Reviewerly/exHarmony huggingface dataset:
Social Recommendation for Social Networks Using Deep Learning Approach: A Systematic Review

The increasing popularity of social networks indicates that the vast amounts of data contained within them could be useful in various implementations, including recommendation systems. Interests and research publications on deep learning-based recommendation systems have largely increased. This study aimed to identify, summarize, and assess studies related to the application of deep learning-based recommendation systems on social media platforms to provide a systematic review of recent studies and provide a way for further research to improve the development of deep learning-based recommendation systems in social environments. A total of 32 papers were selected from previous studies in five of the major digital libraries, including Springer, IEEE, ScienceDirect, ACM, Scopus, and Web of Science, published between 2016 and 2020. Results revealed that even though RS has received high coverage in recent years, several obstacles and opportunities will shape the future of RS for researchers. In addition, social recommendation systems achieving high accuracy can be built by using a combination of techniques that incorporate a range of features in SRS. Therefore, the adoption of deep learning techniques in developing social recommendation systems is undiscovered.
Reduction of Information Asymmetry in the Used Car Market Using the Random Forest Method

Information asymmetry is one of the most common market failures observed in the free market economy. Its various detrimental effects on the economy are especially visible in the used car market. This piece of research aims to develop a tool that by using machine learning could reduce information asymmetry on the used car market. This solution allows for limiting the negative effects of its occurrence. The analysis conducted led to the construction of a tool that enables the reduction of information asymmetry in the used car market. The discussion on the quality of the tool emphasised the importance of exploratory data analysis in the whole process. Also, the main elements that determine the predictive capacity of the random forest model were discussed and the main areas for its further improvement were identified. The interdisciplinary nature of the research was also highlighted, along with the key importance of the domain knowledge in the whole process of analysing data and constructing models.
Cognitive Load Analyzer: A Support Tool for Cognitive-Driven Development

Software modularity refers to the decomposition of complex software to be manageable for the purpose of implementation and maintenance. Most methodologies and practices adopted in the industry follow this principle, often with the benefit of flexibility and variety in use. This is also a recognition that human work can be improved by focus on a limited set of data. However, code more complex than needed continues being produced resulting in cognitive overload for developers. Cognitive-Driven Development (CDD) is an inspiration from the Cognitive Load Theory for software development with the goal of reducing the split-attention effect and the problem space through a feasible cognitive complexity constraint. Implementation units can be kept under this limit even with the continuous expansion of software scale. Experimental studies were carried out and their results suggested that CDD is a promising method when guiding the development focusing on understanding and achieving high-quality code with respect to the quality metrics. In this paper, we present a tool called “Cognitive Load Analyzer” to support the CDD, a plugin for IntelliJ IDEA and Java language. This tool can be useful to support the adoption of the CDD aiming to overcome the to slice the code and help developers to reduce code complexity.
Visualizing Schemes and Logics among Sentences in Paragraph

Authors have been developing algorithms to extract sentences including logical relation of “implication” from specification documents, and have generated executable test program for validating software modules with model checking techniques for years, so as to decrease manufacturing costs in times of system testing. The sentences including the relation “implication” are the key for test case design, because these are regarded as statements of reasonings in the system specifications. The reasoning requires premises, constraints and design rationales, to reinforce reasoning process. The premises, constraints and design rationales are utilized to define test parameters and their range of value in the context of test case design. The sentences corresponding to premises, constraints and design rationales are very likely to be deployed in the same paragraph, where the sentence involving “implication” is included. We tried to visualize logical relations among sentences in a paragraph by focusing the key sentence including the relation “implication (→)” to help test engineers grasp logical scheme of the paragraph at a glance with natural language processing technique. The algorithms and tools for formalizing sentences in natural language and visualizing logical relations among them were developed in the study and applied to visualizing logical scheme for 747 summaries in the technical papers for the validation.
Weighted area constraints-based breast lesion segmentation in ultrasound image analysis

&lt;p style='text-indent:20px;'&gt;Breast ultrasound segmentation is a challenging task in practice due to speckle noise, low contrast and blurry boundaries. Although numerous methods have been developed to solve this problem, most of them can not produce a satisfying result due to uncertainty of the segmented region without specialized domain knowledge. In this paper, we propose a novel breast ultrasound image segmentation method that incorporates weighted area constraints using level set representations. Specifically, we first use speckle reducing anisotropic diffusion filter to suppress speckle noise, and apply the Grabcut on them to provide an initial segmentation result. In order to refine the resulting image mask, we propose a weighted area constraints-based level set formulation (WACLSF) to extract a more accurate tumor boundary. The major contribution of this paper is the introduction of a simple nonlinear constraint for the regularization of probability scores from a classifier, which can speed up the motion of zero level set to move to a desired boundary. Comparisons with other state-of-the-art methods, such as FCN-AlexNet and U-Net, show the advantages of our proposed WACLSF-based strategy in terms of visual view and accuracy.&lt;/p&gt;
Physical Knowledge Driven Multi-scale Temporal Receptive Field Network for Compressed Video Action Recognition

