Reviewerly/exHarmony huggingface dataset:
Quantum information splitting of an arbitrary three‐qubit state by using three sets of GHZ states

Quantum Information Splitting is an important technique used in Quantum Communication where a sender has to comply with more than one recipient for quantum teleportation. We have shown that three sets of three-qubit GHZ states can be used to realize the splitting of an arbitrary three-qubit state securely. We propose a scheme by a new quantum circuit and simulate them on the IBM quantum experience platform and conclude that an arbitrary three-qubit state can be split up experimentally by using three GHZ states. In a way, the presented protocol is deterministic and secure against eavesdropping attacks.
Breast Cancer Classification using the Supervised Learning Algorithms

Breast cancer (BC) has been the second largest cause of death for women around the world for the past few years. BC is characterized by the chronic pain, genes mutation, color (redness), changes in the size and texture of the skin. BC classification helps clinicians to find a comprehensive and accurate response to treatment, with the most common binary classification (benign / malignant cancer). Nowadays, the Machine Learning (ML) techniques are commonly used in the case of classification of breast cancer. They support with high classification accuracy and rapid evaluation technologies. The proposed research work is mainly focused on supervised learning algorithm, which uses four distinct classifiers: K-Nearest Neighbor (KNN), Weighted K-Nearest Neighbor (WKNN), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA) and Artificial Neural Network (ANN) for the classification of breast cancer. Also, this research work suggests the difference between the aforementioned classifiers and determines their accuracy. The performance of the classifier is assessed based on its accuracy, sensitivity, specificity, precision and recall. Results indicate that, ANN provides the highest accuracy of 97.60% than the other classifiers.
RESTEP Into the Future: Relational Spatio-Temporal Learning for Multi-Person Action Forecasting

Multi-person action forecasting is an emerging topic in the computer vision field, and it is a pivotal step toward video understanding at a semantic level. This task is difficult due to the complexity of spatial and temporal dependencies. Yet, the state-of-the-art literature does not seem to be adequately responsive to this challenge. Hence, how to better foresee the forthcoming actions per actor has to be further pursued. Toward this end, we put forth a novel RElational Spatio-TEmPoral learning approach (RESTEP) for multi-person action forecasting. Our RESTEP explores the key that inherently characterizes actions from a perspective of incorporating the spatial and temporal information in a single pass (spatio-temporal dependencies) by extending relational reasoning. As a result, the RESTEP enables simultaneously predicting the actions of all actors in the scene. Our proposal significantly differs from mainstream works that heavily rely on independently processing the spatial and temporal dependencies. The proposed RESTEP first perceives a graph building upon the historical observations, then reasons the relational spatio-temporal context to extrapolate future actions. In order to augment the comprehension of individual actions that might vary over time, we further delve deeper into the essence behind this point – the evolution of spatio-temporal dependencies via optimizing the corresponding mutual information. We assess the RESTEP method on the large-scale Atomic Visual Actions (AVA) dataset, Activities in Extended Videos (ActEV/VIRAT) dataset and Joint-annotated Human Motion Data Base (J-HMDB). The experimental outcomes reveal that RESTEP can introduce considerable improvements with respect to recent leading studies.
Beyond Uber and Lyft: A Decentralized Cab Consortium over Blockchains

Car sharing facilitates car owners and renters to share and use their cars conveniently. The popular car-sharing systems like Car2Go or Zipcar provide such facilities; however, they work line an intermediary or broker and thus pose various security and privacy threats. This paper proposes an alternate car-sharing model by constructing a decentralized cab consortium where the car owners and the renters can directly interact and share the car based on certain predefined agreements. We utilize the concept of smart contracts on top of a blockchain platform to realize such a system that provides a secured, tampered-proof, fair, and transparent interface for car sharing. We have implemented the proposed platform over an Ethereum-based environment and show that its runtime cost and performance are within a considerable margin.
Biplot and cluster analyses to describe some infectious diseases in Southeast Sulawesi

Abstract Infectious diseases are caused by microorganisms that can be transmitted directly or indirectly to humans. Understanding the characteristics of these diseases helps us to prevent and to overcome them. This study aims to discuss the profile of districts and cities in Southeast Sulawesi according to the spread of several types of infectious diseases by employing biplot and cluster analyses. The results showed that when the prevalence of tuberculosis is fairly high in a district or city, the HIV’s also follows. However, if these two diseases occur barely then the prevalence of malaria tends to be high, and vice versa. HIV is more likely to occur in Kota Kendari (Kendari City) and then followed by Buton and Konawe. In contrast, District of Muna with the lowest incidence of HIV and tuberculosis indicates the high prevalence of Malaria. The other types of diseases, such as pneumonia, DHF, and filariasis, are commonly found in Konawe Selatan and Kolaka.
Optimized Item Selection to Boost Exploration for Recommender Systems

