Reviewerly/exHarmony huggingface dataset:
Underwater Acoustic Source Localization via Kernel Extreme Learning Machine

Fiber-optic hydrophones have received extensive research interests due to their advantage in ocean underwater target detection. Here, kernel extreme learning machine (K-ELM) is introduced to source localization in underwater ocean waveguide. As a data-driven machine learning method, K-ELM does not need a priori environment information compared to the conventional method of match field processing. The acoustic source localization is considered as a supervised classification problem, and the normalized sample covariance matrix formed over a number of snapshots is utilized as an input. The K-ELM is trained to classify sample covariance matrices (SCMs) into different depth and range classes with simulation. The source position can be estimated directly from the normalized SCMs with K-ELM. The results show that the K-ELM method achieves satisfactory high accuracy on both range and depth localization. The proposed K-ELM method provides an alternative approach for ocean underwater source localization, especially in the case with less a priori environment information.
User Preferences for Automated Curation of Snackable Content

As the volume of content and the connectivity of social media have grown, snackable content has increasingly become an enjoyable and engaging way to share content. Snackable content is a shortened form of original content focusing on a single theme or motif for entertainment and quick understanding of a video moment. For content owners with a large library of long-form content (movies, television series, documentaries, etc.), one challenge in accommodating snackable content in social media uses is the correct identification and cutting of interesting regions. Related problems have been studied for algorithmic discovery of content for movie trailers, short-duration meme content, and medium duration news stories, but none of these approaches included user preferences as explicit drivers for cuts. This paper analyzes both human and automatic methods for creating snackable clips across different categories of content with two comprehensive user studies. Contrary to initial expectations, findings amongst the surveyed population indicate a preference for slightly longer snackable clips (60-90 seconds) and those that began or ended with a human character.
A Phrase-Level User Requests Mining Approach in Mobile Application Reviews: Concept, Framework, and Operation

Mobile application (app) reviews are feedback about experiences, requirements, and issues raised after users have used the app. The iteration of an app is driven by bug reports and user requirements analyzed and extracted from app reviews, which is a problem that app designers and developers are committed to solving. However, a great number of app reviews vary in quality and reliability. It is a difficult and time-consuming challenge to analyze app reviews using manual methods. To address this, a novel approach is proposed as an automated method to predict high priority user requests with fourteen extracted features. A semi-automated approach is applied to annotate requirements with high or low priority with the help of app changelogs. Reviews from six apps were retrieved from the Apple App Store to evaluate the feasibility of the approach and interpret the principles. The performance comparison results of the approach greatly exceed the IDEA method, with an average precision of 75.4% and recall of 70.4%. Our approach can be applied to specific app development to assist app developers in quickly locating user requirements and implement app maintenance and evolution.
Variational Bayesian partially linear mean shift models for high‐dimensional Alzheimer's disease neuroimaging data

Alzheimer's disease can be diagnosed by analyzing brain images (eg, magnetic resonance imaging, MRI) and neuropsychological tests (eg, mini‐mental state examination, MMSE). A partially linear mean shift model (PLMSM) is here proposed to investigate the relationship between MMSE score and high‐dimensional regions of interest in MRI, and detect the outliers. In the presence of high‐dimensional data, existing Bayesian approaches (eg, Markov chain Monte Carlo) to analyze a PLMSM take intensive computational cost and require huge memory, and have low convergence rate. To address these issues, a variational Bayesian inference is developed to simultaneously estimate parameters and nonparametric functions and identify outliers in a PLMSM. A Bayesian P‐splines method is presented to approximate nonparametric functions, a Bayesian adaptive Lasso approach is employed to select predictors, and outliers are detected by the classification variable. Two simulation studies are conducted to assess the finite sample performance of the proposed method. An MRI dataset with elderly cognitive ability is provided to corroborate the proposed method.
Minimally produced inkjet-printed tactile sensor model for improved data reliability

