Reviewerly/exHarmony huggingface dataset:
Adversarial data-selection based work-hours estimation method on a small dataset in a logistics center

One of the metrics to evaluate the operation quality of a logistics center is work-hours per day; however, the total amount of data is too small for estimation of work-hours even if its accumulation period is several years. In this study, an “adversarial data selection method” leveraging a small dataset is proposed. The proposed method competitively adapts a classifier that classifies the magnitude relationship of work-hours, i.e., a decision boundary of the classifier which cannot distinguish the magnitude of a paired dataset is detected. The experiment of work-hours estimation is conducted with small data which numbers are varied from 100 to 700 on a discrete event simulator. Results such as mean absolute error show the proposed method achieves lower errors than that of conventional methods (SVR, Lasso, k-NN, Random Forest, Gaussian Process, and LightGBM), and it is efficient on the condition of the small number of a dataset.
Fault aware hybrid harmony search technique for optimal resource allocation in cloud

The evolution of cloud computing is increasing exponentially which provides everything as a service. Clouds made it possible to move a huge amount of data over the networks on-demand. It removed the physical necessity of resources as resources are available virtually over the networks. Emerge of new technologies improvising the cloud system and trying to overcome cloud computing challenges like resource optimization, securities etc. Proper utilization of resources is still a primary target for the cloud system as it will increase the cost and time efficiency. Cloud is a pay-per-uses basis model which needs to perform in a flexible manner with the increase and decrease in demand on every level. In general, cloud is assumed to be non-faulty but faulty is a part of any system. This article focuses on the hybridization of Neural networks with the harmony Search Algorithm (HSA). The hybrid approach achieves a better optimal solution in a feasible time duration in the faulty environment to improve the task failure and improve reliability. The harmony Search approach is inspired from the music improvisation technique, where notes are adjusted until perfect harmony is matched. HS (Harmony search) is chosen, as it is capable to provide an optimal solution in a feasible time, even for complex optimization problems. An ANN-HS model is introduced to achieve optimal resource allocation. The presented model is inspired by Harmony Search and ANN. The proposed model considers multi-objective criteria. The performance criteria include execution time, task failure count and power consumption(Kwh).
Control and cable deployment of a tethered PVTOL aircraft

This paper deals with the development of a strategy to stabilize a Planar Vertical TakeOff and Landing Aerial Vehicle powered by a tether system. The tether system consists of a ground station which provides power through a suspended cable to the aerial vehicle. Giving a position reference, the ground station computes the optimal cable length reference, which minimizes the tension force acting on the vehicle due to the cable weight. The optimal cable length is commanded by an automatic winch that retracts/releases the cable forming a catenary curve shape between the ground station and the aerial vehicle. This automatic procedure allows to reduce the disturbances in the vehicle since over-tensions in the cable are avoided. We used a state feedback controller with a tension compensator to control the vehicle altitude as well as its horizontal position, while the vehicle attitude and the winch dynamics are controlled by local state feedback controllers. Finally, in order to demonstrate the effectiveness of the proposed strategy we conducted simulation tests.
Crowd Counting Using Region Convolutional Neural Networks

Monitoring the number of people is essential to estimate the level of crowds in a public area, especially during this Covid19 pandemic. CCTV recording needs to process for counting the number of people in a crowd at a specific time. However, counting people on CCTV is not easy. It can be approached by detecting a specific object from a compilation of frames with a certain size that makes up the image. This study proposed the Faster Region-Convolutional Neural Networks (Faster R-CNN) method with ResNet50 to count the number of people in a crowd from the low-resolution image from CCTV. The research gave that crowd counting with the Faster RCNN needs consideration to choose appropriate architecture. ResNet50 architecture provided an accuracy of 97.20% in detecting the number of people in the crowd image. It was compared to other detectors based on previous studies with the same dataset and gave the highest accuracy. Region Proposal Networks makes Faster RCNN robust. Although the various number of people in the crowd image, quality of the dataset, and anchor aspect ratio values provide good results improve accuracy. Besides, the appropriate learning parameters make the method performance more optimal. This configuration can be applied to real-time testing so that it gave the best results of 86% using Faster RCNN and ResNet50.
Polynomial Description for Nonlinear Time-Varying Systems

