Reviewerly/exHarmony huggingface dataset:
A Critique on Task Allocation Processes in Distributed Agile Software Development

Distributed agile software development is implemented to tackle the increasing competition and changing requirements of the software industry. It inspires the development of emergent software applications and supports user satisfaction with fast delivery to market at low cost. Distributed software development presents a complex distributed team structure of the software industry having various workplaces. Distributed agile software development requires effective and well-organized decisions during task allocation. Task allocation is a challenging activity across the spectrum of committed software developers in a distributed environment. Effective task allocation under the pressure of achieving project objectives is a complex management problem in distributed agile software development. Task allocation process is proposed to support effective task allocation in distributed agile software development. Firstly, we identify different aspects of the task allocation context to explore the basic requirements of decision-making in DASD. Based on the information related to the identified aspects, different types of process activities are defined; those should be fulfilled to support the task allocation decisions. Task allocation process during software project planning requires comprehensive information about various factors that influence decision-making. The proposed task allocation process is evaluated based on the process activities. None of the current state-of-the-art research works focus on an approach for task allocation that completely fulfills all activities related to high-level processes or low-level processes. We fill this gap by identifying, classifying, sequencing, and proposing these activities that can be used to realize a methodical approach for a generic task allocation process in a distributed agile software development setup.
Die Erkennung und Benennung von Themen-Clustern mit dem Dornseiff

No AccessDie Erkennung und Benennung von Themen-Clustern mit dem DornseiffErik KörnerErik Körner Universität Leipzig Deutschland Search for more papers by this authorPublished Online:Jun 2022https://doi.org/10.14220/mdge.2022.69.2.171SectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinkedInRedditEmail About Previous article Next article FiguresReferencesRelatedDetails Download book coverVolume 69Issue 2June 2022 ISSN: 0418-9426eISSN: 2196-8756HistoryPublished online:June 2022 PDF download
A Unified Model Using Distantly Supervised Data and Cross-Domain Data in NER

Named entity recognition (NER) systems are often realized by supervised methods that require large hand-annotated data. When the hand-annotated data is limited, distantly supervised (DS) data and cross-domain (CD) data are usually used separately to improve the performance. The distantly supervised data can provide in-domain dictionary information, and the hand-annotated cross-domain information can be provided by cross-domain data. These two types of information are complemental. However, there are two problems required to be solved before using directly. First, the distantly supervised data may contain a lot of noise. Second, directly using cross-domain data may degrade performance due to the distribution mismatching problem. In this paper, we propose a unified model named PARE (PArtial learning and REinforcement learning). The PARE model can simultaneously use distantly supervised data and cross-domain data as external data. The model uses the partial learning method with a new label strategy to better handle the noise in distantly supervised data. The reinforcement learning method is used to alleviate the distribution mismatching problem in cross-domain data. Experiments in three datasets show that our model outperforms other baseline models. Besides, our model can be used in the situation where no hand-annotated in-domain data is provided.
ECG Signal Denoising based on 2D-LMS Filter in Fractional Fourier Domain

ECG is a non-stationary electrical signal, and the process of collecting ECG signal inevitably contains various noises. This paper proposed a new method for ECG signal denoising in fractional Fourier domain. Firstly transform the signal into fractional Fourier domain, and then use 2D-LMS filter to process the characteristics of signal, finally get the denoised signal in time domain by inverse transformation. Simulation experiments are carried out with the data of MIT-BIH database. The results show that this method can restore the waveform characteristics of ECG signal, effectively improve the SNR of noisy ECG signal and reduce its MSE.
Graph Inspired Geometric Area Allocation for Swarm of Robots in a Warehouse Environment

Swarm robotics has received much spotlight in the past decade in regards to research and has already been deployed in many real life scenarios. From what started to be an interesting application for surveillance, search and rescue based applications has now been scaled to a multitude of applications ranging from the medical industry to industrial warehouse applications. With a plethora of interesting problems seen in such application themes, our work addresses a problem most relatable to the industrial logistics use case. In this paper, a graph inspired geometric area allocation is done which in turn streamlines the process of task allocation not just efficiently but also reduces infrastructure costs in a traditional industrial setting. A fail safe approach is introduced, wherein if a situation arises where robot(s) fail, then the system would realize and dynamically re-adjust the area allocation to make sure the pipeline continues unhampered.
Double-server blind quantum computation based on the GHZ state

