Reviewerly/exHarmony huggingface dataset:
Free choice permission, legitimization and relating semantics

Abstract In this paper, we apply relating semantics to the widely discussed problem of free choice between permitted actions or situations in normative systems. Leaving aside contexts in which the free choice principle is obviously unacceptable (weak permission understood as an absence of prohibition) or uncontroversially valid (strong permission within systems with a closed repertoire of actions or situations), we concentrate on free choice for explicit permissions. In order to construct a formal representation of explicit permissions, we introduce a special constant, $\texttt {permit}$, which is analogous to the constant $\texttt {violation}$ used in the Andersonian–Kangerian approach to deontic logic with respect to prohibition and obligation. Consequently, we define a permission operator on the basis of the $\texttt {permit}$ constant and a relation of legitimization. The general idea is that $P \varphi $ is true if and only if $\texttt {permit}$ is true, which means that a permission is actually issued and $\varphi $ is legitimized by $\texttt {permit}$. The intuitive notion of legitimization is formally represented by an operator of relating implication: a non-classical implication that is semantically defined by adding a constraint to the classical meaning of implication to the effect that arguments are related by a special relation. The properties of this relation are based on an informal, intuitive meaning of legitimization and determine the properties of the permission operator. We show that the resulting permission operator possesses the free choice property and avoids certain unwanted consequences that follow from alternative approaches.
Web-Page Interfaced Real-Time Object Detection Using TensorFlow

Technology has improved fast in recent years. With the development of the powerful microprocessor and good quality camera technologies like artificial intelligence and computer vision making significant strives in modern times. Computer vision-based real-time object detection helps detect, locate, and trace an object from an image or a video with the use of these technologies. This paper proposes a method to interface this real-time system into a web page. To detect objects the TensorFlow object detection API is used and live video is streamed through a password-protected login web page which can be accessed from any device. The system creates a bounding box around the object and also shows the detection accuracy.
A cross-chain technology based on a licensed public chain

With the development of blockchain technology, various kinds of blockchain projects with different characteristics and adapting to different application scenarios have emerged. However, different chains cannot be interconnected, forming many value islands, restricting the promotion and ecological development of blockchain. Cross-chain technology is an important technical means to achieve interconnection between different chains, improve performance, and expand the scope of application; combined with the advantages of nodes and state control in the licensing environment, this article mainly studies the cross-chain technology of licensed public chains.
On Current Limitations of Online Eye-Tracking to Study the Visual Processing of Source Code

Eye-tracking is an increasingly popular instrument to study how programmers process and comprehend source code. While most studies are conducted in a controlled environment, it would be desirable to simplify and scale participation in experiments for users sitting remotely, leveraging home equipment. This study investigates performing eye-tracking studies remotely using open-source algorithms and consumer-grade webcams. Eye-gaze data was gathered remotely from volunteers in a code-comprehension experiment using a for that purpose developed web app. The collected data was analyzed with metrics used in previous software eye-tracking studies, and the limits of the technology were identified. Despite all efforts of extensive calibration and graphical user guidance, the data was of varying quality and inadequate to address the initial research goal to compare programming styles in terms of their comprehensibility. Nonetheless, we have learned some critical lessons the we discern in this paper lead future research. Apart from the overall challenge of vastly diverse equipment, setup, and configuration, two main problems with the current webcam eye-tracking technology were found. The first was the lack of a fixation detection algorithm explicitly developed for low-frequency webcam data. This makes isolation of eye movements more complex and the results possibly unreliable. In response, we here introduce a dispersion threshold algorithm that generates fixations even in low-frequency data. The second problem was the lack of algorithmic support for head movements when predicting gaze points. Online experiments are unsupervised, and subjects are not guaranteed to keep their head still, even if instructed. Consequently, we frequently observed spatial shifts that corrupted many collected datasets. However, we also encountered some positive observations, like consistent spatial dispersion of gaze points and specific recognizable reading patterns. We close the paper by outlining ways forward addressing the current problems to make remote code-reading studies in support of eye-tracking feasible in the future.
Motion-Planning Algorithm for a Hyper-Redundant Manipulator in Narrow Spaces

