Reviewerly/exHarmony huggingface dataset:
Over-the-Air Gaussian Process Regression Based on Product of Experts

This paper proposes a distributed Gaussian process regression (GPR) with over-the-air computation, termed AirComp GPR, for communication-and computation-efficient data analysis over wireless networks. GPR is a non-parametric regression method that can model the target flexibly. However, its computational complexity and communication efficiency tend to be significant as the number of data increases. AirComp GPR focuses on that product-of-experts-based GPR approximates the exact GPR by a sum of values reported from distributed nodes. We introduce AirComp for the training and prediction steps to allow the nodes to transmit their local computation results simultaneously; the communication strategies are presented, including distributed training based on perfect and statistical channel state information cases. Applying to a radio map construction task, we demonstrate that AirComp GPR speeds up the computation time while maintaining the communication cost in training constant regardless of the numbers of data and nodes.
A Generative Approach for Production-Aware Industrial Network Traffic Modeling

The new wave of digitization induced by Industry 4.0 calls for ubiquitous and reliable connectivity to perform and automate industrial operations. 5G networks can afford the extreme requirements of heterogeneous vertical applications, but the lack of real data and realistic traffic statistics poses many challenges for the optimization and configuration of the network for industrial environments. In this paper, we investigate the network traffic data generated from a laser cutting machine deployed in a Trumpf factory in Germany. We analyze the traffic statistics, capture the dependencies between the internal states of the machine, and model the network traffic as a production state dependent stochastic process. The two-step model is proposed as follows: first, we model the production process as a multi-state semi Markov process, then we learn the conditional distributions of the production state dependent packet interarrival time and packet size with generative models. We compare the performance of various generative models including variational autoencoder (VAE), conditional variational autoencoder (CVAE), and generative adversarial network (GAN). The numerical results show a good approximation of the traffic arrival statistics depending on the production state. Among all generative models, CVAE provides in general the best performance in terms of the smallest Kullback-Leibler divergence.
Utilization of extremely precise analytics: A State-of-the-Art Approach and Future Potentials

Critical decisions are now taken with the assistance or relying exclusively on the analysis of massive amounts of complex, volatile, diverse, inconsistent, partially erroneous data, requiring extensive pre/post processing for curation and harmonization, often challenged by privacy considerations and resource/technology limitations. Although many commercial and open-source frameworks were deployed to tackle these challenges, new techniques need to be developed to optimize the prediction accuracy and robustness of artificial intelligence/machine learning and simulation models and validate them in constrained settings. In this paper, progress beyond the state-of-the-art is discussed in the context of extremely precise predictions, simulations, human-centered personalized user interactions, trustworthy artificial intelligence and automated decision-making. The idea of AI-empowered user models that encapsulate parameters such as domain expertise and experience is also presented under the blueprints presentation of a platform that will provide human-centered adaptive data visualizations, facilitating data exploration and enabling effective decision-making.
Convolutional Neural Network and Kernel Extreme Learning Machine for Face Recognition

Due to its good generalization capability and excellent performance, kernel Extreme learning Machine (KELM) has gained attention in face recognition. However, the efficiency of KELM for face classification depends on the effective extraction of the features from the inputted data. Convolutional neural network (CNN) performs good in finding the invariant features. KELM performs well in approximating target function, but does not perform well in learning complicated invariance. Contrarily, CNN cannot be always considered as optimal choice for classification. In this work, a novel learning framework for face recognition based on CNN and KELM (CNN-KELM) is introduced. In the proposed technique, KELM is used as a classifier in CNN architecture for enhancing the performance of face recognition. Due to integration of CNN and KELM, CNN-KELM has the advantage of KELM and CNN and it is easy to learn. Comprehensive experiments on the face databases have verified the enhanced performance of the presented technique when compared with the various state-of-art approaches.
Offline Signature Verification using Gaussian Weighting Based Tangent Angle

Offline signature verification is an important and critical task in biometrics as signature shape is varying with time. We are proposing a novel approach for offline signature verification using the histogram of Gaussian Weighting Based Tangent Angle (GWBTA). As signatures differentiated using shapes, so shape features are playing an important role in offline signature verification. Here the distribution of the pixels around the center pixel of the block is encoded to GWBTA. While computing GWBTA, the coordinates of the pixels are transformed to give more importance to the pixels which are nearer to the center pixels by using Gaussian weighting. The distances between histograms of GWBTA of signatures are utilized to classify the signature. The results show that the GWBTA gives more accurate results than existing tangent angle.
A Robust Slot Filling Model based on LSTM and CRF for IoT Voice Interaction

