Reviewerly/exHarmony huggingface dataset:
Application of the descriptor approach for clustering entities from education sector

Abstract The article proposes the algorithm to solve objects clustering problem for such subject areas as education and labour market. Such objects are competence, discipline, specialty, vacancy, etc. The main problem in clustering algorithm development proved to be the stage of attributes design since the named objects have descriptions in a natural language. Consequently, a descriptive model for the objects was designed at first. The model was based on the fact that all necessary concepts are characterised in the space of descriptors ”know”, ”can”, etc. This allowed the object to be represented as a tuple based on the object name, descriptors (and their values) and keywords related to descriptors. To obtain such structures, the toolkit of context-relative text mining was used. The ability to represent the entities in question as a formal structure allowed the attribute space formation algorithm to be developed and complex metrics to be constructed to solve the stated clustering problem. The developed algorithm permits development of various services for a faster and more objective decision making process in educational and professional sectors. As a result of the work, about two thousand vacancies were obtained and transformed into descriptor entities. Based on the error matrix, it can be judged that the resulting descriptor entities have been clustered with a sufficient level of quality. This demonstrates the applicability of the model for presentation and analysis for elements from the subject area.
Simultaneous Monocular Visual Odometry and Depth Reconstruction with Scale Recover

In this paper, we propose a deep neural network that can estimate camera poses and reconstruct the full resolution depths of the environment simultaneously using only monocular consecutive images. In contrast to traditional monocular visual odometry methods, which cannot estimate scaled depths, we here demonstrate the recovery of the scale information using a sparse depth image as a supervision signal in the training step. In addition, based on the scaled depth, the relative poses between consecutive images can be estimated using the proposed deep neural network. Another novelty lies in the deployment of view synthesis, which can synthesize a new image of the scene from a different view (camera pose) given an input image. The view synthesis is the core technique used for constructing a loss function for the proposed neural network, which requires the knowledge of the predicted depths and relative poses, such that the proposed method couples the visual odometry and depth prediction together. In this way, both the estimated poses and the predicted depths from the neural network are scaled using the sparse depth image as the supervision signal during training. The experimental results on the KITTI dataset show competitive performance of our method to handle challenging environments.
Pinned quantum Merlin-Arthur: The power of fixing a few qubits in proofs

What could happen if we pinned a single qubit of a system and fixed it in a particular state? First, we show that this can greatly increase the complexity of static questions -- ground state properties of local Hamiltonian problems with restricted types of terms. In particular, we show that the Pinned commuting and Pinned Stoquastic Local Hamiltonian problems are QMA complete. Second, we show that pinning a single qubit via often repeated measurements also results in universal quantum computation already with commuting and stoquastic Hamiltonians. Finally, we discuss variants of the Ground State Connectivity (GSCON) problem in light of pinning, and show that Stoquastic GSCON is QCMA complete. We hence identify a comprehensive picture of the computational power of pinning, reminiscent of the power of the one clean qubit model.
Comparative Study of Different Classification Models on Benchmark Dataset of Handwritten Meitei Mayek Characters

This paper reports a comparative study of seven popular classification models namely decision tree, KNN, Linear Support Vector Classifier, Multilayer Perceptron, Random Forest, Support Vector Machine, and Gaussian Naive Bayes on a benchmark dataset of handwritten Meitei Mayek characters. Three different architectures of Convolutional Neural Network are also studied and results show that CNN model achieves state-of-the-art accuracy of 98.11% on the concerned dataset.
OMG I’m Laughing so Hard – Alienation in Digital Communication and Potential Countermeasures

Abstract Nowadays communication is largely dominated by digital text-based channels which naturally only transfer a small part of the information that is present in face-to-face conversations. In particular, information about the communication partner’s emotional state, which is naturally expressed through facial expressions, body language and other non-verbal indicators, can hardly be transferred. Approaches such as emojis address this issue by allowing the sender to show how he (for reasons of readability, the pronoun “he” addresses all genders equally) feels by selecting an appropriate (smiley) face. However, the crucial difference is that this smiley must be deliberately chosen and does not necessarily represent an authentic expression of the sender’s emotional state. The present paper discusses typical challenges and misunderstandings of communication in the digital era by the example of chat communication. It reflects its ramifications on the perceived authenticity of the transferred emotions and discusses possible (technology-based) approaches towards a more direct, authentic way of communication.
Towards Bridging the Value Gap in DevOps

