Reviewerly/exHarmony huggingface dataset:
CORP

The COllaborative Redundant Processing (CORP) algorithm is an approach to prevent unauthorised modification of data in a decent- ralised and distributed computing environment. Built on Ki-Ngā- Kōpuku, a distributed and decentralised security model for Cloud Computing, where redundant nodes are functionally identical, the nodes collectively maintain consistency and integrity of processed data. If a single node is compromised and acts maliciously to modify data, other nodes detect the action. CORP extends the functionality of Ki-Ngā-Kōpuku and is developed mainly for a Cloud Computing context, but the concept can be used in any distributed and decentralised environment to provide consistency, integrity, and availability.
CONSCIOUS USE OF CODE-SWITCHING TO IMPROVE FLUENCY IN SPOKEN ENGLISH OF BANGLADESHI STUDENTS

Purpose: Present-day English language teaching in Bangladesh, despite adopting Communicative Language Teaching (CLT), focuses more on accuracy (i.e. grammar) than fluency which is proved as a weak strategy. Fluency acquisition in speaking includes pronunciation, but focusing on pronunciation first, slows down the process of becoming a fluent speaker.&#x0D; Methodology: As code-switching exists at the tertiary level in Bangladesh and because of the tremendous fascination of Bangladeshi students towards Bengali, code-switching can be utilized as a tool to improve fluency in spoken English. Thus, this research proposes an alternative to existing approaches.&#x0D; Result: Once desired fluency is achieved; grammar and pronunciation will be emphasized respectively to attain proper speaking skills. The success of this process can be studied in three phases. In the first phase, it examines whether allowing code-switching while speaking English helps Bengali students achieve fluency or not.&#x0D; Applications: This research can be used for universities, teachers, and students.&#x0D; Novelty/Originality: In this research, the model of the Conscious Use of Code-Switching to Improve Fluency in Spoken English of Bangladeshi Students is presented in a comprehensive and complete manner.
Marcatori conclusivi del discorso diretto in italiano antico

Abstract In this paper we deal with a specific subset of direct speech markers, to which little or no attention has been given so far: the expressions which codify the ending of the direct speech (“marcatori conclusivi del discorso diretto”). We analyse these markers in Old Italian texts, comparing them with their Latin and, in some cases, Old French equivalents. In the introduction (§1), we take into account various general issues related to ancient texts, namely the practice of spoken-word reading and the lack of systematic punctuation marking that helps text segmentation. After that (§2), we classify the different strategies ancient writers had at their disposal to signal that a direct speech is over, hence that what follows has to be interpreted as the narrator voice; the markers are organized in a range from most explicit to most implicit ( disse &gt; quando ebbe detto &gt; a queste parole &gt; allora &gt; [Ø]). Thereafter (§3), we focus on two specific markers, the participial marker ( detto questo ) and the “connector + finite tense” marker ( quando ebbe detto questo ) in a corpus of nine texts. Though these two markers are roughly synonymic, their occurrence is not uniform among the analysed texts. The explanation of their unequal distribution is that they belong to different discourse traditions ( Diskurstraditionen ): “ quando + finite tense” is a typical expression attested in Romance narrations (the so-called “ quand -Satz”), whereas detto questo appears to be dependent on Latin tradition.
Robust Identification of Dense or Sparse Crowd Based on Classifier Fusion

For a video surveillance system, crowd behavior analysis and crowd managing are important tasks. Along with the event in which crowd participates, its volume and density are also important in managing the crowd. Hence, characterizing the crowd as dense or sparse is an essential component of a crowd handling system. In this context, most of the existing methods try to estimate the headcount. Unlike those, the proposed method exploits the domain-knowledge based low-level features to classify the crowd image as dense or sparse. We present three simple systems working with three different feature sets. These are all free from the burden of background estimation. Experiments are carried on a dataset formed by taking the images from UCF-CC50 and SanghaiTech. Performance of all three feature sets are satisfactory, and Corner-Point based methodology provides the best result.
Management Automation in Multichannel System of Sea Situation Monitoring

The problem of management automation in multichannel system of the sea situation monitoring, in interests of the operator decision-making support is considered.
Creation of Scenarios for Processing Multidimensional Spatial-Temporal Data under Conditions of Uncertainty

