Reviewerly/exHarmony huggingface dataset:
An extensive dataset of handwritten central Kurdish isolated characters

To collect the handwritten format of separate Kurdish characters, each character has been printed on a grid of 14 × 9 of A4 paper. Each paper is filled with only one printed character so that the volunteers know what character should be written in each paper. Then each paper has been scanned, spliced, and cropped with a macro in photoshop to make sure the same process is applied for all characters. The grids of the characters have been filled mainly by volunteers of students from multiple universities in Erbil.
Privacy-Preserving Prediction of Victim’s Mortality and Their Need for Transportation to Health Facilities

Emergency medical services (EMS) provide crucial prehospital care, such as in the case of cardiac arrest, where the victim requires immediate first-aid.For this reason, it is vital to improving EMS response time.This paper proposes a novel methodology based on machine learning (ML) techniques to predict both the victims' mortality and their need for transportation to health facilities using data gathered from the start of the emergency call until the Departmental Fire and Rescue Service of the Doubs (SDIS25) is notified.We first analyzed SDIS25 calls to find out associations between the call processing times and victims' mortality, and to measure the variables' importance.Next, we validated our proposed MLbased methodology, where mortality could be predicted with accuracy and area under the receiver operating characteristic curve (AUC) scores of 96.44% and 96.04% respectively, while the need for transportation achieved an accuracy and AUC scores of 73.62% and 78.91%, respectively.What is more, we found out that it was still possible to predict both targets perturbating the input data by applying k-anonymity and differential privacy techniques.In conclusion, the results showed the potential of ML for EMS, which can be used as a decision-support tool to early identify mortality and the use of resources (transportation) and, thus, help EMS to save more lives and avoid service disruptions.
Digitalization Policy Influence: Implementation of Mobile Learning in the University Educational Process

The digitalization of higher education has led to the emergence of new teaching methods, including mobile learning methods. The purpose of the study is to determine the prospects, advantages, and obstacles to the introduction of mobile learning in the university educational process in the context of the digitalization policy of education. The study presents the prospects for the introduction of mobile learning in the university educational process; identifies the main components of mobile learning, which ensure the effectiveness of the implementation of mobile technologies and types of mobile content that can be used in the university educational process; an analysis of each type has been carried out; the advantages and obstacles of the introduction of mobile learning are highlighted. In conclusion, mobile technologies allow introducing new forms of training organization, involving the interaction of learning subjects with each other not only during lessons but also outside them.
The Application of Group Work in Classroom Interactions of Teaching Reading Comprehension of Students : Case Study SMP Negeri 6 Binjai

This study deals with the application of Group Work in teaching English.  The underlying objective of the study is to see whether teaching reading by using Group Work has significant difference from the students who do not apply it. In carrying out this study, the writer took 40 students of the first year of SMP Negeri 6 Binjai from two classes as the sample of her study.  They were divided into two groups.  The first group is experimental group was taught by using group work in reading comprehension, while the second group is controlled group, the reading text was taught without group work. The instrument used were teacher made test based on the materials that had been taught.  These test used were administrated to find out the reliability, the computation showed that the reliability of the test was 1,5.  After the treatment, a post-test was applied. The results of the study shows that teaching reading comprehension by using group work is more significant than of without group work.
Cryptocurrencies: Key Risks and Challenges

Cryptocurrencies are witnessing a growing interest from investors and the media. They are increasingly perceived as a new class of assets through added benefits, such as hedging capabilities and diversification. However, this does not preclude the fact that cryptocurrencies can be risky assets. Within such a context, diverse studies were carried out at various risk levels. Our chapter bridges this gap and tries to reconcile varying positions on risk across cryptocurrencies. Particularly, we provide a detailed overview on the main risks to be considered by crypto-traders, namely, technology, fraud, legal, market, liquidity, and COVID-19 pandemic risks. The main findings show that the occurrence of any technological failure tends to raise insecurity and distrust in the cryptocurrency technology. This fact can be even further spoiled through fraud schemes and fake trading volumes. Additionally, the legal framework of the cryptocurrencies is still inconclusive. In terms of market risk, these crypto-assets are riskier than fiat currencies and there is a significant risk contagion across large-cap cryptocurrencies. Then, a significant relationship exists between liquidity and efficiency in the cryptocurrency market, since price dynamics can influence the market liquidity. Finally, it sorts out that the COVID-19 pandemic heavily affected the cryptocurrency markets. This chapter underlines current challenges for investors, regulators, and policymakers.
Configurable Hardware Core for IoT Object Detection

