Frequent Elements in Data

Correct logical classifiers the construction of which uses the ideas underlying the well-known “Kora” algorithm and the ideas of estimates calculating algorithms are considered. A modification of the training stage that in certain cases makes it possible to reduce the time cost without loss in the classification quality is proposed. Results of experiments on model and real-life data are presented, and new theoretical estimates of the complexity of training Kora-type classifiers are obtained.
Secret sharing scheme in defense and big data analytics

Nearly 100 years ago, mathematician Dickson [Dickson LE, Mathematics in war perspective. Bull. Amer. Math. Soc. 1919; 25(7):289-311] in his presidential address before the American Mathematical Society focused on the war perspective of mathematics. His presidential address is still relevant from the various perspectives of war and peace. Secret information sharing plays one of the major factors to win wars, military operations, etc. But, it is hard to find methods of general topology or bitopology in information sharing related to defense and big data analytics. Thus, based on Acharjee et al. [Acharjee S, Reilly IL, Sarma DJ. On compactness via bI-open sets in ideal bitopological spaces. Bulletin of the Transilvania University of Brasov series III: Mathematics, informatics, and physics. 2019; 12(61)(1):1-8], this chapter contributes a bitopological evidence on secret information sharing between two military units to conduct joint military operations from big data analytics. Moreover, this chapter reports for the first time since 1933 that Kuratowski’s ideal has the characteristic to carry secret information.
A High-Speed Data Retrieval Model on Blockchain

Blockchain is also known as distributed ledger. All full nodes connected to the blockchain network participate in the maintenance of the data in the ledger. It is a technology in many fields such as computer science, cryptography, distributed storage, and finance. Industrial and academic research on blockchain technology has achieved great results, including research on blockchain networks, consensus mechanisms, and smart contracts. However, limited by the data storage mode and the characteristics of distributed storage at the bottom of the blockchain, there are still problems that need to be solved urgently, such as the single retrieval function and the low retrieval rate of the data retrieval on the blockchain. We focused on this problem, and based on the built-in index and external data warehouse method, we proposed a high-speed data retrieval model on blockchain. The model consists of three parts: a blockchain network with improved index storage, a data processing cluster, and application layer services. The new blockchain network improves the organization of transaction data in the traditional blockchain system, and designs a data structure suitable for high-speed retrieval to organize transaction data; the data processing cluster is responsible for ensuring data consistency and in accordance with high efficiency The synchronization strategy is to synchronize the data on the chain to the relational data warehouse under the chain; the application layer service encapsulates the rich query functions supported by the relational database, and finally provides services to the outside in the form of HTTP, RPC, etc. Experimental results show that the model can effectively expand the blockchain system in terms of query efficiency and query functions, improve the query rate of data on the blockchain, and meet people&#x0027;s needs for blockchain query functions.
Senseful Sensors: Learnings and Optimizations for IoT Sensors in HCI Research

This paper reports learnings and optimizations for the use of simple connected IoT sensors in human-computer interaction research. Starting from requirements for such sensors, i.e. for workshops with people and the use in their homes, we introduce the TI SensorTag devices. It turned out that while these devices were promising, they were not suitable for long deployments, as the coin cell battery life of only two days was not sufficient. We present energy traces of the devices. First the status quo and then with modified firmware and software to allow more suitable sensor parametrization while avoiding pitfalls of Bluetooth Low Energy. We finally managed to create a system for senseful sensing: versatile, responsive, energy efficient, and deployable for weeks on a small coin cell.
Generalization of Machine Learning-Based Compression Method to Hyperspectral Images

