Miracle-dz/UniFAR-FTUs huggingface dataset:
Recognition of Arabic Characters using Spiking Neural Networks

Text Recognition is one of the active and challenging areas of research in the pattern recognition field. It covers many applications like automatic number plate recognition, bank cheques, aid of reading for blind, and hand-written document conversion into form of structural text. In the present work, the spiking neural network (SNN) model is utilized for the identification of Arabic characters in a character set. The structure of considered neural network consists of two layers with Izhekevich neurons. Remote Supervised Method (ReSuMe) has been used as a learning rule for training. Also, the recognition of Arabic characters has been considered based on spike Neural Networks. The network could successfully recognize character set consisting of 28 isolating Arabic characters.
TrAdaBoost Based on Improved Particle Swarm Optimization for Cross-Domain Scene Classification With Limited Samples

Scene classification is usually based on supervised learning, but collecting instances is expensive and time-consuming. TrAdaBoost has achieved great success in transferring source instances to target images, but it has problems, such as the excessive focus on instances harder to classify, the rapid convergence speed of the source instances, and the weight mismatch caused by the big gap between the number of source and target instances, leading to decreased classification accuracy. In this paper, in order to address these problems, classical particle swarm optimization (PSO) is modified to select the optimal feature subspace for classifying the “harder” and “easier” instances by reducing unimportant dimensions. A modified correction factor is proposed by considering the classification accuracy of the instances from both domains, to decrease the convergence speed. Iterative selective TrAdaBoost is also proposed to reduce the weight mismatch by removing the indiscriminate source instances. The experimental results obtained with three benchmark data sets confirm that the proposed method outperforms most of the previous methods of scene classification with limited target samples. It is also proved that modified PSO for optimal feature subspace selection, the modified correction factor, and iterative selective TrAdaBoost are all effective in improving the classification accuracy, giving improvements of 3.6%, 4.3%, and 2.7%, and these three contributions together increase the classification accuracy by about 8% in total.
Node Immunization in Networks with Uncertainty

In immunization strategy, the general focus is to identify and prevent malicious attacks or malware spreading from one node to its neighboring nodes in static networks. However, in many different real-world applications, the transmission routes are uncertain or unstable. The number of uncertain edges determines the number of networks that can be produced, which are referred to as sample networks. Large number of sample networks will significantly increase the computational complexity in the network with uncertainty. In this paper, we first propose a measure to assess the spread of disease or virus in networks with uncertainty (e.g. in a technical network, connections between nodes may fail due to the unstable signal). Specifically, we introduce the concepts of excepted eigenvalue (EE) and excepted fraction of infected node (EF) to quantify the spread strength and influence of disease or viruses. The aim is to minimize the EE and EF values of the remaining network after immunizing with k nodes. Therefore, we design an algorithm based on the characteristics of degree and largest eigenvalue in uncertain networks. We also select an appropriate number of sample networks to reduce the computational cost and guarantee high accuracy level. Findings from the simulations on synthetic and real networks demonstrate the effectiveness of our proposed approach.
Personalized Clothing Recommendation Based on Knowledge Graph

Most clothing recommender systems employed the approach of collaborative filtering and content-based which pay more attention to predicting the user's item preferences based on massive historical data. These systems can easily lead to the dilemma of cold start and data sparseness, furthermore, they overlooked user's context, such as weather, occasion, requirements, emotions and also correlations between clothing items. In this paper, we construct knowledge graph of user, knowledge graph of clothing and knowledge graph of context, utilize Apriori algorithm to capture the intrinsic correlations between clothing attributes and context attributes. Thereafter, we search and match the established knowledge graph according to the user's requirements and combine the Top-N algorithm to generate the recommendation results directly. In the meantime, we recommend which has the most similar to user's collected clothes through the similarity of clothing ontology to improve the efficiency and accuracy of the recommendation. The experimental results show that our method can effectively solve the cold start problem and has higher recommendation quality compared with traditional collaborative filtering.
Recommender Systems with Characterized Social Regularization

Social recommendation, which utilizes social relations to enhance recommender systems, has been gaining increasing attention recently with the rapid development of online social network. Existing social recommendation methods are based on the fact that users preference or decision is influenced by their social friends' behaviors. However, they assume that the influences of social relation are always the same, which violates the fact that users are likely to share preference on diverse products with different friends. In this paper, we present a novel CSR (short for C haracterized S ocial R egularization) model by designing a universal regularization term for modeling variable social influence. Our proposed model can be applied to both explicit and implicit iteration. Extensive experiments on a real-world dataset demonstrate that CSR significantly outperforms state-of-the-art social recommendation methods.
The role of radiotherapy in the management of primary cardiac lymphoma a case report and the literature review

