Reviewerly/exHarmony huggingface dataset:
Adaptive visual cryptography scheme design based on QR codes

QR code based visual encryption schemes are still relatively rare, and the existing schemes are mainly implemented by averaging grayscale maps. They become distorted when recovering complex secret images. In this paper, we propose a visual cryptography scheme based on QR codes. We use two adaptive schemes to improve the distortion problem in secret image recovery. The algorithm for generating the QR code shared matrix is improved, and the secret image can be restored quickly. This scheme could ensure the uniform distribution of secret vectors. Meanwhile individual shared QR codes would never reveal any secret information. The proposed algorithm reduces the space consumed by the secret vector and the probability of falling prey to illegal attacks. Compared with other schemes, the secret image recovered via the proposed method is clearer and the scheme is suitable for scenarios in which the secret images are more complex, as it yields better security and practicality.
Visual state estimation in unseen environments through domain adaptation and metric learning

In robotics, deep learning models are used in many visual perception applications, including the tracking, detection and pose estimation of robotic manipulators. The state of the art methods however are conditioned on the availability of annotated training data, which may in practice be costly or even impossible to collect. Domain augmentation is one popular method to improve generalization to out-of-domain data by extending the training data set with predefined sources of variation, unrelated to the primary task. While this typically results in better performance on the target domain, it is not always clear that the trained models are capable to accurately separate the signals relevant to solving the task (e.g., appearance of an object of interest) from those associated with differences between the domains (e.g., lighting conditions). In this work we propose to improve the generalization capabilities of models trained with domain augmentation by formulating a secondary structured metric-space learning objective. We concentrate on one particularly challenging domain transfer task-visual state estimation for an articulated underground mining machine-and demonstrate the benefits of imposing structure on the encoding space. Our results indicate that the proposed method has the potential to transfer feature embeddings learned on the source domain, through a suitably designed augmentation procedure, and on to an unseen target domain.
Automatic Face Segmentation Using Adaptively Regularized Kernel-Based Fuzzy Clustering Means With Level Set Algorithm

In this research, a new level set-based segmentation algorithm was proposed for human face segmentation. At first, the human facial images were collected from face semantic segmentation (FASSEG) dataset. After collecting the images, pre-processing was accomplished by utilizing contrast limited adaptive histogram equalization (CLAHE). The undertaken methodology effectively improves the quality of facial images by removing the unwanted noise. Then, segmentation was done by using adaptively regularized kernel-based fuzzy clustering means (ARKFCM) clustering with level set, which was a high-level machine learning algorithm for localizing the face parts in complex template. Simulation outcome shows that the proposed segmentation algorithm effectively segments the facial parts in light of precision, recall, Jaccard coefficient, dice coefficient, accuracy, and miss rate. The proposed segmentation algorithm enhanced the segmentation accuracy in face segmentation up to 4.5% compared to the existing methodology (pixel wise segmentation).
A Hybrid Harmony Search

High Utility Association Rule mining discovers high correlated products in the form of rules. Till date there was no existing algorithm for High Utility Association Rules in a multi-level database. This research work proposes a Hybrid Harmony Search - Antlion Optimization algorithm (HHSAO) to retrieve high utility association rules in multi-level transaction database. First the proposed algorithm implements Harmony Search Algorithm to remove less profitable transactions and then apply Antlion trapping behaviour of Antlion Optimization algorithm to recover the rules from reduced database.The performance of proposed algorithm was compared with adopted existing algorithms like GA, PSO, ACO, AMO, BSO, PeSO, HS and ALO. The experiment was carried out with six datasets. The experimental results prove that the HHSAO algorithm works better.
Stance detection of user reviews on social network with integrated structural information

Stance detection is the task of classifying user reviews towards a given topic as either supporting, denying, querying, or commenting (SDQC). Most approaches for solving this problem use only the textual features, including the linguistic features and users’ vocabulary choice. A few approaches have shown that information from the network structure like graph model can add value, in addition to the textual features, by providing social connections and interactions that may be vital for the stance detection task. In this paper, we present a novel model that combines the text features with the network structure by (1) creating a graph-structure model based on conversational structure towards specific topics and (2) constructing a tree-gated neural network model (TreeGGNN) to capture structure information among reviews. We evaluate our model on four baseline models, which shows that the combination of text and network can achieve an improvement of 2–6% over the state-of-the-art baselines.
Optimizing Partial Area Under the Top-K Curve: Theory and Practice

