Reviewerly/exHarmony huggingface dataset:
Utilizing BERT Pretrained Models with Various Fine-Tune Methods for Subjectivity Detection

As an essentially antecedent task of sentiment analysis, subjectivity detection refers to classifying sentences to be subjective ones containing opinions, or objective and neutral ones without bias. In the situations where impartial language is required, such as Wikipedia, subjectivity detection could play an important part. Recently, pretrained language models have proven to be effective in learning representations, profoundly boosting the performance among several NLP tasks. As a state-of-art pretrained model, BERT is trained on large unlabeled data with masked word prediction and next sentence prediction tasks. In this paper, we mainly explore utilizing BERT pretrained models with several combinations of fine-tuning methods, holding the intention to enhance performance in subjectivity detection task. Our experimental results reveal that optimum combinations of fine-tune and multi-task learning surplus the state-of-the-art on subjectivity detection and related tasks.
Secure Card-less ATM Transactions

With the constant developments in technology we have certainly advanced a lot in terms of digital payments. The increasing use of debit and credit cards in different fields has reduced manual efforts to a large extent thereby making payments at and for various tasks way too convenient. This ease comes with the associated risks as technological advancement and cyber- crime go hand-in-hand. In recent years, cash withdrawal through ATM cards has seen an increase in the number of card related frauds; card cloning, shoulder surfing, fake keyboard, skimming etc. being a few of them. To combat these problems, we propose a Secure Cardless Transaction System- a method which would eliminate the usage of ATM PIN and physical cards altogether and hence provide a secure environment for cash withdrawal. The concept of User-Generated One Time Password (OTP) has been introduced in this project. With all these modification in existing systems, the robustness of the machines will increase.
Measuring Instance Hardness Using Data Complexity Measures

Assessing the hardness of each instance in a problem is an important meta-knowledge which may leverage advances in Machine Learning. In classification problems, an instance can be regarded as difficult if it gets systematically misclassified by a diverse set of classification techniques with different biases. The instance hardness measures were proposed with the aim of relating data characteristics to this notion of intrinsic difficulty of the instances. There are also in the literature a large set of measures which are dedicated at describing the difficulty of a classification problem from a dataset-level perspective. In this paper these measures are decomposed at the instance-level, giving a perspective of how each individual example in a dataset contributes to its overall complexity. Experiments on synthetic and benchmark datasets demonstrate the proposed measures can provide a complementary instance hardness perspective when compared to those from related literature.
Blockchain-Enabled Traceable, Transparent Transportation System for Blood Bank

Electronic health records (EHR) and patient health records are an Internet-based health application which is sharable to authorized stakeholders such as doctors, specialist, and patients. One of the important record is blood and sharing the blood record in secured manner is a challenging issue. Blockchain-based blood bank system can resolve the need for individuals to access, trace, manage, share their health and blood-related information, which is immutable. The blockchain-based system provides a secured environment which acts as communication hub between donors, doctors, testing laboratories, and recipients or patients. This work proposes the design of a blockchain-based solution for the blood bank system. Information related to the blood is communicated securely to the intended users from the blood bank. Additionally, the traceability feature included in the proposed system monitors detailed information of blood transfer from excess stock to the nearest place. The system model is designed, implemented and validated using solidity language platform.
Optimization of the Energy Consumption of Electric Motors through Metaheuristics and PID Controllers

The synthesis of electric motor control systems, seeking optimal performance, is a well-known and studied field of automation to date. However, the solutions often use very elaborate mathematical foundations and sometimes require considerable algorithmic complexity. Another approach to the same problem, which offers very interesting results, is the use of artificial intelligence methods to generate controllers. Intelligent methods allow the use of bio-inspired approaches to solve complex problems. This article presents a method to adjust the parameters of a controller for DC motors based on two components in the objective function: High productivity and efficiency. This can be achieved using well-known and low algorithmic complexity PID controllers, and metaheuristic artificial intelligence techniques to adjust a controller to obtain optimal behavior. To validate the benefits of the methodological proposal, a simulator of a DC motor has been rigorously constructed, respecting fundamental physical principles. The adjustment system based on metaheuristics (genetics algorithms) has been designed to work on the simulator and constitutes the central contribution of the paper. This system has been designed to establish the parameters of a PID controller, optimizing its behavior in relation to two variables of interest, such as performance and energy efficiency (a non-trivial problem). The results obtained confirm the benefits of the approach.
Breast Mass Classification Using eLFA Algorithm Based on CRNN Deep Learning Model

