Reviewerly/exHarmony huggingface dataset:
Tableau-based Decision Procedure for Non-Fregean Logic of Sentential Identity

Abstract Sentential Calculus with Identity ( $$\mathsf {SCI}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>SCI</mml:mi> </mml:math> ) is an extension of classical propositional logic, featuring a new connective of identity between formulas. In $$\mathsf {SCI}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>SCI</mml:mi> </mml:math> two formulas are said to be identical if they share the same denotation. In the semantics of the logic, truth values are distinguished from denotations, hence the identity connective is strictly stronger than classical equivalence. In this paper we present a sound, complete, and terminating algorithm deciding the satisfiability of $$\mathsf {SCI}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>SCI</mml:mi> </mml:math> -formulas, based on labelled tableaux. To the best of our knowledge, it is the first implemented decision procedure for $$\mathsf {SCI}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>SCI</mml:mi> </mml:math> which runs in NP , i.e., is complexity-optimal. The obtained complexity bound is a result of dividing derivation rules in the algorithm into two sets: decomposition and equality rules, whose interplay yields derivation trees with branches of polynomial length with respect to the size of the investigated formula. We describe an implementation of the procedure and compare its performance with implementations of other calculi for $$\mathsf {SCI}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>SCI</mml:mi> </mml:math> (for which, however, the termination results were not established). We show possible refinements of our algorithm and discuss the possibility of extending it to other non-Fregean logics.
Design of a Mobile Robot to Work in Hospitals and Trajectory Planning Using Proposed Neural Networks Predictors

Considering the intense and tiring working conditions in hospitals, healthcare personnel’s performance decreases during prolonged working times, and patients are directly affected by this decrease in performance. This study aims to design and implement a mobile robot that can help healthcare professionals improve the healthcare industry conditions. In this context, the focus is on the mobile robot performing two main tasks. The first task is dispensing medication to patients with an eight-chamber mechanical feeding unit. Thus, patients can take only their medicines from the defined reservoir by selecting their names or photos on the touch screen. The second task is to interact with patients to give moral support with phrases such as “good morning”, “you look great today”. Also, drug delivery activity is recorded in a database, and the health status of the patients can be kept under surveillance with the camera on the mobile robot. The designed mobile robot goes to the patient rooms with magnetic strip tracking. For this purpose, a controller is designed for the omni-drive robot using MATLAB, and its performance is simulated. Also, the control velocities that enable tracking the trajectories are taught to artificial neural networks (ANN), and the requirement magnetic strip for trajectory tracking is eliminated. In this direction, two artificial neural networks are compared in terms of their learning performance.
Development of Collaborative Chemistry Experiment Environment Using VR

In recent years, the importance of distance learning has been reaffirmed, but there are some subjects where learning is ineffective owing to the difficulty of interaction. For chemistry experiments, some researchers have tried to conduct remote experiments using head-mounted display (HMD)-based virtual reality (VR). However, in these attempts, there is a concern that collaborative learning, which is important in science education, is lacking. In this study, we proposed a new environment for remote chemistry experiments using network services and HMD-based VR. Specifically, we conducted VR experiments of flame color reaction and metal ion separation and identification, which are often taught in high school chemistry, and then evaluated the effectiveness of the proposed environment using exams and questionnaires. The results confirmed effective learning in the chemistry experiment using HMD-based VR, suggesting that collaborative learning was possible in VR spaces.
A SYSTEMATIC COLLECTION OF NATURAL INTERACTIONS FOR IMMERSIVE MODELING FROM BUILDING BLOCKS

Abstract The increased availability of affordable virtual reality hardware in the last years boosted research and development of such systems for many fields of application. While extended reality systems are well established for visualization of product data, immersive authoring tools that can create and modify that data are yet to see widespread productive use. Making use of building blocks, we see the possibility that such tools allow quick expression of spatial concepts, even for non-expert users. Optical hand-tracking technology allows the implementation of this immersive modeling using natural user interfaces. Here the users manipulated the virtual objects with their bare hands. In this work, we present a systematic collection of natural interactions suited for immersive building-block-based modeling systems. The interactions are conceptually described and categorized by the task they fulfil.
Custom Computing Design and Implementation for Multiple Dedispersion with GPU

Pulsar searching requires a real-time coherent de-dispersion process on an enormous stream of complex voltage data. We present a many-core accelerated de-dispersion pipeline, ACDT, which exploits the custom computing design for multiple de-dispersion on GPUs. The ACDT implementation optimizes the de-dispersion by switching to on-chip shared memory, adopting customized FFT with the overlap-save method, and overlapping the computation with transfer by a two-stage pipeline. The overall performance of ACDT is improved by 2 to 4 times when multiple DMs are processed in sequential compared to the state-of-the-art CDMT package.
Automated Generation of Exam Sheets for Automated Deduction

