Reviewerly/exHarmony huggingface dataset:
Few-Shot Induction of Generalized Logical Concepts via Human Guidance

We consider the problem of learning generalized first-order representations of concepts from a small number of examples. We augment an inductive logic programming learner with 2 novel contributions. First, we define a distance measure between candidate concept representations that improves the efficiency of search for target concept and generalization. Second, we leverage richer human inputs in the form of advice to improve the sample efficiency of learning. We prove that the proposed distance measure is semantically valid and use that to derive a PAC bound. Our experiments on diverse learning tasks demonstrate both the effectiveness and efficiency of our approach.
Insights into the Impactful usage of Virtual Reality for End Users

“Campus Visit” is the most significant facet of college enrolment program. An in-person strolling “campus-tour” is a tiresome task that is generally infeasible for the guests to explore all the amenities within a day due to distance, time constraints, or due to budgetary imperatives. With the innovative movements, the idea of the computerized visit goes under a bigger brolly of Virtual Reality and because of the ongoing COVID-19 pandemic, the demand for virtual exploration is on the rise. VR has an incredible potential to reimagine and reconstruct the reality with magnificent visualizations by allowing virtual access to objects and places that may be out of reach in the real world. Undoubtedly, “Virtual-Tour” has become a celebrated methodology for cheap and cheerful voyaging. This perlustration perspicuously elucidates the impactful usage of VR in assorted fields and spotlights the use of VR in “Campus-Tour”. Additionally, it focusses on featuring the quality and restrictions of prior proposed VR techniques and ultimately, drives out the supreme strategy that offers an immersive experience to the end-user.
Lightfield imaging for industrial applications

Lightfield imaging systems were brought to market for consumer and professional media recording. But so far, this technology is less known for applications in the industrial space. The unique optical concept developed by K Lens allows to capture multiple perspectives of a scene with a single exposure as regular colour images on the camera sensor. The current design is tailored to capture 9 different perspectives. Effectively, such a lens turns any camera without any modification of the camera into a 3D camera. It even makes it possible to record 3D video and instantaneously view it in 3D. This technology has unique benefits specifically for applications requiring the inspection of small and deep structures. For example, capturing 3D information of small pins in electric plugs with high walls is possible with such an optical system. The optical concept can be adapted to different measurement volumes. An optimization routine has been developed to identify if there is a solution for a certain application and which performance parameter could be achieved. Two different types of depth estimation algorithms have been developed which utilize the multiple perspectives. While the optical flow based classical approach allows for manual optimization the machine learning (CNN-based) approach appears to be more robust. The paper outlines the technical principles of the optical concept and points out application areas where such an optical concept has unique advantages. It also gives an indication of the achievable performance in terms of depth resolution based on a mathematical simulation model as well as from measurements using a first prototype. Some examples from real objects conclude the paper.
On the Identification of FOSD-based Non-zero Onset Speech Dataset

Recent trends in voicebot and chatbot application development have enabled utilization of speech-to-text (STT) and text-to-speech (TTS) generation techniques. In order to develop such TTS or STT engines, text and the corresponding recorded speech in an audio file used for training, validating and testing must be aligned. This is to ensure the developed engines achieve the desired conversion quality. In order to align speech and text, an audio alignment tool should be used. In such tools, often onset detection algorithms are utilized for labeling the audio file’s speech start and end times. This information is then stored together with the file’s transcript. In this work, an open nonzero onset Vietnamese speech dataset is provided. This dataset contains 348 audio files filtered from over 25,000 (approximately 30-hours) Vietnamese speech records released publicly by FPT Corporation, Vietnam in 2018. This amount of labeled data is considered to be more than sufficient for a typical onset detection algorithm researches.
Development of Online Examination Management System for Rig Operation Process

In order to complete the closed-loop purpose of optimizing the virtual experiment platform for remote interactive control of drilling rigs from teaching, practice to assessment, on the premise of analyzing the inadequacy of the existing assessment methods, an online examination management system aimed at the operation process of drilling rigs was designed. The system uses MySQL, PHP, Ajax, etc. to achieve the functions of creating test papers, deleting test papers, updating test papers, examinations for the operation process of the rig, automatic examination papers, and feedback. By establishing a reasonable examination mechanism, the system can effectively reduce the risk and accident rate of the actual operation of the drilling rig, and can also provide data for the improvement of the experimental platform, and improve the quality control of the platform teaching.
Machine learning interpretability meets TLS fingerprinting

