Reviewerly/exHarmony huggingface dataset:
Runtime Adaptability of Ambient Intelligence Systems Based on Component-Oriented Approach

Technological improvements of the Internet and connected devices cause increased user expectations. People want to be offered different services in nearly every aspect of their lives. It is a key point that these services can be reached seamlessly and should be dynamically available conforming to the active daily life of today’s people. This can be achieved by having intelligent environments along with smart appliances and applications. The concept of ambient intelligence arises from this need to react with users at runtime and keep providing real-time services under changing conditions. This chapter introduces a component-oriented ontology-based approach to develop runtime adaptable ambient intelligenceAmbient Intelligence (AmI) systems. In this approach, the adaptability mechanism is enabled through a component-oriented method with variability-related capabilities. The outcome supports the find-and-integrate method from the idea formation to the executable system, and thus reducing the need for heavy processes for development. Intelligence is provided through ontology modeling that supports repeatability of the approach in different domains, especially when used in interaction with component variability. In this context, an example problem exploiting the variability in the density of a smart stadium network is used to illustrate the application of the component-driven approach.
Constrained Graph Partitioning via Matrix Differential Equations

A novel algorithmic approach is presented for the problem of partitioning a connected undirected weighted graph under constraints such as cardinality or membership requirements or must-link and cannot-link constraints. Such constrained problems can be NP-hard combinatorial optimization problems. They are restated as matrix nearness problems for the weight matrix of the graph, where the objective is to minimize the distance between the given weight matrix and perturbed weight matrices for which a functional of an eigenvalue and eigenvector of the graph Laplacian takes its minimal value. A key element in the numerical solution of these matrix nearness problems is the use of a gradient system of matrix differential equations for the functional.
HSV Based Histogram Thresholding Technique for MRI Brain Tissue Segmentation

Background: To bring it as a human interactive perceive color process, an automatic color model based segmentation of White Matter (WM), Gray Matter (GM), and Cerebrospinal Fluid (CSF) in Magnetic Resonance Brain images is proposed in this paper. Methods: Preprocessing process is done for the MRI brain images using wavelet based bivariate shrinkage method and Contour based Brain Segmentation method (CBSM). Then segmentation of brain tissues using Hue Saturation Value (HSV) color model Based Histogram Thresholding Technique (HSVBHTT) was applied. Normal and Alzheimer's disease (AD) brain images obtained from Internet Brain Segmentation Repository (IBSR) and Minimal Interval Resonance Imaging in Alzheimer's Disease (MIRIAD) datasets. Results and Conclusions: The results of proposed method was analyzed with similarity measures and quantitative measures like Jaccard (J), Dice (D), Sensitivity (S) and Specificity (SP) and compared with the manual segmented images which produced better results on segmenting WM, GM and CSF compared to other existing methods.
Semantic Distance Spreading Across Entities in Linked Open Data

Recommender systems can utilize Linked Open Data (LOD) to overcome some challenges, such as the item cold start problem, as well as the problem of explaining the recommendation. There are several techniques in exploiting LOD in recommender systems; one approach, called Linked Data Semantic Distance (LDSD), considers nearby resources to be recommended by calculating a semantic distance between resources. The LDSD approach, however, has some drawbacks such as its inability to measure the semantic distance resources that are not directly linked to each other. In this paper, we first propose another variation of the LDSD approach, called wtLDSD, by extending indirect distance calculations to include the effect of multiple links of differing properties within LOD, while prioritizing link properties. Next, we introduce an approach that broadens the coverage of LDSD-based approaches beyond resources that are more than two links apart. Our experimental results show that approaches we propose improve the accuracy of the LOD-based recommendations over our baselines. Furthermore, the results show that the propagation of semantic distance calculation to reflect resources further away in the LOD graph extends the coverage of LOD-based recommender systems.
Bit-Plane Specific Measures and Its Applications in Analysis of Image Ciphers

The paper presents bit-plane specific new measures to visualize the extensive statistical detail of an image. We compute the frequency of ones, maximum run length and correlation among rows (columns) in each bit-plane of an image. The computed measures give row-wise and column-wise structural detail at bit-plane level and help an interpreter to analyze given image deeply for its effective interpretation and understanding. In this paper, the application of these measures is shown in cryptography to statistically analyze the image ciphers. The simulation study shows that the proposed measures are very useful and can be applied in various image processing applications for pattern recognition and understanding of visual objects.
Prediction-Based Lossless Image Compression

