Reviewerly/exHarmony huggingface dataset:
Performance evolution for sentiment classification using machine learning algorithm

Machine Learning (ML) is an Artificial Intelligence (AI) approach that allows systems to adapt to their environment based on past experiences. Machine Learning (ML) and Natural Language Processing (NLP) techniques are commonly used in sentiment analysis and Information Retrieval Techniques (IRT). This study supports the use of ML approaches, such as K-Means, to produce accurate outcomes in clustering and classification approaches. The main objective of this research is to explore the methods for sentiment classification and Information Retrieval Techniques (IRT). So, a combination of different machine learning algorithms is used with a dataset from amazon unlocked mobile reviews and telecom tweets to achieve better accuracy as it is crucial to consider the previous predictions related to sentiment classification and IRT. The datasets consist of user reviews ratings and algorithms utilized consist of K-Means Clustering algorithm, Logistic Regression (LR), Random Forest (RF), and Decision Tree (DT) algorithms. The amalgamation of each algorithm with the K-Means resulted in high levels of accuracy. Specifically, the K-Means combined with Logistic Regression (LR) yielded an accuracy rate of 99.98%. Similarly, the K-Means integrated with Random Forest (RF) resulted in an accuracy of 99.906%. Lastly, when the K-Means was merged with the Decision Tree (DT) Algorithm, the accuracy obtained was 99.83%.We exhibited that we could foresee efficient, effective, and accurate outcomes.
Curated Dataset for Red Blood Cell Tracking from Video Sequences of Flow in Microfluidic Devices

This work presents a dataset comprising images, annotations, and velocity fields for benchmarking cell detection and cell tracking algorithms. The dataset includes two video sequences captured during laboratory experiments, showcasing the flow of red blood cells (RBC) in microfluidic channels. From the first video 300 frames and from the second video 150 frames are annotated with bounding boxes around the cells, as well as tracks depicting the movement of individual cells throughout the video. The dataset encompasses approximately 20,000 bounding boxes and 350 tracks. Additionally, computational fluid dynamics simulations were utilized to generate 2D velocity fields representing the flow within the channels. These velocity fields are included in the dataset. The velocity field has been employed to improve cell tracking by predicting the positions of cells across frames. The paper also provides a comprehensive discussion on the utilization of the flow matrix in the tracking steps.
Management Method Evaluation for Innovation and Entrepreneurship in College with Multi-Scale Feature Convolutional Network

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A hybrid framework for efficient and accurate orientation estimation based on single and multiple orientation vector fields

This article presents a hybrid framework for efficient and accurate orientation estimation. The proposed scheme combines the single orientation information given by a novel method and the multiple orientation information provided by a bank of linear orientated morphological openings. The single orientations are estimated by means of an energy-minimization Gaussian filtering which solves the drawback related to phase changes of other methods. After describing the formulation of these two approaches for estimating the existing orientations in the pixels of an image, several strategies have been analyzed to fuse and discriminate the information of both orientation vector fields in the resulting hybrid orientation vector field. The objective of the proposed hybrid method is to reduce the computational cost involved in calculating multiple orientations only in those pixels where they exist while maintaining the accuracy provided by the single orientation method in the remaining pixels. To this end, strategies ranging from a threshold in the multiple orientation vector field to a convolutional neural network trained with a set of patterns specifically designed to detect pixels with multiple orientations, passing through the Harris corner detector, have been tested to identify those pixels where multiple orientations exist. Results on natural and synthetic images show the accuracy and the computational efficiency achieved by the proposed hybrid framework to provide the vector field with single and multiple orientations.
Assessing MT with measures of PE effort

Recent improvements in quality obtained by neural machine translation (NMT) have boosted its presence in the translation industry. In many domains and language combinations, translators post-edit raw MT output: they edit and correct the pre-translated text to produce the final translation. However, this process can only produce the expected results if the quality of the raw MT can be assured. MT is usually assessed with automatic metrics, as they are faster and cheaper. However, these metrics are not always good quality indicators and do not correlate to the post-editing effort. We suggest a two-step evaluation process for MT intended for post-editing. The automatic evaluations are followed by the assessment of the three dimensions of PE effort. This targeted evaluation can ensure a quality of the raw MT which does not jeopardise the final product or compromise the task of post-editors. We include a detailed description of PosEdiOn, an easy-to-use standalone tool which records PE effort, and a use case of its implementation. 18 translators post-edit texts from English into Spanish from the news domain translated with DeepL and an NMT system trained by the authors to gather PE effort metrics. We compare automatic and PE effort metrics to assess which MT system would be more suitable for post-editing.
Efficient search for relevance explanations using MAP-independence in Bayesian networks

