Reviewerly/exHarmony huggingface dataset:
DETECTION OF VERTICAL TANKS IN MULTISPECTRAL SATELLITE DATA ON ARCTIC TERRITORIES

The most complete information on the state of different man-made objects, collected with on-site and remote sensing methods, is required to ensure environmental and technospheric safety at the regional scale. The procedures of oil tank detection traditionally utilize high-cost, ultra-high-resolution images. The research is devoted to studying the possibility of using high and mediumresolution data from Landsat‑8 and Sentinel‑2 sensors to handle the task. Operating of the parts of the detection algorithms used in practice was analyzed, and some of them were selected as workable options which lead to satisfactory results being applied to data from mentioned instruments. A new technique of tank identifying which consists of classification, blob detection and filtering steps was developed. Testing of proposed solutions on data on the territory of Chaunsky District of Chukotka Autonomous Okrug showed the possibility of their use for detecting objects of the considered category.
Development of Instructional Model App Design for User Experience

This study aims to develop an instructional model App design for effective user experience in self-learning acquisition of origami. In order to develop effective app design, this study conducted a pilot study to compare three formats of instructional models: printed, time-based video, and App instruction. To make the comparison, the study evaluated user tasks in three main areas: (1) time required to complete the given task, (2) number and kinds of errors, and (3) frequency of misunderstanding the instructional information. With an analysis of the result of the usability and exit interview, the findings suggest that users completed the task more effectively in the video and App instructions compared to the printed instruction. However, participants using the App instruction appeared more focused on the performance of each task and worked without interruption. According to both positive responses from the video and App instructions, this study proposes a conceptual framework for the new App design which provides an interactive user interface between static (graphic illustration) and kinetic (video simulation). The App also provides an effective learning acquisition through the online community where users can share their works and instruction model with other online users.
Research on Key Technologies of network GIS cluster based on server virtualization

The integration of geographic information system (GIS) and Internet has greatly expanded the scope of application of GIS, but also made the application of geospatial information wider, so that different regions and different industries can easily and quickly share information, so that geographic information can be more integrated into public life. With the rapid development of GIS network, the information transportation has been constantly updated. The use of virtual server can improve the accuracy and speed of GIS information transmission. This paper analyzes the key technology and development direction.
Game AI hyperparameter tuning in rinascimento

Hyperparameter tuning is an important mixed-integer optimisation problem, especially in the context of real-world applications such as games. In this paper, we propose a function suite around hyperparameter optimisation of game AI based on the card game Splendor and using the Rinascimento framework. We propose several different functions and demonstrate their complexity and diversity. We further suggest various possible extensions of the proposed suite.
HMM-based TTS System Framework

The research focuses on the use of Hidden Markov Model (HMM) to build Khmer text-to-speech (TTS) system. Although the system is based on HMM statistic model, language specific functions were newly designed and developed to cope with the orthographical and grammatical nature of Khmer, some of which included word segmentation, grapheme to phoneme conversion, definitions of full context labels and question sets. In total four-thousand phonemically-balanced Khmer sentences were read aloud by an adult male speaker of Khmer, which were in turn served for training a model for Khmer TTS. The system has been incorporated into VoiceTra, a multilingual speech-to-speech translation app that has been developed and maintained by NICT. The app is publicly released for mobile devices and available to download in both App store and Google Play store.
Atmospheric Light Estimation Using Gradient Information

In this paper, an effective method of using gradient information to estimate atmospheric light is proposed, which contributes to handle the problem of low contrast and detail degradation in outdoor images taken under bad weather. This method based on the observation that the visibility of the region with dense fog in the image is low, which lead to small gradient value. We detect the dense foggy area of the image by cutting the expanding high gradient region in the corresponding place of the input image. Then, the global atmospheric light can be estimated directly. Experimental results on a variety of hazy images demonstrate the efficiency of the proposed method.
Perspectives on Identification Systems

Identification systems such as biometric identification systems have been becoming ubiquitous. Fundamental bounds on the performance of the systems have been established in literature. In this thes ...
Augmented Reality for Troubleshooting Measurement Devices

Augmented Reality (AR) is a rapidly emerging technology used to visualize virtual data superimposed on the real world. It improves the way humans visualize data. Measurement devices form the base of automation pyramid. It is essential that these devices operate accurately and reliably for safe operation of the plant. Troubleshooting these devices can be an arduous task. To this end, finding relevant information in a timely fashion from service manuals can be difficult. Thus, there is a strong need to find alternate approaches to facilitate troubleshooting these devices. This paper presents an approach involving use of AR technology to improve the accuracy and efficiency of troubleshooting measurement device.
Time Series Data Mining in Cloud Model