The article discusses a method for constructing scenarios in an adaptive analytical information system for classifying objects and their movement trajectories in real time based on processing information from a large number of spatially separated information sources (sensors) and analyzing dynamic arrays of distributed multidimensional data under conditions of uncertainty. Examples of scenarios that use statistical, interval, fuzzy and stochastic methods for describing uncertainty are given. When developing scenarios for adaptive data processing and analysis, integrated ontologies of the “hydroacoustics” field of knowledge, ontologies of signal processing methods, image processing methods and data analysis methods are used. Creation of scenarios for automated processing of multidimensional spatial-temporal data is carried out in the OntoMASTER network software environment based on semantic Web standards. To support scientific research, a dynamic scripting interface and their testing in a simulator environment is implemented. The learning environment based on the knowledge base is intended for training and retraining of personnel. The results of the work can be applied both in scientific research and in the process of teaching students.
Reflexive-Integrative Approach to the future IT-specialists teaching

The article is aimed at substantiating the idea of reasonability of the reflexive-integrative approach using in teaching the future IT-specialists. This approach is studied as the students’ experience forms integration reflected in conceptual thinking, reflexion, reflexive value system, conceptualization style actualization in a process of studying. The research is focused on studying the empirical results of using the reflexive-integrative approach on the basis of students drawing up the ampliative texts including key words of conceptual field, characterizing learning process. Two groups of students (studying information systems and technologies) took part in the research: 1 st group – control group (50 persons); 2 nd group – experimental one (50 persons). It is found out that the experimental group is characterized by a significant reflexivity change (increase in indices) as well as by conceptualization variation in a course of developing and categorizing the list of words, characterizing the “education” notion conceptual field. The article proves that the reflexive-integrative approach using in a learning process improves and enlarges informative capacity of training classes as well as helps to check individuality and uniqueness of each learning process, subject’s experience structure and concept content.
Unique Ultra-Rich Rare Metal Ores of the Tomtor Complex and Problem of Their Genesis

Main genetic models of the formation of ultra-rich rare metal ores of the Tomtor deposit (Yakutia) are compared on the basis of their structural-textural features, mineral composition, and geologic setting: 1) reducing epigenesis of laterite weathering crusts of carbonatites and 2) redeposition of weathering products of carbonatites. It is shown that the unique features of the Tomtor deposit are explained by more complex (compared with other deposits of weathering crusts of carbonatites) evolution, which is expressed in two consecutive stages of supergenesis: laterite weathering and reducing epigenesis and their total ore-concentrating efects. The searching-forecast criteria for ores of the Tomtor type are suggested on the basis of epigenetic model of their formation.
Nature-Inspired Protocols for Cloud Computing and Security Systems

This paper describes new nature-inspired security solutions and protocols, oriented for secure authentication and strategic data management in Cloud Computing. In particular will be presented the ways of using advanced cognitive approaches and personal characteristics for creation of secure management protocols, oriented on user authentication, and efficient data management in Big Data infrastructures. Advanced computing procedures connected with cognitive cryptography will be introduced.
The Classification of Representational Forms

Knowledge access and ease of problem-solving, using technology or not, depends upon our choice of representation. Because of our unique facility with language and pictures, these two descriptions are often used to characterize most representational forms, or their combinations, such as flow charts, tables, trees, graphs, or lists. Such a characterization suggests that language and pictures are the principal underlying cognitive dimensions for representational forms. However, we show that when similarity-based scaling methods (multidimensional scaling, hierarchical clustering, and trajectory mapping) are used to relate user tasks that are supported by different representations, then a new categorization appears, namely, tables, trees, and procedures. This new arrangement of knowledge representations may aid interface designers in choosing an appropriate representation for their users' tasks.
5G-CAGE: A Context and Situational Awareness System for City Public Safety with Video Processing at a Virtualized Ecosystem

In this article we present 5G-CAGE, an ongoing project aimed to deploy a city safety solution that enables monitoring and analytics of video streams collected from distributed sources of a Smart City. Unlike current proposals based on inflexible architectures or limited networks, 5G-CAGE leverages 5G's high throughput and low latency, as well as its enhanced dynamism and adaptability with advanced virtualization-based technologies. In this context, 5G-CAGE defines a virtualization-enabled solution called City Object Detection (CODet), which allows recognizing interest objects in safety related situations, such as vehicles (e.g. license plates or brands), obstacles in emergency settings, or human faces recognition, to name a few. It can process multiple streams collected from fixed and moving cameras used as a distributed visual sensing system, adequately combining image processing and computer vision algorithms in a virtualized ecosystem. This paper presents initial tests in the specific task of locating and recognizing vehicle license plates, where the CODet virtualized solution has been successfully integrated and tested in the 5GINFIRE platform, an EU-funded project which provides a playground wherein new components, architectures, and APIs may be tried and proposed before being ported to 5G networks.
On Authenticity Preservation of Positioning in Rounding Autonomous Guided Vehicle

