Reviewerly/exHarmony huggingface dataset:
Lung Nodule Classification Using Taguchi-Based Convolutional Neural Networks for Computer Tomography Images

Lung cancer occurs in the lungs, trachea, or bronchi. This cancer is often caused by malignant nodules. These cancer cells spread uncontrollably to other organs of the body and pose a threat to life. An accurate assessment of disease severity is critical to determining the optimal treatment approach. In this study, a Taguchi-based convolutional neural network (CNN) was proposed for classifying nodules into malignant or benign. For setting parameters in a CNN, most users adopt trial and error to determine structural parameters. This study used the Taguchi method for selecting preliminary factors. The orthogonal table design is used in the Taguchi method. The final optimal parameter combination was determined, as were the most significant parameters. To verify the proposed method, the lung image database consortium data set from the National Cancer Institute was used for analysis. The database contains a total of 16,471 images, including 11,139 malignant nodule images. The experimental results demonstrated that the proposed method with the optimal parameter combination obtained an accuracy of 99.6%.
Dynamic discovery of favorite locations in spatio-temporal social networks

A large volume of data flowing throughout location-based social networks (LBSN) gives support to the recommendation of points-of-interest (POI). One of the major challenges that significantly affects the precision of recommendation is to find dynamic spatio-temporal patterns of visiting behaviors, which can hardly be figured out because of the multiple side factors. To confront this difficulty, we jointly study the effects of users' social relationships, textual reviews, and POIs' geographical proximity in order to excavate complex spatio-temporal patterns of visiting behaviors when the data quality is unreliable for location recommendation in spatio-temporal social networks. We craft a novel framework that recommends any user the POIs with effectiveness. The framework contains two significant techniques: (i) a network embedding method is adopted to learn the vectors of users and POIs in an embedding space of low dimension; (ii) a dynamic factor graph model is proposed to model various factors such as the correlation of vectors in the previous phase. A collection of experiments was carried out on two real large-scale datasets, and the experimental outcomes demonstrate the supremacy of the proposed method over the most advanced baseline algorithms owing to its highly effective and efficient performance of POI recommendation.
Quantifying the Sensitivity to Errors in Analog Quantum Simulation

Quantum simulators are widely seen as one of the most promising near-term applications of quantum technologies.However, it remains unclear to what extent a noisy device can output reliable results in the presence of unavoidable imperfections.Here we propose a framework to characterize the performance of quantum simulators by linking the robustness of measured quantum expectation values to the spectral properties of the output observable, which in turn can be associated with its macroscopic or microscopic character.We show that, under general assumptions and on average over all states, imperfect devices are able to reproduce the dynamics of macroscopic observables accurately, while the relative error in the expectation value of microscopic observables is much larger on average.We experimentally demonstrate the universality of these features in a state-of-the-art quantum simulator and show that the predicted behavior is generic for a highly accurate device, without assuming any detailed knowledge about the nature of the imperfections.
Volcano eruption algorithm for solving optimization problems

Meta-heuristic algorithms have been proposed to solve several optimization problems in different research areas due to their unique attractive features. Traditionally, heuristic approaches are designed separately for discrete and continuous problems. This paper leverages the meta-heuristic algorithm for solving NP-hard problems in both continuous and discrete optimization fields, such as nonlinear and multi-level programming problems through extensive simulations of volcano eruption process. In particular, a new optimization solution named volcano eruption algorithm is proposed in this paper, which is inspired from the nature of volcano eruption. The feasibility and efficiency of the algorithm are evaluated using numerical results obtained through several test problems reported in the state-of-the-art literature. Based on the solutions and number of required iterations, we observed that the proposed meta-heuristic algorithm performs remarkably well to solve NP-hard problem. Furthermore, the proposed algorithm is applied to solve some large-size benchmarking LP and Internet of vehicles problems efficiently.
Edge Detection using Guided Image Filtering and Enhanced Ant Colony Optimization

Edge detection is an important process in many classes of engineering problems. An edge is a boundary between two regions. In the edge detection process, the input is a grayscale image while the output image is a binary image showing edge and non-edge pixels. As a state of art, methods are based on gradient operator, but the accuracy level is poor. Therefore, in recent past soft computing based methods are proposed which are more accurate, still, these methods fail to detect some of the true edges. In this paper, the two-level approach is adopted for edge detection, firstly image edges are enhanced using guided image filtering, and secondly on these enhanced images enhanced ant colony optimization method is applied for edge detection.
A framework for detecting credit card fraud with cost-sensitive meta-learning ensemble approach