For attaining spatial time-series data in past one decade many of the attempts have been implemented on data sets to perform various processes for mining and classifying prediction rules. A novel approach is proposed in this paper for mining time-series data on cloud model we used wallmart data set. This process is performed over numerical characteristic oriented datasets. The process includes theory of cloud model with expectation, entropy and hyper-entropy characteristics. Then data is attained using backward cloud model by implementing on Libvirt. Using curve fitting process numerical characteristics are predicted. The proposed model is considerably feasible and is applicable in performing forecasting over cloud.
Learning Error Patterns from Diagnosis Trouble Codes

Diagnostic trouble codes (DTCs) are steadily produced by a vehicle's control units to support the diagnosis process when the vehicle is maintained or to initiate predictive maintenance. Although, DTCs carry a lot of information, possibly including environmental data such as the engine temperature, the velocity, etc., they are of little help to an automotive engineer if seen without a context. In fact, a concrete problem can mostly be diagnosed if an already known pattern of DTCs is present. However, detecting new patterns in masses of vehicle data gathered each day from thousands of vehicles and recognizing known patterns accurately cannot be performed manually by automotive engineers. We propose an unsupervised DTC pattern learning framework supporting the daily field data analysis of original equipment manufacturers (OEMs).
On adaptive specialisation in genetic improvement

Genetic improvement uses automated search to find improved versions of existing software. Software can either be evolved with general-purpose intentions or with a focus on a specific application (e.g., to improve it's efficiency for a particular class of problems). Unfortunately, software specialisation to each problem application is generally performed independently, fragmenting and slowing down an already very time-consuming search process. We propose to incorporate specialisation as an online mechanism of the general search process, in an attempt to automatically devise application classes, by benefiting from past execution history.
Bio-Inspired EOG Generation from Video Camera: Application to Driver’s Awareness Monitoring

Electrooculogram (EOG), while being a source of useful information. Many researches and applications such as Human computer interaction (HCI) and physiological state monitoring rely match more on it then other resources like video surveillance. In our work we’re interested in awareness level classification for driver drowsiness detection, in the purpose of improving road security. However, EOG acquisition requires placing electrodes around eye’s subject all the time which is not comfortable and convenient for many applications. Therefore, we propose a generation of a Bio-inspired EOG signal (pseudo EOG) using video camera. As for EOG signal, the bio-inspired one contains useful information of eye state and movements in time. Critical features were extracted or calculated from the bio-inspired EOG signal and used as inputs of a fuzzy logic classification of the awareness level in time.
Some Topological Indices of Polar Grid Graph

For over 100 years, chemists, chemo-informatics and biochemists have explored the relationship between chemical structure and biological activity of a molecule using statistical tools (QSPR and QSAR studies, respectively) and have tried to predict them, as well as than other measurable properties. However, the use of graph theory and topological indices has become indispensable. In this case, this exploration can be done in several ways. In this article, we will deploy some bases of so-called molecular indices in order to optimize their calculation.
A New Quantum Private Query Protocol with Better Performance in Resisting Joint-Measurement Attack

Quantum private query (QPQ) as a kind of protocol with strong practicability, its research depth is deepening. However, joint measurement (JM) attack poses a threat to the security of databases in protocol. Specifically, a malicious user can illegally obtain entries more than the average number of honest users from the database. Taking Jakobi et al.’s protocol as an example, a malicious user can obtain up to 500 bits from a database of 104 bits in one query instead of the expected 2.44 bits. In order to prevent JM attack, we design a new quantum private query protocol which has the similar procedure of raw oblivious key generation with Wei Chunyan et al.’s and Jakobi et al.’s. In our protocol, we add a step that Alice has to send back the measured qubits after some operation which ensures she must measure honestly. Therefore, our protocol can protect database security in theoretically, and the protocol can also improve the communication transmission distance because the photons Alice returns to Bob is re-prepared by her. Moreover, our protocol keeps the good peculiarities of QKD-based QPQs, e.g., its loss tolerant and robust against quantum memory attack.
Performance of Common Classifiers on node2vec Network Representations