The Internet Of Things (IoT) is an emerging paradigm representing a network of infrastructure connecting different types of devices in different places. Recently, IoT-based Conversational systems gained more attention, providing users with a new human-computer interaction mode. These new systems allow users to interact with the IoT ecosystem through a virtual assistant. They also heavily rely on Natural Language Processing (NLP) techniques for intelligent and efficient communication. Understanding the user’s needs is the first step toward a more humanized IoT interaction system. This can be achieved using Natural language understanding (NLU) modules. Slot Filling is one of the core sub-tasks in NLU. It is an active research area focusing on extracting attribute values from the user’s utterance. In this study, we propose an LSTM-based model for slot filling. The model benefits from contextual embeddings extracted from the BERT transformer model, the attention mechanism, and the conditional random field (CRF) that is known to model strong dependence among adjacent tags in the output sequence. Experiments show that our model achieved competitive results on two widely used benchmark datasets.
Performance Analysis of Lightweight Algorithm GIFT-COFB for 3-D Printer Security

Secure lightweight algorithms have lately been proving their benefits and applications in resource-constrained environments. 3-D printers in a distributed network need security approval for their authentic and secure utilization. NIST round 2 algorithm GIFT-COFB and its primitive algorithm GIFT have been tested for their performance for STL files used in 3-D printers. GIFT-COFB is an Authenticated Encryption with Associated Data (AEAD) algorithm, an underlying scheme for which is the GIFT-128 lightweight block cipher. We analyze the power consumed, energy consumed and time taken by the algorithm for the encryption and decryption of a particular STL file. Then a comparison of state-of-the-art parameters of GIFT-COFB and GIFT is carried out to show the improvements we achieved by using the COFB lightweight AEAD operating mode. Analysis results show its worth in providing security to STL files. For further improvement in the system, signal processing can be applied.
Q-learning-based Collision-free Path Planning for Mobile Robot in Unknown Environment

With the complexity of application scenarios, higher performance requirements are imposed for the autonomous navigation ability of mobile robots. This paper proposes a Q-learning path planning method to achieve collision-free motion for the mobile robot in an unknown environment. An improved Q-learning algorithm is firstly designed by using unfixed reward function, expanded action space, and dynamic parameters in order to get an optimized and collision-free path. The convergence is reinforced by combining the gravity function in the artificial potential field algorithm and using the deep neural network instead of the Q-table. In the simulation environment, the improved Q-learning-based collision-free path planning is verified using grid map. Compared with comparison Q-learning algorithm, the path length of the improved algorithm is reduced by 17.07%, the path angle is reduced by 85.72%, and the convergence speed is shortened by 36.82%, which significantly improves the efficiency and effectiveness of path planning.
Modeling virtual knowledge graphs using relevant news data by NLP methods for business analysis

Companies aiming at online data sources for extracting business insights often experience the issue of managing different collections of datasets. It is because online business-related data is growing over time. This increasing data demands more storage. But saving the irrelevant data will cost more to companies. To overcome the problems of data integration and scalability, the idea of Virtual Knowledge Graphs (VKGs) is exploited. Moreover, text processing methods based on Natural Language Processing (NLP) are utilized for improving the identification of relevant business news data. The contribution of this work is twofold that is presented using a business case study. First, we have applied the concept of VKG to expand the company database. The expansion is required for adding more business-related data from the news articles to improve the process of identifying its relevance as per the end-user's interests. Second, we have proposed the text relevance resolution matrix and its working is shown through NLP techniques for identifying relevant news data. Populating the modeled VKG with relevant news data will save database space and prevent manual labor. Moreover, it will also help business analysts to understand business data temporally, spatially, and thematically.
Wearable Sensor Feature Fusion for Human Activity Recognition (HAR): A Proposed Classification Framework