In this study, a hyper-redundant manipulator was designed for detection and searching in narrow spaces for aerospace and earthquake rescue applications.A forward kinematics equation for the hyper-redundant manipulator was derived using the homogeneous coordinate transformation method.Based on the modal function backbone curve method and the known path, an improved modal method for the backbone curves was proposed.First, the configuration of the backbone curve for the hyper-redundant manipulator was divided into two parts: a mode function curve segment of the mode function and a known path segment.By changing the discrete points along the known path, the backbone curve for the manipulator when it reached a specified path point was dynamically obtained, and then the joint positions of the manipulator were fitted to the main curve by dichotomy.Combined with engineering examples, simulation experiments were performed using the new algorithm to extract mathematical models for external narrow space environments.The experimental results showed that when using the new algorithm, the hyper-redundant manipulator could complete the tasks of passing through curved pipes and moving into narrow workspaces.The effectiveness of the algorithm was also proven by these experiments.
Enhanced Parallel Sine Cosine Algorithm for Constrained and Unconstrained Optimization

The sine cosine algorithm’s main idea is the sine and cosine-based vacillation outwards or towards the best solution. The first main contribution of this paper proposes an enhanced version of the SCA algorithm called as ESCA algorithm. The supremacy of the proposed algorithm over a set of state-of-the-art algorithms in terms of solution accuracy and convergence speed will be demonstrated by experimental tests. When these algorithms are transferred to the business sector, they must meet time requirements dependent on the industrial process. If these temporal requirements are not met, an efficient solution is to speed them up by designing parallel algorithms. The second major contribution of this work is the design of several parallel algorithms for efficiently exploiting current multicore processor architectures. First, one-level synchronous and asynchronous parallel ESCA algorithms are designed. They have two favors; retain the proposed algorithm’s behavior and provide excellent parallel performance by combining coarse-grained parallelism with fine-grained parallelism. Moreover, the parallel scalability of the proposed algorithms is further improved by employing a two-level parallel strategy. Indeed, the experimental results suggest that the one-level parallel ESCA algorithms reduce the computing time, on average, by 87.4% and 90.8%, respectively, using 12 physical processing cores. The two-level parallel algorithms provide extra reductions of the computing time by 91.4%, 93.1%, and 94.5% with 16, 20, and 24 processing cores, including physical and logical cores. Comparison analysis is carried out on 30 unconstrained benchmark functions and three challenging engineering design problems. The experimental outcomes show that the proposed ESCA algorithm behaves outstandingly well in terms of exploration and exploitation behaviors, local optima avoidance, and convergence speed toward the optimum. The overall performance of the proposed algorithm is statistically validated using three non-parametric statistical tests, namely Friedman, Friedman aligned, and Quade tests.
A Multi-Scale A Contrario method for Unsupervised Image Anomaly Detection

Anomalies can be defined as any non-random structure which deviates from normality. Anomaly detection methods reported in the literature are numerous and diverse, as what is considered anomalous usually varies depending on particular scenarios and applications. In this work we propose an a contrario framework to detect anomalies in images applying statistical analysis to feature maps obtained via convolutions. We evaluate filters learned from the image under analysis via patch PCA and the feature maps obtained from a pre-trained deep neural network (Resnet). The proposed method is multi-scale and fully unsupervised, and is able to detect anomalies in a wide variety of scenarios. While the end goal of this work is the detection of subtle defects in leather samples for the automotive industry, we show that the same algorithm achieves state-of-the-art results in public anomalies datasets.
Collision Avoidance and Rendezvous of Quadric Surfaces Moving on Planar Environments

Achieving collision avoidance or rendezvous between moving objects are important objectives in robotic systems. In performing collision avoidance or rendezvous maneuvers, the relative shapes of the objects play an important role. The literature largely models the shapes of the objects as circles, and this can make the maneuvers very conservative, especially when the objects are of elongated shape. In this paper, we model the shapes of the objects using quadric surfaces. A collision/rendezvous cone concept is employed. A computationally efficient approach to compute the collision/rendezvous cone between finite-sized quadric surfaces (which are not necessarily of the same shape) is presented. Nonlinear analytical guidance laws that perform collision avoidance or rendezvous maneuvers, are subsequently developed. Simulation results demonstrating the working of the developed guidance laws are presented.
An Intensive Literature Review on Machine Translation Using Neural Approach

Researchers since the twentieth century have shown their keen interest in the domain of machine translation. This interest has resulted in tremendous evolutions starting from earlier rule-based (dictionary matched) translation to corpus-based statistical translation and nowadays deep neural machine translation. In this paper, a review of some prominent achievements of machine translation with the help of artificial neural networks, which have gained widespread importance in producing state-of-the-art results, is presented. The paper analyses the recent progress in the field of neural machine translation and will add new knowledge to the existing literature available. The current study will also help future researchers to study various aspects of NMT that will help them to select the suitable NMT model for their research.
Keyword-Based Diverse Image Retrieval With Variational Multiple Instance Graph

