Reviewerly/exHarmony huggingface dataset:
The rise of the new investigative journalism start-ups

<bold>This chapter explores the impact on the field of crime and investigative journalism of three new investigative journalism start-ups in the United Kingdom; how they are challenging journalistic norms through very different working practices; and how this is leading in turn to an expansion of the remit of crime news through the practice of engaged journalism. On The Bristol Cable, this takes the form of working with communities and co-creating crime news, enabling journalists to give voice to under-represented communities. The Bureau for Investigative Journalism challenge journalistic norms through collaborations and the use of big data in their work, enabling them to report crimes often under-reported by mainstream media such as corporate crime and crimes of corruption. The Bureau Local combine both these methods – the use of big data and collaborations with citizens – to produce ‘transformational’ journalism, working with citizens to bring what they term the societal or systemic harms they are reporting upon to wider attention, with the aim of sparking societal change and amelioration of these harms.</bold>
An Overview of Chatbot Structure and Source Algorithms

Chatbots or Chat Robots are the technology of the decade in which a machine having human conversation capabilities interacts with people. The technology uses various Human-Computer Interaction (HCI) mechanisms to communicate with humans. Chatbot technology has grown a lot in the past few years and is used in various fields such as medical, e-commerce, education, and information retrieval. This study aims to understand the existing chatbot conceptual design and explains the overall architecture of the chatbot, which comprises four components of basic input processing, input comprehension, information retrieval, and answer selection and production. Also, the data flow for the chatbot has been evaluated in this study. Furthermore, the portion of a study concentrated on investigating various methods employed by earlier chatbot structures. Natural Language Processing, Pattern Matching Algorithm, Naive Bayes Algorithm, Sequence to Sequence Model, Hybrid Emotion Interference Model, and Long Short-Term Memory are among them. Also, the elements influencing chatbot design have been discovered, which include the manner of input, the usage of Artificial intelligence Mark-up Language (AIML), SQL, the role of user technology acceptance, and the selection of techniques and application sectors. To summarize, this work provides a summary of research on chatbot structure and identifies the most prevalent algorithms employed with the current chatbot structure.
Improving Multihead Finite State Machine With Transformer Neural Network

This paper considers the integration of a neural network into a multi-head automaton designed to recognize multilinear sequences. The main problem of the automaton is a low quality for recognition of sequences that differ from multilinear pattern. This problem is proposed to be solved using neural networks. In this paper a novel technique based on transformer architecture is proposed for detection of the outliers in the multilinear sequence. BERT-like architecture was chosen because of an ability to model long interval dependencies, compared to LSTM or CNN models. These models were trained for detection of irregularities in multilinear sequences. Suggested model was validated on different randomly created sequences.
Improving GNSS Standard Positioning Service Using Boosting Algorithms

Global navigation satellite system performance can be assessed by three key characteristics, namely accuracy, availability, and integrity. Despite the recent development, GNSSs cannot meet the requirements of all users, for instance, autonomous vehicles require high accuracy, availability, and integrity. Improving GLONASS positioning accuracy is one of the main aims of the current modernization stage. The paper describes the approach for calculating reference pseudorange measurements and residual pseudorange error. The article discusses the model building process, including feature selection, and compares the performance of the models that were built using different boosting algorithm implementations– Gradient Boosting, LightGBM, CatBoost, XGBoost. The comparison showed that the performance of the trained models using various implementations has almost identical scores. The experiments showed the ability of the trained and tuned machine-learning model to improve positioning results considerably by compensating for residual pseudorange error. The trained model is able to improve standard positioning service performance by up to 30%.
The airing of grievances: A look at complaint behavior in library reddit

Social media platforms have been adapted into society in various ways during last two decades. Few studies have examined social media content from the perspective of the individual library professional, and in particular, from librarians’ social media posts about their work. This research explores social media complaints from library professionals to reveal insights into the areas of concern related to libraries as expressed in an informal, discursive style. Reddit data from three library subReddits was collected through a Python script. A total of 272 complaint posts with enough information were coded manually. A computational tool, Linguistic Inquiry and Word Count (LIWC), was also used to extract linguistic characteristics of each post. The majority of the sources of the complaint posts came from librarians (86.76%). The top five categories of the target of the complaints were patrons (27.94%), the poster’s organization (20.22%), the poster’s supervisor (11.03%), the library field (11.03%), and the poster’s coworkers (10.29%). The top four topics of the complaint posts were patron behavior (21.69%), negative work environment (16.18%), job expectations (11.76%), and service expectations (10.66%). Organizations should put into place mechanisms and processes through which employees may share their concerns and exercise their voice, with no retribution, to solve workplace problems.
Intellectual work and its role in the formation of new visions of post-war reconstruction