-independence is a novel concept concerned with explaining the (ir)relevance of intermediate nodes for maximum a posteriori () computations in Bayesian networks. Building upon properties of -independence, we introduce and experiment with methods for finding sets of relevant nodes using both an exhaustive and a heuristic approach. Our experiments show that these properties significantly speed up run time for both approaches. In addition, we link -independence to defeasible reasoning, a type of reasoning that analyses how new evidence may invalidate an already established conclusion. Ways to present users with an explanation using -independence are also suggested.
CROWBAR: Natively Fuzzing Trusted Applications Using ARM CoreSight

Abstract Trusted execution environments (TEE) are deployed on many platforms to provide both confidentiality and integrity, and their extensive use offers a secure environment for privacy-sensitive operations. Despite TEE prevalence in the smartphone and tablet market, vulnerability research into TEE security is relatively rare. This is, in part, due to the strong isolation guarantees provided by its implementation. In this paper, we propose a hardware assisted fuzzing framework, CROWBAR, that bypasses TEE isolation to natively evaluate trusted applications (TAs) on mobile devices by leveraging ARM CoreSight components. CROWBAR performs feedback-driven fuzzing on commercial, closed source TAs while running in a TEE protected environment. We implement CROWBAR on 2 prototype commercial-off-the-shelf (COTS) smartphones and one development board, finding 3 unique crashes in 5 closed source TAs that are previously unreported in the TrustZone fuzzing literature.
A novel evaluation framework for recommender systems in big data environments

Recommender systems were first introduced to solve information overload problems in enterprises. Over the last few decades, recommender systems have found applications in several major websites related to e-commerce, music and video streaming, travel and movie sites, social media, and mobile app stores. Several methods have been proposed over the years to build recommender systems. However, very little work has been done in recommender system evaluation metrics. The most common approach to measuring recommender system’s performance in offline settings is to employ micro or macro averaged versions of standard machine-learning measures. Profit or other business-oriented metrics have been proposed for other predictive analytics problems, such as churn prediction. However, no such metrics have emerged for the recommender system context. In this work, we propose a novel evaluation metric that incorporates information from the online-platform userbase’s behavior. This metric’s rationale is that the recommender system ought to improve customers’ repeated use of an online platform beyond the baseline level (i.e. in the absence of a recommender system). An empirical application of this novel metric is also presented in a real-world mobile app store, which integrates the dynamics of large-scale big data environments, which are common deployment scenarios for these types of recommender systems. The resulting profit metric is shown to correlate with the existing metrics while also being capable of integrating cost information, thereby providing an additional business benefit context, which allows us to differentiate between two similarly performing models.
Hyperparameters optimization of convolutional neural network based on local autonomous competition harmony search algorithm

Abstract Because of the good performance of convolutional neural network (CNN), it has been extensively used in many fields, such as image, speech, text, etc. However, it is easily affected by hyperparameters. How to effectively configure hyperparameters at a reasonable time to improve the performance of CNNs has always been a complex problem. To solve this problem, this paper proposes a method to automatically optimize CNN hyperparameters based on the local autonomous competitive harmony search (LACHS) algorithm. To avoid the influence of complicated parameter adjustment of LACHS algorithm on its performance, a parameter dynamic adjustment strategy is adopted, which makes the pitch adjustment probability PAR and step factor BW dynamically adjust according to the actual situation. To strengthen the fine search of neighborhood space and reduce the possibility of falling into local optima for a long time, an autonomous decision-making search strategy based on the optimal state is designed. To help the algorithm jump out of the local fitting situation, this paper proposes a local competition mechanism to make the new sound competes with the worst harmonic progression of local selection. In addition, an evaluation function is proposed, which integrates the training times and recognition accuracy. To achieve the purpose of saving the calculation cost without affecting the search result, it makes the training time for each model depending on the learning rate and batch size. In order to prove the feasibility of LACHS algorithm in configuring CNN superparameters, the classification of the Fashion-MNIST dataset and CIFAR10 dataset is tested. The comparison is made between CNN based on empirical configuration and CNN based on classical algorithms to optimize hyperparameters automatically. The results show that the performance of CNN based on the LACHS algorithm has been improved effectively, so this algorithm has certain advantages in hyperparametric optimization. In addition, this paper applies the LACHS algorithm to expression recognition. Experiments show that the performance of CNN optimized based on the LACHS algorithm is better than that of the same type of artificially designed CNN. Therefore, the method proposed in this paper is feasible in practical application.
Quantum Diffraction: Emergence of the Quantum Substructure of Light