The article deals with the problem of generalizing machine learning-based two-dimensional compression frameworks to hyperspectral (HS) data. The number of HS-floors in the HS-data is usually so large that we can treat the HS-data as three-dimensional arrays with a full third dimension. However, the architecture of machine learning-based compression frameworks usually relies heavily on the two-dimensionality of the source data. Therefore, the task of generalizing two-dimensional compression frameworks based on machine learning to HS-data is non-trivial and relevant. Most HS-floors in HS-data are usually very similar to each other. We use the strong similarity of HS-floors to generalize two-dimensional machine learning-based compression frameworks to HS-data. We perform computational experiments to investigate the effectiveness of the proposed approach for generalizing two-dimensional machine learning-based compression frameworks to HS-data. Experimental results prove that the use of the proposed approach can significantly increase the efficiency of HS-data compression.
A Simple Random-Bit Generator Implemented on FPGA Based on Signum Chaotic Map

This paper presents a simple implementation of random-bit generator. A piecewise-linear signum chaotic map is utilized as a random source, which provides not only truly random signals but also robust chaotic behavior against parameter changes. Chaotic dynamics are described in term of bifurcation diagram, Lyapunov exponent, cobweb plot and time-domain signals. The randomness of binary sequences is investigated by several statistical analysis, i.e., autocorrelation histogram, entropy, and NIST SP800&#x2013;22 test suite. Experimental results of digital random-bit sequences have fully been achieved on FPGA, which realizes speed and compact area. The proposed random-bit generator provides a potential alternative in many applications such as test pattern generator and a robust random bit source for information security and cryptography.
A Novel Nighttime Dehazing Model Integrating Retinex Algorithm and Atmospheric Scattering Model

To solve the problem of low contrast and serious loss of detail information after dehazing directly using the atmospheric scattering model. This paper proposes a novel night image defogging model that combines the Retinex and the atmospheric scattering models. This model incorporates the best of both algorithms. Retinex can enhance the contrast of the image and the detailed information of the image. The atmospheric scattering model can clearly describe the imaging principle of the haze map, and restore the haze-free image from the root. In qualitative and quantitative experiments, this paper compares four methods on public datasets. This algorithm&#x2019;s average image information entropy processed by this algorithm is 6.995, the average peak signal-to-noise ratio is 19.340, and the average structural similarity is 0.766. Compared with the other four algorithms, the comparative test results show that the algorithm in this paper can effectively remove the haze in the nighttime image, restore the detailed information of the image, and promote the progress of the algorithm.
Robust Labeled Multi-Bernoulli Filter with Inaccurate Noise Covariances

In this paper, a robust labeled multi-Bernoulli (RLMB) filter for the multi-target tracking (MTT) scenarios with inaccurate and time-varying process and measurement noise covariances is proposed. The process noise covariance and measurement noise covariance are modeled as inverse Wishart (IW) distributions, respectively. The state together with the predicted error and measurement noise covariances are inferred based on the variational Bayesian (VB) inference. Moreover, a closed-form implementation of the proposed RLMB filter is given for linear Gaussian system and the predictive likelihood function is calculated by minimizing the Kullback-Leibler (KL) divergence by the VB lower bound. Simulation results illustrate that the proposed RLMB filter outperforms the existing LMB filter in the tracking performance.
A Secure Medical Image Retrieval Technique using Encrypted Query Image

In recent, electronic medical records of patients are frequently used and shared among several hospitals and medical clinics to make the medical records easily available to the medical professionals. The patient data contains private information about the individual patient so the database must be accessible to the concerned person in secured way. Relevant medical image searching and its retrieval are crucial in diagnostic and evaluation of suspicious signs to detect deadly and precarious diseases. Content based image retrieval (CBIR) is used for searching and retrieval of images based on the given query image but for the medical database security is also a vital issue so the medical professionals are responsible to retrieve and keep these records in a confidential manner. In this paper, the authors have suggested a model in which not only the retrieved result is secured through encryption process but also image retrieval procedure is carried out using the encrypted query image so that the patient information can be kept hidden from the image provider. The first level image encryption process is suitable to preserve the statistical information of the original medical image while the second level encryption process enhances the security and reduces the chance of cryptography attacks. The overall process is adequate to retrieve the images in secure manner.
Event-Based Kalman Filtering Exploiting Correlated Trigger Information