Breast cancer is known to be common in many developed countries. It is reported as the most common type of cancer in the US, affecting one in eight women. In Korea, thyroid cancer is the most common type of cancer, followed by breast cancer in women. Considering this, early detection and accurate diagnosis of breast cancer are crucial for reducing the associated death rate. Recently, cancer diagnosis systems using medical images have attracted significant attention. Medical imaging methods, such as computed tomography and magnetic resonance imaging, can reveal the overall shape, heterogeneity, and growth speed of carcinoma and are, thus, more commonly employed for diagnoses. Medical imaging has gained popularity since a recent study identified that it could reflect the gene phenotype of a patient. However, an aided diagnosis system based on medical images requires high-specification equipment to analyze high-resolution data. Therefore, this article proposes an edge extraction algorithm and a modified convolutional recurrent neural network (CRNN) model to accurately assess breast cancer based on medical imaging. The proposed algorithm extracts line-segment information from a breast mass image. The extracted line segments were classified into 16 types. Each type was uniquely labeled and compressed. The image compressed in this process was used as the input for the modified CRNN model. Traditional deep learning models were used to evaluate the performance of the proposed algorithm. The results show that the proposed model had the highest accuracy and lowest loss (99.75% and 0.0257, respectively).
Topic-Based Instance and Feature Selection in Multilabel Classification

Multilabel learning has been extensively studied in the past years, as it has many applications in different domains. It aims at annotating the labels for unseen data according to training data, which are often high dimensional in both instance and feature levels. The training data often have noisy and redundant information on these two levels. As an effective data preprocessing step, instance and feature selection should both be performed to find relevant training instances for each testing instance and relevant features for each label, respectively. However, most of the existing methods overlook the input-output correlation in each kind of selection. It will lead to the performance degradation. This article presents a formulation for multilabel learning from a topic view that exploits the dependence between features and labels in a topic space. We can perform effective instance and feature selection in the latent topic space, as the relationship between the input and output spaces is well captured in this space. The results from intensive experiments on various benchmarks demonstrate the effectiveness of the proposed framework.
Explainable machine learning for public policy: Use cases, gaps, and research directions

Abstract Explainability is highly desired in machine learning (ML) systems supporting high-stakes policy decisions in areas such as health, criminal justice, education, and employment. While the field of explainable ML has expanded in recent years, much of this work has not taken real-world needs into account. A majority of proposed methods are designed with generic explainability goals without well-defined use cases or intended end users and evaluated on simplified tasks, benchmark problems/datasets, or with proxy users (e.g., Amazon Mechanical Turk). We argue that these simplified evaluation settings do not capture the nuances and complexities of real-world applications. As a result, the applicability and effectiveness of this large body of theoretical and methodological work in real-world applications are unclear. In this work, we take steps toward addressing this gap for the domain of public policy. First, we identify the primary use cases of explainable ML within public policy problems. For each use case, we define the end users of explanations and the specific goals the explanations have to fulfill. Finally, we map existing work in explainable ML to these use cases, identify gaps in established capabilities, and propose research directions to fill those gaps to have a practical societal impact through ML. The contribution is (a) a methodology for explainable ML researchers to identify use cases and develop methods targeted at them and (b) using that methodology for the domain of public policy and giving an example for the researchers on developing explainable ML methods that result in real-world impact.
e-Governance for healthcare service delivery in India

The past e-Governance initiatives in Indian healthcare sector have created silos of digital health data held in the custody of various healthcare providers. In recent times, as part of the National Health Policy, there have been multifaceted initiatives in the healthcare sector with the focus towards providing patient-centric, cost effective and efficient healthcare service delivery to the citizens. This development is bringing fundamental change in the healthcare sector owing to emergence of a revolutionary Federated Digital Healthcare Ecosystem. The ecosystem components like Healthcare Providers, Disease Repositories, Health Information Exchange(s) are planned to be distributed, scalable and dynamically interconnected for patient care and secondary use. In this paper we discuss security and privacy challenges identified for seamless development of the components of this planned ecosystem in the Indian context. We also provide pointers as ready references for designers and researchers of digital healthcare solutions to delve in these open study areas.
Native Vehicles Classification on Bangladeshi Roads Using CNN with Transfer Learning

To bring an intelligent transportation system in a developing country like Bangladesh, vehicle detection or identification is a major concern to analyze the unique behaviors of the native vehicles. Coming with the tremendous progress in object detection with deep convolutional neural network (CNN), we have addressed the recognition of native vehicles on Bangladeshi Roads. In this paper, a transfer learning approach based on CNN has been applied to the popular You Only Look Once (YOLO) framework for vehicle classification. The goal is to reuse the pre-trained convolutional layers of the YOLO framework and transfer knowledge to detect 15 novel vehicles of Bangladesh. To validate our proposed method, we have introduced Bangladeshi Vehicles Dataset containing 9000 annotated images of 15 vehicles. The images are extracted from Bangladeshi traffic scenes. Experimental results demonstrate that the proposed method has achieved an admissible outcome with 73 % IoU to properly detect native vehicles. Thus, the system can progress the traffic management system of developing countries.
Improved Quantum Circuits via Intermediate Qutrits