Amid the COVID-19 pandemic, distance teaching became default in higher education, urging teachers and researchers to revise course materials into an accessible online content for a diverse audience. Probably one of the hardest challenges came with online assessments of course performance, for example by organizing online written exams. In this teaching-related project paper we survey the setting we organized for our master’s level course “Automated Deduction” in logic and computation at TU Wien. The algorithmic and rigorous reasoning developed within our course called for individual exam sheets focused on problem solving and deductive proofs; as such exam sheets using test grids were not a viable solution for written exams within our course. We believe the toolchain of automated reasoning tools we have developed for holding online written exams could be beneficial not only for other distance learning platforms, but also to researchers in automated reasoning, by providing our community with a large set of randomly generated benchmarks in SAT/SMT solving and first-order theorem proving.
A Technique to Limit Hybrid Covert Channel Capacity via Random Increasing of Packets’ Lengths

Currently, due to the development and widespread use of communication systems, information security problems are very acute. Very often information leakage causes huge damage to both organizations and individuals. One of the mechanisms to organize information leakage during its transmission through communication channels is the construction of covert channels. Everywhere used packet networks provide huge opportunities for covert channels creating, which often leads to leakage of critical data. Packet length covert channels are resistant to traffic encryption, but there are some data transfer schemes that are difficult to detect. Therefore investigating of hybrid covert channel that uses both packet length and time is quite important. The purpose of this paper is to suggest a technique to protect data leakage via random increasing of packets' lengths. The verification of the technique concerns with covert channel capacity examination depending on covert channel input parameters and countermeasure parameters. The authors have chosen the best scheme of a covert channel in terms of a residual covert channel capacity. The construction of covert channel shows that countermeasures applied don't lead to errors. The main advantage of the scheme investigated is as follows: lengths of the transmitted packets take limited number of values that significantly increases the complexity of building such a channel and the ability to detect it.
Human Action Recognition on Exceptional Movement of Worker Operation

Time study is a technique to analyze human action based on ergonomic discipline and has been widely applied in various manufacturing sites. How to identify the standard operation by personnel and extract the operating time duration of each action is an emerging research problem in the human action recognition (HAR) research domain. In the real-world manufacturing, the operators actually conduct exceptional actions beyond the designed operation under standard operating procedures (SOP). These exceptional actions will lead to miss-recognition and disturb the measurement of action time. In this research, an exceptional action detection framework was proposed to detect exceptional actions that are not defined or should be avoided during HAR. First, the skeleton of human workers was computed, and Spatial-Temporal Graph Convolution Network model was implemented to provide the score of each action label. Then, the exceptional action can be classified by score correction with multiple support vector machine (SVM) classifiers. A dataset was created by simulating the actions which may occur during the drilling process. The preliminary experiment shows that the proposed framework is able to detect SOP and exceptional actions with up to 79.1% accuracy
An improved regularization and fitting-based segmentation method for echocardiographic images

Segmenting ultrasonic images is difficult because they consist of many types of noise such as additive and multiplicative noise. Echocardiographic images are greatly affected by speckle noise, which is a type of multiplicative noise. Motion of the heart wall and multi-directional blood flow makes echocardiographic imagery complicated. Closed contour deformable models are the solution to segmenting echocardiographic images. In a conventional approach, segmenting the right boundary of the heart is difficult due to motion and speckle noise. This chapter proposes a technique with a new regularization model to help the curve approach its desired boundary while maintaining its smoothness. The intensity in100formation at different regions in the image also matters for differentiating the boundaries of a curve, therefore we propose a region-based active contour model using intensity information. The speckle density is used for data-fitting energy to find intensity information in local regions. The proposed improved regularization and fitting-based segmentation (IRFS) technique with a new regularization model and fitting function makes it possible for the curve to find its right minima and region. The IRFS technique also improves the capability of the curve to draw the desired boundaries.
Jaskier: A Supporting Software Tool for Continuous Build Outcome Prediction Practice

Continuous Defect Prediction (CDP) is an assisting software development practice that combines Software Defect Prediction (SDP) with machine learning aided modelling and continuous developer feedback. Jaskier is a set of software tools developed under the supervision and with the participation of the authors of the article that implements a lightweight version of CDP called Continuous Build Outcome Prediction (CBOP). CBOP uses classification to label the possible build results based on historical data and metrics derived from the software repository. This paper contains a detailed description of the tool that was already started to be used in the production environment of a real software project where the CBOP practice is being evaluated.
Fuzzy Edge Image Steganography Using Hybrid LSB Method