In networked estimation architectures, event-based sensing and communication can contribute to a more efficient resource allocation in general, and improved utilization of communication resources, in particular. In order to tap the full potential of event-based scheduling, the design of transmission triggers and estimators need to be closely coupled while two directions are promising: First, the remote estimator can exploit the absence of transmissions and translate it into implicit information about the sensor data. Second, an intelligent trigger mechanism at the sensor that predicts future sensor readings can decrease transmission rates while rendering the implicit information more valuable. Such an intelligent trigger has been developed in a recent paper based on a Finite Impulse Response filter, which requires the sensor to transmit an additional estimate alongside the measurement. In the present paper, the communication demand is further reduced by only transmitting the estimate. The remote estimator exploits correlations to incorporate the received information. In doing so, the estimation quality is also improved, which is confirmed by simulations.
Self-Assessment and Robust Anomaly Detection with Bayesian Deep Learning

Deep Learning (DL) models have achieved or even surpassed human-level accuracy in several areas, including computer vision and pattern recognition. The state-of-art performance of DL models has raised the interest in using them in real-world applications, such as disease diagnosis and clinical decision support systems. However, the challenge remains the lack of trustworthiness and reliability of these DL models. The detection of incorrect decisions or flagging suspicious input samples is essential for the reliability of machine learning models. Uncertainty estimation in the output decision is a key component in establishing the trustworthiness and reliability of these models. In this work, we use Bayesian techniques to estimate the uncertainty in the model's output and use this uncertainty to detect distributional shifts linked to both input perturbations and labels shifts. We use the learned uncertainty information (i.e., the variance of the predictive distribution) in two different ways to detect anomalous input samples: 1) a static threshold based on average uncertainty of a model evaluated on the clean test data, and 2) a statistical threshold based on the significant increase in the average uncertainty of the model evaluated on corrupted (anomalous) samples. Our extensive experiments demonstrate that both approaches can detect anomalous samples. We observe that the proposed thresholding techniques can distinguish misclassified examples in the presence of noise, adversarial attacks, anomalies or distributional shifts. For example, when considering corrupted versions of MNIST and CIFAR-10 datasets, the rate of detecting misclassified samples is almost twice as compared to Monte-Carlo-based approaches.
Acoustic Feature Extraction and Isolated Word Recognition of Speech Signal Using HMM for Different Dialects

In this paper improved the recognition rate of isolated words using the hidden Markov model (HMM) with improved accuracy at all frequency ranges of the audio voice. In English and Punjabi, two different dialects of isolated words are recorded using a microphone and audacity tool. These words cover all the phonemes of respective dialects. This work is mainly divided into two sections: feature extraction and recognition. In these techniques, Mel-frequency cepstral coefficients (MFCC) have been used to extract features mathematically. The second part, described the Markov model and HMM with examples for modeling and recognizing the isolated words. Based on the K-mean algorithm, all the samples are clustered. Where the Gaussian mixture contains weight modeling, mean, and variance parameters. The K-folded method used here for training and testing each signal uses the forward and Baum-Welch algorithms. Finally, it gives a minimum misclassification rate from the N number of different hidden states and different values of a number of the feature vector in each frame D. The novelty of this article is to recognize the isolated word using HMM for English and Punjabi Dialect and made a comparison between them. For the computation, five folded cross-validation is used. The misclassification rate of vocabulary is computed using HMM and acoustic models. The database is created using English and Punjabi phonemes in a real-time environment. The training and testing are performed to validate the robustness of the system. The experimental results have shown the system performance to be 93% for English vocabulary and 96% for Punjabi vocabulary.
AvoidPwd: A mnemonic password generation strategy based on keyboard transformation