Natalia Cichowska-Cwali nska , Magdalena Dutka, Andrzej Klapkowski, Rafał PeRksa, Jan Maciej Zaucha and Renata Zaucha Department of Clinical Oncology and Radiotherapy, Medical University of Gda nsk, Gda nsk, Poland; Department of Hematology and Transplantology, Medical University of Gda nsk, Gda nsk, Poland; Department of Cardiac and Vascular Surgery, Medical University of Gda nsk, Gda nsk, Poland; Department of Patomorphology, Medical University of Gda nsk, Gda nsk, Poland
Pioneering models for information interaction in the context of information seeking and retrieval

Purpose The purpose of this paper is to clarify the conceptual issues of information behaviour research by reviewing the approaches to information interaction in the context of information seeking and retrieval (IS&R). Design/methodology/approach The study uses the conceptual analysis focussing on four pioneering models for interactive IS&R proposed by Belkin, Ingwersen and Ingwersen and Jarvelin. Findings A main characteristic of models for information interaction is the tripartite setting identifying information resources accessible through information systems, intermediary/interface and user. Dialogue is a fundamental constituent of information interaction. Early models proposed by Belkin and Ingwersen focussed on the dialogue occurring in user-intermediary interaction, while more recent frameworks developed by Ingwersen and Jarvelin devote more attention to dialogue constitutive of user-information system interaction. Research limitations/implications As the study focusses on four models developed within the period of 1984-2005, the findings cannot be generalised to depict the phenomena of information interaction as a whole. Further research is needed to model the specific features of information interaction occurring in the networked information environments in particular. Originality/value The study pioneers by providing an in-depth analysis of the ways in which pioneering researchers have conceptualised the phenomena of interaction in the context of IS&R. The findings contribute to the elaboration of the conceptual space of information behaviour research.
Skip-gram word embeddings in hyperbolic space

Recent work has demonstrated that embeddings of tree-like graphs in hyperbolic space surpass their Euclidean counterparts in performance by a large margin. Inspired by these results and scale-free structure in the word co-occurrence graph, we present an algorithm for learning word embeddings in hyperbolic space from free text. An objective function based on the hyperbolic distance is derived and included in the skip-gram negative-sampling architecture of word2vec. The hyperbolic word embeddings are then evaluated on word similarity and analogy benchmarks. The results demonstrate the potential of hyperbolic word embeddings, particularly in low dimensions, though without clear superiority over their Euclidean counterparts. We further discuss subtleties in the formulation of the analogy task in curved spaces.
Challenges and Solutions Perceived by Educators in an Early Childcare Program for Refugee Children

Immigration to Germany peaked in 2016. More than 105,000 refugees below the age of 7 years arrived within 12 months. Since then, Germany and other host nations have been in need of strategies to cover the emerging demand for childcare services. The German federal state North-Rhine Westphalia has funded a specialized early childhood education and care (ECEC) program for recently arrived refugees. The present study investigated challenges and possible solutions in this specialized ECEC. In a pilot study, inductive content analysis of n1 = 28 semi-structured interviews with early childhood educators revealed 19 distinct challenges and four generic categories for solutions (provide clear and predictable structures, involve and support parents, ensure adequate structural features of the childcare group, convey trust and feelings of competence). For the main study, identified challenges were transcribed into items for a closed-format questionnaire, which was distributed to a second sample of educators (n2 = 96). Challenges perceived as most difficult concerned language barriers and communication with parents. An exploratory factor analysis of the challenges questionnaire yielded four underlying domains (interpersonal stress, feasibility and attendance, cultural and communication barriers, structural features of a childcare group). Our study provides a first basis to adapt childcare settings for refugees, and to guide staff training for this special group. We discuss evidence in regard to understanding how ECEC programs can successfully promote refugee children’s psychosocial adaptation and educational outcomes.
A CSI Frequency Domain Fingerprint-Based Method for Passive Indoor Human Detection

The passive personnel detection based on Wi-Fi has the advantages of low cost and easy implementation, and can be better applied to elderly care and safety monitoring. In this paper, we propose a passive indoor personnel detection method based on Wi-Fi, which we call as FDF-PIHD (Frequency Domain Fingerprint-based Passive Indoor Human Detection). Through this method, fine-grained physical layer channel state information can be extracted to generate feature fingerprints so as to help determine the state in the scene by matching online fingerprints with off-line fingerprints. In order to improve accuracy, we combine the detection results of three receiving antennas to obtain the final test result. The experimental results show that the detection rate of our proposed scheme all reach above 90%, no matter whether the scene is human-free, stationary or moving human presence. Besides, it can not only detect whether there is a target indoor, but also determine the current state of the target.
Chunk-Based Memory Constraints on the Cultural Evolution of Language