The experience of countries that found ways out of the crisis during the war shows that state support plays a big role during the war. Today, Ukraine and the Ukrainian people are going through difficult times, so the problem of researching the peculiarities of intellectual work and its role in the formation of new visions of post-war reconstruction remains unresolved. The purpose of the article is to study the peculiarities of intellectual work and its role in the formation of new visions of post-war reconstruction. To achieve the goal, the following tasks have been defined: to determine the features of intellectual work in the formation of new visions of post-war reconstruction; to analyze new visions of post-war recovery. Methods. The research used general scientific (generalization, comparison, induction and deduction) and empirical-theoretical methods (analysis, synthesis). The use of system-structural analysis made it possible to distinguish the main features of intellectual work and its role in the formation of new visions of post-war recovery. The results. It has been established that countries such as Germany, Japan, Israel, South Korea, Croatia are noted when it comes to positive experience of economic development in the context of post-war reconstruction of the country, or successes in conditions of constant war threat. It was established that common values and a high level of social consolidation, features of the national character, historical and cultural traditions and a vision of a happy future of their country as a free, independent and successful country with a developed economy and a decent standard of living played an important role in the achievements of the noted countries. Conclusions. On the basis of the conducted research, it was found that the positive experience of economic development in the conditions of constant military threat is interconnected with the state policy of supporting science, education and innovation; stimulation of scientific and technical developments and the latest productions; ensuring the synergy of the nation's human potential and the development of scientific research. The results can be directed to the further study of the social significance of the new visions of the post-war recovery of Ukraine to ensure recovery and the maximum realization of human potential.
Multi‐objective container scheduling and multi‐path routing for elastic business process management in autonomic multi‐tenant cloud

Summary Cloud multi‐tenancy has a variant requirement, due to its resource sharing nature, satisfying such requirements and maintaining a balance between the resources and the business workloads of multiple tenants is a challenging task, and also the communication between scheduled containers leads to high power consumption. To address these issues, this article proposes an autonomic approach to ensure the elasticity of BPM in multi‐tenant cloud. Where it employs the autonomic computing capabilities for scheduling the containers into the available servers then regulates the communication between the containers using multi‐path routing. For the container scheduling, a multi‐objective crow search optimization algorithm is proposed to schedule the containers into appropriate servers. Then, the discrete wolf search algorithm based multipath routing is proposed to route the communication flows between the containers by finding the optimal path with an objective to minimize the energy consumption. The optimal path is constructed as a multi‐tenancy graph with bandwidths determining the shortest distance between the servers and containers. The overall simulations shows that the proposed algorithm outperformed the other compared approaches in terms of make‐span, resource utilization, execution cost, execution time, and energy consumption.
Tangut character image generation based on cycle-consistent adversarial networks

Tangut characters were created by the Tangut of the Western Xia (Xi Xia) Dynasty in ancient China and are over 1000 years old. In deep-learning-based recognition studies on Tangut characters, the lack of category-complete datasets has been problematic. Data augmentation cannot augment the character categories of unknown styles, whereas the use of image generation can effectively solve the problem. In this study, we consider the generation of antique book calligraphy styles of Tangut characters as a problem of learning to map from existing printed styles to personalized antique book calligraphy styles. We present M-ResNet, a multi-scale feature extraction residual unit, and Tangut-CycleGAN, a model for generation Tangut characters that combine M-ResNet and a cycle-consistent adversarial network (CycleGAN). This method uses unpaired data to generate Tangut character images in the calligraphy style of ancient books. To enhance the response of the model to significant channels, a squeezing-and-excitation (SE) module is introduced based on Tangut-CycleGAN to design the Tangut-CycleGAN+SE method for generating images of Tangut characters. This method is not only suitable for Tangut character image generation, but also can effectively generate calligraphy with aesthetic value. In addition, we propose an overall quality discrepancy evaluation metric, FA (Fréchet inception distance + Accuracy), to evaluate the quality of character image generation, which combines style discrepancy and content accuracy metrics.
Data-driven product ranking: A hybrid ranking approach