In this paper, a lossless image compression technique using prediction errors is proposed. To achieve better compression performance, a novel classifier which makes use of wavelet and Fourier descriptor features is employed. Artificial neural network (ANN) is used as a predictor. An optimum ANN configuration is determined for each class of the images. In the second stage, an entropy encoding is performed on the prediction errors which improve the compression performance further. The prediction process is made lossless by making the predicted values as integers both at the compression and decompression stages. The proposed method is tested using three types of datasets, namely CLEF med 2009, COREL1 k and standard benchmarking images. It is found that the proposed method yields good compression ratio values in all these cases and for standard images, the compression ratio values achieved are higher compared to those obtained by the known algorithms.
Lossy Trapdoor Permutations with Improved Lossiness

Lossy trapdoor functions (Peikert and Waters, STOC 2008 and SIAM J. Computing 2011) imply, via black-box transformations, a number of interesting cryptographic primitives, including chosen-ciphertext secure public-key encryption. Kiltz, O'Neill, and Smith (CRYPTO 2010) showed that the RSA trapdoor permutation is lossy under the Phi-hiding assumption, but syntactically it is not a lossy trapdoor function since it acts on $$\mathbb {Z}_N$$ and not on strings. Using a domain extension technique by Freeman et al. (PKC 2010 and J. Cryptology 2013) it can be extended to a lossy trapdoor permutation, but with considerably reduced lossiness. In this work we give new constructions of lossy trapdoor permutations from the Phi-hiding assumption, the quadratic residuosity assumption, and the decisional composite residuosity assumption, all with improved lossiness. Furthermore, we propose the first all-but-one lossy trapdoor permutation from the Phi-hiding assumption. A technical vehicle used for achieving this is a novel transform that converts trapdoor functions with index-dependent domain to trapdoor functions with fixed domain.
Smart Contract-Based Role Management on the Blockchain

Role-based access management is essential in today's business applications. The need for such access control is indisputable, implementation in a centralized way, on the other hand, is not ideal. An improvement could be a decentralized, Smart-Contract-based approach. This paper examines whether corporate applications can use distributed ledger based authorization systems to benefit from the positive properties of blockchain technology, without losing the possibilities and strengths of existing central authorization techniques. The benefit of a prototype with a decentralized approach is to serve as a basis for future decentralized company developments. This paper deals with the implementation and validation of a blockchain-based access control solution for decentralized applications. The feasibility of this on-chain solution for role-based access control (RBAC) is verified through a proof-of-concept using a suitable distributed ledger platform. The implementation of the authorization system aims to fulfill the evaluation requirements and does not claim to be used as a corporate service.
A Public, Blockchain-Based Distributed Smart-Contract Platform Enabling Mobile Lite Wallets Using a Proof-of-Stake Consensus Algorithm

Blockchain-enabled smart contracts that employ proof-of-stake validation for transactions, promise significant performance advantages compared to proof-of-work solutions. For broad industry adoption, other important requirements must be met in addition. For example, stable backwards-compatible smart-contract systems must automate cross-organizational information-logistics orchestration with lite mobile wallets that support the unspent transaction output (UTXO) protocol and simple payment verification (SPV) techniques. The currently leading smart-contract solution Ethereum, uses computationally expensive proof-of-work validation, is expected to hard-fork multiple times in the future and requires downloading the entire blockchain. Consequently, Ethereum smart contracts have limited utility for large industry applications. This paper fills the gap in the state of the art by presenting the Qtum smart-contract framework that allows for managing transaction headers in lite mobile wallets in addition with using a proof-of-stake (PoS) consensus algorithm.
Adaptation of Conventional Technologies with Refugee Language Learners: An Overview of Possibilities

Most research on language acquisition using technology generally investigates collegiate language learners. However, it is unclear as to how well these findings apply to refugee learners, who sometimes have experienced interrupted schooling and had little exposure to technologies found in the resettlement context. Little research concentrates on the use of technology to aid language acquisition among this population. By better understanding the digital literacies refugees already possess, the author are better able to bridge this digital divide (Thorne & Reinhardt, 2008; Warschauer, 2002) and move toward researching how to capitalize on the technological skills refugees already possess in order to facilitate language learning. Therefore, this chapter reviews available literature on how refugees worldwide use multiple forms of technology, their levels of access to such technology, and considerations for pre- and post-resettlement technological options. It identifies best practices for employing technology to facilitate language acquisition in light of the multifaceted constraints refugees face. It concludes by outlining the suitability of different technologies as a means of facilitating language development within a myriad of contexts and gives recommendations for future research on using technology to facilitate language learning at all proficiency levels.
Attitude Detection for One-Round Conversation