In the fields of linguistics and cognitive science, considerable attention has been devoted to the question of how linguistic structure emerged over evolutionary time. Here, we highlight the contribution of a fundamental constraint on processing, the Now-or-Never bottleneck. Language takes place in the here and now, with the transience of acoustic speech signals and our exceedingly limited memory for sound sequences requiring immediate processing. To overcome this bottleneck, the cognitive system employs basic chunking mechanisms to rapidly compress and recode incoming linguistic input into increasingly abstract levels of representation, thereby prolonging its retention in memory. Our suggestion is that these chunk-based memory processes influence linguistic structure across multiple time scales. Chunk-based memory constraints govern language acquisition and processing on the level of the individual. Through usage, linguistic structures that are more easily chunked will tend to proliferate, thus shaping the cultural evolution of language across generations of language users. This results in a selection of learnable structures, from individual words to multiword sequences that are optimally "chunkable," so as to better squeeze through the Now-or-Never bottleneck. From this perspective, language can be thought of as an adaptive system that culturally evolves to fit learners' cognitive capabilities, thereby resulting in the structure it bears today.
Performance Analysis of Wavelet based Spectrum Sensing and Conventional Spectrum Sensing in Fading Environment for Cognitive Radios

This paper discusses about the performance analysis of wavelet based spectrum sensing and conventional spectrum sensing methods in fading environment for cognitive radios. Receiver Operating Characteristics (ROC) which gives the Probability of detection versus Probability of false alarm in terms of signal and noise power is considered to be important performance metrics of Cognitive Radio. The effects of fading is also considered while evaluating the performance in this work. Analysis of results indicates that wavelet based spectrum sensing has an edge over conventional spectrum sensing technique.
General Meaningful Shadow Construction in Secret Image Sharing

Secret image sharing (SIS) is an important technique for image protection. Several shadows are produced in SIS through scrambling pixel values and their correlations of a secret image. We can own sufficient shadows to retrieve the secret image. Meaningful shadow will increase management efficiency as well as decrease encryption suspiciousness. More importantly, it may be applied to encrypted domain searching, which is useful in distributed storage and cloud computing. Hence, the meaningful shadow will save labor and reduce energy consumption. In this paper, based on analyses of the image, SIS, and meaningful shadow, we design a general SIS construction method with meaningful shadow and no pixel expansion, by which and some classic SIS techniques with meaningless (noise-like) shadow some SIS algorithms with meaningful shadow are derived. We take some experiments to demonstrate the effectiveness of our method.
Frequency domain model identification and loop-shaping controller design for quadrotor tail-sitter VTOL UAVs

In this paper, we present a practical and systematic method of model identification and controller design for a quadrotor tail-sitter UAV that is susceptible to significant model uncertainty, various flexible modes and complicated aerodynamic damping effect. Flight experiments are designed and carried out to produce a frequency domain model of the aircraft, from which several flexible modes are observed. This system identification method is well suited to the modeling of complex UAV systems, and allows for the rapid update of aircraft models when mechanical changes are made to the aircraft configuration. Besides this, it clearly captures the flexible modes of an aircraft, and enables explicit analysis of them. Guided by this, the controller design, optimization and limitations can be easily seen. Based on the Bode diagram of the identified aircraft model, loop-shaping techniques are applied to design the feedback controllers for all channels (pitch, roll and yaw) of the angular rate loop, and the system stability and robustness are analyzed. We believe that these frequency-domain-based modeling and controller design techniques are promising to qualitatively characterize the aircraft's robustness and performance, as well as to achieve reliable and safe operations of UAVs in actual environments where both model uncertainties and external disturbances often take place. Finally, simulation and experiments are presented to validate the effectiveness of this framework and to show the controller's performance.
The Investigation of Tightly Packed Spoof Surface Plasmon Polaritons Waveguides with Novel Layouts

