Reviewerly/exHarmony huggingface dataset:
Fall Detection and Direction Judgment Based on Posture Estimation

For the problem of elderly people falling easily, it is very necessary to correctly detect the occurrence of falls and provide early warning, which can greatly reduce the injury caused by falls. Most of the existing fall detection algorithms require the monitored persons to carry wearable devices, which will bring inconvenience to their lives and few algorithms pay attention to the direction of the fall. Therefore, we propose a video-based fall detection and direction judgment method based on human posture estimation for the first time. We predict the joint point coordinates of each human body through the posture estimation network, and then use the SVM classifier to detect falls. Next, we will use the three-dimensional human posture information to judge the direction of the fall. Compared to the existing methods, our method has a great improvement in sensitivity, specificity, and accuracy which reaches 95.86, 99.5, and 97.52 on the Le2i fall dataset, respectively, whereas on the UR fall dataset, they are 95.45, 100, and 97.43, respectively.
A Real- Time Machine learning based cloud computing Architecture for Smart Manufacturing

The monitoring system has become a crucial concept for decision-making and management because of the development of data output in the industrial business. It is possible to use sensor-based technologies, such as the Internet of Things (IoT), to monitor the manufacturing process effectively. IoT and Machine Learning (ML) are offered in this study as a solution for monitoring the production system. Specifically, there is a paucity of cloud-based manufacturing equipment that can provide on-demand manufacturing services through the Internet in this study. The technical problems and enabling technologies of cloud-based manufacturing equipment are discussed in detail in this paper. Data from preprocessed time series data is transmitted to cloud services for trend prediction and data completion using a large short-term memory recurrent neural network, a first-order forward difference, and the original sequence of historical data, and the results are returned. In time-series data processing, machine learning may considerably enhance the efficiency and accuracy, as evidenced by the results of the IIoT oil production IIoT system.
Exploring Experiences with New Musical Instruments through Micro-phenomenology

This paper introduces micro-phenomenology, a research discipline for exploring and uncovering the structures of lived experience, as a beneficial methodology for studying and evaluating interactions with digital musical instruments.Compared to other subjective methods, micro-phenomenology evokes and returns one to the moment of experience, allowing access to dimensions and observations which may not be recalled in reflection alone.We present a case study of five micro-phenomenological interviews conducted with musicians about their experiences with existing digital musical instruments.The interviews reveal deep, clear descriptions of different modalities of synchronic moments in interaction, especially in tactile connections and bodily sensations.We highlight the elements of interaction captured in these interviews which would not have been revealed otherwise and the importance of these elements in researching perception, understanding, interaction, and performance with digital musical instruments.
Acoustic Modeling for End-to-End Empathetic Dialogue Speech Synthesis Using Linguistic and Prosodic Contexts of Dialogue History

We propose an end-to-end empathetic dialogue speech synthesis (DSS) model that considers both the linguistic and prosodic contexts of dialogue history. Empathy is the active attempt by humans to get inside the interlocutor in dialogue, and empathetic DSS is a technology to implement this act in spoken dialogue systems. Our model is conditioned by the history of linguistic and prosody features for predicting appropriate dialogue context. As such, it can be regarded as an extension of the conventional linguistic-feature-based dialogue history modeling. To train the empathetic DSS model effectively, we investigate 1) a self-supervised learning model pretrained with large speech corpora, 2) a style-guided training using a prosody embedding of the current utterance to be predicted by the dialogue context embedding, 3) a cross-modal attention to combine text and speech modalities, and 4) a sentence-wise embedding to achieve fine-grained prosody modeling rather than utterance-wise modeling. The evaluation results demonstrate that 1) simply considering prosodic contexts of the dialogue history does not improve the quality of speech in empathetic DSS and 2) introducing style-guided training and sentence-wise embedding modeling achieves higher speech quality than that by the conventional method.
New Deep Learning Model for Face Recognition and Registration in Distance Learning

The demand for secure, accurate, and reliable identification of individuals using facial recognition has attracted considerable interest in education, security, and many other sectors, not limited because it is robust, secure, and authentic. Recently, the demand for distance learning has increased dramatically. This increase is due to various barriers to learning that arise from enforced conditions such as seclusion and social distancing. Facial feature extraction in distance education is valuable in supporting face authenticity as it prevents the position of participants from changing, especially during the examination phase. In the field of face recognition, there is a mismatch between research and practical application. In this paper, we present a novel but highly efficient Deep Learning model for improving face recognition and registration in distance education. The technique is based on a combination of sequential and residual identity blocking. This makes it possible to evaluate the effectiveness of using deeper blocks than other models. The new model has proven to be able to extract features from faces in a high and accurate manner in compared with other state-of-the-art methods. In registration processing, there are several challenges related to training data limitation, face recognition, and verification. We present a new architecture for face recognition and registration. Experiments have shown that our registration model is capable of recognizing almost all faces and registering the corresponding labels.
Learning Relationship-Enhanced Semantic Graph for Fine-Grained Image–Text Matching