Electronic payment systems continue to seamlessly aid business transactions across the world, and credit cards have emerged as a means of making payments in E-payment systems. Fraud due to credit card usage has, however, remained a major global threat to financial institutions with several reports and statistics laying bare the extent of this challenge. Several machine learning techniques and approaches have been established to mitigate this prevailing menace in payment systems, effective amongst which are ensemble methods and cost-sensitive learning techniques. This paper proposes a framework that combines the potentials of meta-learning ensemble techniques and cost-sensitive learning paradigm for fraud detection. The approach of the proposed framework is to allow base-classifiers to fit traditionally while the cost-sensitive learning is incorporated in the ensemble learning process to fit the cost-sensitive meta-classifier without having to enforce cost-sensitive learning on each of the base-classifiers. The predictive accuracy of the trained cost-sensitive meta-classifier and base classifiers were evaluated using Area Under the Receiver Operating Characteristic curve (AUC). Results obtained from classifying unseen data show that the cost-sensitive ensemble classifier maintains an excellent AUC value indicating consistent performance across different fraud rates in the dataset. These results indicate that the cost-sensitive ensemble framework is efficient in producing cost-sensitive ensemble classifiers that are capable of effectively detecting fraudulent transactions in different databases of payment systems irrespective of the proportion of fraud cases as compared to the performances of ordinary ensemble classifiers.
Optimization of the surface code design for Majorana-based qubits

The surface code is a prominent topological error-correcting code exhibiting high fault-tolerance accuracy thresholds. Conventional schemes for error correction with the surface code place qubits on a planar grid and assume native CNOT gates between the data qubits with nearest-neighbor ancilla qubits. Here, we present surface code error-correction schemes using $\textit{only}$ Pauli measurements on single qubits and on pairs of nearest-neighbor qubits. In particular, we provide several qubit layouts that offer favorable trade-offs between qubit overhead, circuit depth and connectivity degree. We also develop minimized measurement sequences for syndrome extraction, enabling reduced logical error rates and improved fault-tolerance thresholds. Our work applies to topologically protected qubits realized with Majorana zero modes and to similar systems in which multi-qubit Pauli measurements rather than CNOT gates are the native operations.
More Than Human Aesthetics

Species-specific aesthetics is an important consideration for interaction designers working with animals. The paper explores the concept of species-specific aesthetics with particular reference to elephants. Applying existing aesthetic dimensions and design principles to the challenge of designing interactive enrichment for them, we show how the insights gained can inform more than human-centered design in different settings. We offer a multi-faceted, multi-sensory lens for examining an animal-centred aesthetic experience of technology.
RNN-DP: A new differential privacy scheme base on Recurrent Neural Network for Dynamic trajectory privacy protection

Mobile devices furnish users with various services while on the move, but also raise public concerns about trajectory privacy. Unfortunately, traditional privacy protection methods, such as anonymity and generalization, are not secure because they cannot resist attackers with background knowledge. The emergence of differential privacy provides an effective solution to this problem. Still, the existing schemes are almost designed based on the collected aggregate historical data (so-called static trajectory privacy protection), which are not suitable for real-time dynamic trajectory privacy protection of mobile users. Furthermore, due to the complexity and redundancy features of the full trajectory data, the efficiency and accuracy of the privacy protection model are significantly limited by the existing schemes. In this paper, we propose a new differential privacy scheme base on the Recurrent Neural Network for Dynamic trajectory privacy Protection (RNN-DP). We firstly introduce a recurrent neural network model to handle the real-time data effectively instead of the full data. Secondly, we novelty leverage the dynamic velocity attribute to form a quaternion to indicate the status of the users. Moreover, we design a prejudgment mechanism to increase the availability of differential privacy technology. Compared with the current state-of-the-art mechanisms, the experimental results demonstrate that RNN-DP displays excellent performance in privacy protection and data availability for dynamic trajectory data.
Arbitrary Direction Ship Detection in Remote-Sensing Images Based on Multitask Learning and Multiregion Feature Fusion

Ship detection in remote sensing plays an important role in civil and military fields. Owing to the complex background and uncertain direction, ship detection is full of challenge by using the commonly used object-detection methods. In this article, a new framework for detecting the arbitrary direction ships is proposed based on the improvement in the Faster region-based convolutional network (R-CNN), in which the shape of the bounding box is described by three sides, namely, vertical side, horizontal side, and short side, respectively. The inclination of the ship is obtained by calculating the arc-tangent value of the vertical side to the horizontal side. First, the better performing ResNet-101 is adopted to extract features over an entire image, which are shared by the region proposal network (RPN) and the head network. Then, the multidirection proposal regions that may contain ships are generated by the RPN. Next, the global and local features of the proposal regions are combined as the whole features of the regions by a multiregion feature-fusion (MFF) module, which can provide more detailed information of the regions. Finally, the head network uses the whole features of the proposal regions for bounding-box recognition through multitask learning, including classification, regression, and incline direction prediction (left or right). The proposed method is tested and compared with other state-of-the-art ship-detection methods on two open remote-sensing data sets and some large-scale and real images. The experimental results validate that the proposed approach has achieved better performance.
SVD-based factored-form Cubature Kalman Filtering for continuous-time stochastic systems with discrete measurements