Integrated circuits (ICs) have so many advantages, such as small volume, high reliability and good performance to make it outstanding in the practical use. However, the miniaturization makes a great challenge to the signal integrity and interference suppression in ICs. Owing to the extraordinary characteristics of filed confinement and enhancement, natural surface plasmon polaritons (SPPs) at optical frequency would have potential ability to solve the aforementioned problem between miniaturization and signal integrity of ICs design. And the presentation of spoof SPPs at microwave frequency with subwavelength effect makes the idea feasible. In this paper, we proposed three different layouts of two tightly packed spoof SPPs waveguides with deep-subwavelength separation to imitate the arrangement of wires in ICs. We mainly focus on the coupling parameter of the four-port packed spoof SPPs waveguides, and make a compare with the traditional microstrip line. The optimal arrangement and separation of spoof SPPs waveguides with the lowest crosstalk whose coupling parameter remains below −20 dB in the whole transmission band have been found that could be applied in the ICs based on the SPP mode.
A Novel Statistical Cost Model and an Algorithm for Efficient Application Offloading to Clouds

This work presents a novel statistical cost model for applications that can be offloaded to cloud computing environments. The model constructs a tree structure, referred to as the execution dependency tree (EDT), to accurately represent various execution relations, or dependencies (e.g., sequential, parallel and conditional branching) among the application modules, along its different execution paths. Contrary to existing models that assume fixed average offloading costs, each module’s cost is modelled as a random variable described by its Cumulative Distribution Function (CDF) that is statistically estimated through application profiling. Using this model, we generalize the offloading cost optimization functions to those that use more user tailored statistical measures such as cost percentiles. We employ these functions to propose an efficient offloading algorithm based on a dynamic programming formulation. We also show that the proposed model can be used as an efficient tool for application analysis by developers to gain insights on the applications’ statistical performance under varying network conditions and users behaviours. Performance evaluation results show that the achieved mean absolute percentage error between the model-based estimated cost and the measured one for the application execution time can be as small as <inline-formula> <tex-math notation="LaTeX">$5$</tex-math><alternatives><inline-graphic xlink:href="barrameda-ieq1-2513404.gif"/> </alternatives></inline-formula> percent for applications with sequential and branching module dependencies.
Veracity Analysis of Rumors in Social Media

One of the biggest challenges for rumor analysis is the information to be diffused to a large community of social media users in unverified manner. This paper studies and categorizes various approaches for identifying source of rumor and discovers how rumors are diffused. It uses rumor analysis, to categorize the rumor into four types, namely rumor Data Annotation, Classification and Prediction, Visualization and Rumor Diffusion.
Population structure of the raccoon dog (Nyctereutes procyonoides) using microsatellite loci analysis in South Korea: Implications for disease management

The prevention and control of infectious diseases transmitted by wildlife are gaining importance. To establish effective management strategies, it is essential to understand the population structure of animals. Raccoon dogs (Nyctereutes procyonoides) in South Korea play a key role in the maintenance of food web stability and possess genetic compositions that are unique compared to those in other areas. However, wild raccoon dogs play another role as the main host of various infectious diseases. To establish long-term strategies for disease management, we investigated the genetic structure and possible geographic barriers that influence the raccoon dog population in South Korea by analyzing 16 microsatellite loci. The present study showed that mountains were the major factors responsible for genetic structuring, along with distance. We proposed potential management units (MUs) for raccoon dogs based on the genetic structuring and gene-flow barrier data obtained in this study. Four MUs were suggested for the Korean raccoon dog population (Northern, Central, Southwestern, and Southeastern). The Korean raccoon dog population structure determined in this study and the proposed MUs will be helpful to establish pragmatic strategies for managing Korean raccoon dog population and for preventing the transmission of infectious diseases.
On Designing a Questionnaire Based Honeyword Generation Approach for Achieving Flatness

Honeyword (or fake password) based authentication technique has become a well-established security mechanism for protecting the original password from the server-side attack. However, there are few underlying issues associated with this technique which are still a matter of concern for the security community. Achieving flatness or producing honeywords that are equally likely to the original password is one such major concern that requires further attention. Though recent studies have made significant efforts to meet this flatness criterion, our analysis shows that they still fall short to address this issue. In this paper, at first, we have made a detailed investigation to define the basic properties for achieving flatness. Followed by this, we have proposed a questionnaire-based authentication technique which can generate significantly flatter list of honeywords compared to existing state-of-the-art. Further study reveals that the proposed technique passes the other essential evaluation criteria of honeyword based authentication technique with flying colors.
A SIW Horn Antenna with Dynamically Tunable Graphene- Based Attenuator

In this paper, a novel substrate integrated waveguide (SIW) H-plane horn antenna with dynamically tunable graphene-based attenuator is first presented. The antenna is composed of the H-plane horn aperture, the matching transition and the graphene-based attenuator. By using external voltage, the surface impedance of graphene sheets can be changed dynamically. Correspondingly, the gain of antenna can be tuned from 3.045dB to −14.12dB at 17.7GHz. So the switchable horn antenna is in working state with the larger gain and no-working state with the lower gain by changing biased voltage.