InYoung’ double slit, quantum formulation Diffraction, QED description this chapter we discuss an entirely quantum mechanical description of diffraction. We will introduce the remarkably simple new paradigm that diffraction patterns, long associated with wave interference, are instead direct signatures of the quantum states of light. In first order QED (corresponding to conventional quantum mechanics) this link has remained hidden leading to the wave-photon ambiguity. The ambiguity is shown to disappear in second order QED, where only the photon-based description can account for the observed diffraction patterns.
React to the Worst: Lightweight and Proactive Protection of Location Privacy

This work presents a novel optimal control method for privacy protection of mobility data. Protection is based on data obfuscation, consisting in sending to the geolocated service a finely tuned fake location. The objective is twofold, keeping privacy values at an acceptable level and guaranteeing a reasonable utility loss, with a lightweight algorithm able to run on mobile devices. The proposed method consists of an offline modeling stage, based on privacy worst-case anticipation, and a fast algorithm executed online. In the offline stage, the algorithm computes the average amount of allowed utility loss necessary to maintain the privacy value of the following h steps above a given lower bound. For this purpose, the worst possible scenario over the future steps is computed and compared with the privacy function of the solution obtained by an MPC method. The online stage uses the information computed offline to solve an optimization problem whose decision variable is the location to transmit and whose objective is to maintain the privacy value above a minimal level, by avoiding large utility losses. The method is validated on an instance of a database of real records and compared with a state-of-the-art competitor.
Sinogram Interpolation Inspired by Single-Image Super Resolution

Computed tomography is a medical imaging procedure used to estimate the interior of a patient or an object. Radiation scans are taken at regularly spaced angles around the object, forming a sinogram. This sinogram is then reconstructed into an image representing the contents of the object. This results in a fair amount of radiation exposure for the patient, which increases the risk of cancer. Less radiation and fewer views, however, leads to inferior image reconstruction. To solve this sparse-view problem, a deep-learning model is created that takes as input a sparse sinogram and outputs a sinogram with interpolated data for additional views. The architecture of this model is based on the super-resolution convolutional neural network. The reconstruction of modelinterpolated sinograms has less mean-squared error than the reconstruction of the sparse sinogram. It also has less mean-squared error than a reconstruction of a sinogram interpolated using the popular bilinear image-resizing algorithm. This model can be easily adapted to different image sizes, and its simplicity translates into efficiency in both time and memory requirements.
CODet: Component Object Detector Extracting Structural Features Based on Target Characteristics

Deep learning technology has promoted the object detection task in the remote sensing (RS) field to move toward better performance and more demanding requirements. Except for rigid body objects, component objects (COs) with more complex characteristics remain a detection challenge. Its “partial rules and overall disorder” characteristic limits the model learning ability to the structural features. And the internal noise and relatively sparse arrangement are not conducive to optimizing the model by the existing sample assignment strategies. We propose CODet to detect COs in RS scenes. It consists of a cross-hierarchy feature fusion module (CFM) and a noise-sparse sample assignment (NSA) strategy. CFM learns the potential representation and relative position relationship of components by fusing different level features. NSA redefines the optimization process of sample assignment. It aims to alleviate the problems of classification–localization misalignment (CLM) and the positive–negative sample imbalance (PNI) caused by the object’s internal noise and sparse arrangement. The method is verified on the proposed COD dataset of six categories of COs, reaching an average mAP/mAP <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">50</sup> of 54.3/86.0. To be closer to the task requirements of the practical RS scene, we also propose a RS large-scale images inference framework. It includes a dataset (APRoI, labeled with COs and rigid body objects), a large-scale image inference strategy, and a set of evaluation metrics. With CODet as the core, the framework can effectively reduce the inference time by three to four times on images with an average of more than 100 million pixels.
Ubiquitous, Secure, and Efficient Mobile Sensing Systems

The rapid development of mobile sensors and machine learning techniques has enabled the utilization of diverse sensor data in many applications. However, the design and implementation of these algorithms and systems to achieve ubiquitous, secure, and efficient real-world solutions remains challenging. This paper discusses various effective solutions to overcome these challenges and underscores potential future directions.
Minimizing a Smartphone's TCB for Security-Critical Programs with Exclusively-Used, Physically-Isolated, Statically-Partitioned Hardware