We propose a new double-server blind quantum computation protocol based on the Greenberger–Horne–Zeilinger (GHZ) state. In this protocol, the correlation of the GHZ triplets has been utilized to deal with the existing double-server blind quantum computation protocol’s limitation, in which two servers cannot communicate with each other. Moreover, although the two servers in this protocol can obtain different parts of the particles in different GHZ states, they cannot match every GHZ state particle correctly, and this is because the positions of the particles are kept secret. Therefore, the protocol is still secure, in which two servers can communicate with each other. Furthermore, the client can make authentication requests to the trusted center, determining if the first server is honest or not by calculating measurements of part of the GHZ particles. After analyzing the security of the protocol, the protocol is found to be unconditionally secure.
noTrash.AI – An Approach Towards the Hygienic City based on Deep Learning

A city longing to get tagged as a smart city should always aim at the health and public safety of the citizens. Every initiative carried out by the city needs to confine itself with a boundary of safety. The boundary of safety can be defined with the aid of artificial intelligence. In a broad view, a clean and hygienic environment can be achieved by tagging the requirements with deep learning in every single application criterion. Cleanliness of city streets is a significant influence on the urban environment and healthy living. Standard street cleaning involves street sweepers going to clean several places by checking whether to clean or not. Manually detecting and assessing the cleanliness of street takes to a higher cost for cities. To overcome this vulnerability, noTrash.ai -an approach towards the hygienic city uses deep learning to stop further littering by monitoring the litters thrown from the vehicle in real time to classify the trash thrown from moving vehicles.
Evaluating Representation Learning and Graph Layout Methods for Visualization

Graphs and other structured data have come to the forefront in machine learning over the past few years due to the efficacy of novel representation learning methods boosting the prediction performance in various tasks. Representation learning methods embed the nodes in a low-dimensional real-valued space, enabling the application of traditional machine learning methods on graphs. These representations have been widely premised to be also suited for graph visualization. However, no benchmarks or encompassing studies on this topic exist. We present an empirical study comparing several state-of-the-art representation learning methods with two recent graph layout algorithms, using readability and distance-based measures as well as the link prediction performance. Generally, no method consistently outperformed the others across quality measures. The graph layout methods provided qualitatively superior layouts when compared to representation learning methods. Embedding graphs in a higher dimensional space and applying t-distributed stochastic neighbor embedding for visualization improved the preservation of local neighborhoods, albeit at substantially higher computational cost.
Extracting RoIs for Robust Far-infrared Pedestrian Detection on Board

Far-infrared pedestrian detection onboard is more challenging compared to pedestrian detection of visible light. Existing works proved that the output of RoIs extraction, named as the proposal, is significantly related to recall rate and computational cost for pedestrian detection. However, it is non-trivial for RoIs extraction due to low resolution, blurred details, and pedestrian morphological features in far-infrared scenes. The paper proposes a novel RoIs extraction framework for far-infrared pedestrian detection on Board, named FIR-RoIEF, by using edge to obtain pedestrian contour feature and cascade filtering to gain valuable RoIs. Given pedestrian morphological features, we further present a vertical edge strategy to enhance pedestrian vertical features. A T-shaped template and RoIs reordering are used in the bounding box evaluation process to output pure and high-quality RoIs. Under basic and standard metrics, we perform experiments on public datasets, SCUT and KAIST, which both contain large far-infrared pedestrian objects. As for the result, given 100, 400, 1000 proposals, we both achieve the best recall in 1000 proposals, 94%, and 87% respectively. By the way, it can be proved that our method keeps a good balance between computational cost and good real-time.
Aide-mémoire: Improving a Project’s Collective Memory via Pull Request–Issue Links

Links between pull request and the issues they address document and accelerate the development of a software project but are often omitted. We present a new tool, Aide-mémoire, to suggest such links when a developer submits a pull request or closes an issue, smoothly integrating into existing workflows. In contrast to previous state-of-the-art approaches that repair related commit histories, Aide-mémoire is designed for continuous, real-time, and long-term use, employing Mondrian forest to adapt over a project’s lifetime and continuously improve traceability. Aide-mémoire is tailored for two specific instances of the general traceability problem—namely, commit to issue and pull request to issue links, with a focus on the latter—and exploits data inherent to these two problems to outperform tools for general purpose link recovery. Our approach is online, language-agnostic, and scalable. We evaluate over a corpus of 213 projects and six programming languages, achieving a mean average precision of 0.95. Adopting Aide-mémoire is both efficient and effective: A programmer need only evaluate a single suggested link 94% of the time, and 16% of all discovered links were originally missed by developers.
Functional gradient descent for n-tuple regression

n-tuple neural networks have recently been applied to a wide range of learning domains. However, for the particular area of regression, existing systems have displayed two shortcomings: little flexibility in the objective function being optimized and an inability to handle nonstationarity in an online learning setting. A novel n-tuple system is proposed to address these issues. The new architecture leverages the idea of functional gradient descent, drawing inspiration from its use in kernel methods. Furthermore, its capabilities are showcased in reinforcement learning tasks, which involves both nonstationary online learning and task-specific objective functions.
StoryBook. Automatic generation of book trailers