The identification of correct edge location is an important step in edge based image steganography. The edge pixels in an image are characterized by sudden sharp change in intensity; changes made at these locations are inconspicuous to the human eye making it optimal for modification. In this paper, use of Fuzzy inference system to identify the correct edges have been extended to perform edge image steganography, that hides secret message using a hybrid LSB technique. The aim of the proposed hybrid LSB method is to achieve high payload embedding that ensures statistical resemblance between the cover and the generated stego image. It embeds two message bits in the seventh and eighth edge bit using LSB-Matching (LSBM) and LSB-Replacement (LSBR). The performance of our method shows substantial improvement in terms of security and image quality compared to existing edge based image steganography approaches. The experimental analysis shows that the stego image achieves high PSNR of 61 dB and SSIM of 0.99 with minimum level of distortion at high embedding rate observed by gradually increasing % pixels modifications. The method is validated to be secure against RS analysis and Pixel-wise Histogram steganalysis, tested with 2000 grayscale images from BOSSBase 1.0 database.
Digital Image Recovery Based on Lifting Wavelet Transform

Abstract Recently, with the wide distribution of digital media, the need for authenticating digital images was increased. Therefore, many image tamper detection and recovery algorithms were introduced in literature to detect malicious modifications and retrieve the original images. The process of detection and recovery, however, used to have complex operation which requires long processing time. In this paper, a simplified image recovery algorithm is presented by using lifting wavelet transform. In the proposed method, the approximation band is hidden inside the bits of the original image and to be retrieved without relying on source image. For images with hidden data, the average PSNR and SSIM values were 31.22 and 0.977 respectively, and images were successfully retrieved after block attack.
Rating Duration Analysis for Subjective Quality Assessment of 360° Videos

Due to large advances in immersive media systems offering increased resolutions, frame rates, and dynamic ranges, applications such as watching 360° videos on head-mounted displays (HMDs) have become more popular. To assist the development of immersive media applications, subjective tests of 360° videos on HMDs are needed to obtain a ground truth on the quality as perceived by the end-users. The absolute category rating (ACR) method, standardized for subjective quality assessment of conventional videos, has also been used for assessing the subjective quality of 360° videos. Recently, the modified ACR (M-ACR) method has been proposed aiming at subjective quality assessment of 360° videos. Several subjective test campaigns have been conducted to assess the ACR and M-ACR method for evaluating 360° videos covering a wide range of resolutions, bit rates, and quantization parameters. The opinion scores given by the participants in these subjective tests are accumulated to mean option scores (MOSs) and confidence intervals. However, to the best of our knowledge, a detailed statistical analysis and comparison of the rating durations associated with casting opinion scores for 360° test videos in the ACR and M-ACR method has not been reported in the literature. In this paper, we therefore provide a rating duration analysis for subjective quality assessment of 360° videos for the ACR and M-ACR method. The results support experimental designs for 360° video quality assessment and include the following findings: 1) The mean rating duration is shorter for the ACR method, 2) The difference of the standard deviation of rating durations versus MOS between the ACR and M-ACR method is insignificant, 3) For a given mean rating duration, however, the standard deviation is smaller for the M-ACR method indicating that the quality rating task is easier to execute than with ACR. 4) The period to be allocated in subjective tests for rating 360° videos should exceed 5 s but can be kept below or set equal to 10 s.
Diachronic Changes and Multi-dimensional Quantitative Analysis of Sentence Complexity in Contemporary Chinese Written Language

Using the corpus of People’s Daily from 1946 to 2015 as our data, this paper conducts a quantitative study on sentence complexity based on average length, dispersion, fragmentation and lexical density to investigate its historical changes. This paper also aims to conduct a multi-dimensional analysis on the correlation between sentence complexity and structure or function. Our results reveal that the trend of sentence complexity changes from stable to a rise. The amount of verbs in a sentence has a strong positive correlation with sentence length and fragmentation. Emotional value has a positive correlation with average sentence length, as well as dispersion of sentence lengths. The proportion of interrogative sentences is negatively correlated with average sentence length. Furthermore, the proportion of exclamatory sentences is negatively correlated with lexical density, while the proportion of declarative sentences positively correlates with lexical density.
Electric Power Forecasting Based on Logistic Regression and Gradient Boost Regression Tree

With the gradual marketization of power industry, power output forecasting is becoming more and more important in the power industry. The accurate power output of the power plant can be more reasonable for the power plant to make relevant decisions ensure the safe operation of the power plant. At the same time, the cost savings for the power plant to obtain more profits. In order to achieve the prediction of power output of the power plant, this paper uses logistics regression and gradient boosting regression tree on the basis of comprehensive analysis of the characteristics cause the power output to predict the power output of the public data set provided by a power plant. In the end, the average percentage error, root mean square error and relative value evaluation indexes is used to quantitatively analyze the prediction results and to evaluate the achievement of the two models in the prediction of electric energy output.
Tunicate Swarm Magnetic Optimization With Deep Convolution Neural Network For Collaborative Filter Recommendation