Image-text matching of natural scenes has been a popular research topic in both computer vision and natural language processing communities. Recently, fine-grained image-text matching has shown its significant advance in inferring the high-level semantic correspondence by aggregating pairwise region-word similarity, but it remains challenging mainly due to insufficient representation of high-order semantic concepts and their explicit connections in one modality as its matched in another modality. To tackle this issue, we propose a relationship-enhanced semantic graph (ReSG) model, which can improve the image-text representations by learning their locally discriminative semantic concepts and then organizing their relationships in a contextual order. To be specific, two tailored graph encoders, visual relationship-enhanced graph (VReG) and textual relationship-enhanced graph (TReG), are respectively exploited to encode the high-level semantic concepts of corresponding instances and their semantic relationships. Meanwhile, the representations of each graph node are optimized by aggregating semantically contextual information to enhance the node-level semantic correspondence. Further, the hard-negative triplet ranking loss, center hinge loss, and positive-negative margin loss are jointly leveraged to learn the fine-grained correspondence between the ReSG representations of image and text, whereby the discriminative cross-modal embeddings can be explicitly obtained to benefit various image-text matching tasks in a more interpretable way. Extensive experiments verify the advantages of the proposed fine-grained graph matching approach, by achieving the state-of-the-art image-text matching results on public benchmark datasets.
Side Channel Analysis of SPECK Based on Transfer Learning

Although side-channel attacks based on deep learning are widely used in AES encryption algorithms, there is little research on lightweight algorithms. Lightweight algorithms have fewer nonlinear operations, so it is more difficult to attack successfully. Taking SPECK, a typical lightweight encryption algorithm, as an example, directly selecting the initial key as the label can only crack the first 16-bit key. In this regard, we evaluate the leakage of SPECK's operations (modular addition, XOR, shift), and finally select the result of XOR operation as the label, and successfully recover the last 48-bit key. Usually, the divide and conquer method often used in side-channel attacks not only needs to train multiple models, but also the different bytes of the key are regarded as unrelated individuals. Through the visualization method, we found that different key bytes overlap in the position of the complete electromagnetic leakage signal. That is, when SPECK generates a round key, there is a connection between different bytes of the key. In this regard, we propose a transfer learning method for different byte keys. This method can take advantage of the similarity of key bytes, improve the performance starting-point of the model, and reduce the convergence time of the model by 50%.
Virtual Reality as a Tool for Political Decision-Making? An Empirical Study on the Power of Immersive Images on Voting Behavior

One of the strengths of virtual reality (VR) is to provide a highly realistic user experience. How would VR's power of realism affect political decision-making, for example, when experienced by citizens before they cast their vote on an issue? We set out to empirically assess if and how voting information presented in VR would influence people's voting behavior, compared to the traditional text presentation format. In a 2 (format: text vs. VR) × 2 (argumentation: pro vs. con) between-subject factorial experiment, we assessed participants' voting behavior on a fictitious popular initiative. We first asked all participants ( N = 179) to cast their vote based on a brief text, inspired by the traditional Swiss voting booklet (baseline). We then randomly assigned participants to one of four experimental conditions containing the same pro or con arguments concerning the voting issue. Participants could then adjust their previously-cast vote. This was followed by retrospective interviews ( N = 32) to gain deeper insights into the decision-making process of the participants. Our study shows that the presentation format has a reinforcing effect, that is, leading to more YES votes for the VR group, and fewer YES votes for the text group. Irrespective of the pro or con arguments, participants show an overall increase in YES votes in VR, which is not the case for the text group. We identified six factors that may have led to this positive change with VR: (1) the affirmative power of images, (2) the vividness of immersive images, (3) first-person storytelling and storyliving, (4) the greater affordances of VR for engagement through interaction, (5) the design of the VR environment, and (6) the novelty of the VR technology.
Remote Vocal Singing Course Design Based on Embedded System and Internet of Things