Quantum computation is traditionally expressed in terms of quantum bits, or qubits. In this work, we instead consider three-level qu trits . Past work with qutrits has demonstrated only constant factor improvements, owing to the log 2 (3) binary-to-ternary compression factor. We present a novel technique, intermediate qutrits, to achieve sublinear depth decompositions of the Generalized Toffoli and other arithmetic circuits using no additional ancilla—a significant improvement over linear depth for the best qubit-only equivalents. For example, our Generalized Toffoli construction features a 70× improvement in two-qudit gate count over a qubit-only decomposition. This results in circuit cost reductions for important algorithms like quantum neurons, Grover search, and even Shor’s algorithm. Using a previously developed simulator with near-term noise models, we demonstrate for these models over 90% mean reliability (fidelity) for the Toffoli construction, versus under 30% for the qubit-only baseline. For our other constructions, such as the Incrementer, the A + B adder and the +K adder, we demonstrate the power of intermediate qutrits in producing asymptotic depth improvements with no additional ancilla. Together, these results suggest qutrits offer a promising path toward scaling quantum computation.
One-shot assisted distillation of coherence via one-way local quantum-incoherent operations and classical communication

We obtain the general formula for the optimal rate on one of the subsystems that can be distilled from any bipartite quantum state by one-way local quantum-incoherent operations and classical communication. We provide lower and upper bounds on the assisted distillation of coherence using so-called one-shot entropies, and we establish a hierarchy of information quantities that can be used to calculate an assisted distillable rate in the asymptotical limit. We show that they asymptotically coincide with the quantum-incoherent relative entropy, and our findings imply a novel operational interpretation of quantum-incoherent relative entropy.
The varieties of minimal tomographically complete measurements

Minimal Informationally Complete quantum measurements, or MICs, illuminate the structure of quantum theory and how it departs from the classical. Central to this capacity is their role as tomographically complete measurements with the fewest possible number of outcomes for a given finite dimension. Despite their advantages, little is known about them. We establish general properties of MICs, explore constructions of several classes of them, and make some developments to the theory of MIC Gram matrices. These Gram matrices turn out to be a rich subject of inquiry, relating linear algebra, number theory and probability. Among our results are some equivalent conditions for unbiased MICs, a characterization of rank-1 MICs through the Hadamard product, several ways in which immediate properties of MICs capture the abandonment of classical phase space intuitions, and a numerical study of MIC Gram matrix spectra. We also present, to our knowledge, the first example of an unbiased rank-1 MIC which is not group covariant. This work provides further context to the discovery that the symmetric informationally complete quantum measurements (SICs) are in many ways optimal among MICs. In a deep sense, the ideal measurements of quantum physics are not orthogonal bases.
Classification of Bengali Sign Language Characters by Applying a Novel Deep Convolutional Neural Network

With approximately 466 million deaf-mute people in the world, sign language recognition has been an area of interest for researches for a long time now. Previously, many researchers have conducted studies on different sign language datasets, achieving a decent accuracy of recognition. However, not many studies have been conducted on the Bengali sign alphabet recognition dataset despite having approximately 3 million severe deaf cases alone. Previous studies on Bengali sign character detection suggested decent accuracies using traditional machine learning approaches. However, in recent studies, some researches have achieved higher test accuracy using deep learning models. In our study, we've proposed a novel Convolutional Neural Network (CNN) model for the recognition of the Bengali sign alphabets from the Ishara-Lipi dataset. Our model achieved an overall accuracy of 99.22% that has outperformed all previous studies of Bengali sign character recognition for the deaf-mute community.
Deep Feedback Inverse Problem Solver

We present an efficient, effective, and generic approach towards solving inverse problems. The key idea is to leverage the feedback signal provided by the forward process and learn an iterative update model. Specifically, at each iteration, the neural network takes the feedback as input and outputs an update on current estimation. Our approach does not have any restrictions on the forward process; it does not require any prior knowledge either. Through the feedback information, our model not only can produce accurate estimations that are coherent to the input observation but also is capable of recovering from early incorrect predictions. We verify the performance of our model over a wide range of inverse problems, including 6-DoF pose estimation, illumination estimation, as well as inverse kinematics. Comparing to traditional optimization-based methods, we can achieve comparable or better performance while being two to three orders of magnitude faster. Compared to deep learning-based approaches, our model consistently improves the performance on all metrics.
AutoMix: Mixup Networks for Sample Interpolation via Cooperative Barycenter Learning