Top-<inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> error has become a popular metric for large-scale classification benchmarks due to the inevitable semantic ambiguity among classes. Existing literature on top-<inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> optimization generally focuses on the optimization method of the top-<inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> objective, while ignoring the limitations of the metric itself. In this paper, we point out that the top-<inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> objective lacks enough discrimination such that the induced predictions may give a totally irrelevant label a top rank. To fix this issue, we develop a novel metric named partial Area Under the top-<inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> Curve (AUTKC). Theoretical analysis shows that AUTKC has a better discrimination ability, and its Bayes optimal score function could give a correct top-<inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula> ranking with respect to the conditional probability. This shows that AUTKC does not allow irrelevant labels to appear in the top list. Furthermore, we present an empirical surrogate risk minimization framework to optimize the proposed metric. Theoretically, we present (1) a sufficient condition for Fisher consistency of the Bayes optimal score function; (2) a generalization upper bound which is insensitive to the number of classes under a simple hyperparameter setting. Finally, the experimental results on four benchmark datasets validate the effectiveness of our proposed framework.
Survey on Energy Efficient-Load Balancing in Cloud

Cloud computing is a popular technology which delivers virtualized computer resources via the internet.Numerous load balancing considerations substantially determine the performance of the cloud.Load balancing (LB) distributes a dynamic workload among cloud systems & evenly shares resources such that no database server is overloaded or underloaded.Consequently, an active load balancing strategy in the cloud may improve dependability, services, and resource usage.Load balancing task scheduling is a significant issue in cloud systems that directly impacts resource usage.Load balancing scheduling is important for its significant influence on the cloud research industry's back and front end.If an appropriate load balancing is accomplished in the cloud, useful resource utilization is obtained.Therefore, this survey aims to review the recent research papers on existing techniques based on cloud VM migration & load balancing.The literature study examines the various techniques for VM migration & load balancing approaches in the cloud.It analyzes various research articles and provides a detailed analysis.The analytical examination also considers the maximum performance attainments in various contributions.Furthermore, the chronological review and the tools used in the analyzed works are also examined.Furthermore, the survey includes a variety of research problems and gaps that might help researchers to enhance the future study on VM migration & load balancing approaches in cloud technology.
Control of quantum dynamics: non-Markovianity and speedup of a massive particle evolution due to gravity

Abstract We illustrate two linear configurations (one-side model and two-side model) for implementing a non-Markovian speedup evolution of a massive particle gravitationally coupled with a controllable environment: multiple massive particles. By controlling the environment, for instance by choosing a judicious the mass of the environmental particles or by changing the separation distance of each massive particle, two dynamical crossover behaviors from Markovian to non-Markovian and from no-speedup to speedup are achieved due to the gravitational interactions between the system particle and each environmental particle. Numerical calculation also shows that the critical mass of the environmental particles or the critical separation distance for these two dynamical crossover behaviors restrict each other directly. The larger the value of the mass of the environmental particles is, the smaller the value of the critical separation distance should be requested. In this work, the non-Markovian dynamics is the principal physical reason for the speedup evolution of a quantum system. Particularly, the non-Markovianity of the system mass particle in the two-side model has better correspondence with the quantum speed limit time than that in the one-side model.
Anomaly recognition method of perception system for autonomous vehicles based on distance metric

Environmental perception system is the premise of the safety and stability of the autonomous vehicle system. However, studies have shown that the on-board sensors included in the perception system are extremely vulnerable to external attacks and interference, leading to incorrect driving strategies and bringing great security threats. Aiming at the problem, this paper divides the vehicle-mounted sensors into a positioning group and an identification group according to their role in the perception system. Then, based on the information correlation between sensors in the same group and the information correlation of a single sensor on adjacent time series, the distance metric model between sensors in a group and the distance metric model for each sensor of this group on time series is established. And the normal distance intervals corresponding to the confidence interval are calculated respectively. According to the distance metric model between sensors, the authors can detect anomalies in the perception system in real time. Further, according to the distance metric model for each sensor on adjacent time series, the authors can identify anomaly sensors. The authors’ experimental results quantitatively show that the method achieves real-time anomaly recognition, and demonstrate the effectiveness and robustness of the method on the open-source Karlsruhe institute of technology and toyota technological institute (KITTI) dataset.
Reality+: Virtual Worlds and the Problems of Philosophy by David J. Chalmers (W. W. Norton &amp; Company, 2022).

