Reviewerly/exHarmony huggingface dataset:
A novel distant target region detection method using hybrid saliency-based attention model under complex textures

In this paper, a hybrid visual attention model to effectively detect a distant target is proposed. The model employs the human visual attention mechanism and consists of two models, the training model, and the detection model. In the training model, some of the features are selected to train in the process of extracting and combining the early visual features from the training image of the target by bottom-up manner, and these features are trained and accumulated as trained data. When the image containing the target is input into the detection model, a task of selectively promoting only features of the target using pre-trained data is performed. As a result, the desired target is detected through the saliency map created as a result of the feature combination. The model has been tested on various images, and the experimental results demonstrate that the proposed model detected the target more accurately and faster than other previous models.
Entanglement types for two-qubit states with real amplitudes

We study the set of two-qubit pure states with real amplitudes and their geometrical representation in the three-dimensional sphere. In this representation, we show that the maximally entangled states—those locally equivalent to the Bell states—form two disjoint circles perpendicular to each other. We also show that taking the natural Riemannian metric on the sphere, the set of states connected by local gates are equidistant to this pair of circles. Moreover, the unentangled or so-called product states are $$\pi /4$$ units away to the maximally entangled states. This is, the unentangled states are the farthest away to the maximally entangled states. In this way, if we define two states to be equivalent if they are connected by local gates, we have that there are as many equivalent classes as points in the interval $$[0,\pi /4]$$ with the point 0 corresponding to the maximally entangled states. The point $$\pi /4$$ corresponds to the unentangled states which geometrically are described by a torus. Finally, for every $$0< d < \pi /4$$ the point d corresponds to a disjoint pair of torus. Finally, we also show how this geometrical interpretation allows to clearly see that any pair of two-qubit states with real amplitudes can be connected with a circuit that only has single-qubit gates and one controlled-Z gate.
Assortment of Bangladeshi E-commerce Site Reviews using Machine Learning Approaches

In Bangladesh, E-commerce is flourishing day by day, especially in crisis, the world is facing. There are many platforms available on these sites among which Daraz is the most successful marketplace. This online platform allowed people to do shopping, but many reviews and comments made it challenging to opt for the best option. In this paper, the focus is on cataloguing the positive and negative reviews. For this purpose, various classifiers were used by using Python. Data cleaning was done and afterwards Term Frequency -Inverse Data Frequency with Principal Component analysis was applied. It was found that Ridge classifier performed best with more training time than other classifiers and depicted high accuracy. This classifier could help different businesses on different platforms to identify the positive and negative reviews and provide customers with details about the quality of products.
DyEgoVis: Visual Exploration of Dynamic Ego-Network Evolution

Ego-network, which can describe relationships between a focus node (i.e., ego) and its neighbor nodes (i.e., alters), often changes over time. Exploring dynamic ego-networks can help users gain insight into how each ego interacts with and is influenced by the outside world. However, most of the existing methods do not fully consider the multilevel analysis of dynamic ego-networks, resulting in some evolution information at different granularities being ignored. In this paper, we present an interactive visualization system called DyEgoVis which allows users to explore the evolutions of dynamic ego-networks at global, local and individual levels. At the global level, DyEgoVis reduces dynamic ego-networks and their snapshots to 2D points to reveal global patterns such as clusters and outliers. At the local level, DyEgoVis projects all snapshots of the selected dynamic ego-networks onto a 2D space to identify similar or abnormal states. At the individual level, DyEgoVis utilizes a novel layout method to visualize the selected dynamic ego-network so that users can track, compare and analyze changes in the relationships between the ego and alters. Through two case studies on real datasets, we demonstrate the usability and effectiveness of DyEgoVis.
MAPPING LITHOLOGICAL AND MINERALOGICAL UNITS USING HYPERSPECTRAL IMAGERY