In this paper, a singular value decomposition (SVD) approach is developed for implementing the cubature Kalman filter. The discussed estimator is one of the most popular and widely used method for solving nonlinear Bayesian filtering problem in practice. To improve its numerical stability (with respect to roundoff errors) and practical reliability of computations, the SVD-based methodology recently proposed for the classical Kalman filter is generalized on the nonlinear filtering problem. More precisely, we suggest the SVD-based solution for the continuous-discrete cubature Kalman filter and design two estimators: (i) the filter based on the traditionally used Euler-Maruyama discretization scheme; (ii) the estimator based on advanced It\^{o}-Taylor expansion for discretizing the underlying stochastic differential equations. Both estimators are formulated in terms of SVD factors of the filter error covariance matrix and belong to the class of stable factored-form (square-root) algorithms. The new methods are tested on a radar tracking problem.
Towards automatically generating block comments for code snippets

Code commenting is a common programming practice of practical importance to help developers review and comprehend source code. There are two main types of code comments for a method: header comments that summarize the method functionality located before a method, and block comments that describe the functionality of the code snippets within a method. Inspired by the effectiveness of deep learning techniques in the NLP field, many studies focus on using the machine translation model to automatically generate comment for the source code. Because the data set of block comments is difficult to collect, current studies focus more on the automatic generation of header comments than that of block comments. However, block comments are important for program comprehension due to their explanation role for the code snippets in a method. To fill the gap, we have proposed an approach that combines heuristic rules and learning-based method to collect a large number of comment-code pairs from 1,032 open source projects in our previous study. In this paper, we propose a reinforcement learning-based method, RL-BlockCom, to automatically generate block comments for code snippets based on the collected comment-code pairs. Specifically, we utilize the abstract syntax tree (i.e., AST) of a code snippet to generate a token sequence with a statement-based traversal way. Then we propose a composite learning model, which combines the actor-critic algorithm of reinforcement learning with the encoder-decoder algorithm, to generate block comments. On the data set of the comment-code pairs, the BLEU-4 score of our method is 24.28, which outperforms the baselines and state-of-the-art in comment generation.
Automated Hate Speech Detection on Twitter

With the sudden increase in micro-blogging websites such as Twitter, Facebook, and Tumbler, the communication between people becomes indirect and reliable; people from different educational backgrounds, cultures share their opinion on different aspects of life every day. This has resulted in conflicts among people. As a result, the use of hate speech becomes a very serious problem. Manual detection of such content from these websites is a very tedious task. Hate speech is the use of aggressive, violent or offensive language which targets a specific group of people sharing common property, this property can be their gender, ethnic group or their believes and regions. The proposed model is capable to detect hate content on Twitter automatically. This approach is based on a bag of words and TFIDF (term frequency-inverse document frequency) approach. These features are used to train machine learning classifiers. Exhaustive experiments are conducted on existing twitter dataset and the accuracy obtained by logistic regression classifier is equal to 94.11% on detecting whether a particular tweet is hateful or not.
Improving Affect Detection in Game-Based Learning with Multimodal Data Fusion

Accurately recognizing learner affect is critically important for enabling affect-responsive learning environments to support student learning and engagement. Multimodal affect detection combining sensor-based and sensor-free approaches has shown significant promise in both laboratory and classroom settings. However, important questions remain regarding which data channels are most predictive and how they should be combined. In this paper, we investigate a multimodal affect detection framework that integrates motion tracking-based posture data and interaction-based trace data to recognize the affective states of students engaged with a game-based learning environment for emergency medical training. We compare several machine learning-based affective models using competing feature-level and decision-level multimodal data fusion approaches. Results indicate that multimodal affect detectors induced using joint feature representations from posture-based and interaction-based data channels yield improved accuracy relative to unimodal models across several learner-centered affective states. These findings point toward implications for the design of multimodal affect-responsive learning environments that support learning and engagement.
Robust video watermarking using a hybrid DCT-DWT approach

