Miracle-dz/UniFAR-FTUs huggingface dataset:
Band-Notched UWB Antenna with Switchable and Tunable Performance

A band-notched UWB antenna is presented, which can switch between two notch bands and tune the central frequency simultaneously. It is the first time that the switchable and tunable behaviours are combined together in band-notched UWB antennas. In the band-notched structure, PIN diodes are used to switch the lower and upper frequency bands, while varactors could vary the central frequency of each notch band continuously. Measurement results show that the notch bands could switch between 4.2 GHz and 5.8 GHz when the state of varactors is fixed, and the ranges of tuning are 4.2–4.8 GHz and 5.8–6.5 GHz when the state of PIN diodes is ON and OFF, respectively.
Serum Nerve Growth Factor Levels as a Predictor of Bull Candidate Semen Quality of Madura Cattle

Madura cattle are the germplasm of native cattle on the verge of extinction because of crossbreeding. Therefore, the present study was aimed to determine the serum nerve growth factor (NGF) concentration as a predictor of fresh ejaculate fertility parameters in Madura bull candidates. Eleven Madura bull candidates used for frozen semen production were selected for the study. Blood samples were collected using a vacutainer from the jugular vein for analyzing serum NGF and testosterone levels. Meanwhile, semen collection was conducted using an artificial vagina for sperm motility, viability, and concentration assessment. Data were analyzed to determine the correlation among variables and the linear regression of NGF concentration to other significant variables. The result showed that NGF had a significant correlation (p < 0.05) with testosterone levels, sperm motility, viability, and concentration. A significant correlation was observed between testosterone levels, sperm concentration, and sperm viability. The regression equation among significantly correlated variables was determined. For artificial insemination, suitable bull ejaculates for obtaining frozen semen should reach at least 2.12 ng/mL of NGF levels, with sperm viability, sperm concentration, and testosterone levels of more than 78.63%, 1,462.177 million/mL ejaculate, and 25.67 ng/mL, respectively. This is the first study to identify NGF as a predictor of male fertility in bull candidates of Madura cattle. Therefore, NGF levels could be used as a marker of male fertility in Madura bull cattle candidates. Thus, based on the minimum NGF levels, the ejaculate of Madura bull candidate that meets the requirements for frozen semen production could predict fertility.
The changes after physical therapy in non-specific neck pain patients

Non-specific neck pain is one of the most common pain after low back pain. Physiotherapy interventions are used as a treatment to reduce pain and functional status in such patients. This study aims to see the changes after physiotherapy in non-specific neck pain. To see the changes after physiotherapy in non-specific neck pain. A case series was performed on 35 patients in Dhiraj General Hospital at physiotherapy department. Physiotherapy interventions including electro therapeutic modalities (like moist pack, ultrasound) along with stretching and strengthening exercise, massage to neck musculature was given for 3 days to patients with non-specific neck pain who were referred for physiotherapy. Outcome measure was taken in form of NPRS & NDI. A Decrease in NPRS and improvement in NDI was noted after the treatment sessions for 3 consecutive days. It is concluded that physiotherapy intervention is effective in reducing pain and improving functional status in patients with nonspecific neck pain.
ANÁLISIS SHIFT-SHARE PARA LA TOMA DE DECISIONES DE INTERNACIONALIZACIÓN EN PYMEs

Este artículo tiene por objetivo presentar una aplicación del análisis Shift-Share para la selección de países con fines de internacionalización, como una herramienta significativa para la toma de decisiones de las pequeñas y medianas empresas. Se llevó a cabo el análisis para examinar a los países ganadores y perdedores de las cuotas de mercado en cuanto a importación del producto que el emprendedor hipotético deseaba internacionalizar. El ejemplo presentado muestra la viabilidad del análisis para una correcta orientación en mercados internacionales para las PYMEs. El análisis Shift-Share llevado a cabo reveló que cinco países (Australia, Austria, Burkina Faso, Bulgaria y Bahréin), de los 11 analizados en la región, ganaron participación en el mercado internacional de servicios durante el período de estudio, demostrando su viabilidad para un proceso de internacionalización.
Phishing Classification Techniques: A Systematic Literature Review