Object detection is an important task for many applications, like transportation, security, and medical applications. Many of these applications are needed on edge devices to make local decisions. Therefore, it is necessary to provide low-cost, fast solutions for object detection. This work proposes a configurable hardware core on a field-programmable gate array (FPGA) for object detection. The configurability of the core allows its deployment on target devices with diverse hardware resources. The object detection accelerator is based on YOLO, for its good accuracy at moderate computational complexity. The solution was applied to the design of a core to accelerate the Tiny-YOLOv3, based on a CNN developed for constrained environments. However, it can be applied to other YOLO versions. The core was integrated into a full system-on-chip solution and tested with the COCO dataset. It achieved a performance from 7 to 14 FPS in a low-cost ZYNQ7020 FPGA, depending on the quantization, with an accuracy reduction from 2.1 to 1.4 points of mAP50.
Tunet: A Block-Online Bandwidth Extension Model Based On Transformers And Self-Supervised Pretraining

We introduce a block-online variant of the temporal feature-wise linear modulation (TFiLM) model to achieve bandwidth extension. The proposed architecture simplifies the UNet backbone of the TFiLM to reduce inference time and employs an efficient transformer at the bottleneck to alleviate performance degradation. We also utilize self-supervised pretraining and data augmentation to enhance the quality of bandwidth extended signals and reduce the sensitivity with respect to downsampling methods. Experiment results on the VCTK dataset show that the proposed method outperforms several recent baselines in both intrusive and non-intrusive metrics. Pretraining and filter augmentation also help stabilize and enhance the overall performance.
Machine Learning approach towards real time assessment of hand-arm vibration risk

In industry 4,0, the establishment of an interconnected environment where human operators cooperate with the machines offers the opportunity for substantially improving the ergonomics and safety conditions of the workplace. This topic is discussed in the paper referring to the vibration risk, which is a well-known cause of work-related pathologies. A wearable device has been developed to collect vibration data and to segment the signals obtained in time windows. A machine learning classifier is then proposed to recognize the worker’s activity and to evaluate the exposure to vibration risks. The experimental results demonstrate the feasibility and effectiveness of the methodology proposed.
Photon-Driven Neural Reconstruction for Path Guiding

Although Monte Carlo path tracing is a simple and effective algorithm to synthesize photo-realistic images, it is often very slow to converge to noise-free results when involving complex global illumination. One of the most successful variance-reduction techniques is path guiding, which can learn better distributions for importance sampling to reduce pixel noise. However, previous methods require a large number of path samples to achieve reliable path guiding. We present a novel neural path guiding approach that can reconstruct high-quality sampling distributions for path guiding from a sparse set of samples, using an offline trained neural network. We leverage photons traced from light sources as the primary input for sampling density reconstruction, which is effective for challenging scenes with strong global illumination. To fully make use of our deep neural network, we partition the scene space into an adaptive hierarchical grid, in which we apply our network to reconstruct high-quality sampling distributions for any local region in the scene. This allows for effective path guiding for arbitrary path bounce at any location in path tracing. We demonstrate that our photon-driven neural path guiding approach can generalize to diverse testing scenes, often achieving better rendering results than previous path guiding approaches and opening up interesting future directions.
Robot line structured light vision measurement system: light strip center extraction and system calibration

We investigate the light strip center extraction and system calibration method of a robot line structured light vision measurement system to realize fast on-line measurement of complex objects. First, the model of a robot vision measurement system is established to analyze the key technologies involved for improving the measurement accuracy of the system. Second, on the basis of the traditional gray gravity method, a gray gravity method based on the normal direction is proposed to extract the center of the laser stripe. Considering the influence of camera distortion, the accuracy of extracting the center stripe can be further improved. Third, a two-dimensional checkerboard is applied to synchronously calibrate the camera parameters, laser plane parameters, and hand-eye relationship of the robot, thus improving the efficiency and accuracy of system calibration. Finally, three-dimensional topography and the geometric dimensions of a complex part are measured by the constructed system with high accuracy. The experimental results show that the average measurement error is <0.2 mm and the measurement accuracy is <1.3 % , indicating that the system can be effectively used in actual industrial measurement.
Foregrounding Daily Data Collection on Archaeological Fieldwork

Overview Daily data collection during archaeological fieldwork forms the basis for later interpretation and analysis. Across the world, we observe a wide variety of digital data collection methods and tools employed during fieldwork. Here, we detail the daily practices at four recent survey and excavation projects in the South Caucasian country of Armenia. As archaeology continues to become ever more digital, it is useful to consider these day-to-day recording processes at a typical field project. We provide details on both the types of data collected and the ways they are collected so as to foreground these topics. Finally, we reflect on how our work is currently impacted by digital changes and how it may continue to change in the future.
A Computational Exploration of Pejorative Language in Social Media

