Reviewerly/exHarmony huggingface dataset:
SecureRec: Privacy-Preserving Recommendation with Distributed Matrix Factorization

Recommender systems have received much attention recently because of their abilities to capture the interests of users. A standard solution is to collect and analyze users' historical behavior data, which might raise privacy concerns, e.g., Facebook-Cambridge Analytica data scandal. Collaborative filtering has been widely used in recommender systems for its simplicity. However, it suffers from an efficiency issue owing to a large amount of data and time-consuming operations. Therefore, an interesting question arises: how to provide recommendation services and protect users' privacy at the same time based on distributed matrix factorization? The paradox is that sharing inaccurate information about user data makes it difficult for the recommender to infer personal preference. In this paper, we propose an item recommender system named SecureRec. We formulate the notion of $$(\alpha , \beta )$$ -accuracy. We prove that SecureRec is $$(\alpha , \beta )$$ -accurate and $$\epsilon $$ -differentially private. Experimental results on three real-world datasets show that SecureRec achieves comparable precision to non-private item recommendation methods while offering privacy guarantees to users.
Summarization With Self-Aware Context Selecting Mechanism

In the natural language processing family, learning representations is a pioneering study, especially in sequence-to-sequence tasks where outputs are generated, totally relying on the learning representations of source sequence. Generally, classic methods infer that each word occurring in the source sequence, having more or less influence on the target sequence, should all be considered when generating outputs. As the summarization task requires the output sequence to only retain the essence, classic full consideration of the source sequence may not work well on it, which calls for more suitable methods with the ability to discard the misleading noise words. Motivated by this, with both relevance retaining and redundancy removal in mind, we propose a summarization learning model by implementing an encoder with copious contextual information represented and a decoder with a selecting mechanism integrated. Specifically, we equip the encoder with an asynchronous bi directional parallel structure, in order to obtain abundant semantic representation. The decoder, different from the classic attention-based works, employs a self-aware context selecting mechanism to generate summary in a more productive way. We evaluate the proposed methods on three benchmark summarization corpora. The experimental results demonstrate the effectiveness and applicability of the proposed framework in relation to several well-practiced and state-of-the-art summarization methods.
Entanglement and Entropy Squeezing for Moving Two Two-Level Atoms Interaction with a Radiation Field

In this paper, we analyzed squeezing in the information entropy, quantum state fidelity, and qubit-qubit entanglement in a time-dependent system.The proposed model consists of two qubits that interact with a two-mode electromagnetic field under the dissipation effect.An analytical solution is calculated by considering the constants for the equations of motion.The effect of the general form of the time-dependent for qubit-field coupling and the dissipation term on the temporal behavior of the qubit-qubit entanglement, quantum state fidelity, entropy, and variance squeezing are examined.It is shown that the intervals of entanglement caused more squeezing for the case of considering the time-dependent parameters.Additionally, the entanglement between the qubits became more substantial for the case of time dependence.Fidelity and negativity rapidly reached the minimum values by increasing the effect of the dissipation parameter.Moreover, the amount of variance squeezing and the amplitude of the oscillations decreased considerably when the time dependence increased, but the fluctuations increased substantially.We show the relation between entropy and variance squeezing in the presence and absence of the dissipation parameter during the interaction period.This result enables new parameters to control the degree of entanglement and squeezing, especially in quantum communication.
Attention-Modulated Triplet Network for Face Sketch Recognition

In this paper, a novel triplet network is proposed for face sketch recognition.A spatial pyramid pooling layer is introduced into the network to deal with different sizes of images, and an attention model on the image space is proposed to extract features from the same location in the photo and sketch.Our attention mechanism builds and improves recognition accuracy by searching similar regions of the images, which include abundant information in order to distinguish different persons in photos and sketches.So that the cross-modality differences between photo and sketch images are reduced when they are mapped into a common feature space.Our proposed solution is tested on composite face photo-sketch datasets, including UoM-SGFS and e-PRIP dataset, and achieves better performance than the state-of-the-art result.Especially for Set B in UoM-SGFS dataset, the accuracy is higher than 81%.
NichePSO and the Merging Subswarm Problem