The task of cross-modal image retrieval has recently attracted considerable research attention. In real-world scenarios, keyword-based queries issued by users are usually short and have broad semantics. Therefore, semantic diversity is as important as retrieval accuracy in such user-oriented services, which improves user experience. However, most typical cross-modal image retrieval methods based on single point query embedding inevitably result in low semantic diversity, while existing diverse retrieval approaches frequently lead to low accuracy due to a lack of cross-modal understanding. To address this challenge, we introduce an end-to-end solution termed variational multiple instance graph (VMIG), in which a continuous semantic space is learned to capture diverse query semantics, and the retrieval task is formulated as a multiple instance learning problems to connect diverse features across modalities. Specifically, a query-guided variational autoencoder is employed to model the continuous semantic space instead of learning a single-point embedding. Afterward, multiple instances of the image and query are obtained by sampling in the continuous semantic space and applying multihead attention, respectively. Thereafter, an instance graph is constructed to remove noisy instances and align cross-modal semantics. Finally, heterogeneous modalities are robustly fused under multiple losses. Extensive experiments on two real-world datasets have well verified the effectiveness of our proposed solution in both retrieval accuracy and semantic diversity.
Integrated Approach of Heavy Metal Evaluation Using Geostatistical and Pollution Assessment Index in Soil of Bauxite Mining Area

Heavy metals contamination in soil is one of the global issues, posing a threat not just to the environment but also to human health. Identifying the source and distribution of heavy metal pollutants around mining areas can provide a scientific basis for future environmental control. Distributions of the heavy metals (Cd, Cr, As, and Ni) in this study were evaluated using descriptive and multivariate statistics and further described using a geostatistical approach and pollution indices. The total content of Cr, Cd, and Ni in surface soil was observed with a higher concentration level according to the Dutch target values and the 95% Investigation Levels determined for Malaysia soil. Statistical analyses, geostatistics, and GIS mapping suggested that Cd, Cr, and Ni were derived mainly from anthropogenic sources, including mining and agricultural activities, while As could be derived from lithogenic and anthropogenic sources. Geoaccumulation index analysis demonstrated that the contamination that occurred with Cd posed the greatest risk of contamination, followed by Cr, Ni, and As. A spatial interpolated map showed a higher concentration of heavy metals in the vicinity of the mining area. These findings highlight the effectiveness of principal component analysis, geostatistics, and geospatial analyses in evaluating heavy metal contents in the study area. The obtained results could be used by authorities to identify areas requiring remediation management and establish scientific baseline data related to soil quality.
Particle Swarm Optimization for Clustering Ensemble

Clustering ensemble is an open proposition that aims to solve the limitation of a single cluster algorithm on the diversity of data structures in data partitions. It obtains a consensus model by integrating multiple clustering algorithms. Compared with a single clustering algorithm, it can process the data more accurately and stably, and has a better tolerance for the structure and diversity of the data. This paper proposes to integrate particle swarm optimization for clustering ensemble model. Particularly, the idea is to first use multiple classic clustering algorithms to cluster the original data set to obtain a preliminary clustering result set, and then search for a clustering result that is most consistent with the preliminary results by a particle swarm optimization procedure. In the experiments, we make use of public data sets and micro-precision to evaluate the effectiveness of the proposed clustering ensemble model, and compare it with both base clustering algorithms and other common clustering ensemble algorithms. The evaluation results show that this model can significantly improve the base clustering algorithms and outperforms common clustering ensemble algorithms in most cases.
Dynamic Sign Language Recognition Based on CBAM with Autoencoder Time Series Neural Network

The CNN-LSTM network has a low generalization ability, and the backward relevance of actions is not strong. In this work, a convolutional self-encoding timing network with a fusion of attention mechanism, namely, convolutional block attention module (CBAM), is proposed. The model first designs a convolutional self-encoding network for pretraining to obtain feature vectors of smaller dimensions. Second, it uses the BN network to speed up the training process and enhance the network generalization ability. Then, we use the encoder part of the pretrained convolutional autoencoder, embed the attention mechanism to further focus on the weight of important parts in image features, and use Bi-LSTM to form a CNN-Bi-LSTM network. As compared with the traditional CNN-LSTM model, the proposed method continuously expands the training samples through the pretrained network to improve the generalization performance. The experimental results show that the method proposed in this paper effectively recognizes the sign language video. The recognition rate reaches 89.90%, which is higher as compared to other methods. These results verify the feasibility and effectiveness of the proposed method.
Face To BMI: A Deep Learning Based Approach for Computing BMI from Face