Smartphone owners often need to run security-critical programs on the same device as other untrusted and potentially malicious programs. This requires users to trust hardware and system software to correctly sandbox malicious programs, trust that is often misplaced. Our goal is to minimize the number and complexity of hardware and software components that a smartphone owner needs to trust. We present a split-trust hardware design composed of statically-partitioned, physically-isolated trust domains. We introduce a few simple, formally-verified hardware components to enable a program to gain provably exclusive and simultaneous access to both computation and I/O on a temporary basis. To manage this hardware, we present OctopOS, an OS composed of mutually distrustful subsystems. We present a prototype of this machine (hardware and OS) on a CPU-FPGA board and show that it incurs a small hardware cost compared to modern smartphone SoCs. For security-critical programs, we show that this machine significantly reduces the required trust compared to mainstream TEEs while achieving usable performance. For normal programs, performance is similar to a legacy machine.
Real-Time Privacy Preserving Human Activity Recognition on Mobile using 1DCNN-BiLSTM Deep Learning

Human Activity Recognition (HAR) from sensors has real-world applications such as fall detection of elderly or improvement in human-robot interactions, but privacy is of concern in videos of such applications. We investigated the feasibility of developing a real-time HAR mobile application that detects 3 different human poses: standing, sitting and lying, while preserving privacy by a facial detection method that also masks faces in the video. We proposed a hybrid 1-Dimensional Convolutional Neural Network with Bidirectional Long Short-Term Memory (1DCNN-BiLSTM) algorithm to recognise these 3 poses. We trained our proposed 1DCNN-BiLSTM algorithm using RGB videos from 3 public datasets (URFall, NTU RGB+D, Fall Detection) whereby features were extracted using MoveNet. The results yielded 82.1% test accuracy and 0.930 macro average AUC-ROC score on the combined dataset, hence demonstrating feasibility of deploying the proposed algorithm for balanced as well as imbalanced datasets. We then implemented our proposed 1DCNN-BiLSTM algorithm with face detection and masking in a mobile application. The results showed that the mobile application could perform human pose detection inference in real time (3 people at 23.0-25.3 FPS). Hence this study showed promise of a larger scale-study of these HAR systems, such as monitoring potential falls in video surveillance.
Per-pixel accuracy as a weighting criterion for combining ensemble of extreme learning machine classifiers for satellite image classification

Reliable classification of satellite images is essential for various applications, including land cover and crop (LCC) mapping. In recent years, ensemble classifiers have shown remarkable success in satellite image classification as they provide solutions to combine and integrate multiple classifiers. This research presented a novel satellite image classification method called PAELM. The PAELM algorithm builds a diverse set of extreme learning machine (ELM) classifiers and combines them using the pixel-based LCC accuracy values in such a way that, for a given pixel, the most highly accurate ELM classifier among the ensemble of ELMs assigns the LCC class of that pixel. We validated PAELM on six experimental sites with varying geographical environments in the United States and compared it with three advanced machine learning classifiers, namely support vector machine, conventional ELM, and extreme gradient boosting and one advanced ensemble classifier. Our results showed that PAELM improved the accuracy of LCC mapping in comparison to the benchmark classifiers. The LCC maps generated by PAELM had an averaged overall accuracy and aggregated F1 score of 0.811 and 0.804, respectively, while these values for the most accurate benchmark classifier were 0.787 and 0.781, respectively. The results also implied that all the classifiers were sensitive to scene heterogeneity and LCC class composition, with PAELM being the least sensitive classifier to these factors. Overall, our findings suggested that PAELM was a promising approach for accurate LCC mapping, demonstrating the practicality of spatial accuracy as a weighting factor in the integration of ensemble of classifiers.
Estimating Gaze Duration Error from Eye Tracking Data

Eye tracking technologies produce a series of timestamped gaze fixation points that can be attributed to objects within a subject’s field of vision. These fixation points are often aggregated in order to generate a measurement of the gaze duration in Area of Interest studies. Error is typically measured on the basis of the variation between individual gaze fixation points and the intended point of fixation. These underlying errors in fixation have no inherent transformation for making a statement about error in gaze duration, even though they are expected to be related.
Audio Super-resolution Using Feed-Forward Neural Networks

In this paper, a system for reducing the file size of an audio signal, and then performing super-resolution on the resultant signal to estimate the original, is proposed and designed. This design takes influence from the principles of audio sampling, as well as super-resolution systems designed for visual media, and is split into an encoder and a decoder. The encoder successfully reduces the file size of the audio file by a significant amount. The super-resolution-based decoder can also successfully generate a matching high-frequency audio track that can be combined with the encoded lossy audio in order to estimate the original audio with a reasonable degree of audible accuracy. While a number of improvements to the system can be made in the future, it shows great promise, as it accomplishes the goals it was designed to meet.