Abstract Collaborative filtering (CF) is a well-known and eminent recommendation technique to predict the preference of new users by revealing the structures of historical records of the examined users. Even though CF is effectively adapted in several commercial areas, many limitations still exist, particularly in the sparsity of rating data that raises many issues. This paper devises a novel deep learning strategy for CF to recognize user preferences. Here, black hole entropic fuzzy clustering (BHEFC) is devised for clustering item sequences to form groups with similar item sequences. Moreover, cluster centroids are optimized using the tunicate swarm magnetic optimization algorithm (TSMOA), which is devised by combining tunicate swarm algorithm and magnetic optimization algorithm. After grouping similar items together, the group matching is performed based on a deep convolutional neural network (Deep CNN). Subsequently, the visitor sequence and query sequence are compared using Jaro–Winkler distance, which contributes to the best visitor sequence. From this best visitor sequence, the recommended product is acquired. The proposed TSMOA–BHEFC and Deep CNN outperformed other methods with minimal mean absolute error of 0.200, mean absolute percentage error of 0.198 and root mean square error of 0.447, respectively.
Impact of Viewing Distance to Virtual Screen upon Kraepelin-Task Performance and Its Psychological Fatigue

This manuscript discusses an impact of viewing distances to a virtual screen in an immersive environment upon psychological fatigue by assessing calculation performance of Kraepelin test. The viewing distance can be adjusted from 0.5 m to 128 m. The result shows that there is a certain tendency to alleviate psychological fatigue at the furthest viewing distance.
B-plane compression technique for lossless and lossy coding of LAS files

In this paper, a new technique called B-plane compression is presented. This technique is used to compress LAS files, producing slightly better overall compression gain than the popular LAZ compression engine. Furthermore, the B-plane compression technique has other advantages. B-plane compression processes each of the ten attributes within the LAS files independently, allowing for parallel processing of LAS files, unlike LAZ which uses the return number and number of return attributes to provide context for compression of X, Y, and intensity attributes. Additionally, B-plane compression processes each attribute in a similar manner, with only minor adjustments to the compression technique for the intensity and return attributes as they are compressed as unsigned integers. All other attributes are compressed with the exact same algorithm. Overall, B-plane compression produces on average 1.48% increased compression gain when compared to LAZ compression for lossless compression of LAS files. Additionally, we introduce a quantization step in the compression of X, Y, Z, and intensity attributes, allowing for lossy compression and large compression gain.
Deep Semantics Sorting of Voice-Interaction-Enabled Industrial Control System

In recent years, voice-interaction-based control systems have attracted considerable attention for industrial control systems implementing Industrial Internet of Things (IIoT) technologies. The development of automated semantic understanding relates to the industrial Internet equipment used to realize remote voice control as well as to its intelligent management and control. In these emerging voice-interaction-enabled industrial central control systems, sorting technologies are considered critical. For complex user questions, the level of satisfaction regarding the answers given by such systems tends to be low. Driven by these challenges and opportunities, the optimization of conventional retrieval-based question answering through deep learning methods has become popular. In this study, we propose three deep semantic sorting models based on deep learning, including a multilayer convolutional matching sorting model for single documents and two interactive pairwise bidirectional encoder representations from transformers (BERT) sorting models for document pairs. Two main network architectures are proposed to model document pairs, named Pairwise-Twin-BERT and Pairwise-Triple-BERT. Experimental results indicate that proposed models performed better than state-of-the-art methods based on text matching in a candidate document sorting task.
VOTA: A 2.45TFLOPS/W Heterogeneous Multi-Core Visual Object Tracking Accelerator Based on Correlation Filters

VOTA is a domain-specific accelerator for correlation filter (CF)-based visual object tracking (VOT). It encompasses a Winograd convolution core, a FFT core and a vector core in a high-bandwidth starring topology. VOTA’s frame-based instructions and execution enable a 537GFLOPS performance and reduce the code size. An instruction-chaining mechanism permits inter-core pipelining to improve the utilization to 84.2%. A 10.2mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> 28nm FP16 VOTA prototype incorporating a RISC-V host CPU is measured to achieve 2.45TFLOPS/W at 0.72V. Running OPCF, a CF-based VOT enhanced by adaptive boosting and particle filters, the chip achieves 1157FPS on 640×480 input frames at 0.9V and 175MHz, consuming 296mW.