Inkjet-printing as an on-the-go, inexpensive, and green method of creating instant flexible sensors and circuits will not proliferate until reliable device fabrication is possible outside the research environment. Shortfalls exist due to non-uniform fabrication/curing, environmental humidity/temperature influence, and uncontrollable deposition conditions, particularly in low-production setups. Electrical non-uniformity and variations from low-quality prints made by a minimally produced inkjet-printed sensor may be overcome by training a machine learning model to interpret the variabilities and output a high-confidence prediction of the signal. In this report, an inkjet-printed tactile sensor is modeled to simulate generate a rich data-set for training and testing an echo state network. The end goal of the reported work is to attach the echo state network to the imperfect, on-the-go, inkjet-printed sensor as an edge computing device, transforming the unreliable data into a more stable readout. In this way, the sensor design may be printed using any suitable inkjet-printer with minimal production effort and still extract reliable data. This enables inkjet-printers to be used at home by those in isolated/restrictive settings, poor communities, resource starved environments, or by enthusiasts. Applications include biometric, environmental, electro-chemical and -mechanical sensing, and the concept may be extended to inkjet-printed circuits for signal stabilization.
Jamais vu: thwarting microarchitectural replay attacks

Microarchitectural Replay Attacks (MRAs) enable an attacker to eliminate the measurement variation in potentially any microarchitectural side channel—even if the victim instruction is supposed to execute only once. In an MRA, the attacker forces pipeline flushes in order to repeatedly re-execute the victim instruction and denoise the channel. MRAs are not limited to transient execution attacks: the replayed victim can be an instruction that will eventually retire. This paper presents the first technique to thwart MRAs. The technique, called Jamais Vu, detects when an instruction is squashed. Then, as the instruction is re-inserted into the pipeline, Jamais Vu automatically places a fence before it to prevent the attacker from squashing it again. This paper presents several Jamais Vu designs that offer different trade-offs between security, execution overhead, and implementation complexity. One design, called Epoch-Loop-Rem, effectively mitigates MRAs, has an average execution time overhead of 13.8% in benign executions, and only needs counting Bloom filters. An even simpler design, called Clear-on-Retire, has an average execution time overhead of only 2.9%, although it is less secure.
Adaptive multi-swarm in dynamic environments

Multi-population is a promising approach to optimization in dynamic environments. To appropriately distribute multiple populations to distinct areas of the search space and refine the best solution found by each population, an adaptive multi-swarm framework for dynamic optimization problems is proposed, in which several adaptations of multi-population approaches are developed for a better exploration/exploitation tradeoff. As the first intention, a basic adaptation is the combination of a group of active swarms and a group of inactive swarms. The group of active swarms are devoted to exploring new areas of the search space, and the group of inactive swarms are devoted to preserving useful experiences. One kind of swarm can be transformed into another. An active swarm becomes inactive after it converges. An inactive swarm will become active and search for new optima again when an environmental change occurs. For the second intention, another basic adaptation is the application of a local search to the best individual of a stagnated swarm. The experimental results on various moving peaks benchmarks show that the proposed framework is competitive with other state-of-the-art methods and more effective for dynamic environments under many peaks, severe changes, and high dimensionalities.
Learning Dynamic User Interest Sequence in Knowledge Graphs for Click-Through Rate Prediction

Despite that path-based and embedding-based models with knowledge graphs (KGs) achieve better recommendation performance compared with other deep learning based methods, such improvement is limited due to a lack of modeling user's dynamic interest. To address this issue, we explore a principled model to provide semantic understanding of each item in user's historical interest sequence in KGs. Specifically, we propose a multi-granularity dynamic interest sequence learning method, which is based on knowledge-enhanced path mining and interest fluctuation signal discovery, to obtain semantic-enhanced paths. Furthermore, the paths are embedded by the SEP2Vec, and merged through the proposed entropy-aware pooling layer to obtain the user preference representation, which is then used to learn dynamic user interest sequence. Experimental results on two public datasets of movie and music recommendation, and two industrial datasets of personalized local service recommendation in Alipay App have illustrated that the proposed model can achieve significantly better prediction performance compared with other known baselines.
A Machine Learning Approach to Recognize Speakers Region of the United Kingdom from Continuous Speech Based on Accent Classification

Speech is one of the primary modes of communication with a lot of identical features for measuring performance and behavior of human voice. Accent is an important element and can play a vital role in spoken language. In this paper, we propose a region detection approach of UK citizens by recognizing their accent from continuous speech. The ultimate goal of this paper is to detect the region of UK citizens from which region among Ireland, Midland, Northern England, Scotland, Southern England and Wales he/she belongs using continues speech. Firstly, we use Mel Frequency Cepstral Coefficient (MFCC) for extracting the feature from continuous speech. Then we applied several Machine Learning classifiers to train and test our model. After evaluating performance we find that k-Nearest Neighbors (k-NN), Support Vector Machine (SVM), and Random Forest classifier provide comparatively better accuracy than others. We also perform a comparative analysis of these three algorithms. We got the best accuracy of 98.48% by applying k-NN classifier.
Towards Activity Recognition through Multidimensional Mobile Data Fusion with a Smartphone and Deep Learning