Intelligent terminal based action recognition is important to smart cities. However, due to the dependency on training data and high complexity of extracting information, the existing image based methods cannot be implemented. Moreover, recognizing the actions with different durations is still a challenge. Due to the issues, we first extend traditional image domain to the compressed domain to efficiently extract the information of key frames and physical knowledge MVs (Motion Vectors), which can reflect the multi-scale temporal feature, without complete decoding. Then, to recognize the actions with different durations, a multi-scale temporal receptive field network including short-term and long-term branches, is proposed to capture the action's instant change based on the extracted MVs, the long temporal feature between adjacent key frames and the interaction between them simultaneously. Results show that our algorithm can achieve better balance between accuracy and computation complexity.
CNN-LSTM Hybrid Model for Kinematic Feature Analysis and Parabolic Radian Prediction in Basketball Videos

With the improvement of living standards around the world, people's love for sports has also increased; basketball is especially loved by people. It is of great importance to provide sound motor instruction for basketball. To this end, this paper comprehensively investigates the dependence between the optimal release conditions and the corresponding shooting arm movements in basketball players. We carry out kinematic feature analysis of basketball sports videos, propose a hybrid CNN-LSTM model that can predict the arc of the shooting parry, and identify the key movements of the arm joint that produce optimal release velocity, angle, and backspin in short-, mid-, and long-range shots. The experiment demonstrates that the model has three rigid planar links with rotational joints that mimic the shoulder, elbow, and wrist joints of the upper arm, forearm, and hand, which are better at guiding the optimal ball release speed, angle, and backspin for different players with the fastest ball speed being about 4.6 m/s and the slowest being about 1.7 m/s.
A game theory-based approach to discourage fake reviews

The introduction of a review system in e-commerce platforms results in an effective business model. The effects of positive/negative reviews on the success of a product are well studied in the literature. Therefore, for the vendors, it is crucial to obtain positive reviews of their products. This fact opens fraudulent scenarios. Indeed, some vendors can pay an incentive to the buyers to obtain, in exchange, a fake positive review. This fake-review system (FRS) is so widespread that vendors rely on ad-hoc companies that, through intermediaries, find buyers available to release fake reviews. In this paper, we propose a game-theory-based strategy to discourage the above fraudulent business model, and an effective way to implement this strategy leveraging an Ethereum smart contract. Specifically, the contribution of the paper is two-fold. First, we formalize the current business model as a sequential game, and we identify sufficient conditions making FRS advantageous. Second, we propose to introduce in the review system a new mechanism, thanks to which, the corresponding sequential game requires conditions necessary to make advantageous FRS that are strictly less convenient than the previous ones. We implemented the solution and evaluate the cost of the smart contract execution.
Holistic Pose Graph: Modeling Geometric Structure among Objects in a Scene using Graph Inference for 3D Object Prediction

Due to the missing depth cues, it is essentially ambiguous to detect 3D objects from a single RGB image. Existing methods predict the 3D pose for each object independently or merely by combining local relationships within limited surroundings, but rarely explore the inherent geometric relationships from a global perspective. To address this issue, we argue that modeling geometric structure among objects in a scene is very crucial, and thus elaborately devise the Holistic Pose Graph (HPG) that explicitly integrates all geometric poses including the object pose treated as nodes and the relative pose treated as edges. The inference of the HPG uses GRU to encode the pose features from their corresponding regions in a single RGB image, and passes messages along the graph structure iteratively to improve the predicted poses. To further enhance the correspondence between the object pose and the relative pose, we propose a novel consistency loss to explicitly measure the deviations between them. Finally, we apply Holistic Pose Estimation (HPE) to jointly evaluate both the independent object pose and the relative pose. Our experiments on the SUN RGB-D dataset demonstrate that the proposed method provides a significant improvement on 3D object prediction.
Paving the Way for Distributed Artificial Intelligence Over the Air