In this paper we evaluate the performance of different multi-class classifiers on network graphs. Since the node embedding techniques have been widely used to represent and analyze networks structures, we decide to transform network data (nodes and links) into attributes which are descriptive and contain correct information of its structure. For this purpose, we use a state-of-the-art algorithmic framework node2vec, which has been shown to outperform other popular methods when applied to multilabel classification as it manages to efficiently learn a mapping of nodes to a low-dimensional space of features. Applying this framework, we generate a set of representations for nodes of multiple network data sets. Using the generated representations, we evaluate the performance of common classifiers. We perform crossvalidation and parameter tuning to get the best possible model of each classifier type. To compare their performance, we computed Precision, Recall and F1-score for each model on each data set. Following that, the obtained results are analyzed and compared.
Fast 3D Body Measurement Based on Multi-directional Point Cloud Piecing

Abstract In order to realize fast, accurate and device-lightweight 3D body measurement, this paper proposes a quick measurement scheme which obtains measurement results by combining multi-directional human point cloud data. In this paper, the depth camera captures the point cloud data in front, side and rear of the human body, and combine the point cloud data with the skeleton point data provided by the depth camera somatosensory function. Taking the measurement of waist circumference as an example, the method of extracting the point cloud data of the waist is described. The curve equation of the waist curve is obtained by polynomial curve fitting with the discrete points. Finally, the length of the waist curve is calculated by the integral method. The measurement results are satisfactory.
Text Modeling Based on the Analysis of Categorical Words

Abstract Recently the most essential research in machine learning is to discover correct topics from large scale of electronic documents, and many studies about text modeling have been made in natural language process with the environment of big data. This paper discusses a fundamental way for people to form words while talking and describing ideas, the document is considered as words convergence and a new method for text modeling based on the relational analysis about verbs, nouns, and modifiers is proposed. Additionally the paper introduces a new algorithm for estimating parameters in the generative probabilities of the model, which can alleviate the process about statistics-based text analysis from computational complexity. Experimental results show that the method improves the quality for learning a document.
An analysis of control parameters of copula-based EDA algorithm with model migration

The copula-based EDA algorithms nowadays represent a promising technique for problem optimization in the continuous domain. This paper provides a detailed analysis on how six key parameters of the parallel copula-based EDA with model migration (mCEDA) influence the quality of optimization. In order to improve the performance of that kind of algorithm the most suitable setting of these control parameters is evaluated on the well known CEC 2013 benchmark using inferential statistics.
Work-in-Progress: Silent Speech Recognition Interface for the Differently Abled

Silent speech or unvoiced speech can be interpreted by lip reading, which is difficult, or by using EMG (Electromyography) electrodes to convert the facial muscle movements into distinct signals. These signals are processed in MATLAB and matched to a predefined word by using Dynamic Time Warping algorithm. The identified word is then converted to speech and can be used to control a nearby device such as a motorized wheelchair. Thus, a silent speech interface has the potential to enable a differently-abled person to communicate and interact with objects in their surroundings to ease their lives.
Hand range of motion evaluation for Rheumatoid Arthritis patients

We introduce a framework for dynamic evaluation of the fingers movements: flexion, extension, abduction and adduction. This framework estimates angle measurements from joints computed by a hand pose estimation algorithm using a depth sensor (Realsense SR300). Given depth maps as input, our framework uses Pose-REN [1], which is a state-of-art hand pose estimation method that estimates 3D hand joint positions using a deep convolutional neural network. The pose estimation algorithm runs in real-time, allowing users to visualise 3D skeleton tracking results at the same time as the depth images are acquired. Once 3D joint poses are obtained, our framework estimates a plane containing the wrist and MCP joints and measures flexion/extension and abduction/adduction angles by applying computational geometry operations with respect to this plane. We analysed flexion and abduction movement patterns using real data, extracting the movement trajectories. Our preliminary results show this method allows an automatic discrimination of hands with Rheumatoid Arthritis (RA) and healthy patients. The angle between joints can be used as an indicative of current movement capabilities and function. Although the measurements can be noisy and less accurate than those obtained statically through goniometry, the acquisition is much easier, non-invasive and patient-friendly, which shows the potential of our approach. The system can be used with and without orthosis. Our framework allows the acquisition of measurements with minimal intervention and significantly reduces the evaluation time.
Discovery of robust protocols for secure quantum cryptography

Quantum Key Distribution (QKD) allows for two parties to establish a shared secret key which is secure against an all-powerful adversary (a task impossible to achieve using only classical communication). Furthermore, any attack against these systems creates an observable "noise signature." This work develops a new solution representation for QKD protocols allowing a GA to evolve optimal protocols to counter observed attacks against the communication.
Beyond counting: point supervised attention guided neural network for crowded object locating