An abstract is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
Implicit Session Contexts for Next-Item Recommendations

Session-based recommender systems capture the short-term interest of a user within a session. Session contexts (i.e., a user's high-level interests or intents within a session) are not explicitly given in most datasets, and implicitly inferring session context as an aggregation of item-level attributes is crude. In this paper, we propose ISCON, which implicitly contextualizes sessions. ISCON first generates implicit contexts for sessions by creating a session-item graph, learning graph embeddings, and clustering to assign sessions to contexts. ISCON then trains a session context predictor and uses the predicted contexts' embeddings to enhance the next-item prediction accuracy. Experiments on four datasets show that ISCON has superior next-item prediction accuracy than state-of-the-art models. A case study of ISCON on the Reddit dataset confirms that assigned session contexts are unique and meaningful.
CloudRecoMan: Cloud adoption made easy

Although the adoption and use of cloud computing in large, medium and small enterprises is increasing every year, statistics show that medium and especially small enterprises have a much slower adoption than large enterprises. This suggests that various types of issues may affect Small to Medium Enterprises (SMEs) in making the choice to transit towards the public cloud. Based on experts and literature, SMEs' adoption of cloud is impacted by lack of resources, concerns on security and data control and uncontrolled increasing cloud costs due to the lack of cloud experience. A platform is not yet available that would assist non-technical SME managers in specifying non-technical business requirements in order to receive assistance in the form of recommendations of cloud resources, cloud applications and deployment plans. The CloudRecoMan platform aims to enable this through the development of a web-based cloud recommender and deployment platform.
Developing a Measurement Instrument for Teacher Key Competencies

Based on perspectives of exemplary teachers, this study constructed a theory on key competencies for teachers and employed the Rasch model to develop an instrument for measuring the key competencies.The theory conceptualizes teacher key competencies to consist of three dimensions: professional ethics, professional teaching and professional learning.There are four sub-competencies of teachers' professional ethics: ideal and belief, dedication and love, diligence and enterprise, grit and pragmatic.There are also four sub-competencies of teachers' professional teaching: nature of discipline, nature of leaning, traditional culture, information and technology.Lastly, there are four sub-competencies of professional learning, i.e., professional autonomy, reflective inquiry, cooperation and innovation, humanistic literacy.Among the three dimensions of teacher key competencies, it was found that teachers scored high in professional ethics and professional teaching, but low in professional learning.One-way ANOVA showed that teachers' competencies were significantly different on many demographic characteristics (p=0.00).The validated measurement instrument and findings from this study are informative for government education departments and schools in understanding and developing teachers' competencies.
5D-fusion imaging for surface shape, polarization, and hyperspectral measurement

We present a five-dimensional (5D) imager that is capable of simultaneous detection of the surface shape, spectral characteristics, and polarization states of macroscopic objects, and straightforwardly fuse collected data into a 5D data set. A polarized module that uses a polarized camera obtains polarized images, while a 3D hyperspectral module reconstructs the target as a 3D point cloud using a fringe projection technique. A liquid-crystal tunable filter is placed in front of the camera of this module to acquire spectral data that can be assigned to corresponding point clouds directly. The two modules are coupled by a dual-path configuration that allows the polarization information to be merged into a comprehensive point cloud with spectral information, generating a new 5D model. The 5D imager shows excellent performance, with a spectral resolution of 10 nm, depth accuracy of 30.7 µm, and imaging time of 8 s. Sample experiments on a toy car with micro scratch defects and a yellowing plant are presented to demonstrate the capabilities of the 5D imager and its potential for use in a broad range of applications, such as industrial manufacturing inspection, plant health monitoring, and biological analysis.
Interac table Topographical Map with Remote Cross Reality Collaboration Support

An Interac table Topographical Map (ITM) is a generalization of the modern GPS-based applications, such as the Google Maps. In the case of an ITM system, the pinpointing, tracking, and augmenting of relevant information are presented on approximated 3D geographical terrains instead of 2D maps. Similar to GPS-based maps, an ITM system can support pre-planned journeys or explorations by augmenting appropriate journey information such as path and time to destination, traveling speed and direction. In contrast to traveling through urban traffics, an ITM system is better served as the guide or reference for journeying through undeveloped terrains. Such journeys are often required for missions into these regions, often critical and sometimes dangerous such as fighting forest fires, or search and rescues. The planning of such operations often involves the collaboration of various disciplinary experts and frontline personnel who may be distributed geographically. Additionally, especially for frontline personnel who must execute the potentially dangerous missions, the ability to be immersed in the actual 3D environment can be essential. This paper examines existing ITM systems, derives a design guideline, and discusses a prototype implementation that supports the collaboration of remote users across reality boundaries, i.e., users configured with distinct reality configurations, e.g., PC users and VR users. Though only an initial step, the delivered system demonstrated interesting potentials and insights.
Relationship between entanglement and polarization in tripartite states