As an economic and reliable transportation system, rounding autonomous guided vehicle is widely researched and implemented in logistics. However, higher automation implies less involving of manual control, which makes the unattended workshop prone to malicious attacks. Since the operation of rounding autonomous guided vehicle system largely depends on the accuracy of scheduling strategy, authenticity of rounding autonomous guided vehicle position must be preserved to avoid collision, priority breach, steal, etc. In order to ensure the correctness of positioning, a novel location acquisition and authentication scheme is proposed in this paper, with the help of message authentication code and verifiable threshold secret sharing. According to security and performance analysis, our scheme is resistant against chosen plaintext attack and feasible in rounding autonomous guided vehicle environment.
Blur Identification of the Degraded Images Based on Convolutional Neural Network

A method of automatic blur identification for the degraded images based on convolutional neural network is presented. There are significant differences in the Fourier spectra of degraded images with different blur types. According to this, the Fourier spectra of a large number of motion blurred images and defocus blurred images which have different blur parameters are used as the training samples to train the convolutional neural network, so that it can recognize the Fourier spectrum corresponding to the degraded image with motion blur or defocus blur. The experimental results show that the trained convolutional neural network can accurately recognize the degraded images of the two blur types with an accuracy of 99%, which lays the foundation for subsequent image restoration.
Soft Transferring and Progressive Learning for Human Action Recognition

In action recognition, many different network structures of spatiotemporal features extractions has been proposed, and performed well on several mainstream datasets. Inevitably, one question occurs to us: is there any transferable characterizes between different models? In this paper, we discuss such problem by introducing a cross-architecture transferring learning scheme, dubbed soft transferring learning, aiming to overcome the limitation of divergence of different network structures. A multi-stage semi-supervision training procedure is conducted to keep the consistency internally between two different models from bottom to top. To this end, we introduce two kinds of cross-structure metric strategy to compute the mismatch value in different features level, together with entropy classification loss, integrated to be a three-stage supervision method. We additionally design a new network to learn from the supervisors, which have been trained on large-scale datasets. We fine-tune supervision model and train our new model on UCF101 and HMDB51 datasets, experiment results demonstrate the feasibility of soft transferring method, extend transfer learning to a broader sense, and show the flexibility of deploying of existing models. Our method is designed easily generalized to different networks in other computer vision task.
Semantic-Based Linguistic Platform for Big Data Processing

The paper deals with the development of a semantic-based linguistic platform. Special attention is paid to semantic patterns.
Applying a User-Centered Method to Develop 3D Gestural Inputs for In-Vehicle Tasks

Gestures are a natural input method for human communication and may be effective for drivers to interact with in-vehicle infotainment systems (IVIS). Most of the existing work on gesture-based human-computer interaction (HCI) in and outside of the vehicle focus on the distinguishability of computer systems. The purpose of this study was to identify gesture sets that are used for IVIS tasks and to compare task times across the different functions for gesturing and touchscreens. Task times for user-defined gestures were quicker than for a novel touchscreen. There were several functions that resulted in relatively intuitive gesture mappings (e.g., zooming in and zooming out on a map) and others that did not have strong mappings across participants (e.g., decreasing volume and playing the next song). The findings of this study suggest that user-centric gestures can be utilized to interact with IVIS systems instead of touchscreens, and future work should evaluate how to account for variability in intuitive gestures. Understanding the gesture variability among the end users can support the development of an in-vehicle gestural input system that is intuitive for all users.
Light Field Retrieval via Focus Variation

Light field imaging is a new computational imaging method in recent years, which is a representation of full four-dimensional (4D) radiance of all rays with spatial and angular information in free space. However, most current light field imaging systems are limited by the contradiction between spatial resolution and angular resolution, that is, trading spatial resolution for angular resolution. In this paper, we present a full resolution light field microscopy technique based on 3D intensity transport. The advantage of our method is that it does not sacrifice spatial resolution, and the reconstructed light field is of full resolution, comparable to that of the image sensor itself. First, a non-linear iterative reconstruction algorithm is proposed to reconstruct the full resolution light field from the 3D intensity stack which is captured by axial scanning. Then, we investigate the key factors that influence the quality of the reconstructed 4D light field, which is proved to be related to the number of reconstructed viewing angles and the number of captured images of each stack. Finally, experiments are carried out with real 3D samples, and the above methods are tested and evaluated with simple two-layer samples and complex multi-layer samples as Cyclops and Drosophila. The reliability and accuracy of our method are verified by simulation and experimental results.
Multi-view Similarity Learning of Manifold Data

In recent years, multi-view learning methods have developed rapidly where graph-based approaches have achieved good performance. Usually, these learning methods construct information graph for each view or fuse different views into one graph. In this paper, a novel multi-view learning model that learns one similarity matrix for all views named Multi-view Similarity Learning (MSL) is proposed, where adaptive weights are learned for each view. The multi-view similarity learning method is further extended to kernel space. Experiments of classification, clustering and semi-supervised classification on different real-world datasets show the effectiveness of the proposed method.
ReDesigning intercultural exchanges through the use of augmented reality