Phishing has become a serious and concerning problem within the past 10 years, with many reviews describing attack patterns and anticipating different method utilizations. This indicates that the results are still not comprehensive, subsequently leaving a critical gap in phishing reports. Therefore, this study aims to conduct a systematic review, to show a more crucial issue in phishing attacks, namely classification techniques. These issues were categorized into techniques, datasets, performance evaluation, and phishing types. The obtained results are expected to help developers prevent future phishing attacks more effectively, especially in selectively and carefully determining the techniques and evaluations to address specific types of phishing.
A Pragmatic Ensemble Strategy for Missing Values Imputation in Health Records

Pristine and trustworthy data are required for efficient computer modelling for medical decision-making, yet data in medical care is frequently missing. As a result, missing values may occur not just in training data but also in testing data that might contain a single undiagnosed episode or a participant. This study evaluates different imputation and regression procedures identified based on regressor performance and computational expense to fix the issues of missing values in both training and testing datasets. In the context of healthcare, several procedures are introduced for dealing with missing values. However, there is still a discussion concerning which imputation strategies are better in specific cases. This research proposes an ensemble imputation model that is educated to use a combination of simple mean imputation, k-nearest neighbour imputation, and iterative imputation methods, and then leverages them in a manner where the ideal imputation strategy is opted among them based on attribute correlations on missing value features. We introduce a unique Ensemble Strategy for Missing Value to analyse healthcare data with considerable missing values to identify unbiased and accurate prediction statistical modelling. The performance metrics have been generated using the eXtreme gradient boosting regressor, random forest regressor, and support vector regressor. The current study uses real-world healthcare data to conduct experiments and simulations of data with varying feature-wise missing frequencies indicating that the proposed technique surpasses standard missing value imputation approaches as well as the approach of dropping records holding missing values in terms of accuracy.
A Survey on Advanced Encryption Standard

: Rijndael’s Advanced Encryption Standard (AES) is the block cipher based symmetric-key cryptography to protect the sensitive information. The key sizes of AES are 128, 192, 256 bits. AES is based on substitution-permutation strategy. It is accepted by NIST in 2001 after the five year of security evaluation. It is highly secured and efficient than Data Encryption Standard (DES) and other symmetric-key cryptographic algorithms. This paper depicts all the valuable work done on the Advanced Encryption Standard since it is accepted by National Institute of Standards and Technology (NIST).
Machine Learning-Based Performance Comparison to Diagnose Anterior Cruciate Ligament Tears

In recent times, knee joint pains have become severe enough to make daily tasks difficult. Knee osteoarthritis is a type of arthritis and a leading cause of disability worldwide. The middle of the knee contains a vital portion, the anterior cruciate ligament (ACL). It is necessary to diagnose the ACL ruptured tears early to avoid surgery. The study aimed to perform a comparative analysis of machine learning models to identify the condition of three ACL tears. In contrast to previous studies, this study also considers imbalanced data distributions as machine learning techniques struggle to deal with this problem. The paper applied and analyzed four machine learning classification models, namely, random forest (RF), categorical boosting (Cat Boost), light gradient boosting machines (LGBM), and highly randomized classifier (ETC) on the balanced, structured dataset of ACL. After oversampling a hyperparameter adjustment, the above four models have achieved an average accuracy of 95.72%, 94.98%, 94.98%, and 98.26%. There are 2070 observations and eight features in the collection of three diagnosis ACL classes after oversampling. The area under curve value was approximately 0.998, respectively. Experiments were performed using twelve machine learning algorithms with imbalanced and balanced datasets. However, the accuracy of the imbalanced dataset has remained under 76% for all twelve models. After oversampling, the proposed model may contribute to the investigation of ACL tears on magnetic resonance imaging and other knee ligaments efficiently and automatically without involving radiologists.
Opleiding Informatica & Economie Explainable Artiﬁcial Intelligence: a Checklist for the Insurance Market