Recommender Systems have become the backbone of personalized services that provide tailored experiences to individual users. Still, data sparsity remains a common challenging problem, especially for new applications where training data is limited or not available. In this paper, we formalize a combinatorial problem that is concerned with selecting the universe of items for experimentation with recommender systems. On one hand, a large set of items is desirable to increase the diversity of items. On the other hand, a smaller set of items enable rapid experimentation and minimize the time and the amount of data required to train machine learning models. We show how to optimize for such conflicting criteria using a multi-level optimization framework. Our approach integrates techniques from discrete optimization, unsupervised clustering, and latent text embeddings. Experimental results on well-known movie and book recommendation benchmarks demonstrate the benefits of optimized item selection.
From Machine Learning to Deep Learning for Detecting Abusive Messages in Arabic Social Media: Survey and Challenges

The pervasiveness of social networks in recent years has revolutionized the way we communicate. The chance is now opened up for every person to freely and anonymously share his thoughts, opinions and ideas in a real-time manner. However, social media platforms are not always considered as a safe environment due to the increasing propagation of abusive messages that severely impact the community as a whole. The rapid detection of abusive messages remains a challenge for social platforms not only because of the harm it may cause to the users but also because of its impact on the quality of service they provide. Furthermore, the detection task proves to be more difficult when contents are generated in a specific language known by its complexity, richness and specificities like the Arabic language. The aim of this paper is to provide a comprehensive review of the existing approaches for detecting abusive messages from social media in the Arabic language. These approaches extend from the use of traditional machine learning to the incorporation of the latest deep learning architectures. Additionally, a background on abusive messages and Arabic language specificities will be presented. Finally, challenges are described for better analysis and identification of the future directions.
A Peer Recommendation Model to Avoid Hate Speech Engagements in Multiplex Social Networks

Multiplex social network relationships are quite strong in most occurrences, especially within a strong peer network (a cluster of near engaging friends). Moreover, hate speech is found on most online social media platforms. Hence, this study aims to identify hate speech discussions among peer networks. This paper discusses a novel model to recommend a peer under the context of multiplex social networks to minimize the hate speech engagements; Facebook, Twitter, and YouTube social media networks (SMN) were used in this experiment. Collaborative filtering defines an interest-based recommendation model. Under the context of user engagements, some topics become of more user interest. Hence, some social media posts drastically spread over multiplex layers rapidly, initiating a high social impact on a specific topic. The research gap is identifying the peer network that reduces hate speech in multiplex social networks. Hence, this study provides a social innovation platform for peer recommendations to avoid social splits. First, this research contributes by proposing a novel methodology for identifying user engagements on online social networks by mining interactive social network graphs. Secondly, it provides an algorithm for recommending a multi-dimensional recommendation model by using collaborative filtering. Upon the proposed algorithm, a system that recommends engagements in any given online social network to minimize hate speech was implemented. Accordingly, the novel algorithm evaluates by using recommendation precision. The results show that the novel algorithm is highly applicable for peer recommendation in multiplex social networks to avoid hate speech discussions.
Integrating Modalities into Context Aware eLearning System Using Cloud Computing

ICT and digital Technologies have influenced the everyday life and the way how people learn and work. Nowadays, there is a variety of learning systems supporting innovative digital approaches and many schools, universities and organizations worldwide are investing in building their own e-Learning platforms. However, learners” performance still a concern that requests focus for improvement. The learning processes can be adapted to suit the needs of particular users when the proposed e-learning systems include context-awareness computing. We believe that instead of designing an e-Learning environment with hidden contextual information, e.g.: user's activity, location or nearby devices, we must consider a new e-Learning application approach including context-awareness capabilities. Such a system will be more efficient and adaptive to users' needs. Existing e-Learning systems suffer a lack of dynamic scalability. It's also complexes to extend, and expensive to integrate with other e-Learning platforms. In particular students who have visual processing disorder or hearing impairments can manipulate digital text or use adaptive modules like Subtitles which help them in processing the information effectively. This paper focus on the development of a hybrid e-Learning cloud system that combines the National Institute of Standards and Technology (NIST) architecture and various contexts aware computing such as User context, system context, and environment context that can quickly adjust to any environment change is expected. Were viewed traditional e-Learning architectures and their limitations and then proposed a hybrid cloud computing architecture to offer resources efficiently to all learning stakeholders and improve the education system quality at an affordable cost. To demonstrate the usefulness of the proposed system, we implemented and evaluated a simulation scenario. Based on our results, we highlight facts for increasing the cooperation and communication levels among learners in the proposed e-Learning environments and provide possible directions for future work.
Towards Tunable RDMA Parameter Selection at Runtime for Datacenter Applications