Protecting users’ privacy over the Internet is of great importance; however, it becomes harder and harder to maintain due to the increasing complexity of network protocols and components. Therefore, investigating and understanding how data are leaked from the information transmission platforms and protocols can lead us to a more secure environment. In this paper, we propose a framework to systematically find the most vulnerable information fields in a network protocol. To this end, focusing on the transport layer security (TLS) protocol, we perform different machine-learning-based fingerprinting attacks on the collected data from more than 70 domains (websites) to understand how and where this information leakage occurs in the TLS protocol. Then, by employing the interpretation techniques developed in the machine learning community and applying our framework, we find the most vulnerable information fields in the TLS protocol. Our findings demonstrate that the TLS handshake (which is mainly unencrypted), the TLS record length appearing in the TLS application data header, and the IV field are among the most critical leaker parts in this protocol, respectively.
Automatically Extract Semi-Transparent Motion-Blurred Hand From a Single Image

When we use video chat, video game, or other video applications, motion-blurred hands often appear. Accurately extracting these hands is very useful for video editing and behavior analysis. However, existing motion-blurred object extraction methods either need user interactions, such as user supplied trimaps and scribbles, or need additional information, such as background images. In this letter, a novel method which can automatically extract the semi-transparent motion-blurred hand just according to the original RGB image is proposed. The proposed method separates the extraction task into two subtasks: alpha matte prediction and foreground prediction. These two subtasks are implemented by Xception based encoder-decoder networks. The images of extracted motion-blurred hands are calculated by multiplying the predicted alpha mattes and foreground images. Experiments on synthetic and real datasets show that the proposed method has promising performance.
L1 Norm Based Principal Component Analysis and Its Multiresolution Analysis

The L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> norm based principal component analysis (PCA) is used for reducing the dimensions of the signals in many science and engineering disciplines recently. This is because the errors between the reconstructed vectors and the original training vectors are sparse. Different from the conventional L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> norm based PCA, as both the projection matrix and the reconstruction matrix are not necessary to be unitary, these two matrices are required to be determined. Another approach is to employ the same matrix for both the projection and the reconstruction. In this case, the total L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> norm error between the reconstructed vectors and the original training vectors is minimized subject to the exact unitary condition on the transform matrix. However, the errors between the reconstructed vectors and the original training vectors are not sparse. To address this issue, this paper minimizes both the total L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> norm error between the reconstructed vectors and the original training vectors as well as the unitary error of the transform matrix. Also, this concept is applied to the multiresolution analysis. That is, the training vectors are reduced from the first dimension to the second dimension and then from the second dimension to the third dimension.
Vehicle trajectory recognition based on video object detection

Highway always has high-speed traffic flow, which means that frequent lane changes will easily cause large risk. So, lane changes are often not allowed on complex sections, such as tunnel, long downhill section, etc. Vehicle trajectory recognition from the video can help the administration monitor and analyze the movement of the vehicles. In this paper, we choose a one stage object detection network called Yolo to detect vehicles from the surveillance video camera. Data augmentation, focal loss, and synchronized batch normalization are applied to improve the performance of detector. After successful vehicles detection, a vehicle box matching method based on IOU is applied to identify whether a detected vehicle is a recorded vehicle or new one. The results show that the object detection and tracking method can detect and track vehicle is stable, the trajectory recognition achieves high reliability.
On Performance Evaluation of Security Monitoring in Multitenant Cloud Applications

In this paper we present a modeling approach suitable for practical evaluation of the delays that may affect security monitoring systems in (multitenant) cloud based architecture, and in general to support professionals in planning and evaluating relevant parameters in dealing with new designs or migration projects. The approach is based on modularity and multiformalism techniques to manage complexity and guide designers in an incremental process, to help transferring technical knowledge into modeling practice and to help easing the use of simulation. We present a case study based on a real experience, triggered by a new legal requirement that Italian Public Administration should comply about their datacenters.
DNN Based Iterative Detection for High Order QAM OFDM Systems With Insufficient Cyclic Prefix