While the debate on breakthrough technologies has focused on inept, dexterous, and socially transforming technologies such as Artificial Intelligence (AI) assistants and robot dexterity, in second/foreign language learning, particular emphasis is placed on AI, Augmented Reality (AR) and Virtual Reality (VR). This study takes a closer look at the role of three newly developed AR applications in promoting a better understanding of complex concepts such as the Zone of Proximal Development (ZPD), strategies in dealing with disruptive students, and an immigrant’s perspective in moving to a foreign country with no knowledge of the language(s) spoken in that country. The AR applications were developed and implemented during intercultural exchanges among students enrolled in academic institutions in the UK and Cyprus. The aim was to develop AR applications that were geared toward the learning needs of future language teachers and examine what students could achieve through the use of these applications during goal-driven tasks and activities.
Space-Time Graphs Based on Interest Point Tracking for Sign Language

A hand tracking method is presented in this work, which achieves the best results found in the literature for the public RWTH-BOSTON-50 dataset, with a tracking error rate of 8.5%. Its main contribution is the extraction of intrinsic features from RGB sign language movies. In order to avoid some common limitations of model and appearance-based tracking methods, a movement pattern analysis was used as a feature basis. Such feature can be succinctly described as a space-time graph of interest point movement similarity, which is arranged as trees of dense trajectory connections to track hands in RGB sign language movies. In addition to basic geometry operations, simple graph methods are employed in the process, making it effective for parallel processing of large movie datasets.
A Study of Anticipating the Flood Hazard in Agricultural Areas in Ular Watershed by Using Geospatial Approach

Abstract Indonesia is a country which is extremely vulnerable to the natural disasters, one of which is Indonesia is a country vulnerable to various natural disasters such as flood. Flood in a coastal area can cause plant damages and increase of plant diseases. Flood is potential to disrupt the food security because the production of crops decreases due to the damages to agricultural and fishery areas. Therefore, it is necessary to make some efforts to develop the anticipationability to face flood in order to reduce the flood impacts. This research is a descriptive quantitative encompassing the analysis of potentials of flood hazard in the agricultural areas and the implementation of forms of anticipation in the agricultural areas which are vulnerable to flood hazard. The mapping of distribution areas that are vulnerable to flood hazard in Ular watershed by utilizing the technology of Geographic Information System through the overlay method for rainfall, geomorphologic, land use, and flood mappings. According to the research results, The area with high flood vulnerability class is 1,105.56 ha The anticipation efforts which the farmers have carried out are: (1) building some dams and embankments along the borderline of the Ular River, (2) selecting the types of plants and patterns of planting, and (3) preparing some budget to anticipate the damage due to flood. The effort that has been done to anticipate the flood, is pay Agricultural Insurance preimium of Rp. 50.000, - per hectare, where with the sum insured price of Rp.6.000.000, - per hectare, then the cost of compensation to be prepared by the government is Rp. 6,000,000 x 1,105.56 ha = Rp. 6.630.000.000,00 per planting season.
A Multi-resolution Coarse-to-Fine Segmentation Framework with Active Learning in 3D Brain MRI

Precise segmentation of key tissues in medical images is of great significance. Although deep neural networks have achieved promising results in many medical image segmentation tasks, it is still a challenge for volumetric medical image segmentation due to the limited computing resources and annotated datasets. In this paper, we propose a multi-resolution coarse-to-fine segmentation framework to perform accurate segmentation. The proposed framework contains a coarse stage and a fine stage. The coarse stage with low-resolution data provide high semantic cues for the fine stage. Moreover, we embed active learning processes into coarse-to-fine framework for sparse annotation, the proposed multiple query criteria active learning methods can select high-value slices to label. We evaluated the effectiveness of proposed framework on two public brain MRI datasets. Our coarse-to-fine networks outperform other competitive methods under the condition of fully supervised training. In addition, the proposed active learning method only need 30% to 40% slices of one scan to produce relatively better dense prediction results than non-active learning method and one query criteria active learning methods.
How Central European countries fare in speech and language technology research?

Speech and language technologies are parts of AI family of tools and methods that already have an economic impact; they are assumed to extend that impact in the near future. We analyze the contributions of several average and small central European countries to these fields in terms of papers published. We compare these contributions with those of France and Germany and establish relationships between the efficiency measured by scientific publications between subfields of speech and language technologies. We find that Czech Republic is more efficient in terms of papers per inhabitant even than Germany. Czech Republic excels too when the number of papers is divided by the population of tertiary education. Also, we find interesting strong and low correlations between subdomains.