Artiﬁcial intelligence (AI) can be a powerful tool to accomplish a great many tasks. This exciting branch of technology is being adopted increasingly across varying sectors, including healthcare and insurance. With that potential often arise several complications. One of which is a lack of transparency and explainability of an algorithm for experts and non-experts alike. This brings into question both the usefulness as well as the accuracy of the algorithm, coupled with an added difﬁculty to assess potential biases within the model. In this thesis, we investigate the current usage of AI algorithms in the Dutch insurance industry and the adoption of explainable artiﬁcial intelligence (XAI) techniques. Armed with this knowledge we design a checklist for insurance companies that should help assure quality standards and a solid foundation for co-operation between ﬁrms. This checklist extends an existing checklist that SIVI, a knowledge and standardisation institute for ﬁnancial services, offers its member organisations.
Delving Deep into Regularity: A Simple but Effective Method for Chinese Named Entity Recognition

Recent years have witnessed the improving performance of Chinese Named Entity Recognition (NER) from proposing new frameworks or incorporating word lexicons. However, the inner composition of entity mentions in character-level Chinese NER has been rarely studied. Actually, most mentions of regular types have strong name regularity. For example, entities end with indicator words such as"company"or"bank"usually belong to organization. In this paper, we propose a simple but effective method for investigating the regularity of entity spans in Chinese NER, dubbed as Regularity-Inspired reCOgnition Network (RICON). Specifically, the proposed model consists of two branches: a regularity-aware module and a regularityagnostic module. The regularity-aware module captures the internal regularity of each span for better entity type prediction, while the regularity-agnostic module is employed to locate the boundary of entities and relieve the excessive attention to span regularity. An orthogonality space is further constructed to encourage two modules to extract different aspects of regularity features. To verify the effectiveness of our method, we conduct extensive experiments on three benchmark datasets and a practical medical dataset. The experimental results show that our RICON significantly outperforms previous state-of-the-art methods, including various lexicon-based methods.
Automated and manual testing as part of the research software development process of RCE

Research software is often developed by individual researchers or small teams in parallel to their research work. The more people and research projects rely on the software in question, the more important it is that software updates implement new features correctly and do not introduce regressions. Thus, developers of research software must balance their limited resources between implementing new features and thoroughly testing any code changes. We present the processes we use for developing the distributed integration framework RCE at DLR. These processes aim to strike a balance between automation and manual testing, reducing the testing overhead while addressing issues as early as possible. We furthermore brieﬂy describe how these testing processes integrate with the surrounding processes for development and releasing.
NumGLUE: A Suite of Fundamental yet Challenging Mathematical Reasoning Tasks

Given the ubiquitous nature of numbers in text, reasoning with numbers to perform simple calculations is an important skill of AI systems. While many datasets and models have been developed to this end, state-of-the-art AI systems are brittle; failing to perform the underlying mathematical reasoning when they appear in a slightly different scenario. Drawing inspiration from GLUE that was proposed in the context of natural language understanding, we propose NumGLUE, a multi-task benchmark that evaluates the performance of AI systems on eight different tasks, that at their core require simple arithmetic understanding. We show that this benchmark is far from being solved with neural models including state-of-the-art large-scale language models performing significantly worse than humans (lower by 46.4 %). Further, NumGLUE promotes sharing knowledge across tasks, especially those with limited training data as evidenced by the superior performance (average gain of 3.4 % on each task) when a model is jointly trained on all the tasks as opposed to task-specific modeling. Finally, we hope that NumGLUE will encourage systems that perform robust and general arithmetic reasoning within language, a first step towards being able to perform more complex mathematical reasoning.
Do Not Fire the Linguist: Grammatical Profiles Help Language Models Detect Semantic Change