The NichePSO algorithm was the first particle swarm optimization algorithm to utilize parallel swarms as an approach to solve multimodal optimization problems. Despite its strong results over many years of research, the NichePSO algorithm has always suffered from a major fundamental issue: The NichePSO algorithm uses multiple smaller subswarms that each search independent sections of the search area, searching for the different optima across the problem landscape. However, each run often ends with a single large subswarm absorbing every other subswarm that has been created, losing track of many of the optima found. This paper analyzes the NichePSO algorithm in detail, and shows evidence that this problem is caused by the subswarm merging strategy being used. Alternative merging approaches are proposed and it is shown that they do not suffer from the same issue as the traditional algorithm.
Age group classification to identify the progress of language development based on convolutional neural networks

Speech pathology is a scientific study of speech disorders. In this field, the study also analyzes and evaluates language abilities for the purpose of improving speech and hearing. Speech therapy first performs evaluation of speech ability, which is expensive. In order to solve this problem, software methodologies have been applied to language analysis, but most of them have been applied to only part of the whole process. In this study, the degree of language development is judged by determining the age group of the speaker (Pre-school children, Elementary school, Middle and high school, Adults, and Senior citizen) using deep learning and simple statistics. We use transcription data from the counseling contents and multi-kernel CNN model. At this time, in order to understand the characteristics of Korean language belonging agglutinative languages, experiments are carried out in words, morphemes, characters, Jamo, and Jamo with POS tag-level. And we analyze the distribution of the results for each sentence of the speakers to predict their age groups and to check the degree of language development. The proposed model shows an average accuracy of about 74.6 %.
Time-Frequency Analysis of Daily Activities for Fall Detection

Fall events in elderly populations often result in serious injury and may lead to long-term disability and/or death. While many fall detection systems have been developed using wearable sensors to distinguish falls from other daily activities, detection sensitivity and specificity decreases when exposed to more rigorous activities such as running and jumping. This research uses time-frequency analysis of accelerometer-only activity data to develop a strategy for improving fall detection accuracy. In this study, a wireless sensor system (WSS) consisting of a three-axis accelerometer, microprocessor and wireless communications module is used to collect daily activities performed following a script in the laboratory setting. Experiments were conducted on 36 healthy human subjects performing four types of falls (i.e., forward, backward, and left/right sideway falls) as well as normal movements such as standing, walking, stand-to-sit, sit-to-stand, stepping, running and jumping. In total, 1227 different activities were collected and analyzed. The developed algorithm computes the magnitude of three-axis accelerometer data to detect if a critical fall threshold is passed, then analyzes the power spectral density within a critical fall duration window (500 ms) to differentiate fall events from other rigorous activities. Fall events were observed with high energy in the 2–3.5 Hz range and distinct from other rigorous activities such as running (3.5–5.5 Hz) and jumping (1–2 Hz). Preliminary results indicate the power spectral density (PSD)-based algorithm can detect falls with high sensitivity (98.4%) and specificity (98.6%) using lab-based daily activity data. The proposed algorithm has the benefit of improved accuracy over existing time-domain only strategies and multisensor strategies.
Three-dimensional Human Tracking of a Mobile Robot by Fusion of Tracking Results of Two Cameras

This paper proposes a process that uses two cameras to obtain three-dimensional (3D) information of a target object for human tracking. Results of human detection and tracking from two cameras are integrated to obtain the 3D information. OpenPose is used for human detection. In the case of a general processing a stereo camera, a range image of the entire scene is acquired as precisely as possible, and then the range image is processed. However, there are problems such as incorrect matching and computational cost for the calibration process. A new stereo vision framework is proposed to cope with the problems. The effectiveness of the proposed framework and the method is verified through target-tracking experiments.
Online Examination System (Electronic Learning)

An online examination system is a software solution that allows a company or a teaching institute to prepare and manage examinations across an online platform. This can be done through Internet, Intranet and/or LAN environments. The purpose of this paper is to create an online examination system application that utilizes network laboratories available in any college. The application greatly reduces the time required to administer the examination and handout the results. The examination will consist of objective questions, and the system will provide an easy-to-use graphical user interface for lecturers to ask questions and for students to answer them. The application was developed with ASP.net, providing a better performance than scripting languages. This web application uses ASP.net with C# in the front end and MySQL as the background of the interface on the Apache Server Framework and supports the HTTP/HTTPs protocol with English.
Development and Testing of the PVSPEC Model of Photovoltaic Spectral Mismatch Factor

The PVSPEC model of spectral mismatch factor (SMM) for photovoltaic modules was developed using spectral irradiance and other meteorological data from eight locations. PVSPEC depends on clear sky index and absolute air mass. Cross-validation was used to evaluate model performance: the model was trained on data from seven locations and tested on data from the remaining location, going through all permutations. Root mean square errors on SMM were 3.2% on average, while absolute errors on long-term spectral effects were 0.8% on average. PVSPEC outperformed existing models developed by First Solar and by the European Commission's Joint Research Center.
Compact representations for efficient storage of semantic sensor data