The sudden COVID-19 epidemic has caused consumers to gradually switch to online shopping, the increasing number of online consumer reviews (OCR) on Web 2.0 sites has made it difficult for consumers and merchants to make decisions by analyzing OCR. Much of the current literature on ranking products based on OCR ignores neutral reviews in OCR, evaluates mostly given criteria and ignores consumers’ own purchasing preferences, or ranks based on star ratings alone. This study aims to propose a new decision support framework for the evaluation and selection of alternative products based on OCR. The decision support framework mainly includes three parts: 1) Data preprocessing: using Python to capture online consumer comments for data cleaning and preprocessing, and extracting key features as evaluation criteria; 2) Sentiment analysis: using Naive Bayes to analyze the sentiment of OCR, and using intuitionistic fuzzy sets to describe the emotion score; 3) Benchmark analysis: a new IFMBWM-DEA model considering the preference of decision makers is proposed to calculate the efficiency score of alternative schemes and rank them according to the efficiency score. Then, the OCR of 15 laptops crawled from JD.com platform is used to prove the usefulness and applicability of the proposed decision support framework in two aspects: on the one hand, the comparison of whether the preference of decision makers is considered, and on the other hand, the comparison with the existing ranking methods. The comparison also proves that the proposed method is more realistic, the recommendations are more scientific and the complexity of the decision is reduced.
“Innovare” program, based on b-learning, to improve the level of achievement of the communication area for secondary school students - Trujillo, 2019

The purpose of the study was to determine if the 'INNOVARE' program, based on b-learning, influences the improvement of the level of achievement of learning in the area of communication of the students of the 5th grade of secondary education of the I.E. 81001 Republic of Panama in the year 2019. A quasi-experimental design was used, with a population of 120 students and a sample of 60 students, 30 for the control group and 30 for the experimental group. For data analysis, the Wilcoxon signed rank test was applied. The "Innovare" program, based on b-learning, comprises a set of learning sessions, using the "Aulantic" virtual platform. The test statistic confirms that in the experimental group YES there is a significant difference (significance &lt; 1%) between the pre-evaluation and the post-evaluation due to the application of the Innovare program; while in the control group there is NO significant difference (significance &gt; 5%) between both tests due to the NO application of the Innovare program. In conclusion, the Innovare program, based on b-learning, YES has influenced the improvement of the level of achievement of learning in communication of the students of the 5th grade of secondary education of the I.E. 81001 Republic of Panama in the year 2019.
Variability analysis of monthly precipitation vector time series in Australia by a new spatiotemporal entropy statistic

Abstract Changing climate in Australia has significant impacts on the country’s economy, environment and social well-being. Addressing such impacts, particularly that of precipitation change, entails immediate action due to the more frequent occurrence of extreme dry or wet events in Australia in recent decades. In this paper we investigate the intra-annual Australian precipitation variability and how it changes over space and time. We quantify this variability using information entropy—a statistical tool for measuring the uncertainty of a random variable over its sample space, and propose a compositional data model to compute optimal spatiotemporal estimators of this entropy using 1/1979-to-3/2022 monthly satellite precipitation estimates from the National Oceanic and Atmospheric Administration. The results enable us to identify those locations/times where/when extreme intra-annual precipitation variation or unevenness occurred. We find this variability has been changing over time in large regions of southeastern Queensland and on the coast of South Australia, which would be difficult to find without using the proposed approach. We uncover the development of extreme entropy in the months leading up to, and in the location of, four extreme precipitation events in Australia where inter-annual precipitation amounts and/or trends proved insignificant. In marked contrast to annual precipitation, we found entropy has a weak association with the El Niño Southern Oscillation cycle.
Suppression of the Block Effect while Enhancing Dark Images Using Asumu Derivative

Image contrast enhancement is one of the most important tasks in the field of image digital processing. With the help of computers and programming languages, many mathematical methods have been implemented to improve the visual quality of an image. However, the majority of these methods have a major drawback: they tend to make the image look blocked. This effect is visually unpleasant and is likely to cause a computer vision system to falsely recognize as edges the boundaries of different blocks that belong to the same smooth area in the original image. To avoid the block effect while enhancing the quality of dark images, we propose a new fractional differential mask based on the Asumu fractional derivative. Experiments with dark natural images show that, even though our method does not have a better performance in statistical quality, it achieves a solution to the problem that was the motivation to write this paper, suppressing efficiently the block effect while improving the visual qualities of dark images.
To say almost the same thing? A study on cross-linguistic variation in ancient texts and their translations