Because of the low-latency and high-throughput benefits of RDMA, an increasing number of collaborative applications in datacenters are re-designed with RDMA to boost the performance. Among various low-level hardware primitives provided by RDMA, exposed as parameters of APIs, the application designers select and hardcode them to exploit all the performance benefits of RDMA. However, with the dynamic nature of datacenter application, the hardcoded and fixed parameter selection fails to take full advantages of RDMA capabilities, which can cause up to 35% throughput performance loss. To address this issue, we present a tunable RDMA parameter selection framework, which allows parameter tuning at runtime, adaptive to the dynamic application and server status. To attain the native RDMA performance, we use a lightweight decision tree to reduce the overhead of RDMA parameter selection. Finally, we implement the tunable RDMA parameter selection framework with native RDMA API to provide a more abstract API. To demonstrate the effectiveness of our method, we implement a key-value service based on the abstract API. Experiment results show that our implementation has only a very small overhead compared with the native RDMA, while the optimized key-value service achieves 112% more throughput than Pilaf and 66% more throughput than FaRM.
Scalar Adjective Identification and Multilingual Ranking

The intensity relationship that holds between scalar adjectives (e.g., nice < great < wonderful) is highly relevant for natural language inference and common-sense reasoning.Previous research on scalar adjective ranking has focused on English, mainly due to the availability of datasets for evaluation.We introduce a new multilingual dataset in order to promote research on scalar adjectives in new languages.We perform a series of experiments and set performance baselines on this dataset, using monolingual and multilingual contextual language models.Additionally, we introduce a new binary classification task for English scalar adjective identification which examines the models' ability to distinguish scalar from relational adjectives.We probe contextualised representations and report baseline results for future comparison on this task.
Semantic Image Segmentation based on SegNetWithCRFs

In order to solve the problems of rough segmentation results and loss of image details due to the lack of smooth- ing constraints and continuous downsampling in semantic segmentation tasks. In this article, we propose a end-to-end network model based on SegNetWithCRFs. Conditional Random Fields(CRFs) with Gaussian pairwise potentials and mean-field approximate inference is the last layer of the SegNet network, so that the model has the characteristics of both Deep Convolutional Neural Networks(DCNN) and CRFs, and can learn the parameters of DCNN and CRFs together in a unified deep network. We train the entire deep neural network end-to-end through the backpropagation algorithm, avoiding separate post-processing of the image. Through the qualitative analysis of the experiment on the zebra crossing and KITTI-Road datasets, it can be found that the SegNet model after adding CRFs unified training can effectively solve the problem of image detail loss, even unmarked details can be identified. The algorithm is also effective for segmentation targets with obvious geometric features.
Multi-peak Graph-based Multi-instance Learning for Weakly Supervised Object Detection

Weakly supervised object detection (WSOD), aiming to detect objects with only image-level annotations, has become one of the research hotspots over the past few years. Recently, much effort has been devoted to WSOD for the simple yet effective architecture and remarkable improvements have been achieved. Existing approaches using multiple-instance learning usually pay more attention to the proposals individually, ignoring relation information between proposals. Besides, to obtain pseudo-ground-truth boxes for WSOD, MIL-based methods tend to select the region with the highest confidence score and regard those with small overlap as background category, which leads to mislabeled instances. As a result, these methods suffer from mislabeling instances and lacking relations between proposals, degrading the performance of WSOD. To tackle these issues, this article introduces a multi-peak graph-based model for WSOD. Specifically, we use the instance graph to model the relations between proposals, which reinforces multiple-instance learning process. In addition, a multi-peak discovery strategy is designed to avert mislabeling instances. The proposed model is trained by stochastic gradients decent optimizer using back-propagation in an end-to-end manner. Extensive quantitative and qualitative evaluations on two publicly challenging benchmarks, PASCAL VOC 2007 and PASCAL VOC 2012, demonstrate the superiority and effectiveness of the proposed approach.
Multi-stage Indian sign language classification with Sensor Modality Assessment