&nbsp;Hyperspectral images such as the Earth Observer-1 (EO-1) provides an efficient method of mapping surface mineralogy because it can measures the energy in narrower bands compared with multispectral sensors. The Kirkuk anticline northern Iraq is one of the most petroleum-rich provinces. It is characterized, that is an asymmetrical, cylindrical anticline, with a fold axis trend towards North West- East, South East. The study’s primary goal is to apply satellite processing and techniques on the Eo-1 imagery for identifying lithological and mineral units at a part of Kirkuk anticline northern Iraq. The EO-1 image was corrected at the beginning of atmospheric impacts using the FLAASH module in ENVI software. Processing of (Minimum Noise Fraction- MNF) processing was applied and then it&nbsp;reduced&nbsp;the&nbsp;dimensionality&nbsp;of&nbsp;data, as well as, the processing of (PPI) pixel purity index was applied to spatial reduction. This study tested the potential of (spectral angle mapper supervised classification-SAM) classification for mapping the lithological and mineral units using the Hyperion imagery. three different sources of endmembers or spectra are used for SAM classifications. The one:&nbsp; is done by Analytical Spectral Devices (ASD) Spectrometer. The second: reference spectra have been taken from the spectral library of USGS. Third: extracting endmembers from the purest pixels of the hyperion image, which was done by applying (MNF and PPI). The endmembers were provided, generated as the training area for SAM classification. &nbsp;The present results demonstrated the great potential of data used to map the distribution of alteration of minerals and lithological units in a part of Kirkuk anticline. The classified Hyperion image shows that Jarosite and illite are the most dominant altered minerals, as well as, the main lithological units of the upper member of Fatha formation are revealed in the core of the Kirkuk anticline with scattered and small outcrops towards the flanks.
A Simple Low Cost Parallel Architecture for Big Data Analytics

Big Data Systems (Hadoop, DBMSs) require a complicated setup and tuning to store and process big data on a parallel cluster. This is mainly due to static partitioning when data sets are loaded or copied into the file system. Parallel processing thereafter works in a distributed manner, aiming for balanced parallel execution across nodes. Node synchronization, data redistribution and distributed caching in main memory are difficult to tune in the system. On the other hand, there exist analytical problems and algorithms, which can be computed in parallel, with minimal synchronization and fully independent computation. Moreover, some problems can be solved in one pass or few passes. In this paper, we introduce a low cost, yet useful, processing architecture in which data sets are dynamically partitioned at run-time and storage is transient. Each node processes one partition independently and partial results are gathered at the master processing node. Surprisingly, we show this architecture works well for some popular machine learning models as well as some graph algorithms. We attempt to identify which problem characteristics enable such efficient processing, and we also show the main bottleneck is the initial data set partitioning and distribution across nodes. We anticipate our architecture can benefit parallel processing in the cloud, where a dynamic number of virtual processors is decided at runtime or when the data set is analyzed for a short time.
Softmax Model as Generalization upon Logistic Discrimination Suffers from Overfitting

The motivation behind this paper is to investigate the use of Softmax model for classification.We show that Softmax model is a nonlinear generalization for the logistic discrimination, that can approximate the posterior probabilities of classes where other Artificial neural network (ANN) models don't have this ability.We show that Softmax model has more flexibility than logistic discrimination in terms of correct classification.To show the performance of Softmax model a medical data set on thyroid gland state is used.The result is that Softmax model may suffer from overfitting.
Fractional Rider Deep Long Short Term Memory Network for Workload Prediction-Based Distributed Resource Allocation Using Spark in Cloud Gaming

The modern development in cloud technologies has turned the idea of cloud gaming into sensible behaviour.The cloud gaming provides an interactive gaming application, which remotely processed in a cloud system, and it streamed the scenes as video series to play through network.Therefore, cloud gaming is a capable approach, which quickly increases the cloud computing platform.Obtaining enhanced user experience in cloud gaming structure is not insignificant task because user anticipates less response delay and high quality videos.To achieve this, cloud providers need to be able to accurately predict irregular player workloads in order to schedule the necessary resources.In this paper, an effective technique, named as Fractional Rider Deep Long Short Term Memory (LSTM) network is developed for workload prediction in cloud gaming.The workload of each resource is computed based on developed Fractional Rider Deep LSTM network.Moreover, resource allocation is performed by fractional Rider-based Harmony Search Algorithm (Rider-based HSA).This Fractional Rider-based HSA is developed by combining Fractional calculus (FC), Rider optimization algorithm (ROA) and Harmony search algorithm (HSA).Moreover, the developed Fractional Rider Deep LSTM is developed by integrating FC and Rider Deep LSTM.In addition, the multi-objective parameters, namely gaming experience loss QE, Mean Opinion Score (MOS), Fairness, energy, network parameters, and predictive load are considered for efficient resource allocation and workload prediction.Additionally, the developed workload prediction model achieved better performance using various parameters, like fairness, MOS, QE, energy and delay.
Review of GAN-Based Person Re-Identification