Nowadays, there is a rapid increase in the number of sensor data generated by a wide variety of sensors and devices. Data semantics facilitate information exchange, adaptability, and interoperability among several sensors and devices. Sensor data and their meaning can be described using ontologies, e.g., the Semantic Sensor Network (SSN) Ontology. Notwithstanding, semantically enriched, the size of semantic sensor data is substantially larger than raw sensor data. Moreover, some measurement values can be observed by sensors several times, and a huge number of repeated facts about sensor data can be produced. We propose a compact or factorized representation of semantic sensor data, where repeated measurement values are described only once. Furthermore, these compact representations are able to enhance the storage and processing of semantic sensor data. To scale up to large datasets, factorization based, tabular representations are exploited to store and manage factorized semantic sensor data using Big Data technologies. We empirically study the effectiveness of a semantic sensor’s proposed compact representations and their impact on query processing. Additionally, we evaluate the effects of storing the proposed representations on diverse RDF implementations. Results suggest that the proposed compact representations empower the storage and query processing of sensor data over diverse RDF implementations, and up to two orders of magnitude can reduce query execution time.
RETRACTED: Visual management of sports training based on embedded wearable devices and machine vision

Visual need magnificent vision in their great athletic presentation, numerous competitors are turning vision preparing program, to build their customary preparing programs. The act of "sports vision preparing" relies progressively upon the idea of requesting visual observation, perception, practice or coulometer errands can improve the capacity to measure and react seen to improve athletic execution. Virtual Control Algorithm (VCA). This venture isn't really new, yet as of now can be conveyed essentially progressed in the previous few years by new computerized advancements in preparing exercises of nature, through perceptual learning style instructional class and pass an augmented experience reenactment, can remake and extend sports setting to encourage the development of certain one of a kind visual and psychological capacity. These improved abilities may, thusly, imparted an upper hand in the games field, focused on the likely estimation of these methodologies. Sports vision preparing techniques in the remarks of this article showed up, advancements and patterns. In the event that accessible, give supporting logical examination.
Learning to estimate smooth and accurate semantic correspondence

We tackle the problem of estimating dense semantic correspondence between two images depicting different instances of the same category. In this paper, we consider semantic context and correspondence information from the neighborhood in order to overcome the drawback of previous works that estimate the correspondence of each pixel or patch independently. To this end, a novel network, called SANet, with a trainable spatial aggregation module is proposed, which is trained in an end-to-end manner and outputs semantic flow. We train this SANet by adopting two complementary loss terms: landmark loss, focusing on keypoints with ground truth, and consistency loss, applicable to all pixels without ground truth. Qualitative and quantitative experimental results demonstrate the improved network achieves a better balance between accuracy and smoothness comparing with the baseline and warps images with better visual quality.
Exploiting a multi-device knowledge meshing to agent-based activity tracking

Nowadays, systems of systems, composed of multiple cooperative smart devices, reached popularity in many areas, including surveillance, digital forensics, agriculture and more. The analysis of great amounts of data coming from different sources could be very time consuming for humans, so they require automatic tools that help them to monitor vast and complex environments to detect anomalous situations. To this purpose, this paper introduces an agent-based model to allow IoT systems to monitor outdoor environments and detect suspicious or critical situations. The agent-modeling allows the accomplishment and coordination of various tasks, including object detection and data collection achieved through tracking, environmental context detection by using frame classification and semantic segmentation, contextual knowledge generation and activity detection through ontology reasoning. Finally, the agents report to humans about what happened by estimating the scene criticality through a fuzzy controller. A case study shows the potential of the whole framework and experiences evaluate the skills of the framework for activity detection.
Playground for Early Automotive Service Architecture Design and Evaluation

Context: We consider the structure of service-oriented architectures in vehicular software. Aim: We aim at evaluating the structure and grouping of service architectures. Method: We propose and discuss architectural metrics tailored towards automotive service-oriented architectures. We apply the metrics on an adaptive cruise control case example extracted from the AUTOSAR standard. Results: The application of the proposed metrics to two different service groupings for ACC points clearly to the same service grouping that we consider, after a thorough analysis, to be better with respect to coupling and cohesion attributes. Conclusion: We demonstrate the usefulness of proposed service group metrics in early design phases of the development process and validate the metrics on the case example of an adaptive cruise control function.
Improved Particle Swarm Optimization Algorithm Based on Dynamic Change Speed Attenuation Factor and Inertia Weight Factor