Sign Language used around the world by deaf community for communication. To enable conversation between sign language users and non-signers, an electronic sign language translator may prove useful. In this paper, classification of 50 signs from the Indian sign language is performed using data acquired from multiple surface electromyograms, and gyroscopes and accelerometers on one forearm of the signers. A novel multistage classification of signs with ensemble machine learning models is proposed. First, a binary classifier is used to classify the sign as a static posture or containing dynamic motion of hand. Then, the sign is classified using one of the two multi-class classifiers, each trained to classify the sign from among the set of static signs or the dynamic signs. Random forest (RF) and extreme gradient boosting machine compared for determining the features important for classification of signs from the two categories. It is observed that more of the electromyogram features are important for classification of static signs, while those extracted from accelerometer and gyroscope signals are important for classification of dynamic signs. The proposed multi-stage classification approach gave an overall accuracy of classification increases to a maximum of 98% with RF, which is greater than that achieved when a single classifier is used to classify all the signs.
Existential Semiotics and Its Application to Music: The Zemic Theory and Its Birth from the Spirit of Music

Existential semiotics, a new theory stemming from combination of classical semiotics (Paris school) and continental philosophy, has led us to two notions of transcendence and—zemic. By zemic we understand four modes of being (Hegel) interpreted via the categories of Moi and Soi, inserted first to a semiotic square and then dynamized by two movements of sublimation and embodiment. A new model for music analysis grows from this starting point. In the background loom the two ways of transcending: transascendence and transdescendence (Jean Wahl), which appear as gestures of musical discourse. Ultimately this is close to the’breathing of the hand’ at the piano playing method by the Parisian teacher Jules Gentil. Moreover, historically, the idea occurs also in some notes in the Brown Book by Richard Wagner referring to Indian Hindu philosophy. This has its consequences in the whole of theory of existential semiotics as a metaphysical system.
User Centric and Collaborative Movie Recommendation System Under Customized Platforms

Topping the entertainment industry, movies have become a really life-changing factor for this industry. The only difficulty individuals are facing right now is to find the right content which fits their choice of demands. To solve this problem, recommendation systems have been brought into use. Various filters can be applied to find the content of your desire. The aim of this paper is to predict user's movie rating along with genre in order to recommend movies. The movies are recommended based on user activity and content to which they potentially give high ratings. User choices and history makes it easier to predict movies and filter out content. Many methods have been used to implement a recommendation system, but in this paper, we have majorly used collaborative filtering for recommending and content-based filtering for sentiment analysis. It will be used to output a list of recommended movies based on an individual's choice, ratings, likes and past behavioral pattern. In this paper, to improve accuracy and effectiveness, similarity score is used. It is a numerical value that ranges between 0 to 1 and helps to determine how much a group of items are similar to each other. Cosine similarity makes the process effective by quantifying the similarity in the form of angular distance. As compared to Euclidean distance, cosine similarity is used because the precision of the cosine angle and the equi-distance of movies are almost identical.
Design and Application of Hybrid Teaching Platform Based on Internet +

By constructing a mixed teaching mode of courses, students' needs for knowledge learning can be better met, and it is also the need for the connotative development of schools to improve teaching quality. "Internet+education" has formed a new educational ecology relying on the network, and also endowed mixed teaching with a new and unique connotation. Based on this, it is also necessary to reconstruct the basic attributes of blended teaching. Based on the analysis of the problems existing in the hybrid teaching model of colleges and universities in the "Internet +" era, a method for constructing an online and offline hybrid teaching platform based on "Internet +" is proposed. So as to meet the needs of students' self-construction under the premise of effectively playing the leading role of teachers.
Enhancing the Reliability of Cloud Data by Implementing AES Algorithm

Cloud computing can be said as a technology which is based on the networks. Cloud computing has it origin from distributed computing, grid computing, utility computing. In cloud the users may pay for what they are using. To reduce the infrastructure the data can be moved from their environment to the cloud environment by the users. The users can loss the control over the data. The data moving on infrastructure will face several attacks. There is a possibility that the attacker will spoof the data from an authenticated user with ease. This paper concentrates on improving cloud computing data protection through Tclouds and trusted computing. Keywords: cloud computing, threats, attacks.
Classifying Functional Data from Orthogonal Projections – Model, Properties and Fast Implementation

We consider the problem of functional, random data classification from equidistant samples. Such data are frequently not easy for classification when one has a large number of observations that bear low information for classification. We consider this problem using tools from the functional analysis. Therefore, a mathematical model of such data is proposed and its correctness is verified. Then, it is shown that any finite number of descriptors, obtained by orthogonal projections on any differentiable basis of \(L_2(0,\, T)\), can be consistently estimated within this model.Computational aspects of estimating descriptors, based on the fast implementation of the discrete cosine transform (DCT), are also investigated in conjunction with learning a classifier and using it on-line. Finally, the algorithm of learning descriptors and classifiers were tested on real-life random signals, namely, on accelerations, coming from large bucket-wheel excavators, that are transmitted to an operator’s cabin. The aim of these tests was also to select a classifier that is well suited for working with DCT-based descriptors.