Distance learning, a new style of teaching, can create a strong sensation of presence through real and vivid communication. It is set to reach a communication effect of deep impression and a lasting memory. It can also obtain students’ course learning records in a timely manner. Students can adjust the teaching content through various learning feedback to meet the requirements of learners. However, for those traditional courses that must be taught in person, such as vocal singing teaching, there is an urgent need to adapt to the new distance learning model. In the teaching methodology of vocal singing, there are often high requirements for equipment in terms of sound, such as radio, encoding, and playback. However, there are very few requirements for images. Nowadays, the distance course platform cannot meet the basic content of vocal teaching. It is even more unable to promote teaching according to the material and personalized teaching. Therefore, this paper proposes a remote vocal singing course design based on an embedded system and the Internet of Things. The research paper meets the basic vocal teaching needs in terms of not only hardware but also remote learning. It can improve student motivation. Combined with network technology, it greatly stimulates students’ sensory thinking of learning. It also improves the teachers’ understanding of students’ singing skills and personalized teaching strategies for different students with specified content. According to their own learning aptitude and learning history, the system may select the instructional materials that the students have not learned. It may provide pupils with the best learning option based on their past selection records. Distance education may give a tailored interface using artificial intelligence. In conclusion, the distance learning system based on the AI networks project is feasible.
DETERMINING CRITERIA WEIGHTS FOR VEHICLE TRACKING SYSTEM SELECTION USING PIPRECIA-S

Vehicle tracking systems are generally used to determine the location of vehicles, monitor them, and guide them when appropriate. This study aims to define the criteria that logistics companies consider when selecting a vehicle tracking system, as well as the relative importance of these criteria. PIPRECIA-S, a multi-criteria decision analysis method, was used in this context. According to the analysis results, the most important criterion in the selection of a vehicle tracking system is real-time tracking of the vehicle's location. When it comes to selecting a vehicle tracking system, logistics firms should prioritize instant vehicle tracking, compliance with local rules, compatibility with new technologies and software, quality certification, and compatibility with external systems-devices over other criteria. Other important criteria to consider when selecting a vehicle tracking system are system maintenance and technical support, providing statistical data collection and effective reporting, allowing the vehicle to be diverted, comprehension, simplicity of implementation and visual geo-information presentation for users, design and quality of hardware, system cost, communication infrastructure, and reducing the operating costs of companies. Also, when developing vehicle tracking systems, system developers can prioritize the aforementioned criteria.
Pruning convolutional neural networks via filter similarity analysis

Deep learning has shown excellent performance in many fields, especially image recognition and retrieval in recent years. The performance of convolutional neural networks (CNNs) is particularly outstanding. CNNs, however, are usually computationally intensive, which hinders the deployment of CNNs in resource-limited devices. Methods of network compression, pruning methods in particularly, removing redundant structures of CNNs, can significantly reduce the computational complexity of CNNs. Most of the state-of-the-art pruning methods for CNNs, however, have two defects. (1) Filters, also called convolutional kernels that are matrices used to extract features in an image, are pruned by ranking their weight without considering the effects of their actual output, which results in the deletion of important filters and the difficulty in determining the pruning threshold on weight. (2) Filters are pruned either in the forward direction or in isolation, which are difficult to control the loss of accuracy. This paper proposes a novel pruning method called filter similarity analysis with backward pruning (FSABP). FSABP calculates the similarity coefficients of filters in each layer, and deletes the filters associated with small similarity coefficients. The smaller the coefficient the more similar the filters. Filters are pruned layer by layer in the backward direction starting from the last convolution layer, which can effectively control the loss of accuracy by avoiding early removal of the shallow convolution filters. Experiments on LENET, VGG-16 and ResNet-50 show that FSABP can reduce parameter redundancy at the cost of negligible loss of accuracy and even improve accuracy in some cases. The results on LENET also suggest that FSABP is applicable to both deep and shallow CNNs.
A Dual-Pointer guided transition system for end-to-end structured sentiment analysis with global graph reasoning

Structured sentiment analysis is a newly proposed task, which aims to summarize the overall sentiment and opinion status on given texts, i.e., the opinion expression, the sentiment polarity of the opinion, the holder of the opinion, and the target the opinion towards. In this work, we investigate a transition-based model for end-to-end structured sentiment analysis task. We design a transition architecture which supports the recognition of all the possible opinion quadruples in one shot. Based on the transition backbone, we then propose a Dual-Pointer module for more accurate term boundary detection. Besides, we further introduce a global graph reasoning mechanism, which helps to learn the global-level interactions between the overlapped quadruples. The high-order features are navigated into the transition system to enhance the final predictions. Extensive experimental results on five benchmarks demonstrate both the prominent efficacy and efficiency of our system. Our model outperforms all baselines in terms of all metrics, especially achieving a 10.5% point gain over the current best-performing system only detecting the holder-target-opinion triplets. Further analyses reveal that our framework is also effective in solving the overlapping structure and long-range dependency issues.
Research on Path Planning Strategy of Rescue Robot Based on Reinforcement Learning