In order to solve the limitations of the digital video watermarking algorithm, this paper proposes a new robust video watermarking algorithm using combining discrete cosine transform (DCT) and discrete wavelet transform (DWT) techniques. First of all, the video frames are randomly selected and then the DCT algorithm is applied to the selected video frames. After that, the first column of the selected video frames is scrambled using the Arnold algorithm. Furthermore, every column with 4 direct current (DC) coefficients is reshaped and transformed into four different sub-bands using the DWT technique. Next, the watermark is embedded into the approximation (LL) sub-band. The proposed algorithm is easy to carry out because it provides random frames with no special requirements for video frames. The experiment results indicate that this algorithm can resist against different kinds of watermarking attacks, such as the Gaussian filter attack and sharpen attack. In addition, it also illustrates that the proposed algorithm has a better result than some other watermarking algorithms.
Quantum Algorithm for Petz Recovery Channels and Pretty Good Measurements

The Petz recovery channel plays an important role in quantum information science as an operation that approximately reverses the effect of a quantum channel. The pretty good measurement is a special case of the Petz recovery channel, and it allows for near-optimal state discrimination. A hurdle to the experimental realization of these vaunted theoretical tools is the lack of a systematic and efficient method to implement them. This Letter sets out to rectify this lack: Using the recently developed tools of quantum singular value transformation and oblivious amplitude amplification, we provide a quantum algorithm to implement the Petz recovery channel when given the ability to perform the channel that one wishes to reverse. Moreover, we prove that, in some sense, our quantum algorithm's usage of the channel implementation cannot be improved by more than a quadratic factor. Our quantum algorithm also provides a procedure to perform pretty good measurements when given multiple copies of the states that one is trying to distinguish.
HMTL: Heterogeneous Modality Transfer Learning for Audio-Visual Sentiment Analysis

Multimodal sentiment analysis is an extended approach to traditional language-based sentiment analysis, which uses other relevant modality data. Multimodal sentiment analysis usually applies visual, textual, and acoustic representations for sentiment prediction. Recently, various data fusion methodologies have been proposed for multimodal sentiment analysis. In most cases, textual modality plays a major role, and visual and acoustic modalities are used as auxiliary sources for multimodal sentiment analysis. However, in general multimedia such as video, text transcripts of an individual's speech are not provided. Research on an audio-visual sentiment analysis methodology that does not depend on text modality is essential for multimodal sentiment analysis in real-world industrial applications. Therefore, it is important to improve audio-visual sentiment analysis because it currently exhibits lower performance than multimodal sentiment analysis, including text modality. In this paper, we propose heterogeneous modality transfer learning (HMTL) to utilize the knowledge of aligned text data as a source modality in transfer learning to improve audio-visual sentiment analysis performance. Our approach uses a decoder and adversarial learning techniques to reduce the gap between the source and target modalities in the embedded space for multimodal representation. Our proposed methodology experimentally outperformed recent unimodal and bimodal audio-visual sentiment analysis achievements.
Verifiable and Privacy-Preserving Traffic Flow Statistics for Advanced Traffic Management Systems

Crowdsourcing-based traffic monitoring plays an important role in advanced traffic management systems due to its high accuracy and low costs, but it may expose drivers real identities and sensitive locations that results in the privacy leakage of drivers. In this paper, we propose a crowdsourcing-based traffic monitoring scheme that enables a transportation management center (TMC) to achieve traffic flow statistics at road intersections in an efficient, verifiable, and privacy-preserving manner. Specifically, by integrating a homomorphic encryption primitive and a super-increasing sequence, traffic flow can be flexibly structured and encrypted by drivers, i.e., each drivers travel direction at T-junctions or crossroads is protected. As a middle-ware between drivers and TMC, roadside units (RSUs) are introduced to aggregate and further perturb the aggregated encrypted traffic flow based on a differential privacy mechanism. In this way, TMC is capable of acquiring the traffic flow statistics by decrypting the perturbed encrypted traffic flow, without disclosing each individual drivers traffic information. In addition, based on a lightweight commitment proof, the correctness of the encrypted drivers data can be guaranteed, i.e., a selfish driver cannot arbitrarily manipulate his data to poison the aggregated traffic flow. Finally, security analysis demonstrates that the proposed scheme satisfies all desirable security properties, including confidentiality, verifiability, unlinkability, and traceability. Extensive simulations are also conducted to show that the proposed scheme is efficient in terms of low computation and communication costs.
Gut-Tracking as Cultivation

Contemporary self-tracking devices take forms that reflect particular cultures of self-tracking that emphasize clinical perspectives and numerical precision. In this pictorial, we explore alternative forms to speculate on other possible self-tracking cultures. We present how we involved experts on the relationship between the gut and the brain to inspire a design brief for an alternative gut tracking device. This led to the design of Loupe and Lightbox, which together operate to externalize gut biota for closer examination, aesthetic appreciation, and reflection on the self. The device represents an example of self-tracking as cultivation-building a longer-term relationship with the self as something to be nurtured, tended to, and cared about.