Distributed Artificial Intelligence (DAI) is one of the most promising techniques to provide intelligent services under strict privacy protection regulations for multiple clients. By applying DAI, training on raw data is carried out locally. At the same time, the trained outputs, e.g., model parameters from multiple local clients, are sent back to a central server for aggregation. DAI is recently studied in conjunction with wireless communication networks to achieve better practicality, incorporating various random effects brought by wireless channels. However, because of wireless channels&#x2019; complex and case-dependent nature, a generic simulator for applying DAI in wireless communication networks is still lacking. To accelerate the development of DAI in wireless communication networks, we propose a generic system design in this paper and an associated simulator that can be set according to wireless channels and system-level configurations. Details of the system design and analysis of the impacts of wireless environments are provided to facilitate further implementations and updates. We employ a series of experiments to verify the effectiveness and efficiency of the proposed system design and reveal its superior scalability.
Integrated Approach to Detect Cyberbullying Text: Mobile Device Forensics Data

Mobile devices and social networks provide communication opportunities among the young generation, which increases vulnerability and cybercrimes activities.A recent survey reports that cyberbullying and cyberstalking constitute a developing issue among youngsters.This paper focuses on cyberbullying detection in mobile phone text by retrieving with the help of an oxygen forensics toolkit.We describe the data collection using forensics technique and a corpus of suspicious activities like cyberbullying annotation from mobile phones and carry out a sequence of binary classification experiments to determine cyberbullying detection.We use forensics techniques, Machine Learning (ML), and Deep Learning (DL) algorithms to exploit suspicious patterns to help the forensics investigation where every evidence contributes to the case.Experiments on a real-time dataset reveal better results for the detection of cyberbullying content.The Random Forest in ML approach produces 87% of accuracy without SMOTE technique, whereas the value of F1Score produces a good result with SMOTE technique.The LSTM has 92% of validation accuracy in the DL algorithm compared with Dense and BiLSTM algorithms.
Lorenzen Won the Game, Lorenz Did Too: Dialogical Logic for Ellipsis and Anaphora Resolution

We propose a novel solution to anaphora and ellipsis resolution using multi-sorted first order logic. Our theory is proof-theoretic, employing methods from the study of dialogical logic. The first order propositions are extracted from reduced lambda terms, which are themselves derived from Lambek Categorial Grammar proofs.
Reshaping Education Through Augmented Reality and Virtual Reality

Augmented reality (AR) is the real world to which a virtual world including images sounds, etc. Augmented reality can be defined as a medium connecting digital information with the real world spatially and temporally and is instantly interactive. Virtual reality (VR) is a new technology that allows users to be present and interact with a computer simulation environment. Most virtual reality environments are devices for human-to-computer interaction with computers. Advances in technology in today's world have made a lot of information about our environment turn into a virtual environment. A type of information that has a special place in education today is augmented reality. This form of the virtual world is a living, direct or indirect physical view that adds elements around people's real world. This paper presents the study conducted by data collection and library studies and looks at how augmented reality and virtual reality are used as digital technology in school classrooms around the world. It seems to be one of the most appropriate ways to benefit from education in the age of Covid-19 and the closure of schools in many parts of the world and forcing students to enter the virtual world, which may continue and use these methods.
Controlled cyclic joint remote state preparation of arbitrary single-qubit states

A novel scheme for controlled cyclic joint remote state preparation (CCJRSP) is proposed where arbitrary single-qubit state can be mutually transmitted between three communication participants Alice, Bob and Charlie under the control of the supervisor David. In our scheme, one participant can reconstruct the desired states by performing POVM measurement and basic unitary transformation with the help of other two participants in a cyclic manner. In addition, one non-maximally entangled channel is utilized in this work. Hence, the requirement for the quantum channel is reduced.
DoubleRYOLO: Rotated Prohibited Item Detection for X-ray Security Inspection System

research-article DoubleRYOLO: Rotated Prohibited Item Detection for X-ray Security Inspection System Share on Authors: Jianping Xiong Zhejiang Dahua Technology Co.,Ltd., China Zhejiang Dahua Technology Co.,Ltd., ChinaView Profile , Dong Hu Zhejiang Dahua Technology Co.,Ltd., China Zhejiang Dahua Technology Co.,Ltd., ChinaView Profile , Lijian Mao Zhejiang Dahua Technology Co.,Ltd., China Zhejiang Dahua Technology Co.,Ltd., ChinaView Profile , Min Wu Zhejiang Dahua Technology Co.,Ltd., China Zhejiang Dahua Technology Co.,Ltd., ChinaView Profile , Jingsong Zhu Zhejiang Dahua Technology Co.,Ltd., China Zhejiang Dahua Technology Co.,Ltd., ChinaView Profile Authors Info & Claims ICGSP 2021: 2021 the 5th International Conference on Graphics and Signal ProcessingJune 2021 Pages 32–40https://doi.org/10.1145/3474906.3474921Online:06 October 2021Publication History 0citation39DownloadsMetricsTotal Citations0Total Downloads39Last 12 Months39Last 6 weeks7 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
SyLPEnIoT: Symmetric Lightweight Predicate Encryption for Data Privacy Applications in IoT Environments