In this paper, we consider high order QAM orthogonal frequency-division multiplexing (OFDM) systems with insufficient cyclic prefix (CP) which will lead to intersymbol interference (ISI) and intercarrier interference (ICI) in the receiver. To cope with the error floor induced by ICI and ISI in one-tap equalization and iterative serial interference cancellation (ISIC), we develop a maximum likelihood (ML) based iterative grouping detection algorithm (ML-IG), which utilizes the correlation among the received signals on adjacent subcarriers to improve the detection accuracy. Since ML-IG for OFDM systems with high order QAM modulation is of significant complexity, detection network (DetNet) based iterative grouping detection algorithm (DetNet-IG) is designed to imitate ML-IG. Simulations show that both ML-IG and DetNet-IG can provide better BER performance than ISIC, and DetNet-IG exhibits distinguished robustness against channel model incongruity.
Declarative Residual Network for Robust Facial Expression Recognition

Automatic facial expression recognition is of great importance for the use of human-computer interaction (HCI) in various applications. Due to the large variance in terms of head position, age range, illumination, etc, detecting and recognizing human facial expressions in realistic environments remains a challenging task. In recent years, deep neural networks have started being used in this task and demonstrated state-of-the-art performance. Here we propose a reliable framework for robust facial expression recognition. The basic architecture for our framework is ResNet-18, in combination with a declarative $$L_p$$ sphere/ball projection layer. The proposed framework also contains data augmentation, voting mechanism, and a YOLO based face detection module. The performance of our proposed framework is evaluated on a semi-natural static facial expression dataset Static Facial Expressions in the Wild (SFEW), which contains over 800 images extracted from movies. Results show excellent performance with an averaged test accuracy of $$51.89\%$$ for five runs, which indicates the considerable potential of our framework.
Towards an Infostructure Maturity Model for Disaster Management

Maturity models has been accepted as a widely accepted tool in guiding the development of improvement program to transform an organization to a better state. Due to the ad-hoc and dynamic state of disaster management, activities conducted in dealing with disaster need to be assessed so it meets the objectives. This paper highlights a preliminary investigation of a suitable assessment tool, aimed at the development of an infostructure maturity model for the domain of disaster management. The development of this model is justified to the extent that there is no existing maturity model to assess infostructure, which is a new term developed specifically for disaster management. The research also aims to serve as a preliminary work in developing a tool to determine the maturity of the activities involved in disaster management, specifically for electricity company.
Augmented Reality Signage in Mixed-Use Shopping Mall Focusing on Visual Types of Directional Signage

This study aims to explore the effective types of signage in augmented reality (AR) environments. Although AR, a state-of-the-art technology, has been utilized in diverse contexts, its application in real-world situations is limited. This study examines the effectiveness of AR signage in mixed-use shopping malls in terms of wayfinding, which is a continuous problem. First, the time it took to make a decision was measured for three categories of signage: existing signage, AR-textual signage, and AR-graphic signage. Thirty directional sign images from the three categories of signage were used in the experiment. The sign images of the current sign system at the mixed-use mall were used as the control condition. Second, subjects were asked in a questionnaire to evaluate each AR-enhanced sign type in 5 aspects: uniformity, continuity, legibility, clarity, and aesthetics. The result found that AR-graphic signage is the most effective type in terms of decision-making time and aesthetics. However, it was found that for legibility and clarity of signage, textual elements should be adjusted in AR signage environments. Thus, this research suggests the use of mixed graphic and textual signage in AR environments.
E-commerce Item Identification Based on Improved SqueezeNet

Abstract In order to improve the recognition rate of product images in e-commerce recommendation scenes, we proposed a high-performance improved SqueezeNet convolutional neural network, which uses a Fire Module structure containing two large convolution kernels to reduce the complexity of the model while fully extracting features, and adding a pooling layer behind each Fire Module to effectively filter key features of the classification. Experiments show that the algorithm in this paper has a better application in image recognition in e-commerce scenes, and the recognition accuracy is improved by 1%.
Gaussian Poisson Multi Bernoulli Mixture Filter and its Applications