In this paper, a structure called as Polynomial Time-Varying Fuzzy Model (PTVFM) for representing nonlinear systems with time-varying parameters is proposed by using polynomial descriptions. The descriptions as Takagi-Sugeno (T-S) fuzzy model, polynomial fuzzy model and Linear Parameter Varying (LPV) model are respectively applied for nonlinearity, polynomial structure and time-varying parameters. Comparing with the existing works, only few fuzzy rules are required by the proposed structure for approximating the nonlinear systems. Based on the proposed model, only few fuzzy rules are required to describe the systems that reduces the difficulty and complexity of stability analysis. Finally, the comparisons between each model are presented for the approximation accuracy with mean squared error.
Application study of UAV path planning based on the balanced search factor artificial bee colony algorithm

Abstract In this paper, aiming at the shortcomings of slow convergence speed and weak local search ability of traditional artificial bee colony algorithm in path planning, an artificial bee colony algorithm based on balanced search factor is proposed for UAV path planning. Using a search strategy based on balanced search factor, the depth search is carried out while maintaining a certain population diversity. The global search ability and local development ability are balanced, the average accuracy of path planning is improved, the robustness of path planning is enhanced, and the ability to obtain better path solutions is improved.
Mobile APP Development for Recording Car Incidents in Public Transport Companies Promoting SmartCity Models

The aim of this study was to propose the development of a mobile application that helps prevent traffic incidents, in addition to establishing a Smartcity initiative. The study starts from the WHO, which mentions that traffic accidents represent a public health problem worldwide. This project proposes the development of a mobile application by flutter allowing to record incidents in an agile way. The purpose is group all incidents or actions of public vehicles according to the transport company belonging in a certain region or city. This seeks to establish a SmartCity initiative that contributes to improvements in the accident rate and the city's road safety system. A survey of 32 people and quality plan for the functionality of the mobile app was conducted. It is concluded that the mobile app will help with the problem of transport incidents.
Formal Specification of Non-Functional Properties of Context-Aware Systems

During the recent years, there is an increasing demand for software systems that dynamically adapt their behavior at run-time in response to changes in user preferences, execution environment, and system requirements, being thus context-aware. Authors are referring here to requirements related to both functional and non-functional aspects of system behavior since changes can also be induced by failures or unavailability of parts of the software system itself. To ensure the coherence and correctness of the proposed model, all relevant properties of system entities are precisely and formally described. This is especially true for non-functional properties, such as performance, availability, and security. This article discusses semantic concepts for the specification of non-functional requirements, taking into account the specific needs of a context-aware system. Based on these semantic concepts, we present a specification language that integrates non-functional requirements design and validation in the development process of context-aware self-adaptive systems.
Lex2KG: Automatic Conversion of Legal Documents to Knowledge Graph

Legal documents are generally available in the form of PDF which is not machine-readable. A knowledge graph (KG) is a graph describing real-world entities and their relationships, providing structured, machine-readable information. In this paper, we present Lex2KG, a framework for converting legal (PDF) documents into a KG. The legal KG contains various kinds of structured data, such as metadata, document structures, textual content, and relations between legal resources (e.g., amendments and citations). Through Lex2KG, we have successfully converted 784 Indonesian laws into a KG with a total size of over 1.1 million triples. We also present use cases of the legal KG for SPARQL querying, simple chatbots, and legal visualizations, showing how the legal KG generated can be useful in practice.
Normalized and orthogonal phase retrieval algorithm based on Lissajous ellipse fitting

A normalized and orthogonal phase retrieval algorithm based on Lissajous ellipse fitting (NOLEF) is proposed in this study, which can extract phase distributions under random phase shifts with high accuracy. In this algorithm, the parameters related to the phase can be calculated via Lissajous ellipse fitting after the normalization and orthogonalization processes. Compared with other high-accuracy phase-shifting algorithms, the proposed method has a shorter calculation time and provides stable and accurate phase extraction with random phase-shifting interferograms, in which the accuracy is not affected by the number of fringes and phase shifts; it is also suitable for interferograms with different backgrounds and modulations. This method's feasibility and performance at different number of fringes and noise levels are verified via experimental and simulation analyses.
A New Steganography Technique Based on Dotted Arabic Letters Features

Because of the great evolution of communication technologies, the subject of information security has become a sensitive topic that must be given great importance in order to protect confidential information. Steganography is one of the effective ways to improve security and information protection while transferring data over the Internet. It consists of hiding information within an unremarkable cover object that can be either of text, image, audio, or video. Arabic language and other similar languages such as Urdu and Persian, possess some special features that make them excellent covers for steganography. In this work, we propose to use for the first time in the literature, the position of points in dotted Arabic letters combining with other Arabic text features to create a new steganography technique. Based on this idea, we introduce new secret bit patterns that could allow us to hide three bits of secret information into a cover object instead of one bit, and hence greatly enhance the capacity performance. Experiments show the effectiveness of our proposed technique by outperforming other existing steganography methods.
Program Obfuscation via ABI Debiasing