The prevalence of video content consumption prompted book publishers to adopt this medium as a way to advertise books, through so called book trailers. Beyond advertising, a book’s video presentation may provide information to potential future readers so they can make an informed choice on whether consider reading it or not. StoryBook is a software tool that, through a process of knowledge extraction and media collection, supports the semi-automatic creation of multimedia trailers of books. A proof-of-concept of the tool and its user interface has been built and is available online.
ORGANIZATION OF TEACHER TRAINING TO FACILITATIVE INTERACTION WITH STUDENTS

The article introduces the pedagogical education organization model according to the psychological regularities of the personality’s standing mental new features formations.
Local neighborhood gradient pattern: A feature descriptor for content based image retrieval

Local feature descriptors are efficient encoders for capturing repeated local patterns in many of the computer vision applications. Majority of such descriptors consider only limited local neighborhood pixels to encode a pattern. One of the major issues while considering more number of neighborhood pixels is that it increases the dimensionality of the feature descriptor. The proposed descriptor addresses these issues by describing an effective encoding pattern with optimal feature vector length. In this paper, we have proposed Local Neighborhood Gradient Pattern (LNGP) for Content-Based Image Retrieval (CBIR) in which the relationship between a set of neighbours and the centre pixel is considered to obtain a compact 8-bit pattern in the respective pixel position. The relationship of the gradient information of immediate, next-immediate, and diagonal neighbours with the centre pixel is considered for pattern formation, and thus the local information based on pixels in three directions are captured. The experiments are conducted on benchmarked image retrieval datasets such as Wang’s 1K, Corel 5K, Corel 10K, Salzburg (Stex), MIT-Vistex, AT &amp; T, and FEI datasets and it is observed that the proposed descriptor yields average precision of 71.88%, 54.57%, 40.66%, 71.85%, 86.12%, 82.54%, and 68.54% respectively in the mentioned datasets. The comparative analysis of the recent descriptors indicates that the proposed descriptor performs efficiently in CBIR applications.
A Fragile Watermarking Algorithm Based on Multiple Watermarking

A fragile watermarking algorithm based on multiple watermarking is proposed. Watermarking generation: First, divide the image into block, and generate an authentication watermarking and two average watermarking based on their average value. Then, use block truncation coding to encode each image block to generate the characteristic watermarking. Then, the image block of generates three different mapping blocks according to the three different keys. By embedding three recovery watermarking, the algorithm can provide three recovery opportunities for the image block, which improves the recovery ability of the image under random multi-region tampering. In the experiment, through experiments such as multi-region tampering and large-region tampering on images of different scenes, it is shown that the algorithm can realize the authentication and recovery of tampering in multiple regions of the image. Finally, through comparative experiments with recent typical methods, the advantages of the algorithm in image quality after watermarking and restoration of image quality are verified.
Q-Free Walk Ant Hybrid Architecture for Mobile Robot Path Planning in Dynamic Environment

This work provides a novel hybrid system for the trajectory planning of an autonomous mobile robot named Q-Free Walk Ant Hybrid Architecture (Q-FWAHA). The proposed approach combines two layers: a deliberative layer (global planning) and a reactive one (local planning), it is adapted to partially unknown environments (dynamic). It includes both reactive planning methods based on the Perception &amp; Action principle (Sense &amp; Act) and deliberative methods based on the Perception-Planning &amp; Action principle (Sense-Plan &amp; Act). The deliberative layer consists of a global planning algorithm and a model (a map) based on current knowledge of the environment. It is based on a method inspired by nature called Ant Colony Optimization (ACO) for optimal trajectory planning in known environments. The next layer is the reactive layer, it allows the robot to avoid collisions with dynamic obstacles by directly using sensory data. The reactive planning method used is a reinforcement learning approach for planning in dynamic environments; The Q-learning method. The deliberative layer is compared alone with an existing system for path planning, results show that the final path produced by our system is more efficient in terms of safety and energy saving. Simulations and experimental results to validate the proposed hybrid system are presented.
Mineralogical Criteria for the Prediction of Gold Mineralization in the Structures of the Siberian Craton