Random Finite Sets became a prominent method for the definition of Bayesian multi target tracking. Often these tracking methods are derived by probability generating functional which requires a broader mathematical background. This paper considers a concrete implementation of a rather advanced type of Bayesian Multi Target Tracking - the so called Gaussian Poisson Multi Bernoulli Mixture Filter. The aim is to introduce this tracking technique, its implementation, specifics and possible applications.
Visual Question Answering Based on Question Attention Model

Abstract Visual Question Answer (VQA), the natural language question of Visual images, has become popular in the field of artificial intelligence. At present, most of the VQA models extract the whole image features, which consume a large amount of computation and have a complex structure. In this paper, we propose a VQA method based on question attention model. Firstly, the Convolutional Neural Networks (CNN) is used to extract image features from the input images, and the question text is processed by the Long Short-Term Memory (LSTM). Then, we design a question attention module to let the learning algorithm focus on the most relevant features of the input text. According to question features, our method utilities the attention module to add the corresponding weights to the image features and extract the meaningful information for the generation of answer sequence words. Our method performed significantly better than the LSTMQ+I model on the MS COCO visual question answer (VQA) dataset with an accuracy improvement of 2%.
An Efficient Public Batch Auditing Scheme for Data Integrity in Standard Model

The cloud storage auditing constructions derived from the primitives of homomorphic linear authenticator and polynomial-based authentication tag outperform other types of constructions in terms of the efficiency in verifier’s side. However, the batch auditing overheads regarding the storage and the computation in known constructions can be further reduced. And these constructions are improper for the standard batch auditing model. In this paper, we propose an efficient cloud storage auditing scheme supporting the batch auditing in standard model. To this end, the only nonce in the existing constructions is replaced with multiple nonces that are corresponding to each involved data owner. And the extended Euclidean algorithm is employed to generate the aggregated proof for batch auditing. In the proposed scheme, the overheads regarding storage and computation are both reduced to be as approximately large as the number of the involved data owners. The security analysis and the performance evaluation show that the proposed scheme is secure and efficient as expected.
Sparse Hierarchical Modeling of Deep Contextual Attention for Document-Level Neural Machine Translation

Document-level machine translation has shown its advantages and importance, but we still have to face some challenges due to the difficulty in efficiently using document context for translation. In this work, we propose a model based on the Transformer to translate the whole paragraph or document. We extend the Transformer model with a new context encoder, which can be incorporated into the original sentence encoder. Then we use a sparsity gate on the context encoder to extract document-level context attention, which is then incorporated into the hierarchical decoder so that we can feedback the document-level inter-sentence consistency and coherence to each word to distinguish different translations of a word according to its specific surrounding context. In addition, we use the pre-training strategy instead of the two-step strategy to take advantage of large-scale parallel sentence pairs and a small-scale corpus with in-domain parallel document pairs to achieve the domain adaptability. The results of experiment on three language-pair corpora have shown that our proposed model can achieve better translation performance quality than baselines.
Analysis of the Earth’s Surface Type in Remote Sensing Using Fractal Dimension

The possibility of applying the fractal theory to the analysis of the surface type in the Earth's remote sensing is considered in the article. The method of conducting experimental studies of remote sensing of various types of earth surfaces is given. The analysis of radar signals reflected from the earth's surface using fractal dimensions is carried out. Based on the conducted research, an approach to the analysis of the underlying surface type using fractal dimension is proposed.
Association analysis method of drug-related cases based on Apriori and FP-growth algorithm

Drug crimes seriously endanger human health and social peace. Strengthening the correlation analysis of the features of drug-related cases and fully mining up the correlation rules from the features of the cases have become the key links for effectively preventing and responding to drug crimes. After performing performance analysis on the two association analysis algorithms, in view of the lack of existing association analysis research and association rule mining methods in drug-related cases, this paper proposes a method for analyzing association rules of drug-related cases, Through the FP-growth algorithm and K-supported expectation method to calculate the weighted frequent items of drug-related personnel, and use the Apriori algorithm to obtain the feature association rules. The results show that the proposed method makes a good trade-off between the amount of data and the minimum support (called minsup), and can effectively mine association rules from the features of drug-related cases. It is of great significance to prevent and control the development of drug-related crimes in time and improve the ability of public security departments to deal with drug-related cases.