Privacy preserving mechanisms are essential for protecting data in IoT environments. This is particularly challenging as IoT environments often contain heterogeneous resource-constrained devices. One method for protecting privacy is to encrypt data with a pattern or metadata. To prevent information leakage, an evaluation using the pattern must be performed before the data can be retrieved. However, the computational costs associated with typical privacy preserving mechanisms can be costly. This makes such methods ill-suited for resource-constrained devices, as the high energy consumption will quickly drain the battery. This work solves this challenging problem by proposing SyLPEnIoT – Symmetric Lightweight Predicate Encryption for IoT, which is lightweight and efficient compared with existing encryption schemes. Based on the bitwise-XOR operation, we use this basic gate to construct a scheme that transfers encrypted data onto more powerful machines. Furthermore, for resource-constrained IoT devices, the requester can authenticate devices at different levels based on the type of communication. SyLPEnIoT was meticulously designed to run on a gamut of IoT devices, including ultra low-power sensors that are constrained in terms of CPU processing, memory and energy consumption, which are widely deployed in real IoT ecosystems.
Ship Instance Segmentation in Foggy Scene

Fog is a common weather phenomenon at sea. In foggy conditions, image quality collected by imaging equipment is relatively poor, and ship targets are blurred. This interference seriously affects the accuracy of ship instance segmentation. To this end, we propose a new instance segmentation with channel attention module, called CondInstAtt for ship segmentation in foggy scene. Meanwhile, to solve data scarcity in foggy scene, a foggy image simulation method based on an atmospheric scattering model for the marine scene is used. We also label the instance segmentation dataset with 2929 ship images in fog. Finally, the numerical and visual results clearly show that our method outperforms a few recent methods including Mask RCNN and CondInst.
Reconstructing Group Wavelet Transform From Feature Maps With a Reproducing Kernel Iteration

In this article, we consider the problem of reconstructing an image that is downsampled in the space of its SE(2) wavelet transform, which is motivated by classical models of simple cell receptive fields and feature preference maps in the primary visual cortex. We prove that, whenever the problem is solvable, the reconstruction can be obtained by an elementary project and replace iterative scheme based on the reproducing kernel arising from the group structure, and show numerical results on real images.
Analysis of the distribution types of depth and magnitude of tectonic earthquake 2018 in Lombok Island based on clusterization results using K-Means method

Tectonic earthquakes are vibrations or shocks that occur on the earth’s surface due to the sudden release of energy from below the surface which creates seismic waves. Tectonic earthquakes with large magnitudes and shallow depths can cause casualties and material losses. This research focuses on the earthquake that occurred in 2018 on Lombok Island. The research objective was to determine the distribution types of depth and magnitude of tectonic earthquake using the K-S test approach. In addition, we will show histogram graph and probability density function based on distribution of the K-S test results. Previously, tectonic earthquake data were clustered using the non-hierarchical cluster method of K-Means approach.
Speaker Adaptation with Continuous Vocoder-Based DNN-TTS

Traditional vocoder-based statistical parametric speech synthesis can be advantageous in applications that require low computational complexity. Recent neural vocoders, which can produce high naturalness, still cannot fulfill the requirement of being real-time during synthesis. In this paper, we experiment with our earlier continuous vocoder, in which the excitation is modeled with two one-dimensional parameters: continuous F0 and Maximum Voiced Frequency. We show on the data of 9 speakers that an average voice can be trained for DNN-TTS, and speaker adaptation is feasible 400 utterances (about 14 min). Objective experiments support that the quality of speaker adaptation with Continuous Vocoder-based DNN-TTS is similar to the quality of the speaker adaptation with a WORLD Vocoder-based baseline.
Dynamic Coalition Logic: Granting and Revoking Dictatorial Powers

One of the classic formalisms for reasoning about multi-agent coalitional ability is coalition logic (CL). In CL it is possible to express what a coalition can achieve in the next step no matter what agents outside of the coalition do at the same time. We propose an extension of CL with dynamic operators that allow us to grant dictatorial powers to agents or to revoke them. In such a way we are able to reason about the dynamics of coalitional ability. We also discuss some logical properties of the proposed formalisms and compare their relative expressive power.