We tackle Attitude Detection, which we define as the task of extracting the replier's attitude, i.e., a target-polarity pair, from a given one-round conversation. While previous studies considered Target Extraction and Polarity Classification separately, we regard them as subtasks of Attitude Detection. Our experimental results show that treating the two subtasks independently is not the optimal solution for Attitude Detection, as achieving high performance in each subtask is not sufficient for obtaining correct target-polarity pairs. Our jointly trained model AD-NET substantially outperforms the separately trained models by alleviating the target-polarity mismatch problem. Moreover, we proposed a method utilising the attitude detection model to improve retrieval-based chatbots by re-ranking the response candidates with attitude features. Human evaluation indicates that with attitude detection integrated, the new responses to the sampled queries from are statistically significantly more consistent, coherent, engaging and informative than the original ones obtained from a commercial chatbot.
Quantum Dialogue Based on Entanglement Swapping and Hadamard Operation via Cavity QED

A quantum dialogue scheme based on entanglement swapping and Hadamard operation via cavity QED is proposed, which effectively uses the evolution law of atom in cavity QED. The proposed scheme uses Bell states and GHZ states as quantum resource and only needs single-atom measurements. The proposed scheme is secure against active attacks from an eavesdropper and can transmit 4 secret bits per round communication.
Dolphin Swarm Algorithm for Cryptanalysis

This work focuses on the application of Dolphin Swarm Algorithm (DSA) to cryptanalysis. It is a relatively new optimization algorithm that is based on the collective behavior of dolphins to hunt for food in their natural habitat. Experimental results show that DSA is more efficient than well-known algorithms like genetic algorithm and ant colony optimization when utilized for the cryptanalysis of the Data Encryption Standard. It also produces solutions with higher fitness in a shorter period of time and with fewer individuals. The DSA can also be used to attack other block ciphers like Advanced Encryption Standard or Elliptic Curve Cryptography due to the independence of the fitness function with respect to the cipher.
clstk

Cross-lingual summarization (CLS) aims to create summaries in a target language, from a document or document set given in a different, source language. Cross-lingual summarization can play a critical role in enabling cross-lingual information access for millions of people across the globe who do not speak or understand languages having large representation on the web. It can also make documents originally published in local languages quickly accessible to a large audience which does not understand those local languages. Though cross-lingual summarization has gathered some attention in the last decade, there has been no serious effort to publish rigorous software for this task. In this paper, we provide a design for an end-to-end CLS software called clstk. Besides implementing a number of methods proposed by different CLS researchers over years, the software integrates multiple components critical for CLS. We hope that this extremely modular tool-kit will help CLS researchers to contribute more effectively to the area.
Tarsier Goggles: a virtual reality tool for experiencing the optics of a dark-adapted primate visual system

Charles Darwin viewed eyes as the epitome of evolution by natural selection, describing them as organs of extreme perfection and complication. The visual system is therefore fertile ground for teaching fundamental concepts in optics and biology, subjects with scant representation during the rise and spread of immersive technologies in K-12 education. The visual system is an ideal topic for three-dimensional (3D) virtual reality learning environments (VRLEs), and here we describe a 3D VRLE that simulates the vision of a tarsier, a nocturnal primate that lives in southeast Asia. Tarsiers are an enduring source of fascination for having enormous eyes, both in absolute size and in proportion to the size of the animal. Our motivation for developing a tarsier-inspired VRLE, or Tarsier Goggles, is to demonstrate the optical and selective advantages of hyperenlarged eyes for nocturnal visual predation. In addition to greater visual sensitivity, users also experience reductions in visual acuity and color vision. On a philosophical level, we can never know the visual world of another organism, but advances in 3D VRLEs allow us to try in the service of experiential learning and educational outreach.
Semi-supervised clustering of unknown expressions

Automated facial expressions recognition is an important area in computer vision and machine learning due to its important role in socially aware human-computer interaction and affective analysis. However, expression recognition is not trivial as it may vary across different genders, age groups and ethnicities, despite the universality of basic expressions. We propose here a semi-supervised learning algorithm with the motivation to cluster known, as well as unknown facial behavior. We detect and align faces and then reduce their dimensionality using various linear and non-linear, supervised and unsupervised methods. The transformed faces in new dimensions are then clustered with various methods, including but not limited to, consensus clustering, which is also used for automated class discovery in genetics. The linear dimensionality reduction methods, that we employ, include principal component analysis and linear discriminant analysis; while the non-linear methods include learning embeddings with Deep Convolution Neural Networks (CNNs). Deep CNNs can learn highly complicated non-linear transformations in a complex feature space. We get the best results using embeddings that we learn using deep convolution neural networks with consensus clustering. The novelty of our work is to show that we can cluster facial expressions, which were not included while learning the embeddings in the first place. This shows that we can generalize our work to non-standard expressions and can learn expression classes from the datasets themselves.
Techniques and Trends for Fine-Grained Opinion Mining and Sentiment Analysis: Recent Survey