The field of activity recognition has evolved relatively early and has attracted countless researchers. With the continuous development of science and technology, people’s research on human activity recognition is also deepening and becoming richer. Nowadays, whether it is medicine, education, sports, or smart home, various fields have developed a strong interest in activity recognition, and a series of research results have also been put into people’s real production and life. Nowadays, smart phones have become quite popular, and the technology is becoming more and more mature, and various sensors have emerged at the historic moment, so the related research on activity recognition based on mobile phone sensors has its necessity and possibility. This article will use an Android smartphone to collect the data of six basic behaviors of human, which are walking, running, standing, sitting, going upstairs, and going downstairs, through its acceleration sensor, and use the classic model of deep learning CNN (convolutional neural network) to fuse those multidimensional mobile data, using TensorFlow for model training and test evaluation. The generated model is finally transplanted to an Android phone to complete the mobile-end activity recognition system.
Automated Detection of Leadership Qualities Using Textual Data at the Message Level

Efficient leadership plays an important role in organizations, with the military being one of the more obvious examples of this statement. In this context, it is not surprising that ensuring a culture of excellence is at the heart of Navy leadership. However, it is not easy to maintain or increase the quality of leadership among staff, as such efforts require constant training and practice. To address this need for continuous monitoring and improvement in human leadership expressed in everyday communication, we demonstrate the feasibility of automatically detecting and classifying military leadership messages. We achieve this goal by 1) curating a data set of short text messages that are written in the military-specific language, have some characteristics of spoken language, and are human-annotated with labels referring to selected leadership roles and 2) demonstrating the performance of selected automation methods that allow classes to be predicted for each analyzed message. This study shows that recent deep learning methods provide reasonable performance, even when limited data is provided. Future efforts should focus on creating an automated self-assessment tool that would enable continuous monitoring and training of leadership skills required in the Navy domain.
Semantic segmentation based on DeeplabV3+ with multiple fusions of low-level features

For the problem that DeeplabV3+ semantic segmentation model uses downsampling operation several times in the encoder process, a large amount of object boundary information is lost, resulting in inaccurate segmentation at the object boundary location, this paper proposes a new semantic segmentation model that fuses low-level features several times based on DeeplabV3+ algorithm. Firstly, we use the encoding module of DeeplabV3+ algorithm to extract the high-level semantic information of the object; then we use the method of this paper to extract the low-level features several times to obtain the detailed information of the object boundary; finally, we fuse the high-level semantic information and the detailed information of the object to obtain the optimized semantic segmentation result. The experimental results on the current open source dataset PASCAL VOC 2012 show that compared with the DeeplabV3+ algorithm, the algorithm model proposed in this paper has better semantic segmentation results by fusing the low-level features multiple times, especially at the boundary position of the object, with a pixel accuracy of 94.1% and a mean intersection over union of 77.5%. The overall performance of the algorithm model proposed in this paper has reached the current leading level.
Fast motion planning for a laboratory 3D gantry crane in the presence of obstacles

In this paper, a concept is presented for the fast motion planning of a 3D laboratory crane in a static environment with obstacles taking into account the dynamic constraints on the state variables and control inputs. The focus is set on the possibility of a fast (re)planning if the starting and/or target state is changing. The proposed concept consists of two parts: an offline trajectory planner to set up a database of collision-free, time optimal trajectories from the starting to the target space, with an average computing time of 0.17 s for one trajectory, and an online planner based on a constrained quadratic program, with an average computing time of 7 ms for one trajectory. Simulation results for a validated mathematical model demonstrate the feasibility of the proposed approach.
Estimating the state of a One-sided quantum Cavity Driven by Vacuum field in Schrödinger Picture

A major component of establishing quantum technologies in the quantum optic platform is the cavity. This paper concerns state estimation (filtering) of a quantum one-sided cavity driven by a vacuum field while the output field is monitored by either Homodyne or photodetector. The stochastic master equation in the schrödinger picture is presented for Homodyne and photon measurements. Then, the infinite state space of the cavity is truncated by considering some reasonable simplifying assumptions. Finally, the number of photons in the cavity is estimated using the derived equations. Simulation results are validated by demonstrating that the average of several paths of the derived filter matches the corresponding master equation.
Experimental Verification of a Control System for Autonomous Navigation