In this paper we study pejorative language, an under-explored topic in computational linguistics.Unlike existing models of offensive language and hate speech, pejorative language manifests itself primarily at the lexical level, and describes a word that is used with a negative connotation, making it different from offensive language or other more studied categories.Pejorativity is also context-dependent: the same word can be used with or without pejorative connotations, thus pejorativity detection is essentially a problem similar to word sense disambiguation.We leverage online dictionaries to build a multilingual lexicon of pejorative terms for English, Spanish, Italian, and Romanian.We additionally release a dataset of tweets annotated for pejorative use.Based on these resources, we present an analysis of the usage and occurrence of pejorative words in social media, and present an attempt to automatically disambiguate pejorative usage in our dataset.
Classification-Regression for Chart Comprehension

Chart question answering (CQA) is a task used for assessing chart comprehension, which is fundamentally different from understanding natural images. CQA requires analyzing the relationships between the textual and the visual components of a chart, in order to answer general questions or infer numerical values. Most existing CQA datasets and models are based on simplifying assumptions that often enable surpassing human performance. In this work, we address this outcome and propose a new model that jointly learns classification and regression. Our language-vision setup uses co-attention transformers to capture the complex real-world interactions between the question and the textual elements. We validate our design with extensive experiments on the realistic PlotQA dataset, outperforming previous approaches by a large margin, while showing competitive performance on FigureQA. Our model is particularly well suited for realistic questions with out-of-vocabulary answers that require regression.
Machine Learning in Neuro-Oncology, Epilepsy, Alzheimer’s Disease, and Schizophrenia

Applications of machine learning (ML) in translational medicine include therapeutic drug creation, diagnostic development, surgical planning, outcome prediction, and intraoperative assistance. Opportunities in the neurosciences are rich given advancement in our understanding of the brain, expanding indications for intervention, and diagnostic challenges often characterized by multiple clinical and environmental factors. We present a review of ML in neuro-oncology, epilepsy, Alzheimer's disease, and schizophrenia to highlight recent progression in these field, optimizing machine learning capabilities in their current forms. Supervised learning models appear to be the most commonly incorporated algorithm models for machine learning across the reviewed neuroscience disciplines with primary aim of diagnosis. Accuracy ranges are high from 63% to 99% across all algorithms investigated. Machine learning contributions to neurosurgery, neurology, psychiatry, and the clinical and basic science neurosciences may enhance current medical best practices while also broadening our understanding of dynamic neural networks and the brain.
Improved YOLOv5 network-based object detection for anti-intrusion of gantry crane

In response to the current lack of intelligence and security research on outdoor gantry cranes, the method based on the improved you-only-look-once (YOLO)v5 network for intelligent anti-intrusion detection is proposed. First an overall detection scheme is proposed. Then the following improvement tricks are made to the YOLOv5 network to achieve the highest possible detection accuracy while ensuring speed: incorporate multi-layer receptive fields and fine-grained modules into the backbone network to improve the performance of features; use dilated convolution to replace the pooling operation in the SPP module to reduce the loss of network information; further enrich the fusion of non-adjacent deep and shallow features in the network by using cross-layer connections; then use the K-means algorithm to cluster the target size to improve the positioning accuracy of the model; Finally, the non-maximum suppression algorithm is optimized by the weighting algorithm to effectively alleviate the inaccurate positioning of the YOLO series of bounding boxes. By combining multiple tricks, the improved YOLOv5s model can achieve a better balance between effectiveness (75.81% mAP) and efficiency (83 FPS) in anti-intrusion detection. At the same time, compared with the original YOLOv5s network on the VOC data set, the mAP value of the improved YOLOv5s is increased by 7.05%.
Shifted Rayleigh filter: a novel estimation filtering algorithm for pervasive underwater passive target tracking for computation in 3D by bearing and elevation measurements

Purpose Marine exploration is becoming an important element of pervasive computing underwater target tracking. Many pervasive techniques are found in current literature, but only scant research has been conducted on their effectiveness in target tracking. Design/methodology/approach This research paper, introduces a Shifted Rayleigh Filter (SHRF) for three-dimensional (3 D) underwater target tracking. A comparison is drawn between the SHRF and previously proven method Unscented Kalman Filter (UKF). Findings SHRF is especially suitable for long-range scenarios to track a target with less solution convergence compared to UKF. In this analysis, the problem of determining the target location and speed from noise corrupted measurements of bearing, elevation by a single moving target is considered. SHRF is generated and its performance is evaluated for the target motion analysis approach. Originality/value The proposed filter performs better than UKF, especially for long-range scenarios. Experimental results from Monte Carlo are provided using MATLAB and the enhancements achieved by the SHRF techniques are evident.
Supervisors' knowledge hiding and knowledge-based trust: from the lens of social impact theory