This paper proposes new ways of sample mixing by thinking of the process as generation of barycenter in a metric space for data augmentation. First, we present an optimal-transport-based mixup technique to generate Wasserstein barycenter which works well on images with clean background and is empirically shown complementary to existing mixup methods. Then we generalize mixup to an AutoMix technique by using a learnable network to fit barycenter in a cooperative way between the classifier (a.k.a. discriminator) and generator networks. Experimental results on both multi-class and multi-label prediction tasks show the efficacy of our approach, which is also verified in the presence of unseen categories (open set) and noise.
BOSCA—A Hybrid Butterfly Optimization Algorithm Modified with Sine Cosine Algorithm

Nature-inspired metaheuristic algorithms along with their improved and hybrid versions have been gaining intrinsic popularity in solving nonlinear constrained complex real-world problems. On this presentation, a new hybrid butterfly optimization algorithm (BOA), viz. BOSCA combined with sine cosine algorithm (SCA) is suggested to develop a balanced yet powerful optimization technique through enhancing and stabalizing the global exploration and local exploitation ability. In this, metaheuristic hybridization is done in such a way to get both the exploration and exploitation phases for each of the butterfly with sufficient chance to improvise each solution. To prove the efficiency and robustness of the developed BOSCA, it has been applied to solve twenty-five classical benchmark functions. A comparative study has been done by taking some of the popular algorithms in available in literature and this developed algorithm is found to be superior to the compared algorithms. Again to validate its efficiency in real-world problems, it has been applied to two real-world problems; One is gas transmission compressor design problem and another is optimal capacity of gas production facilities. Results of these real-world problems have been compared to that of some other algorithms and the proposed method found to be superior in real-world optimization problems also.
Tracking and Localization based on Multi-angle Vision for Underwater Target

With the cost reduction of underwater sensor network nodes and the increasing demand for underwater detection and monitoring, near-land areas, shallow water areas, lakes and rivers have gradually tended to densely arranged sensor nodes. In order to achieve real-time monitoring, most nodes now have visual sensors instead of acoustic sensors to collect and analyze optical images, mainly because cameras might be more advantageous when it comes to dense underwater sensor networks. In this article, image enhancement, saliency detection, calibration and refraction model calculation are performed on the video streams collected by multiple optical cameras to obtain the track of the dynamic target. This study not only innovatively combines the application of AOD-Net’s (all-in-one network) image defogging algorithm with underwater image enhancement, but also refers to the BASNet (Boundary-Aware Salient network) network architecture, introducing frame difference results in the input to reduce the interference of static targets. Based on the aforementioned technologies, this paper designs a dynamic target tracking system centered on video stream processing in dense underwater networks. As part of the process, most nodes carried underwater cameras. When the dynamic target could be captured by at least two nodes in the network at the same time, the target position could then be calculated and tracked.
Talking with Familiar Strangers: An Empirical Study on HTTPS Context Confusion Attacks

HTTPS is principally designed for secure end-to-end communication, which adds confidentiality and integrity to sensitive data transmission. While several man-in-the-middle attacks (e.g., SSL Stripping) are available to break the secured connections, state-of-the-art security policies (e.g., HSTS) have significantly increased the cost of successful attacks. However, the TLS certificates shared by multiple domains make HTTPS hijacking attacks possible again.
Nonlinear Two-Time-Scale Stochastic Approximation: Convergence and Finite-Time Performance

Two-time-scale stochastic approximation, a generalized version of the popular stochastic approximation, has found broad applications in many areas including stochastic control, optimization, and machine learning. Despite its popularity, theoretical guarantees of this method, especially its finite-time performance, are mostly achieved for the linear case while the results for the nonlinear counterpart are very sparse. Motivated by the classic control theory for singularly perturbed systems, we study in this article the asymptotic convergence and finite-time analysis of the nonlinear two-time-scale stochastic approximation. Under some fairly standard assumptions, we provide a formula that explicitly characterizes the rate of convergence of the main iterates to the desired solutions. In particular, we show that the mean square error generated by the method convergences to zero at a rate <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\mathcal O}(1/k^{2/3})$</tex-math></inline-formula> , where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> is the number of iterations. The key idea in our analysis is to properly choose the two step sizes to characterize the coupling between the fast and slow time-scale iterates.