Morphological and syntactic changes in word usage — as captured, e.g., by grammatical profiles — have been shown to be good predictors of a word’s meaning change. In this work, we explore whether large pre-trained contextualised language models, a common tool for lexical semantic change detection, are sensitive to such morphosyntactic changes. To this end, we first compare the performance of grammatical profiles against that of a multilingual neural language model (XLM-R) on 10 datasets, covering 7 languages, and then combine the two approaches in ensembles to assess their complementarity. Our results show that ensembling grammatical profiles with XLM-R improves semantic change detection performance for most datasets and languages. This indicates that language models do not fully cover the fine-grained morphological and syntactic signals that are explicitly represented in grammatical profiles. An interesting exception are the test sets where the time spans under analysis are much longer than the time gap between them (for example, century-long spans with a one-year gap between them). Morphosyntactic change is slow so grammatical profiles do not detect in such cases. In contrast, language models, thanks to their access to lexical information, are able to detect fast topical changes.
How to design a network architecture using capacity planning

Building a network architecture must answer to organization needs, but also to two major elements which are the need for dependability and performance. By performance, we must understand the ability to meet an immediate need and the ability to scale without reducing the performance of the whole as new elements are added to the network infrastructure. This last point is covered by Capacity Planning domain.
A Survey on Crowdsourcing Applications in Smart Cities

With the emergence of the Internet of things (IoT), human life is now progressing towards smartification faster than ever before. Thus, smart cities become automated in different aspects such as business, education, economy, medicine, and urban areas. Since smartification requires a variety of dynamic information in different urban dimensions, mobile crowdsourcing has gained importance in smart cities. This chapter systematically reviews the related applications of smart cities that use mobile crowdsourcing for data acquisition. For this purpose, the applications are classified as environmental, urban life, and transportation categories and then investigated in detail. This survey helps in understanding the current situation of smart cities from the viewpoint of crowdsourcing and discusses the future research directions in this field.
Mining Logical Event Schemas From Pre-Trained Language Models

We present NESL (the Neuro-Episodic Schema Learner), an event schema learning system that combines large language models, FrameNet parsing, a powerful logical representation of language, and a set of simple behavioral schemas meant to bootstrap the learning process. In lieu of a pre-made corpus of stories, our dataset is a continuous feed of “situation samples” from a pre-trained language model, which are then parsed into FrameNet frames, mapped into simple behavioral schemas, and combined and generalized into complex, hierarchical schemas for a variety of everyday scenarios. We show that careful sampling from the language model can help emphasize stereotypical properties of situations and de-emphasize irrelevant details, and that the resulting schemas specify situations more comprehensively than those learned by other systems.
Free Monads, Intrinsic Scoping, and Higher-Order Preunification

Type checking algorithms and theorem provers rely on unification algorithms. In presence of type families or higher-order logic, higher-order (pre)unification (HOU) is required. Many HOU algorithms are expressed in terms of $\lambda$-calculus and require encodings, such as higher-order abstract syntax, which are sometimes not comfortable to work with for language implementors. To facilitate implementations of languages, proof assistants, and theorem provers, we propose a novel approach based on the second-order abstract syntax of Fiore, data types \`a la carte of Swierstra, and intrinsic scoping of Bird and Patterson. With our approach, an object language is generated freely from a given bifunctor. Then, given an evaluation function and making a few reasonable assumptions on it, we derive a higher-order preunification procedure on terms in the object language. More precisely, we apply a variant of $E$-unification for second-order syntax. Finally, we briefly demonstrate an application of this technique to implement type checking (with type inference) for Martin-L\"of Type Theory, a dependent type theory.
ADFF: Attention Based Deep Feature Fusion Approach for Music Emotion Recognition