Abstract Entanglement and polarization of pure, two-qubit systems are known to be constrained by the relationship <?CDATA $C^2+P^2 = 1$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msup> <mml:mi>C</mml:mi> <mml:mn>2</mml:mn> </mml:msup> <mml:mo>+</mml:mo> <mml:msup> <mml:mi>P</mml:mi> <mml:mn>2</mml:mn> </mml:msup> <mml:mo>=</mml:mo> <mml:mn>1</mml:mn> </mml:math> . Here, C stands for concurrence, a standard measure of entanglement, and P refers to the degree of polarization of either of the two, single-qubit subsystems. The above constraint may be understood as reflecting that a system cannot be in a pure state, if it is entangled with another one. In order to explore the connection between entanglement and polarization beyond two-qubit systems, we addressed states in which at least one of the parties has dimensionality greater than two. The case of pure, tripartite states offered itself as a timely candidate. We focussed on three qubits and studied the connection between one qubit’s polarization and its entanglement with the other two taken together. We derived new constraints, akin to the above one, and submitted them to experimental tests using single photons entangled in polarization. The latter provided two qubits, each one attached to one photon. The third qubit was chosen to be the path (momentum) of one of the photons. Our experimental results confirmed the validity of the new constraints. Our theoretical results hold also for classical light. Possible experimental tests could be done with so-called structured light beams.
Reverse Image Search Engine for Garment Industry

The Internet provides an extensive quantity of data in the form of text and images. Search Engines like Google and Bing are intended to retrieve the most relevant answers for our textual queries. In search engines, queries are generally matched between text-to-text or text-to-image which excludes the entire domain of image-to-image matching. Searching for images based on text poses the problem of retrieving results primarily based on image description and tagging. The ideal solution to tackle the flaws of text-based image queries and achieve image-to-image matching is obtained by extracting features of the image and using these features to retrieve similar images. Extracting information from visual data and similarity matching fall under the domain of Content Based Image Retrieval (CBIR). CBIR analyzes image information using low level representation including color, shape, texture, and spatial representation of objects to set up feature vectors for image indexing. Deep Learning techniques like Convolutional Neural Networks (CNN) serve the purpose of fulfilling the feature extraction task. Retrieval methods are achieved based on the low-level features of an image and mathematical similarity metrics aid in determining the relevant images satisfying the query for similarity. However, low level features do not capture the idea behind the image and hence limiting the images to a specific domain ensures that accuracy and similarity are as expected. The proposed project is specific to the domain of garments in the fashion industry. The proposed project conducted a study on the use of different pre-trained CNN models for the purpose of image feature extraction and attempted to fine-tune the selected ResNet50 model for the custom dataset. The feature similarity matching was performed by selecting a similarity metric famously used by music streaming giant Spotify&#x0027;s algorithm, Annoy Indexing, optimized for time and accuracy.
PFET: Multi-Vehicle Tracking with Pseudo-Feature Embeddings for Traffic Video Surveillance

Advances in multi-object tracking (MOT) provide possibilities for unmanned monitoring across intelligent transportation. The tracking-by-detection paradigm aims at associating detection boxes whose scores are higher than a threshold with active trajectories. However, when there is no valid detection associated with certain trajectories due to occlusions or false negatives (FN) may leads to fragmented trajectories and ID switches. An effective trajectory recovery method is proposed for multi-vehicle tracking in traffic surveillance in this paper. The proposed method provides a set of pseudo-feature embeddings to fill the gaps in the fragmented trajectories. These embeddings with motion and appearance features can be re-associated with new detections through a cascaded matching strategy that improves the quality of data association. Experimental results prove that our method can effectively reduce the number of ID switches and fragmentations. It is worth noting that the proposed method without a deep model achieves high tracking speed with considerable accuracy compared to state-of-the-art methods.