Recent advances in convolutional neural networks have achieved prominent results for object counting or locating in images. In these networks, some adopt detection-based methods which require providing bounding-box annotations for training and these methods usually cannot deal with the crowded cases well. Other networks adopt density regression-based methods which require only point annotations for training but these methods can only predict the total count without providing any location information. In this Letter, the authors propose an attention guided convolutional neural network (AGNet) for both crowded object locating and counting using point annotation as supervision without annotated bounding boxes which are far more time-consuming to label. Accordingly, they define a multi-task objective loss function to lead the training of AGNet to achieve the goal of crowded object locating. The experimental results on four challenging benchmark datasets for crowded objects counting demonstrate the superiority of the proposed AGNet over recent state-of-the-art approaches.
Automatic Facial Expression Recognition Using Geometrical Features

Every person expresses himself or herself with different facial expressions. Nowadays, recognition of facial expression is a popular topic for researchers due to the practical function in a wide variety of areas. The learning of human facial expressions is a tough and tricky domain in pattern research society. Each individual facial expression is produced by non-rigid object contortions, and these contortions are man-dependent. The main motto of this paper was to invent and apply a structure for the automatic recognition of human facial expression.
Purchase and Its Sign Analysis from Customer Behaviors Using Deep Convolutional Neural Networks

In this paper, we predict purchase from access log data and consider customer behavior about purchase sign on E-commerce site. In addition, applying Convolutional neural networks to this study, we discuss probability of purchase or not purchase and data preprocessing. By extracting hidden layer of model, we consider customer behaviors about purchase and not purchase. Furthermore, we discuss the way of transforming no image datasets to image-like data. We think about probability of using its networks for no images data through this study.
PRO2S - System for the Protection and Monitoring of the Elderly and Mentally I11 in Outdoor

Monitoring of the elderly or people with a particular type of dementia, such as Alzheimer's, is of great importance, especially when some functional capacities, such as reasoning, orientation, among others, are beginning to be compromised. The present work presents a system model that will allow the real time monitoring of elderly people or who suffer from some neurodegenerative pathology or mental deficiency, more likely to suffer occasional outbreaks of memory loss that may compromise their safety. Real-time monitoring includes vital signs monitoring equipment and the approximate location within and shape of the facilities, which will allow monitoring technicians at Private Social Solidarity Institutions (IPSS) to verify in real time the physical condition of their users. This system intends to make a significant leap in the use of ubiquitous and pervasive, nonintrusive computing, in the support of entities related to the Social Protection sector. In this way, the monitoring and followup of the users will be carried out by a specialized technician who can at any moment initiate a process of search and rescue of some users who may be in danger, that leaves the protection zone confined to IPSS, that loses vital signs, or is facing any other adverse event that could compromise their safety and, in the limit, their own life.
Computational Evolutionary Art: Artificial Life and Effective Complexity

On the field of Evolutionary Computational Art, artists frequently adopt a top-down process of creation, employing the algorithms only as a mean to express a previously conceived composition. In this sense, the present paper aims to discuss the use of Genetic Algorithms for the development of systems with greater level of emergence, running towards the increase of its effective complexity, understood as suggested by Gell-Mann. In this context it is presented the system Morphogenesis. It was developed as a Multi-Agent Adaptive System, built with Genetic Algorithms to generate movement, feeding, fighting and reproductive behaviors. All these behaviors are programed at the individual level, from which emerge the macro patterns of the groups, simulating the evolutionary process. The system analysis suggests that the fitness function should not be focused at the arrangements of the agents’ genotype, but at the adaptation of the phenotype itself. It is expected that the use of algorithms that allow expressions closer to the evolutionary process has a greater affinity with the aesthetic notion proposed for the field of Evolutionary Computational Art. Hereupon, a qualitative exploratory study was conducted to compare the perception of the high effective complexity arrangements against random arrangements. Preliminary results show that the evolutionary process could be associated with a greater evaluation of intentionality of the compositions and could be also related with a deeper aesthetic evaluation.
Comparison of Graduate Specialist Study of Computing and Specialist Study of Information Security and Digital Forensics at the Zagreb University of Applied Sciences

The aim of this article is to demonstrate the purposefulness of the existence of two distinct but very related studies in the field of computing at the Zagreb University of Applied Sciences, given the similarities and differences, and the satisfaction of students with the study and the contents of the subjects and the manner of the knowledge transfer. The graduate specialist Study in Computing and specialist Study of Information Security and Digital Forensics are compared, at the level of the first two semesters. The comparison is done by using qualitative analysis and comparing the contents of the subjects and related outcomes, and the quantitative analysis of the student survey at the end and the beginning of professional subjects.