Generalization of the results of the study of placer gold and the mechanisms of its distribution in the east of the Siberian Platform made it possible for the first time to reveal that the ore sources of gold-bearing placers were mainly mineralization of the gold-sulfide formation formed under the influence of hydrothermal metasomatic processes occurred in deep fault zones. It is determined that the gold-bearing hydrothermal-metasomatic formations of the gold-sulfide rock formation are amagmatic, near-surface, low-temperature and spatially confined to deep faults. These formations are widespread in terrigenous-carbonate strata and sand deposits of PZ-MZ age and are observed in the zones of brecciation, silicification, and ferruginization. The analysis of the mechanisms of distribution of hydrothermal-metasomatic gold-bearing formations in the east of the Siberian Platform, based on previously conducted research of predecessors and our field observations, allowed us to establish the following. In the northeast, hydrothermal-metasomatic formations occur in the field of development of the Zhigansky fault, the Molodo-Popigai and Anabar-Eekite fault system, in the central part of the east of the Siberian Platform, the Vilyui paleorift (Kempendyai dislocations), and in the southeast in the Baikal-Patom thrust belt in ancient faults (Bappagai, Khorinsky, etc.). Metasomatites of hydrothermal origin form extended narrow formations along ancient faults activated in the Mesozoic, as well as conformable deposits in the Vendian, Cambrian carbonate strata, Jurassic and Cretaceous sandstones. It is proved that these formations are amagmatic and are not related to the widespread magmatism of the basic composition, which is confirmed by the homogenous mineral composition of metasomatites, independent of the composition of magmatic bodies spatially located with them. Zones of metasomatites with gold mineralization contain Au from 0.5 to 3.0 g/t, and in isolated cases up to 24–32 g/t. Gold is represented from finely-dispersed to visible fine, with a size from 0.01 to 0.5 mm or more. It is known that hydrothermal-metasomatic processes on the territory of Central Aldan contributed to the formation of large deposits such as Kuranakh, Tabornoye and others. It is possible that the identified gold-ore metasomatites, developed along the zones of regional faults and not confined to magmatic formations, widely occurred both in the northeast and in the southeast of the Siberian Platform, represent a huge metallogenic potential.
Simulation Technologies Enhancing Educational Physics Laboratory Workshops at Lomonosov Moscow State University

This article presents the issues of using simulation technologies to enable educational research of difficult to implement scenarios. It shows that it is possible to introduce students to modern scenarios of electromagnetic compatibility problem, including compatibility between satellite systems, via face-to-face, blended or distant education. Validity of simulation results is based on using relevant internationally recognized documentation of International Telecommunication Union’s Radiocommunication Sector and methodologies approved at international level as theoretical basis. Practical application examples of designed series of workshops are discussed.
Secure Data Storage in The Cloud by Using DNA and Chaos Cryptography

Encryption is a technique for securing and protecting images against unauthorized access. Security is a major concern in everyone's life, and encryption plays a critical part in ensuring it. Because of the digital world, it is now incredibly easy to hack any information stored in the cloud. Many picture encryption techniques are based on chaotic logistic maps, but they have drawbacks such as limited key space and insecure security. In this research, we present an image encryption approach based on chaotic logistic mapping and Deoxyribose Nucleic Acid (DNA) encoding. The analysis results reveal that the proposed scheme has an acceptable level of security, efficiency, complexity, and speed.
Towards a Teacher Application to Support Semantic Annotations of Learning Tasks in Cultural Heritage

New generations should learn about the Cultural Heritage (CH) that surrounds them to value and preserve it. Common strategies for learning CH include lectures, textbooks, and also cultural visits to CH sites. Cultural visits allow very rich learning experiences but are quite challenging to prepare and carry out. This paper presents Cultural Heritage Educational Semantic Tool (CHEST), a web application that enables teachers to identify monuments of interest and annotate them with learning tasks. The created learning tasks will be carried out by students using an existing application, named Casual Learn, that makes use of a dataset of learning tasks created from information available as Linked Open Data. CHEST overcomes an identified limitation of Casual Learn by giving the teachers the possibility of enriching the existing semantic dataset of monuments and associated learning tasks with customized content while hiding the technical complexity of Semantic Web technologies. Additionally, CHEST promotes the reusability and adaptation of learning tasks by different teachers. An early version of CHEST was used by two secondary school teachers in real-life situations to design customized tasks for their students. These tasks were then carried out by the students via Casual Learn. These teachers gave a high usability score to CHEST and were interviewed to obtain further feedback. They were keen to publish new tasks, share them with other teachers, and reuse tasks from other teachers.
A Methodological Approach for The Dual Axis Solar Tracking System

Solar energy is fast gaining traction as a viable source of renewable energy. Tracking the panel to keep it aligned with the sun at a correct angle to the light beams produces more energy. With the use of a mechanically modelled model, this study covers in detail the design and building of a prototype for a solar tracking system with two degrees of freedom. The testing findings and design details are displayed.