Abstract This article presents a study of several parallel corpora of historical languages and their translations. The aligned corpora are the result of a large crowdsourcing project, named Ugarit, aimed at supporting translation alignment for ancient and historical languages: the study of the resulting translation pairs allows us to observe cross-linguistic dynamics in a range of languages, some of which have never been systematically aligned before. The corpora considered are divided into two distinct groups: English translations of ancient languages, including Greek, Latin, Persian, and Coptic; and translations of ancient Greek into other languages, including Latin, English, Georgian, Italian, and Persian. We evaluated different ratios of word matching across each language pair (one-to-one, one-to-many, many-to-one, and many-to-many), and analyzed the resulting trends across the corpus. We propose some observations on how and why different types of alignment links are established in a given language pair, and what factors affect their creation beyond the control of the user: we propose two complementary hypotheses to explain the changes, one based on structural linguistic factors and the other based on cultural difference.
An End-to-End Data-Adaptive Pancreas Segmentation System with an Image Quality Control Toolbox

With the development of radiology and computer technology, diagnosis by medical imaging is heading toward precision and automation. Due to complex anatomy around the pancreatic tissue and high demands for clinical experience, the assisted pancreas segmentation system will greatly promote clinical efficiency. However, the existing segmentation model suffers from poor generalization among images from multiple hospitals. In this paper, we propose an end-to-end data-adaptive pancreas segmentation system to tackle the problems of lack of annotations and model generalizability. The system employs adversarial learning to transfer features from labeled domains to unlabeled domains, seeking a dynamic balance between domain discrimination and unsupervised segmentation. The image quality control toolbox is embedded in the system, which standardizes image quality in terms of intensity, field of view, and so on, to decrease heterogeneity among image domains. In addition, the system implements a data-adaptive process end-to-end without complex operations by doctors. The experiments are conducted on an annotated public dataset and an unannotated in-hospital dataset. The results indicate that after data adaptation, the segmentation performance measured by the dice similarity coefficient on unlabeled images improves from 58.79% to 75.43%, with a gain of 16.64%. Furthermore, the system preserves quantitatively structured information such as the pancreas’ size and volume, as well as objective and accurate visualized images, which assists clinicians in diagnosing and formulating treatment plans in a timely and accurate manner.
ZigZag Algorithm: Scanning an Unknown Maze by an Autonomous Drone

We consider the problem of a drone (quadcopter) that autonomously needs to scan or search an unknown maze of walls and obstacles (no GPS and no communication). This ability (navigating in an unknown indoor environment) is a fundamental problem in the area of drones (even in general robotics) and has applications in military, security, search & rescue and surveillance tasks. Typically, previous works proposed systems that construct a 3D map (via camera images or distance sensors) of the drone’s surroundings. This 3D map is then analyzed to determine the drone’s location and an obstacle-free path. The algorithm proposed here skips over the 3D map and the computation of the obstacle-free path by using random “blind” billiard zig-zag movements to scan the maze. This way, the drone simply bounces from walls and obstacles disregarding the need to find an obstacle-free path in a 3D map. Thus the algorithm requires only a simple form of obstacle detection, one that alerts the drone that there is a close obstacle in its direction of flight. Just using zigzag movements was not enough to obtain efficient cover of the maze were “efficient” cover is when the drone performs no more than one pass per corridor/room (OPTtime). Hence, a more complex algorithm was developed on top of these random zigzag movements. Experimental results using a realistic flight simulation in a random maze showed about 95% cover in OPTtime.
Patterns and tools in Robotic Systems Integration

Software integration activity is crucial to develop robotic applications. However, there is little specific literature dedicated to the integration process in robotics. The research in this field today is mostly focused on a concrete implementation phase and on the development of new technologies, but few researchers target their integration as an application-agnostic process. To shed light on the state of robotics software integration, we conducted a survey among researchers and practitioners in the field. As part of this survey we inquired how robotics software integration is currently performed. This study allowed us to find patterns in the way this process is carried out.
Implemention of Reinforcement Learning Environment for Mobile Manipulator Using Robo-gym

Many studies utilize reinforcement learning in simulation environments to control robots. Since simulation environments do not provide reinforcement learning environments for all robots, it is important for researchers to choose a simulation environment with the robots they use. This paper adds and expands a new robot-platform to the robot-gym environment, a reinforcement learning framework used in the Gazebo simulation environment. The added robot-platform is Husky-ur3, a mobile manipulator robot, and it can recognize the coordinates of the target point by itself through the camera. It was confirmed that the mobile manipulator learning environment was well established through experiments of recognizing and following target.