Person re-ID is becoming increasingly popular in the field of modern surveillance.The purpose of person re-ID is to retrieve person of interests in non-overlapping multi-camera surveillance system.Due to the complexity of the surveillance scene, the person images captured by cameras often have problems such as size variation, rotation, occlusion, illumination difference, etc., which brings great challenges to the study of person re-ID.In recent years, studies based on deep learning have achieved great success in person re-ID.The improvement of basic networks and a large number of studies on the influencing factors have greatly improved the accuracy of person re-ID.Recently, some studies utilize GAN to tackle the domain adaptation task by transferring person images of source domain to the style of target domain and have achieved state of the art result in person re-ID.
Concept Identification of Directly and Indirectly Related Mentions Referring to Groups of Persons

Unsupervised concept identification through clustering, i.e., identification of semantically related words and phrases, is a common approach to identify contextual primitives employed in various use cases, e.g., text dimension reduction, i.e., replace words with the concepts to reduce the vocabulary size, summarization, and named entity resolution. We demonstrate the first results of an unsupervised approach for the identification of groups of persons as actors extracted from a set of related articles. Specifically, the approach clusters mentions of groups of persons that act as non-named entity actors in the texts, e.g., “migrant families” $$=$$ “asylum-seekers.” Compared to our baseline, the approach keeps the mentions of the geopolitical entities separated, e.g., “Iran leaders” $$\ne $$ “European leaders,” and clusters (in)directly related mentions with diverse wording, e.g., “American officials” $$=$$ “Trump Administration.”
Penentuan Kelas Menggunakan Algoritma K Medoids Untuk Clustering Siswa Tunagrahita

So far, the class placement of mentally retarded students is based on the age of entering the child when registering at SLB C Muzdalifah, the Intelligence Quotient (IQ) test has not been tried for mentally retarded students in classifying student classes. It is important to group mentally retarded children to make it easier for teachers to prepare programs and implement educational services. It is important for the school to understand that in mentally retarded children there are individual differences with very large variations. That is, being at almost the same age level (calendar age and mental age) and the same education level, in fact individual abilities differ from one another. Thus, of course, special strategies and programs are needed that are adapted to individual differences. This research was made to classify and analyze data mining for class clustering students with the K-Medoids algorithm to help group students who want to occupy classes according to their level of intellectual disability. From the grouping results obtained 3 clusters, which have the highest number of students is the moderate mental retardation class and the lowest cluster is mild mental retardation, the Muzdalifah special school can prepare classes based on grouping for teaching and learning activities.
Model-Driven Approach of Virtual Interactive Environments for Enhanced User Experience

The video game and entertainment industry has been growing in recent years, particularly those related to Virtual Reality (VR). Therefore, video game creators are looking for ways to offer and improve realism in their applications in order to improve user satisfaction. In this sense, it is of great importance to have strategies to evaluate and improve the gaming experience in a group of people, without considering the fact that users have different preferences and, coupled with this, also seeks to achieve satisfaction in each user. In this work, we present a model to improve the user experience in a personal way through reinforcement learning (RL). Unlike other approaches, the proposed model adjusts parameters of the virtual environment in real-time based on user preferences, rather than physiological data or performance. The model design is based on the Model-Driven Architecture (MDA) approach and consists of three main phases: analysis phase, design phase, and implementation phase. As results, a simulation experiment is presented that shows the transitions between undesired satisfaction states to desired satisfaction states, considering an approach in a personal way.
What Do You Meme?

Users of the Internet have established a unique form of communication tool commonly used on social media platforms; these are referred to as Internet memes. The term meme, however, was conceived many years before its appropriation into an Internet sphere. Where memes are units of cultural information propagated like a virus from brain to brain, Internet memes are dynamic digital artifacts that can easily be created and quickly distributed to a large social group with the intention of communication. Internet memes are not static; rather their format inspires social interactions that allow them to be modified and reworked with an infinite amount of communicative outcomes. This paper offers an exploration of the different features of Internet memes, which are identified as creational/distributional, social, as well as communicative. When combined, these features in relation to each other constitute a sociolinguistic potential.
THE IMPACT OF STRATEGIC AWARENESS ON ENHANCING ORGANIZATIONAL IMMUNITY SYSTEM: AN APPLIED STUDY ON JORDANIAN FOOD MANUFACTURING COMPANIES