Music emotion recognition (MER), a sub-task of music information retrieval (MIR), has developed rapidly in recent years. However, the learning of affect-salient features remains a challenge. In this paper, we propose an end-to-end attention-based deep feature fusion (ADFF) approach for MER. Only taking log Mel-spectrogram as input, this method uses adapted VGGNet as spatial feature learning module (SFLM) to obtain spatial features across different levels. Then, these features are fed into squeeze-and-excitation (SE) attention-based temporal feature learning module (TFLM) to get multi-level emotion-related spatial-temporal features (ESTFs), which can discriminate emotions well in the final emotion space. In addition, a novel data processing is devised to cut the single-channel input into multi-channel to improve calculative efficiency while ensuring the quality of MER. Experiments show that our proposed method achieves 10.43% and 4.82% relative improvement of valence and arousal respectively on the R2 score compared to the state-of-the-art model, meanwhile, performs better on datasets with distinct scales and in multi-task learning.
Discovery of ATLAS17jrp as an Optical-, X-Ray-, and Infrared-bright Tidal Disruption Event in a Star-forming Galaxy

We hereby report the discovery of ATLAS17jrp as an extraordinary tidal disruption event (TDE) in the star-forming galaxy SDSS J162034.99+240726.5 in our recent sample of mid-infrared outbursts in nearby galaxies. Its optical/UV light curves rise to a peak luminosity of ∼1.06 × 1044 erg s−1 in about a month and then decay as t −5/3 with a roughly constant temperature around 19,000 K, and the optical spectra show a blue continuum and very broad Balmer lines with FWHM ∼ 15,000 km s−1, which gradually narrowed to 1400 km s−1 within 4 yr, all agreeing well with other optical TDEs. A delayed and rapidly rising X-ray flare with a peak luminosity of ∼1.27 × 1043 erg s−1 was detected ∼170 days after the optical peak. The high MIR luminosity of ATLAS17jrp (∼2 × 1043 erg s−1) has revealed a distinctive dusty environment with a covering factor as high as ∼0.2, which is comparable to that of a torus in active galactic nuclei but at least one order of magnitude higher than normal optical TDEs. Therefore, ATLAS17jrp turns out to be one of the rare unambiguous TDEs found in star-forming galaxies, and its high dust-covering factor implies that dust extinction could play an important role in the absence of optical TDEs in star-forming galaxies.
GORDA: Graph-Based Orientation Distribution Analysis of SLI Scatterometry Patterns of Nerve Fibres

Scattered Light Imaging (SLI) is a novel approach for microscopically revealing the fibre architecture of unstained brain sections. The measurements are obtained by illuminating brain sections from different angles and measuring the transmitted (scattered) light under normal incidence. The evaluation of scattering profiles commonly relies on a peak picking technique and feature extraction from the peaks, which allows quantitative determination of parallel and crossing in-plane nerve fibre directions for each image pixel. However, the estimation of the 3D orientation of the fibres cannot be assessed with the traditional methodology. We propose an unsupervised learning approach using spherical convolutions for estimating the 3D orientation of neural fibres, resulting in a more detailed interpretation of the fibre orientation distributions in the brain.
Video Captioning: a comparative review of where we are and which could be the route

Video captioning is the process of describing the content of a sequence of images capturing its semantic relationships and meanings. Dealing with this task with a single image is arduous, not to mention how difficult it is for a video (or images sequence). The amount and relevance of the applications of video captioning are vast, mainly to deal with a significant amount of video recordings in video surveillance, or assisting people visually impaired, to mention a few. To analyze where the efforts of our community to solve the video captioning task are, as well as what route could be better to follow, this manuscript presents an extensive review of more than 105 papers for the period of 2016 to 2021. As a result, the most-used datasets and metrics are identified. Also, the main approaches used and the best ones. We compute a set of rankings based on several performance metrics to obtain, according to its performance, the best method with the best result on the video captioning task. Finally, some insights are concluded about which could be the next steps or opportunity areas to improve dealing with this complex task.