Background: Nowadays, with the appearance of web 2.0, more users express their opinions, judgments, and thoughts towards certain objects, services, organizations, and their attributes via social networking, forum entries, websites, and blogs and so on. In this way, the volume of raw content generated by these users will increase rapidly with enormous size, where people often find difficulties in identifying and summarizing fine-grained sentiments buried in the opinion-rich resources. The traditional opinion mining techniques, which focused on the overall sentiment of the review, fails to uncover the sentiments expressed on the aspects of the reviewed entity. For that, researchers in Aspect-based opinion mining community try to solve and handle this problem. Objective: Our proposed study aims to present, survey and compare in the first place the important recent Aspect-based opinion mining approaches relevant to important languages such English, Arabic and Chinese and commonly datasets used in literature so that future researchers could improve their results. The cited approaches used the last techniques in the area on Opinion mining field, relevant to the Deep Learning models. In the second place, we try to highlight and give special attention to the Arabic language by introducing a dashboard of deep learning methods dedicated to the Arabic language. Finally, we emphasize the research gaps and future challenges in both English and Arabic languages that provide some new potential research fields. Methods: We have carefully summarized 48 models according to their algorithm into three categories: supervised, semi-supervised and unsupervised. Due to a large number of approaches with diverse datasets and techniques, we propose some statistical graphics to compare different experimentation results namely precision, Recall, and F-measure. Also, the study has conferred a comparative analysis and a comprehensive discussion of different approaches and techniques dedicated to the aspect extraction sub-task using the new tendency that of deep learning on both Arabic, English and Chinese language. We have introduced some future challenges, research gaps, and new trends in the opinion mining task, which need more efforts and investigations to present new solutions that make the opinion mining field more pervasive and give more ideas about the different researches done in the field of OM. Methods: We have compared the different approaches and techniques dedicated to the extraction of aspects using the new tendency that of deep learning. Our contribution illustrates the add values given by deep learning models in the treatment of user reviews expressed in the Arabic language. At the same time, this work is mainly based on the use of the evaluation performance metrics (precision, recall, and f-measure).
A Recommendation Model Based on Multi-Emotion Similarity in the Social Networks

This paper proposed a recommendation model called RM-SA, which is based on multi-emotional analysis in networks. In the RM-MES scheme, the recommendation values of goods are primarily derived from the probabilities calculated by a similar existing recommendation system during the initiation stage of the recommendation system. First, the behaviors of those users can be divided into three aspects, including browsing goods, buying goods only, and purchasing–evaluating goods. Then, the characteristics of goods and the emotional information of user are considered to determine similarities between users and stores. We chose the most similar shop as the reference existing shop in the experiment. Then, the recommendation probability matrix of both the existing store and the new store is computed based on the similarities between users and target user, who are randomly selected. Finally, we used co-purchasing metadata from Amazon and a certain kind of comments to verify the effectiveness and performance of the RM-MES scheme proposed in this paper through comprehensive experiments. The final results showed that the precision, recall, and F1-measure were increased by 19.07%, 20.73% and 21.02% respectively.
On modal logics arising from scattered locally compact Hausdorff spaces

For a topological space X, let L(X) be the modal logic of X where □ is interpreted as interior (and hence ◇ as closure) in X. It was shown in [3] that the modal logics S4, S4.1, S4.2, S4.1.2, S4.Grz, S4.Grzn (n≥1), and their intersections arise as L(X) for some Stone space X. We give an example of a scattered Stone space whose logic is not such an intersection. This gives an affirmative answer to [3, Question 6.2]. On the other hand, we show that a scattered Stone space that is in addition hereditarily paracompact does not give rise to a new logic; namely we show that the logic of such a space is either S4.Grz or S4.Grzn for some n≥1. In fact, we prove this result for any scattered locally compact open hereditarily collectionwise normal and open hereditarily strongly zero-dimensional space.
Extensions of Self-Improving Sorters

Ailon et al. (SIAM J Comput 40(2):350–375, 2011) proposed a self-improving sorter that tunes its performance to an unknown input distribution in a training phase. The input numbers $$x_1,x_2,\ldots ,x_n$$ come from a product distribution, that is, each $$x_i$$ is drawn independently from an arbitrary distribution $${{{\mathcal {D}}}}_i$$. We study two relaxations of this requirement. The first extension models hidden classes in the input. We consider the case that numbers in the same class are governed by linear functions of the same hidden random parameter. The second extension considers a hidden mixture of product distributions.