The Itanium ABI is the most popular C++ ABI that defines data structures essential to implement underlying object-oriented concepts in C++. Specifically, name mangling rules, object and VTable layouts, alignment, etc. are all mandated by the ABI. Adherence to the ABI comes with undesirable side effects. While it allows interoperability, past research efforts have shown that it provides robust inference points that an attacker can leverage to reveal sensitive design information through binary reverse engineering. In this work, we aim to reduce the ability of an attacker to successfully reverse engineer a binary. We do this via removal of what we call ABI Bias, i.e., the reverse engineering bias that manifests due to a compiler’s adherence to the ABI.
Measuring User Experience with Modular Questionnaires

A good user experience is a key factor for the market success of products. Therefore, it is important that we can measure this factor. We describe the advantages of carefully constructed standard questionnaires for UX measurement. A particular problem with this approach is that UX is a very heterogeneous concept. Thus, there are many semantically different aspects of UX. Which of these aspects should be measured in a specific product evaluation depends on the product category. We explain this dependency and show how modular approaches can be used to solve the associated problems. Two new modular approaches are described as examples.
Exploring semantic segmentation of related subclasses from a superset of classes

Image segmentation is a very important topic in the field of computer vision. We present a method for semantic segmentation of selected stuff classes from a superset of classes. We show that in situations where only select stuff classes are required if we group them as per a strategy then it can attain much higher accuracy than the models trained on the original dataset with all classes intact. The COCO-Stuff Dataset is used for demonstrating the aforesaid strategy. For training purposes, the DeepLabv3+ with Mobilenet-v2 architecture is used. We have achieved an 80.2 percent mean Intersection over Union (mIoU) on these selected classes. We also refine the masks using Learning/Computer Vision (CV) methods and hence obtain better visualization results as compared to the existing DeepLabv3+ results.
Correction of Vector Representations of Words to Improve the Semantic Similarity

The computational complexity of obtaining vector representations of words is constantly growing due to both an increase in the volume of input data and an increase in the complexity of the models. The design and training of a new model, which would take into account additional expert information about the semantic similarity between words, today is estimated at thousands of hours of computational experiments. In the study, existing vector representations are adjusted by transforming them into secondary representations taking into account the semantic similarity. The representation transformation and the learning procedure are proposed. Thus, instead of building a new model, the existing one is corrected, which significantly reduces computational costs compared to developing a new model.
Lung Parenchyma Segmentation Based on CT Images

Novel Coronavirus targets the lung posing a serious threat to human health and causing huge social and economic losses. Extraction of lung parenchyma from CT images is an important step in the diagnosis of Novel Coronavirus. Therefore, accurate segmentation of lung parenchyma is highly significant for the diagnosis of disease. A lung parenchyma segmentation method based on OTSU and morphological operation is proposed. First of all, according to the CT image noise type, bilateral filtering is selected as preprocessing to filter out image noise. Then, binary images are obtained by the OTSU-based algorithm. Secondly, the residual interference of the trachea and blood vessels in the image is removed by morphological operation, and connected areas are marked and holes are filled. Finally, the original image is multiplied by the mask to obtain the lung parenchyma image. Experimental results show that this method can accurately segment lung parenchyma.
Discriminative Siamese Tracker Based on Multi-Channel-Aware and Adaptive Hierarchical Deep Features

Most existing Siamese trackers mainly use a pre-trained convolutional neural network to extract target features. However, due to the weak discrimination of the target and background information of pre-trained depth features, the performance of the Siamese tracker can be significantly degraded when facing similar targets or changes in target appearance. This paper proposes a multi-channel-aware and adaptive hierarchical deep features module to enhance the discriminative ability of the tracker. Firstly, through the multi-channel-aware deep features module, the importance values of feature channels are obtained from both the target details and overall information, to identify more important feature channels. Secondly, by introducing the adaptive hierarchical deep features module, the importance of each feature layer can be determined according to the response value of each frame, so that the hierarchical features can be integrated to represent the target, which can better adapt to changes in the appearance of the target. Finally, the proposed two modules are integrated into the Siamese framework for target tracking. The Siamese network used in this paper is a two-input branch symmetric neural network with two input branches, and they share the same weights, which are widely used in the field of target tracking. Experiments on some Benchmarks show that the proposed Siamese tracker has several points of improvement compared to the baseline tracker.
Justification Logic and Type Theory as Formalizations of Intuitionistic Propositional Logic