The DevOps movement, which combines software development with information technology operations, enables the more frequent delivery of changes to a software system. Adopting DevOps practices is seen as enabling the ability to deliver more. But is the more that is getting done actually of value to the end user or to the producing organization? In this paper, we describe how the ideas of value streams are being applied to software development and how more systematic handling of features is key to enabling an increased focus on the delivery of value.
Pedestrian detection method in vehicle video based on AMSSD

Pedestrian detection in vehicle video has high requirements for speed and accuracy of detection. To improve the detection speed, the underlying network of the SSD framework, the VGG-16 network, is replaced with MobileNet. In order to improve the detection accuracy of small targets, a deconvolution layer is added to this framework. With this layer, the feature map of low resolution and high semantic information with the feature map of high resolution and little semantic information is fused to increase the ability to extract the shallow feature. The VOC dataset and COCO dataset are used as the training set, and the Cityscapes dataset is used as the test set to verify the effect of the constructed framework AMSSD. The experimental results show that the proposed method can improve the detection speed and accuracy.
Effort Estimation of Web Based Applications Using ERD, Use Case Point Method and Machine Learning

Effort estimation of a web-based application is treated as the most important process before the development of the application. It is stated by many studies and researches that accurate effort estimation rate increases the success rate for developing the application. There are quite a few methodologies for estimating the effort the web-based application. Earlier the effort estimation process did not include the back-end part or the database part of the application. Since nowadays, the software are more data-centric and thus database has become such a crucial part of the software product, therefore to get more accurate estimations, the back-end part of web based application has to be taken into consideration. This paper deals with finding more accurate effort prediction of a web application with the inclusion of the back-end part of application using different techniques. In this paper, the proposed effort estimation method using the backend has been discussed and benchmarked against the current models including ML models for effort estimation.
Rethinking Autonomy of Humans and Robots

In the field of artificial intelligence, autonomy is defined as an expected capacity for systems to behave without human control, and there are currently many items with such capabilities. However,...
Reconstructing HDR Image from a Single Filtered LDR Image Base on a Deep HDR Merger Network

In this paper, a novel Deep HDR Merger network, which is called MergeNet, is proposed to reconstruct a HDR image from a single filtered LDR image. Filtered images are adopted as input since they contain more dynamic range than traditional ones. By learning the correlation between filtered LDR images and HDR images, the MergeNet successfully achieves HDR reconstruction of filtered images. We used five evaluation methods to make qualitative and quantitative comparisons to show that our method produced excellent results. Experimental results show that the proposed method performs favorably against state-of-the-art HDR image reconstruction methods.
Effectiveness Of Learning Media Using Contextual Based Macromedia Flash for Junior School Students Al Hikmah Medan

Abstract Macromedia Flash is a software for interactive learning media so that it can improve student learning achievement. 21st-century learning is the result of the 4.0 industrial revolution in the world of education, it is hoped that students can actively participate in learning. This is discussed with the 2013 curriculum, so students become the center of learning and learning can run effectively. The objectives of this study are: (1) to learn the 75% completeness of student learning outcomes with KKM 70; (2) for learning Improvement of student learning outcomes; (3) to learn the timeliness in learning. The study population denied 280 people consisted of 8 classes, while the class VIII-5 sample released 35 people. This research is a quasi-experimental study. Testing the research hypothesis using a one-party test and the cumulative frequency, paired t-test. The results showed (1) completeness of student learning outcomes classically that is 80%, indicating mastery achievement has been achieved; (2) the value of sig &lt;α, then the student’s posttest learning outcomes are better than the pretest; (3) appropriate use of time in accordance with the planned use.
Image Tampering Location and Restoration Watermarking Based on Blockchain Technology

With the development of multimedia technology and network communication, digital watermarking technology has become one of the research hotspots in the field of signal processing and information security in recent years. In this paper, an effective fragile block watermarking method for image tampering localization is proposed. Compared with the existing algorithms, it has better localization effect. With the idea of blockchain, the algorithm first forms a linear cyclic chain from all blocks in the image on the basis of the key, which is called blockchain. Then the watermarking of the image block is hidden in the subsequent blocks, which ensures the content of the image block and the security of the embedded watermarking. Theoretical analysis and experimental results show that this method can resist VQ and collage attacks well, and has high positioning accuracy.
A hierarchical insurance recommendation framework using GraphOLAM approach