Body Mass Index (BMI) is a measure of how healthy a person is with respect to their body weight. BMI has shown a correlation with various factors like physical health, mental health, popularity. BMI calculation often requires accurate height and weight, which would take manual work to measure. Largescale automation of BMI calculation can be utilized for analyzing various aspects of society and can be used by governments and companies to make effective decisions. Previous works have used only geometric facial features discarding other information, or a data-driven deep learning-based approach in which the amount of data becomes a bottleneck. We used the state of the art pre-trained models such as Inception-v3, VGG-Faces, VGG19, Xception and fine-tuned them on the comparatively large public dataset with discriminative learning. We used the larger Illinois DOC labeled faces dataset for training and Arrest Records, VIP_attribute for evaluation purposes.
Anomaly Detection from Distributed Data Sources via Federated Learning

Anomaly detection is an important task to identify rare events such as fraud, intrusions, or medical diseases. However, it often needs to be applied on personal or otherwise sensitive data, e.g. business data. This gives rise to concerns regarding the protection of the sensitive data, especially if it is to be analysed by third parties, e.g. in collaborative settings, where data is collected by different entities, but shall be analysed together to benefit from more effective models. Besides various approaches for e.g. data anonymisation, one approach for privacy-preserving data mining is Federated Learning – especially in settings where data is collected in several distributed locations. A common, global model is obtained by aggregating models trained locally on each data source, while the training data remains at the source. Therefore, data privacy and machine learning can coexist in a decentralised system. While Federated Learning has been studied for several machine learning settings, such as classification, it is still rather unexplored for anomaly detection tasks. As anomalies are rare, they are not picked up easily by a detection method, and the representation in the model dedicated to recognise them might be lost during model aggregation. In this paper, we thus study anomaly detection task on two different benchmark datasets, in supervised, semi-supervised, and unsupervised settings. We federate Multi-Layer Perceptrons, Gaussian Mixture Models, and Isolation Forests, and compare them to a centralised approach.
Depth-Guided Progressive Network for Object Detection

Multi-scale object detection in natural scenes is still challenging. To enhance the multi-scale perception capability, some algorithms combine the lower-level and higher-level information via multi-scale feature fusion strategies. However, the inherent spatial properties among instances and relations between foreground and background are ignored. In addition, the human-defined “center-based” regression quality evaluation strategy, predicting a high-to-low score based on a linear relationship with the distance to the center of ground-truth box, is not robust to scale-variant objects. In this work, we propose a Depth-Guided Progressive Network (DGPNet) for multi-scale object detection. Specifically, besides the prediction of classification and localization, the depth is estimated and used to guide the image features in a weighted manner to obtain a better spatial representation. Therefore, depth estimation and 2D object detection are simultaneously learned via a unified network, where the depth features are merged as auxiliary information into the detection branch to enhance the discrimination among multi-scale objects. Moreover, to overcome the difficulty of empirically fitting the localization quality function, high-quality predicted boxes on scale-variant objects are more adaptively obtained by an IoU-aware progressive sampling strategy. We divide the sampling process into two stages, i.e., “statistical-aware” and “IoU-aware”. The former selects thresholds for positive samples based on statistical characteristics of multi-scale instances, and the latter further selects high-quality samples by IoU on the basis of the former. Therefore, the final ranking scores better reflect the quality of localization. Experiments verify that our method outperforms state-of-the-art methods on the KINS and Cityscapes dataset.
Using BTA Algorithm for finding Nash equilibrium problem aiming the extraction of rules in rule learning

It is crystal clear that discovering the rules for finding a specific pattern among given data for extraction of association rules in rule-based learning systems has been defined in previous researches. Making use of game theory for the processes contributing to discovery of rules can be seen in numerous researches. In recent years, modeling based on game theory in rule learning sphere has gained much more attention for computer scientists. When two or more players use different strategies independently, the strategy game modeling could be used. In this view, strategic play is a desirable model for situations with no permanent strategic relationship among interactions. In addition, Nash equilibrium is the most widely used solution concept in game theory. This concept is a state-of-the-art interpretation of a strategy game. Each player has an accurate prediction of other players’ behavior and acts according to such a rational prediction. In the present study, by extracting rules from frequent patterns we have presented a model that can extract learning rules by abstraction based on game theory, which can be used not only for association rules but also for rule-based learning systems. Also, the introduced method can be easily generalized to fuzzy data. To find Nash equilibrium (FNE) in the proposed method, we used meta-heuristic bus transportation algorithm. The results indicated that the method reduces computational complexity in the associate rule discovery process and rule learning, provided that FNE is solved.