Abstract Compared with other optimization algorithms, the particle swarm optimization algorithm (pso) has the advantages of fast convergence, simple calculation, and fewer parameters to be adjusted. Therefore, it has been greatly developed in the past period of time. In order to further accelerate convergence, in recent years, more efficient and convenient particle swarm optimization algorithms such as LDWPSO, NLPSO, and DPSO have been proposed.However, these algorithms have many problems such as large amount of calculation, lack of precision, and easy to fall into local optimum.Therefore, the algorithm still needs to be improved. In order to obtain an efficient global search ability early in the algorithm iteration and better local optimization performance in the later stage of the algorithm iteration, this paper proposes a special expression form of dynamic change speed attenuation factor and inertia weight factor, and in the algorithm some random parameters are added to the iterative formula and applied to the algorithm iteration formula to improve the optimization speed and optimization effect of the algorithm.Finally, the effectiveness and correctness of the algorithm are also demonstrated by computer simulation experiments.
Blockchain-based Distributed Authentication Mechanism for Internet-of-Things Devices

Internet of Things (IoT) is rapidly becoming a one of the dominant technologies in today's world. However, due to the decentralized nature of the network, many vulnerabilities are introduced. The risks associated with these vulnerabilities are becoming significant due to the increasing use of the IoT devices. One of the main vulnerabilities is the authentication of different nodes in IoT. The decentralized nature of the network impedes the ability to create a traditional authentication system. In this paper, we introduce a novel decentralized authentication mechanism for IoT devices. This system is based on the blockchain technology and utilizes the unique environmental features available in the environment of the IoT device. We explain the unique features of the IoT networks, the different threats, and we discuss the different aspects of our proposed mechanism. We provide a security analysis for this introduced mechanism, and how it addresses different security attacks.
Understanding Strengths and Weaknesses of Complementary Sensor Modalities in Early Fusion for Object Detection

In object detection for autonomous driving and robotic applications, conventional RGB cameras often fail to sense objects under extreme illumination conditions and on texture-less surfaces, while LIDAR sensors often fail to sense small or thin objects located far from the sensor. For these reasons, an intuitive and obvious choice for perception system designers is to install multiple sensors of different modalities to increase (in theory) the detection robustness. In this paper we focus on the analysis of an object detector that performs early fusion of RGB images and LIDAR 3D points. Our goal is to go beyond the intuition of simply adding more sensor modalities to improve performance, and instead analyze, quantify, and understand the performance differences, strengths and weaknesses of the object detector under three different modalities: 1) RGB-only, 2) LIDAR-only, and 3) Early fusion (RGB and LIDAR), and under two key scene variables: 1) Distance of objects from the sensor (density), and 2) Illumination (Darkness). We also propose methodologies to generate 2D weak semantic training masks, and a methodology to evaluate the object detection performance separately at different distance ranges, which provides a more reliable detection performance measure and correlates well with object LIDAR point density.
Reconstruction of Multi-Temporal Satellite Imagery by Coupling Variational Segmentation and Radiometric Analysis

Digital images, and in particular satellite images acquired by different sensors, may present defects due to many causes. Since 2013, the Landsat 7 mission has been affected by a well-known issue related to the malfunctioning of the Scan Line Corrector producing very characteristic strips of missing data in the imagery bands. Within the vast and interdisciplinary image reconstruction application field, many works have been presented in the last few decades to tackle the specific Landsat 7 gap-filling problem. This work proposes another contribution in this field presenting an original procedure based on a variational image segmentation model coupled with radiometric analysis to reconstruct damaged images acquired in a multi-temporal scenario, typical in satellite remote sensing. The key idea is to exploit some specific features of the Mumford–Shah variational model for image segmentation in order to ease the detection of homogeneous regions which will then be used to form a set of coherent data necessary for the radiometric reconstruction of damaged regions. Two reconstruction approaches are presented and applied to SLC-off Landsat 7 data. One approach is based on the well-known histogram matching transformation, the other approach is based on eigendecomposition of the bands covariance matrix and on the sampling from Gaussian distributions. The performance of the procedure is assessed by application to artificially damaged images for self-validation testing. Both of the proposed reconstruction approaches had led to remarkable results. An application to very high resolution WorldView-3 data shows how the procedure based on variational segmentation allows an effective reconstruction of images presenting a great level of geometric complexity.