Graph has been widely used for modeling complex relationship datasets in different application fields. Social networks based recommendation system have obtained satisfactory results in Business Intelligence(BI). However, current personalized recommendation methods based on graph structure generally lack interactivity and seldom consider efficient data management. To address these problems, Graph OnLine Analytical Mining (GraphOLAM) is a promising method, which combines OLAP technology with social networks. We first propose an efficient recommendation framework based on GraphOLAM data cube technology for the recommendation in the insurance service. Based on this framework, a new algorithm framework named RU-GOLAM for insurance is proposed, which combines GraphOLAM dimensional aggregation operation and specific recommendation methods. A series of graphs can be generated by GraphOLAM dimensional aggregation operations, which reflect the relationships of nodes under the constraints of different hierarchical dimensions. Node similarities are calculated to generate the Top-N sequential recommendation based on all of these graphs, which can achieve the balance between the topology of the original graph and high-dimensional information of the nodes. Experiments show that our approach outperforms other baseline algorithms on an insurance service dataset.
A Precise Fingertip Tracking Algorithm Based on 3D Motion Mapping for HoloLens

Fingertip's motion is an important input channel of Human-Computer-Interaction in Augmented Reality, but HoloLens' depth-image is not accessible nowadays. In this paper, a precise and fast fingertip tracking algorithm is proposed. Firstly, we get 2D fingertip's position from monocular camera video stream by segmentation method. Then, we setup a spherical coordinate system according to the hierarchical finger-hand-arm's motion model to project the fingertip's motion in 2D image to 3D space. Meanwhile, a composite Kalman motion model is designed for 3D tracking prediction and smoothing. With the 3D Kalman filter's help, high-precise fingertip motion is projected back to the 2D space, to promote the fingertip detection of the next state. The fingertip detection is performed in a small neighbourhood of the predicted position. Experiments show that the accuracy and performance are both promoted than traditional fingertip detection method. The algorithm is applied in HoloLens AR gesture interaction system and can provide accurate and fast gesture tracking.
Author Profiling: Predicting Gender from Document

Mamgain, Sunakshi Balabantaray, Rakesh C. Das, Ajit K. Kumar, SrikantAs the Internet is aging, a massive amount of data is being created on the web, out of which mostly is text. Therefore, authorship of the contents and prediction of characteristics of the author is becoming a new domain of data analytics making Author Profiling a research area with huge scope of possibilities and outcomes. The ability to describe the features or traits of an author has a key application in many security and forensic areas. The PAN labs provide a platform for scholars by organizing author profiling tasks, for example, language, gender prediction, etc. In this paper, we are attempting to predict gender of a particular author, for which we have considered English dataset of PAN 2017.
Evaluation of an Automatic Separation Algorithm for Unmanned Aircraft Systems

This paper presents results gained within the MoNIfly project, an EU funded research project to investigate a low-level unmanned traffic management system. With the use of the mobile network infrastructure, MoNIfly is contributing to a safe and socially acceptable integration of remotely piloted aircraft systems (RPAS) into existing airspace and urban areas. This requires RPAS to respect privacy and safety of the general public and all airspace users. MoNIfly proposes to equip all RPAS with a communications module, enabling to transmit data via the mobile network infrastructure. With the positional data gained that way, the proposed centralized UTM Management Software can issue warnings and evasive manoeuvres. In this paper, an algorithm for automatic cooperative separation among remotely piloted aircraft system and other air traffic is presented. This algorithm will make use of the existing infrastructure as well as vehicle communications developed in this project. The algorithm is based on the concept of modified velocity obstacles. In this work, the algorithm is introduced and evaluated in both a two-dimensional and a three-dimensional version, based on simulations of RPAS in different traffic densities. The results of this work will be a basis for optimizations and modifications towards a prototype development. Since the proposed algorithms will be demonstrated in field tests within the scope of the MoNIfly project, this paper also presents challenges and requirements for the transfer and the implementation of these methods for real flight tests.
A Salient Object Detection Model Based on Local-region Contrast for Night Security and Assurance

Nowadays, night security and assurance are necessary and monitoring systems have been set up all over China. However, it is difficult to analyze the monitoring area because of the dark night light. In order to further maintain peace and ensure the safety and security of our study focused on the design of a more effective and robust approach to identify the salient area in a nighttime image. Therefore, this paper proposed an optimized salient object detection (SOD) model for night security and assurance. Firstly, enhance the image, then simple linear iterative clustering optimization (SLICO) method was used to divide the image into superpixel regions. Constructed the covariance matrix (CM) of the image based on various features, local saliency was calculated by non-linear fusion of these salient information. After this, integrated local saliency with the saliency of color space (CS) contrast. Finally, the regularization of the image was utilized to refine the salient graph. Experiments show that our method outperforms state of the art in the popular evaluation metrics.
RZcash: A Privacy Protection Scheme for the Account-based Blockchain