We explore two ways of formalizing Kreisel’s addendum to the Brouwer-Heyting-Kolmogorov interpretation. To do this we compare Artemov’s justification logic with simply typed $$\lambda $$ calculus, by introducing a map from justification terms into $$\lambda $$ terms, which can be viewed as a method of extracting the computational content of the justification terms. Then we examine the interpretation of Kreisel’s addendum in justification logic along with the image of the resulting justification terms under our map.
Stellar calibration of the single-photon receiver for satellite-to-ground quantum key distribution

Abstract Satellite quantum communication is the technology that allows to deploy large-scale quantum networks with a communication range of thousands kilometres We report the ground receiver for downlink quantum key distribution (QKD) with satellite. An optical part of this system including an active tracking loop is mounted on a 600-mm Ritchey-Chretien telescope and permits to distinguish polarization states to perform QKD between ground and satellite. Moreover, a procedure of calibration the receiver using stars with known brightness is presented. Measurements of the photon count rate of stars in the spectral range of 845 nm - 855 nm are performed and compared with an estimate.
Disruptions to shared mental models from poor quality of service in collaborative virtual environments

Collaborative virtual environments are being used in various applications ranging from online games to complex team training scenarios. The key to the success of such environments is the ability of the participants to form a shared mental model of the collaborative task being performed. Poor quality of service can deteriorate user performance and quality of experience, leading to a disruption of this mental model. While the effects of quality of service have been analyzed for traditional desktop environments, these effects remain unclear in collaborative virtual environments during user-to-user interactions. Here, we analyze the role of latency and packet bursts, two common problems in collaborative applications, on both simulator perception and actual task performance in a collaborative fire-fighting simulator. This exploratory study indicates that large latencies have a significant (p < 0.05) impact on the quality of experience, but not task performance. In contrast, packet bursts have a much larger impact on both the quality of experience and performance. Additionally, the network role, such as whether a user is a client or server, showed a significant (p < 0.05) impact on task performance in conditions impaired by packet bursts.
Research of special abilities of future informatics teachers in the process of training to work with gifted children

The article substantiates the relevance of improving the training of future informatics teachers to work with gifted children in accordance with the needs of society in the context of new educational standards. The analysis of the works of domestic and foreign authors devoted to research in the area under consideration is presented. A brief description of a special module “Work of an informatics teacher with gifted children”, developed by one of the authors of the article, is presented and included in the work program of the discipline “Methods of teaching informatics”. The comparative results of the input and output questionnaires of students are given, which make it possible to identify the level of knowledge of the psychological characteristics of children with high educational needs among future teachers, as well as the levels of subject and methodological readiness to work with this category of pupils. The results of the study of the personal qualities of the participants in the pedagogical experiment, obtained using test diagnostic techniques, are described. In the process of experimental work, the effectiveness of the combination within the framework of one course of psychological, subject and methodological components of training of a future informatics teacher for educational activities with gifted pupils has been substantiated. The results of the study may be of interest to teachers and methodologists of institutions of higher and secondary education, courses of advanced training for teachers.
Garage Geographic Information System Using Haversine Method Based On Android

The development of technology in the field of information and communication, especially Android-based smartphone devices in everyday life. Observations and experiences of researchers are the background of the design of this system. This system provides information about garage locations and garage info such as address, telephone number, opening hours, opening days and services. The system is expected to help motorized vehicle users in finding garage services when experiencing problems while traveling around the Badung Regency area. The haversine method is a method used in calculating the shortest distance in the designed system. The haversine theorem is used to calculate the distance between 2 points based on the length of a straight line between 2 points on longitude and latitude. With the nearest garage search system, it is able to help motorized vehicle users in Badung Regency to find the nearest garage from where the user is located. The system helps users to get the nearest garage route by utilizing GPS and the google maps application on the user&#x0027;s android device. Users can save time in finding a garage without asking the local community. By conducting field tests on users of this system, the results of respondents&#x0027; satisfaction were 88.5&#x0025; in terms of benefits, 91.1&#x0025; for effectiveness, 86.5&#x0025; for interface and 89&#x0025; for content.