A flexible architecture is developed with the purpose of supporting education and research on the field of autonomous vehicles. A pure electric vehicle is equipped with on-board computational units, sensors and actuator interfaces. This paper presents the current status of the control system and its validation by means of navigation experiments. With the cascade control architecture, problems of actuator dead-zone, sensor offset errors, path tracking and redesign for obstacle avoidance are addressed.
A Cloud-based Fall Detection System for Elderly Care with Passive RFID Sensor Tags

Along with the aging population, the high incidence of fall accidents by elderly people has been a serious worldwide issue. Even though early detection of accidents is indispensable to avoid the fatal risk, a practical situation is severe due to the lack of caregivers. Especially, it is required to keep delays to a minimum when deploying a detection system in nursing homes. Therefore, a real-time monitoring system for elderly care is essential in nursing homes. This paper proposes a cloud-based system for detecting the fall accidents by the elderly in real-time with passive RFID sensor tags. We have developed and implemented a prototype system using a public cloud service.
News Classification from Microblogging Dataset using Supervised Learning

Today, Microblog is one of the most popular social networks. There is a lot of data floating on this microblog that is useful for both general and unique purposes (e.g. business, current trend). From here, people can also get different types of news. We may use a supervised machine learning algorithm in order to reduce the search effort. One of the basic tasks for categorizing news is classification. We have proposed a model in this paper to identify news from the Twitter dataset and find the best outcome for the microblogging dataset. This task began with basic data crawling and after applying four supervised learning algorithms, ended with the selection of the best one. Eventually, we chose our best template for the crawled dataset.
Object Detector based on Enhanced Multi-scale Feature Fusion Pyramid Network

Constructing the pyramidal architecture for the feature is currently a very effective way to obtain feature information of objects at different scales. Although the feature pyramid can realize the recognition and detection of multi-scale objects in the object detection task well, it still has some limitations. Since the feature information of different levels is often not from the same layer of the network, it is difficult to obtain the feature of different objects information at a certain scale from a certain level feature map of the pyramid network. To solve this problem, we present a novel object detection architecture, named Enhanced Multi-scale Feature Fusion Pyramid Network (EMFFPNet). Our network consists of Enhanced Multi-scale Feature Fusion Module (EMFFM) and Predictor Optimization Module (POM). In EMFFM, Features at different levels can be fused into the Enhanced features as outputs, which are more representative and deterministic. In order to enable the enhanced features to play their respective roles in the pyramid network, we assign different weights to fusion features of different levels in POM. We perform the experiments on the COCO detection benchmark. The experimental results indicate that the performance of our model is much better than the state-of-the-art model.
Search Patterns Based on Trajectories Extracted from the Response of Second-Order Systems

Recently, several new metaheuristic schemes have been introduced in the literature. Although all these approaches consider very different phenomena as metaphors, the search patterns used to explore the search space are very similar. On the other hand, second-order systems are models that present different temporal behaviors depending on the value of their parameters. Such temporal behaviors can be conceived as search patterns with multiple behaviors and simple configurations. In this paper, a set of new search patterns are introduced to explore the search space efficiently. They emulate the response of a second-order system. The proposed set of search patterns have been integrated as a complete search strategy, called Second-Order Algorithm (SOA), to obtain the global solution of complex optimization problems. To analyze the performance of the proposed scheme, it has been compared in a set of representative optimization problems, including multimodal, unimodal, and hybrid benchmark formulations. Numerical results demonstrate that the proposed SOA method exhibits remarkable performance in terms of accuracy and high convergence rates.
Vehicle Detection in UAV Videos Using CNN-SVM

Conventional monitoring devices are usually kept at fixed locations which yields a fixed surveillance coverage. Unmanned aerial vehicles (UAVs) are receiving much attention from researchers in traffic monitoring due to their low cost, high flexibility, and wide view. Unlike stationary surveillance, the camera platform of UAVs is in constant motion and makes it difficult to process for data extraction. The inaccuracy in detection rates of vehicles from UAV videos becomes the motivation for combining optical flow methods with supervised learning algorithms. The proposed method incorporates steps that make use of the Kanade–Lucas optical flow method for moving object detection, connected graphs theory and CNN-SVM for further classification. Optical flow generated contains some background objects detected as vehicle when the camera platforms are moving. The classifier rules out the presence of any other moving objects to be detected as vehicles. The proposed method is tested on few stationary and moving aerial videos. The system is found to be 100% accurate in case of stationary aerial videos and 98% accurate in moving videos.