This study aimed at identifying the impact of strategic awareness on enhancing the regulatory immune system, an applied study on Jordanian food processing companies.For the purpose of achieving the objectives of the study, the researcher used the descriptive and analytical approach, the researcher developed a questionnaire to collect data related to the study variables.(160) questionnaires were distributed on an intentional sample consisting of (13) food processing companies.The questionnaire targeted managers and workers in the directorates of production, quality, and planning in the searched companies.The number of retrieved questionnaires reached (150) questionnaires are suitable for analysis, the researcher used multiple regression analysis through the Statistical Package in Social Sciences (SPSSv.19) to test the study hypotheses, and the study reached results, the most important results are: That the study sample individuals 'perceptions of the strategic awareness in the researched companies were of a high level, and the study sample's perceptions of the organizational immune system in the researched companies were of a high level.The study recommends the need to strengthen the supporting culture to the concept of organizational immunity between senior management and employees, the need to work with strategic awareness because of its great impact on strengthening the regulatory immune system and its ability to convert knowledge into applied practices, in addition to following an interest in strategic awareness in its various dimensions, development and enhancing it among Jordanian food processing companies.
A Proposed Optimized Steganography Technique using ROI, IWT and SVD

Current advancement in communication technology have made rapid delivery and sharing of multimedia information promising, but these advancements in technology are leading violation to information security and private information. Information security prevents unauthorized and unlawful access of information. So, the key concern of the communication industries is to protect information from unauthorized users. Many techniques exist in the communication technology that preserves the Integrity, Authentication, Non- Repudiation, and Availability of information. Steganography is a process of sending secret message over the internet without letting anyone knows the existence of the secret message. Only sender and receiver know about existence of the message. In this research paper, an optimized Steganography Technique is proposed, which shows robustness against different types of attacks by utilizing the benefits of the properties of ROI, IWT and SVD. This paper will also focus on gaining essential and vibrant functions of steganography in requisites of imperceptibility, complexity, robustness and superior hiding capability.
Policy-Guided Heuristic Search with Guarantees

The use of a policy and a heuristic function for guiding search can be quite effective in adversarial problems, as demonstrated by AlphaGo and its successors, which are based on the PUCT search algorithm. While PUCT can also be used to solve single-agent deterministic problems, it lacks guarantees on its search effort and it can be computationally inefficient in practice. Combining the A* algorithm with a learned heuristic function tends to work better in these domains, but A* and its variants do not use a policy. Moreover, the purpose of using A* is to find solutions of minimum cost, while we seek instead to minimize the search loss (e.g., the number of search steps). LevinTS is guided by a policy and provides guarantees on the number of search steps that relate to the quality of the policy, but it does not make use of a heuristic function. In this work we introduce Policy-guided Heuristic Search (PHS), a novel search algorithm that uses both a heuristic function and a policy and has theoretical guarantees on the search loss that relates to both the quality of the heuristic and of the policy. We show empirically on the sliding-tile puzzle, Sokoban, and a puzzle from the commercial game `The Witness' that PHS enables the rapid learning of both a policy and a heuristic function and compares favorably with A*, Weighted A*, Greedy Best-First Search, LevinTS, and PUCT in terms of number of problems solved and search time in all three domains tested.
AES AND RSA-BASED HYBRID ALGORITHMS FOR MESSAGE ENCRYPTION & DECRYPTION

File encryption is an easy means of securing personal or business data protection. The RSA and AES representative encryption algorithms are not capable of satisfying the criteria of file encryption reliability and security when used separately. A hybrid encryption algorithm mixing AES and RSA algorithms is suggested in this paper to overcome the above issues in order to solve file encryption performance and security problems. The experimental results suggest that the RSA and AES hybrid encryption algorithm can not only encrypt files, but also provide the benefits of efficiency and protection of the algorithm.
A Single-Shot 3D Measuring Method Based on Quadrature Phase-Shifting Color Composite Grating Projection

A single-shot three-dimensional measuring method based on quadrature phase-shifting color composite grating projection is proposed. Firstly, three quadrature phase-shifting sinusoidal gratings are encoded in red (R), green (G), and blue (B) channels respectively, composed single- frame color composite grating. This color composite grating is projecting obliquely on the object by DLP. After that, the color camera which is placed in a specific location is used to capture the corresponding color deformed pattern and send it to the PC. Then, by color separation, the color deformed pattern is demodulated as the corresponding three-frame monochromatic deformed patterns with a shifted quadrature phase. Due to the existences of sensitivity differences and color crosstalk among the tricolor channels, we propose a gray imbalance correction method based on the DC component’s consistency approximation. By the established 3D reconstruction physical model, the measurement of 3D shape can be achieved. Many experimental results for static and moving objects prove the proposed method’s feasibility and practicability. Owing to the single-shot feature of the proposed method, it has a good application prospect in real-time and high-speed 3D measurement.