Human Activity Recognition (HAR) focuses on detecting people's daily regular activities based on time-series recordings of their actions or motions. Due to the extensive feature engineering and human feature extraction required by traditional machine learning algorithms, they are time-consuming to develop. To identify complicated human behaviors, deep learning approaches are more suited since they can automatically learn the features from the data. In this paper, a feature-fusion concept on handcrafted features and deep learning features is proposed to increase the recognition accuracy of diverse human physical activities using wearable sensors. The deep learning model Long-Short Term Memory based Deep Recurrent Neural Network (LSTM-DRNN) will be used to extract deep features. By fusing the handcrafted produced features with the automatically extracted deep features through the use of deep learning, the performance of the HAR model can be improved, which will result in a greater level of accuracy in the HAR model. Experiments conducted on two publicly available datasets show that the proposed feature fusion achieves a high level of classification accuracy.
Neural Networks Based Prayer Monitoring and Recognition Framework

Daily prayer is an important part of every muslim around the globe and muslims makes up 24.7% of the world population. The rapid advancement in smartphones and wearable devices sensors', enabled research to utilize these sensors for more complex human activity recognition tasks. In this paper, we propose a framework that leverages smartwatch sensors for prayer monitoring and recognition. Our work is mainly targeting smartwatch due their convenience for the users, but challenging nature of unlimited movement and noise during prayer. Our framework allows user to store their prayer using a smartwatch with the help of our web applications. We further propose a deep learning-based timeseries data classification model for prayer recognition. In this study, we have used three sensors namely heartrate electrocardiogram, an accelerometer, and a gyroscope. The collected data is used to train a prayer classification model. The raw prayer data is pre-processed using moving average, data aggregation, and data segmentation. Our proposed prayer classification model showed an accuracy of 87%. Finally, we discussed a suggested future work and further research in prayer analysis area.
Dual-View Preference Learning for Adaptive Recommendation

While recommendation systems have been widely deployed, most existing approaches only capture user preferences in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">macro-view</i> , i.e., the user's general interest across all kinds of items. However, in real-world scenarios, user preferences could vary with items of different natures, which we call the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">micro-view</i> . Both views are crucial for fully personalized recommendation, where an underpinning macro-view governs a multitude of finer-grained preferences in the micro-view. To model the dual views, in this paper, we propose a novel model called Dual-View Adaptive Recommendation (DVAR). In DVAR, we formulate the micro-view based on item categories, and further integrate it with the macro-view. Moreover, DVAR is designed to be adaptive, which is capable of automatically adapting to the dual-view preferences in response to different input users and item categories. To the best of our knowledge, this is the first attempt to integrate user preferences in macro- and micro- views in an adaptive way, without relying on additional side information such as text reviews. Finally, we conducted extensive quantitative and qualitative evaluations on several real-world datasets. Empirical results not only show that DVAR can significantly outperform other state-of-the-art recommendation systems, but also demonstrate the benefit and interpretability of the dual views.
Straggler-Exploiting Fully Private Distributed Matrix Multiplication With Chebyshev Polynomials

In this paper, we consider coded computation for matrix multiplication tasks in distributed computing to mitigate straggler effects. We assume that the stragglers’ computation results can be leveraged at the master by assigning multiple sub-tasks to the workers. We propose a new coded computation scheme, namely Chebyshev coded fully private matrix multiplication (CFP), to preserve the privacy of a master in a scenario where a master wants to obtain a matrix multiplication result from the libraries which are shared by the workers, while concealing both of the two indices of the desired matrices from each worker. The key idea of CFP is to introduce Chebyshev polynomials, which have commutative property, in queries sent to workers to allocate sub-tasks. We also extend CFP to keep the privacy of a master from colluding workers. In conclusion, we show that CFP can preserve the privacy of a master from each worker and efficiently mitigate straggler effects compared to existing schemes.
Multi-Party Private Function Evaluation for RAM

Private function evaluation (PFE) is a special type of MPC protocols that, in addition to the input privacy, can preserve the function privacy. In this work, we propose a PFE scheme for RAM. In particular, we first design an efficient 4-server distributed ORAM scheme with amortized communication <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(\log n)$ </tex-math></inline-formula> per access (both reading and writing). We then simulate a RISC RAM machine over the MPC platform, hiding (i) the memory access pattern, (ii) the machine state (including registers, program counter, condition flag, etc.), and (iii) the executed instructions. Our scheme can naturally support a simplified TinyRAM instruction set; if a public RAM program <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$P$ </tex-math></inline-formula> with given inputs <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$x$ </tex-math></inline-formula> needs to execute <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$z$ </tex-math></inline-formula> instruction cycles, our PFE scheme is able to securely evaluate <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$P(x)$ </tex-math></inline-formula> on private <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$P$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$x$ </tex-math></inline-formula> within <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5z+1$ </tex-math></inline-formula> online rounds. We prototype and benchmark our system for set intersection, binary search, and quicksort algorithms. For instance, obliviously performing the binary search algorithm on a 210 array takes <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5.81s$ </tex-math></inline-formula> with function privacy.
Impact of Mobile Technology on Collaborative Learning in Engineering Studies