Past studies have largely focused on leaders' influence on employees' attitudes and behaviors, largely ignoring the followership and its consequences. This study investigates the social impact that followers induce on leaders through their intentions and actions. Following social impact theory (SIT), this study contributes to the growing research on supervisory knowledge hiding (KH) and related positive consequences beyond the traditional leader-centered approach. This paper investigates the serial mediation link between supervisory KH and supervisory knowledge-based trust (KBT) via perceived prosocial impact and supervisor directed citizenship behavior.,Time-lagged dyadic data of 348 employees working in a bank under 54 supervisors were collected from Saudi Arabia.,The findings suggest that supervisory KH entails a potential prosocial impact on employees to engage in supervisor-directed citizenship behavior that builds the KBT in supervisors about subordinates. The empirical support provides an understanding of the social impact of subordinates' influence on supervisors above and beyond traditional leadership literature by depicting the active role of followers in influencing leaders' behavior in building trust in knowledge management. Theoretical and practical implications of the findings are also discussed.,By studying the followership effect on leadership, this study extends the social impact process beyond a social phenomenon to the workplace in a supervisor–subordinate relationship. Moreover, examining the positive framing of a leader's KH to transform such behaviors through active followership role provides a new insight into positive consequences of supervisory behavior through social impact.
From the graphical representation to the smart contract language: a use case in the construction industry

From the graphical representation to the smart contract language: a use case in the construction industry Xuling Ye and Markus König Pages 272-279 (2021 Proceedings of the 38th ISARC, Dubai, UAE, ISBN 978-952-69524-1-3, ISSN 2413-5844) Abstract: With the growing popularity of blockchain technology in the construction industry, smart contracts are becoming increasingly common. A smart contract is a self-executing contract, which contains if-then rules that automatically execute certain processes when certain conditions are met. Such smart contracts serve as programmable blockchain applications. Using blockchain-enabled smart contracts, many processes like construction contracting and payments can be automated. Since research on blockchain-enabled smart contracts in the construction industry is still theoretical, researchers usually assume that users (e.g. clients, contractors) can directly program the conditions in a smart contract. However, it is difficult for stakeholders to program smart contracts themselves due to a lack of knowledge. The smart contracts developed by programmers might not fully represent stakeholders' ideas. Therefore, this paper proposes an approach that illustrates how graphical workflow notations (e.g. BPMN, YAWL) can be translated into smart contract programming languages (e.g. Solidity, Vyper). In this way, non-programmers can also design and generate their own smart contracts. To test the feasibility of this approach, an illustrative example is presented for generating smart contracts displaying automated the reporting, checking and payment process of construction works. In particular, the smart contracts in this example are translated from YAWL graphical representations into Solidity smart contract languages. Finally, improvements and further developments of the approach are discussed in several aspects. Keywords: process modeling; smart contract; blockchain; construction industry DOI: https://doi.org/10.22260/ISARC2021/0039 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
Passive RFID-based Music Player Textile

Music has always been an important way of expressing ourselves. Creating music from bodily interaction has gained lots of attention: Various gestures, body touch, movements of hand and foot are used as inputs for creating music. The traditional identification and sensing technology, passive ultra-high frequency (UHF) radio frequency identification (RFID) technology, can also be turned into a music player textile for creating music with simple touch or gesture on clothing. When the player touches a specific integrated circuit (IC) copper pad with finger, the unique ID is read by the reader and translated to music via our music software. With this preliminary version of the music player textile, it is possible to play drum and piano. In the future, the technology can be applied to daily clothing for educational musical purposes such as teaching music to kids, as well as professional musical purposes such as performers (showmen and hosts) and lecturers to have playful performances.
Modern technologies and mobile apps in developing speaking skill

Speaking is one of the abilities that students must develop when studying English. Speaking is a necessary technique for communication. Improving pupils' speaking ability has long been a priority in the classroom. Various novel technologies are being developed to educate speaking skills in classrooms in the rapidly expanding twenty-first century. Technology is the means through which we may gain access to this updated environment. Technology has been viewed as a means of assisting pupils in improving language abilities such as speaking ability. The Internet, podcasts, video conferencing, movies, and voice recognition software are regarded as the most effective instruments for training public speaking. The purpose of this paper is to explore some of the current tools that are accessible to English instructors today to help second or foreign language learners improve their speaking skills.