In recent years, the Ethereum platform has rapidly emerged, but its privacy issue has become the largest obstacle to the implementation of Ethereum projects. Unlike UTXO-based Bitcoin, Ethereum, the account-based blockchain, requires realtime updates of the account balance. Therefore, its corresponding hidden operation has certain difficulties. We propose a privacy protection scheme called RZcash to solve this problem. It implements the hiding of account balances that are updated in real time, and uses rangeproofs to ensure that the amount of each transaction does not exceed the payer's existing balance. For hiding the payee, we improve the related technology of Monero by using the ciphertext equivalent commitment scheme. We also provide the conversion mechanism and trading method between hidden coins and open coins and describe how RZcash can be combined with an existing account-based blockchain system (such as Ethereum). Finally, we implement the core model of RZcash and evaluate its performance. The evaluation results show that the scheme not only guarantees the privacy and security of the blockchain, but also has good performance in practical applications.
Concept System Formalization: Ontology Models in Organic Chemistry Intelligent System Design and Development

Due to organic synthesis achievements, the amount of data and knowledge in organic chemistry grows exponentially. Despite the existence of diverse software and information products, there is a need for high-quality intelligent systems that can facilitate the decision-making process in the tasks scientists solve every day. The quality of intelligent system knowledge base defines the overall intelligent system quality. In case of the ontological approach to intelligent system design, the expert forms an application area knowledge base in reliance on the concept system and the structure that the application area ontology model forms. This is due to the semantic constraints of the problem input data. It is impossible to define the input data precisely; however, it is possible to denote a larger set the input data belong to. The article is devoted to the application of the ontological approach for the design and development of the intelligent system, the major task of which is organic compound reaction capacity identification. The use of this approach is driven by the intelligent system requirement analysis. The foreground requirements that involve alternating intelligent system knowledge and metaknowledge and extending intelligent system functionality (by means of adding problem-solvers for new classes of tasks) can be satisfied if the intelligent system architecture considers key features of the knowledge-intensive application domain and is based on its ontology model.
Efficient Delivery of Very High Dynamic Range Compressed Imagery by Dynamic-Range-of-Interest

JPEG 2000 allows scenes to be encoded in a highly scalable and accessible manner, so that only the content that is relevant to a region or resolution of interest need be transmitted and decoded. This paper extends this property to allow efficient access into compressed images that have a very high dynamic range, based on a dynamic-range-of-interest. Optimized re-prioritization of the encoded content is used to stream imagery based on a potentially dynamic set of viewing conditions that implicitly identify the visual significance of content. We propose and validate a framework for doing this, based on a single compressed representation, a sparse set of display-independent luminance statistics and a novel algorithm for inferring the display-dependent significance of code-block distortions.
Management of Big Textual Data in Qualitative Research

In structured indexes, classification systems, thesauri, conceptual structures or semantic networks, relationships are too often vague. For instance, in terminology, the relationships between concepts are often reduced to the distinction established by standard between hierarchical relationships (genus-species relationships and part/whole relationships) and non-hierarchical relationships ("time, space, causal relationships, etc."). The semantics of relationships are vague because the principal users of these relationships are industrial actors (translators of technical handbooks, terminologists, data-processing specialists, etc.). Nevertheless, the consistency of the models built must always be guaranteed... One possible approach to this problem consists in organizing the relationships in a typology based on logical properties. For instance, we typically use only the general relation "Is-a". It is too vague. We assume that general relation "Is-a" is characterized by asymmetry. This asymmetry is specified in: (1) the belonging of one individualizable entity to a distributive class, (2) Inclusion among distributive classes and (3) relation part of (or "composition"). With the view to better designing the knowledge structures underlying the concepts of a field, and more specifically, the indexing of documents and/or information retrieval, we propose a structured set of relationships, based on a linguistic model, the Applicative and Cognitive Grammar (ACG) of Jean-Pierre Descles [7], [8]. This model was extended to terminology by Christophe Jouis [32], and applied by Widad Mustafa and Christophe Jouis, [33], [34], [35] and [36].
Multi-task Digital Verification Code Recognition Based on Improved ALexNet

Verification code is a human-machine test method that is widely used in the Internet. It can essentially distinguish between human and network robots. However, with the development of technology, more and more problems has been found in the verification code. This paper takes the digital verification codes commonly used in university student management system as the research object. The verification code after scaling and graying is used as the data set. The eight-layer ALexNet convolutional neural network is constructed by using the Google deep learning framework TensorFlow to train the verification code. The data set is tested to obtain a network model that can identify different complexity digital verification codes. The recognition rate of digital verification codes commonly used in college student management systems can reach 99%.