Identity authentication is the first line of defense for network security. Passwords have been the most widely used authentication method in recent years. Although there are security risks in passwords, they will be the primary method in the future due to their simplicity and low cost. Considering the security and usability of passwords, we propose AvoidPwd, which is a novel mnemonic password generation strategy that is based on keyboard transformation. AvoidPwd helps users customize a "route" to bypass an "obstacle" and choose the characters on the "route" as the final password. The "obstacle" is a certain word using any language and the keys adjacent to the "obstacle" are typed with the "Shift" key. A two-part experiment was conducted to examine the memorability and security of the AvoidPwd strategy with other three password strategies and three leaked password sets. The results showed that the passwords generated by the AvoidPwd strategy were more secure than the other leaked password sets. Meanwhile, AvoidPwd outperformed the KbCg, SpIns, and Alphapwd in balancing security and usability. In addition, there are more symbols in the character distribution of AvoidPwd than the other strategies. AvoidPwd is hopeful to solve the security problem that people are difficult to remember symbols and they tend to input letters and digits when creating passwords.
Marker-less Kendo Motion Prediction Using High-speed Dual-camera System and LSTM Method

Kendo is becoming popular worldwide nowadays. This research aims to develop a high-speed vision system to capture Kendo motions clearly and predict the intent attacking target of the trainee. We proposed a method to increase the joints detecting frequency by combining the joints tracking algorithm with OpenPose. For motion prediction, we applied LSTM method to achieve real-time predicting. In the end, we got 66% accuracy for basic Kendo moves prediction.
An Analysis of Distributional Shifts in Automated Driving Functions in Highway Scenarios

We investigate the distributional shifts between datasets which pose a challenge to validate safety critical driving functions which incorporate Machine Learning (ML)-based algorithms. First, we describe the possible distributional shifts which can occur in highway driving datasets. Following this, we analyze–both qualitatively and quantitatively–-the distributional shifts between two publicly available, and widely used, highway driving datasets. We demonstrate that a safety critical driving function, e.g., a lane change maneuver prediction, trained on one dataset will not generalize as expected to the other dataset in the presence of these distributional shifts. This highlights the impact which distributional shifts can have on safety critical driving functions. We suggest that an analysis of the datasets used to train ML-based algorithms incorporated in safety critical driving functions plays an important role in building a safety-argument for validation.
Optimization design of amorphous metal distribution transformer based on improved quantum particle swarm optimization algorithm

As an energy-saving and environmentally friendly power distribution equipment, amorphous metal core distribution transformer (AMDT) has a huge market. The designing efficiency and main material cost of AMDT play an important role in improving the competitiveness and economic benefit of transformer manufacturers. Based on Visual Basic 6.0 software, AMDT was taken as the object of optimization design, and the main material cost of AMDT was taken as the objective function of optimization. Compared with the results obtained by the original design scheme, particle swarm optimization (PSO), quantum particle swarm optimization (QPSO), and improved quantum particle swarm optimization (IQPSO) after optimizing the design variables, the results Shown that the IQPSO provides a much minimized solution and computation time than the other three kinds of methods. The feasibility and effectiveness of the IQPSO algorithm during the optimal design of AMDT are verified.
Should Quantum Processor Design be Considered a Topic in Computer Architecture Education?

New trends in computer architecture include non-general purpose architectures, brain-inspired design, agile hardware development and environmentally responsible design. Computer Science students need to understand computer architecture to develop programs that can achieve high performance through a programmer's awareness of parallelism and latency. In a re-visit of curriculum guidelines, students could develop skills and competencies in less mature yet cutting-edge topics, including quantum computing. For example, should students understand and appreciate quantum processor design's components and characteristics? In a task to revise curricular guidelines, one faces the decision of which topics may be obsolete and should be dropped or modified. Equally important is deciding the set of topics that must be included. Such questions are hard to address, particularly for a curriculum intended to remain fresh under a 10-years horizon. For example, the danger of not properly promoting the discussion of quantum processor design in the new ACM/IEEE-CS/AAAI CS202X curricula is the long waiting cycle (15 years+) for any new revision. This BOF will foster the participants' interactions into a debate of whether new curricular guidelines should contain quantum processor design as part of the Architecture and Organization (AR) Knowledge Area.