&lt;p&gt;How rescue robots reach their destinations quickly and efficiently has become a hot research topic in recent years. Aiming at the complex unstructured environment faced by rescue robots, this paper proposes an artificial potential field algorithm based on reinforcement learning. Firstly, use the traditional artificial potential field method to perform basic path planning for the robot. Secondly, in order to solve the local minimum problem in planning and improve the robot’s adaptive ability, the reinforcement learning algorithm is run by fixing preset parameters on the simulation platform. After intensive training, the robot continuously improves the decision-making ability of crossing typical concave obstacles. Finally, through simulation experiments, it is concluded that the rescue robot can combine the artificial potential field method and reinforcement learning to improve the ability to adapt to the environment, and can reach the destination with the optimal route.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Interests of Croatian primary school pupils about elective Technology Teaching and school activities

Allowing pupils to choose the content of their learning is an important step towards individualized development. Despite the need proclaimed by society for the development of competences in STEM area, educational cycles in this field are the least represented in the Croatian general education curriculum. This research was conducted in order to gain insight into pupils’ interests in the contents of technical culture and for school activities. The research was conducted on a stratified sample of primary school pupils in Croatia (N = 2155) aged 11 to 15 years. The analysis of the research results showed that the activities from ICT, robotics, programming and woodworking are the most attractive. When it comes to school activities, pupils most prefer sports activities, computer activities, experiments in science subjects and then technical activities. This result suggests that the school curricula needs to be redesigned so that pupils can develop according to their interests.
Fast algorithm for box‐constrained fractional‐order total variation image restoration with impulse noise

In this paper, a novel variational model with box constraints is proposed to restore images corrupted by impulse noise. The proposed model is composed of fractional-order total variation regularization and Lp-fidelity term ( 0 < p < 1 ) $(0<p<1)$ . Moreover, the new model possesses the advantages of preserving sharp edges and removing blocking effect. To solve the proposed model, some auxiliary variables are first introduced to transform it into some easy-to-solve subproblems. Further, the alternating direction method of multipliers, iteratively re-weighted ℓ1 algorithm and fast iteration technique are adopted to solve the related subproblems. Numerical results show that the proposed model performs better in comparison with the several existing methods, in terms of both quantitative evaluation and visual quality.
A Framework for Text Classification Using Evolutionary Contiguous Convolutional Neural Network and Swarm Based Deep Neural Network

To classify the texts accurately, many machine learning techniques have been utilized in the field of Natural Language Processing (NLP). For many pattern classification applications, great success has been obtained when implemented with deep learning models rather than using ordinary machine learning techniques. Understanding the complex models and their respective relationships within the data determines the success of such deep learning techniques. But analyzing the suitable deep learning methods, techniques, and architectures for text classification is a huge challenge for researchers. In this work, a Contiguous Convolutional Neural Network (CCNN) based on Differential Evolution (DE) is initially proposed and named as Evolutionary Contiguous Convolutional Neural Network (ECCNN) where the data instances of the input point are considered along with the contiguous data points in the dataset so that a deeper understanding is provided for the classification of the respective input, thereby boosting the performance of the deep learning model. Secondly, a swarm-based Deep Neural Network (DNN) utilizing Particle Swarm Optimization (PSO) with DNN is proposed for the classification of text, and it is named Swarm DNN. This model is validated on two datasets and the best results are obtained when implemented with the Swarm DNN model as it produced a high classification accuracy of 97.32% when tested on the BBC newsgroup text dataset and 87.99% when tested on 20 newsgroup text datasets. Similarly, when implemented with the ECCNN model, it produced a high classification accuracy of 97.11% when tested on the BBC newsgroup text dataset and 88.76% when tested on 20 newsgroup text datasets.
Blockchain &amp; Multi-Agent System: A New Promising Approach for Cloud Data Integrity Auditing with Deduplication

Recently, data storage represents one of the most important services in Cloud Computing. The cloud provider should ensure two major requirements which are data integrity and storage efficiency. Blockchain data structure and the efficient data deduplication represent possible solutions to address these exigencies. Several approaches have been proposed, some of them implement deduplication in Cloud server side, which involves a lot of computation to eliminate the redundant data and it becomes more and more complex. Therefore, this paper proposed an efficient, reliable and secure approach, in which the authors propose a Multi-Agent System in order to manipulate deduplication technique that permits to reduce data volumes thereby reduce storage overhead. On the other side, the loss of physical control over data introduces security challenges such as data loss, data tampering and data modification. To solve similar problems, the authors also propose Blockchain as a database for storing metadata of client files. This database serves as logging database that ensures data integrity auditing function.