&lt;p style="text-align: justify;"&gt;Collaborative learning has been identified as an essential aspect in the process of learning. As accelerated advancement continues to characterize the developments of technology, innovative mobile technology appears to be transforming the way collaborative learning is taking shape. This study focused on identifying whether mobile technology has a significant impact on collaborative learning in engineering studies in a private University in Malaysia. Using a quantitative approach, an online survey was administered for the data collection. Some 221 participants were selected randomly among undergraduate engineering students in the University. Data were analyzed using SmartPLS. The research findings revealed that mobile technology has a significant impact on collaborative learning. The findings also indicated that two of the mobile technology dimensions, namely mobility and immediacy have significant impact on collaborative learning. Consequently, this research suggests engineering educators can integrate mobile technology into their future instruction for more collaborative learning and create a smart workforce consisting of fast and adaptive engineers as well as other learners in Malaysia.&lt;/p&gt;
Human Resource Network Information Recommendation Method Based on Machine Learning

Human resource network information recommendation method is affected by similarity information, which leads to poor recommendation effect. A human resource network information recommendation method based on machine learning is proposed. The machine learning statistical model is established to filter the noise information. Temporal behavioral preference features were constructed to calculate the average number of information clicks. Data preprocessing is accomplished through data format conversion, data cleaning and data specification. The network information resources were retrieved, the pattern matching results were obtained, the loss function based on content association rules was constructed, and the recommendation model was constructed combined with hybrid genetic algorithm. The experimental results show that the highest recommendation accuracy of this method reaches 94%, and the highest recall rate is 0.90, which indicates that the application of research methods to recommend human resources network information has a good effect.
Secure Storage Method for Network Resources of Professional Works Based on Decision Tree Algorithm

Because the work network resources quantity are large, the storage difficulty is great, the security is very difficult to obtain safeguard. Therefore, a method of secure storage for network resources of professional works based on decision tree algorithm is proposed. According to the network resource storage requirements of professional works, the network layer, middleware layer and application layer of the network resource security storage system are constructed. On this basis, the secure storage network communication and its open network protocol are designed through CSocket class and CConsection class, the decision tree is constructed to predict the network load performance, and the work resource cache model is built. By encoding the transmitted work resources, complete the encryption and decryption process of network resources, find network resources based on POP3 protocol, and create the retransmission failure task of main thread and monitoring thread, so as to realize the safe storage of professional work network resources. Experimental results show that the maximum caching efficiency of the proposed method is 94.21%, and the maximum safety factor of the resource storage is 0.9.
Content-Aware Deep Feature Matching

Feature matching is a fundamental and critical step for many tasks of computer vision. In recent years, many methods for feature matching have been proposed. However, the image alignment algorithm used in the state-of-the-art matching method is not robust enough, and the number of the features extracted from the images are insufficient, thus the performance obtained by the state-of-the-art matching method is unsatisfactory. To address these issues, firstly, we use a more robust geometric estimation algorithm to align image pairs. Secondly, based on aligned image pairs, we design a content-aware block to more focus on effective features by using a feature extractor and a mask predictor. Experimental results show that our method achieves more superior results, and outperforms several classical and deep learning methods on the HPatches dataset.
A Textual Adversarial Attack Scheme for Domain-Specific Models

Most of the textual adversarial attack methods generate adversarial examples by searching solutions from a perturbation space, which is constructed based on universal corpus. These methods possess high performance when attacking models trained on universal corpus, whereas have a greatly reduced attack capability when attacking domain-specific models. In this paper, we inject domain-specific knowledge into the perturbation space and combine the new domain-specific space with the universal space to enlarge the candidate space for attacking. Specifically, for a domain-specific victim model, the corresponding corpus is used to construct a domain-specific word embedding space, which is utilized as the augmented perturbation space. Besides, we use beam search to augment the search range to further improve the attack ability. Experiment results, involving multiple victim models, datasets, and baselines, reflect that our attack method realized significant improvements on domain-specific model attack.