Reviewerly/exHarmony huggingface dataset:
A Sound Definitional Interpreter for a Simply Typed Functional Language

In this paper, we develop, in the proof assistant Coq, a definitional interpreter and a type-checker for a simply typed functional language, and formally prove that the mentioned type-checker is sound with respect to the definitional interpreter via progress and preservation. To represent binders, we embark on the choice of “concrete syntax” in which parameters are just names (or strings).
Analyzing Noise Robustness of Cochleogram and Mel Spectrogram Features in Deep Learning Based Speaker Recognition

The performance of speaker recognition systems is very well on the datasets without noise and mismatch. However, the performance gets degraded with the environmental noises, channel variation, physical and behavioral changes in speaker. The types of Speaker related feature play crucial role in improving the performance of speaker recognition systems. Gammatone Frequency Cepstral Coefficient (GFCC) features has been widely used to develop robust speaker recognition systems with the conventional machine learning, it achieved better performance compared to Mel Frequency Cepstral Coefficient (MFCC) features in the noisy condition. Recently, deep learning models showed better performance in the speaker recognition compared to conventional machine learning. Most of the previous deep learning-based speaker recognition models has used Mel Spectrogram and similar inputs rather than a handcrafted features like MFCC and GFCC features. However, the performance of the Mel Spectrogram features gets degraded in the high noise ratio and mismatch in the utterances. Similar to Mel Spectrogram, Cochleogram is another important feature for deep learning speaker recognition models. Like GFCC features, Cochleogram represents utterances in Equal Rectangular Band (ERB) scale which is important in noisy condition. However, none of the studies have conducted analysis for noise robustness of Cochleogram and Mel Spectrogram in speaker recognition. In addition, only limited studies have used Cochleogram to develop speech-based models in noisy and mismatch condition using deep learning. In this study, analysis of noise robustness of Cochleogram and Mel Spectrogram features in speaker recognition using deep learning model is conducted at the Signal to Noise Ratio (SNR) level from −5 dB to 20 dB. Experiments are conducted on the VoxCeleb1 and Noise added VoxCeleb1 dataset by using basic 2DCNN, ResNet-50, VGG-16, ECAPA-TDNN and TitaNet Models architectures. The Speaker identification and verification performance of both Cochleogram and Mel Spectrogram is evaluated. The results show that Cochleogram have better performance than Mel Spectrogram in both speaker identification and verification at the noisy and mismatch condition.
The Linguistic Characteristics of Article Titles in Applied Linguistics Published in Accredited National Journals of Different Sinta Scores

The journal article (henceforth JA) title is regarded as an advertisement to promote the content of the research article to potential readers. The authors must write and organize their titles to be attractive by paying attention to the syntactic features of titles. This research aims at investing the average length, syntactic features, and the differences of JA titles in Applied Linguistics at different Sinta scores. The method used in this research was a mixed-method with an exploratory sequential design. 120 JA titles were included as corpora of this research taken from six journals at different Sinta scores. The analyses of data were conducted following Cheng et al. (2012)’s framework on the syntactic features, while the analysis of the average length of JA titles by counting the parts of speech of the titles. The results show that the average length of JA titles was in the range of 10.5 to 13.55 words. Moreover, there are three types of syntactic features of JA titles found in this research, namely nominal structure, compound structure, and Verb-Ing phrases. Furthermore, all groups of journals in applied linguistics at different Sinta scores tended to have the same average length and syntactic features of JA titles. The findings of this research give useful significance for Indonesian authors in applied linguistics especially new or novice authors should learn and use the common syntactic features of JA titles when writing an article in English to be submitted to reputable international journals.
SentiSfaction: New cultural way to measure tourist COVID-19 mobility in Italy

From a psycho-linguistic and marketing perspective, the research fits into the evaluation of in the context of tourism and, in particular, tourism mobility, targeting one of the leading Italian rail transport companies, namely Trenitalia. This study, conducted on tweets, aims to examine how talks about the transport service offered by Trenitalia. A total of 674 tweets for the tourist season 2019 and 100 tweets for the tourist season 2020 were collected following the pre-COVID-19 and COVID-19 period. The methodology is the application of sentiment analysis (SA) that produces quantitative and qualitative results. For the quantitative part, the sentiment was calculated first automatically via the Sentistrength software, then an extraction of the frequencies and calculation of the dependence (Chi-square statistic and t-test) between year and polarity was conducted with R, statistical software. The results show that SA is a good methodology of analysis of the online reputation and customer satisfaction of a company that deals with tourism, also in the difference between pre-COVID-19 and COVID-19 period.
PEDAGOGICAL CONDITIONS FOR THE FORMATION OF POLYCULTURAL COMPETENCES OF OLDER ADOLESCENTS IN AN EDUCATIONAL ENVIRONMENT CHILDREN'S MUSIC SCHOOL

The article reveals the problem of the formation of multicultural competences of older teenagers in the educational and educational environment of a children's music school. The author has proven that the effectiveness of the mentioned process will depend on the implementation of the following pedagogical conditions: the implementation of the methodology for the development of interpersonal interaction skills of teenagers in the multicultural environment of a children's music school; implementation of a tolerant approach in the process of forming multicultural competences of older teenagers in the educational environment of a children's music school. It is noted that the multicultural educational environment of the State Secondary School contributes to the formation of a system of knowledge about different cultures in students, the motivation for further familiarization and interaction with their speakers develops, a variety of emotions and feelings are revealed, a positive attitude towards different cultures is formed, interpersonal ties and relations are established in a multinational the student body. The second pedagogical condition is aimed at the readiness and ability of the participants of the educational process to hear and respect the opinion of others, based on the principles of respect and recognition of equality.
The Android-Based Vegetable Retail Sales System uses The Firebase API Method

Developments in the economic sector so far have brought quite a rapid impact in the business world, this has led to differences in sales at the market level. Porong Market is a traditional market located in Porong District, Sidoarjo Regency, East Java Province. Not all traders in Porong market have their own proper land or place to sell their wares. This creates problems when the rainfall is erratic and causes the traders' land to become flooded or muddy. This sales system is designed with the Java programming language which is then implemented on an Android-based system. The results of this study are the android based sales system using the firebase api which has been tested with blackbox testing on several respondents. The benefits of the research are expected to make it easier for the public to find out information about sales at the Porong market. And users can manage merchandise to keep getting a stable income for sellers
Real‐time video anomaly detection for smart surveillance

Abstract Human monitoring of surveillance cameras for anomaly detection may be a monotonous task as it requires constant attention to judge if the captured activities are anomalous or suspicious. This paper exploits background subtraction (BS), convolutional autoencoder, and object detection for a fully automated surveillance system. BS was performed by modelling each pixel as a mixture of Gaussians (MoG) to concatenate only the higher‐order learning in the foreground. Next, the foreground objects are fed to the convolutional autoencoders to filter out abnormal events from normal ones and automatically identify signs of threat and violence in real time. Then, object detection is introduced on the entire scene and the region of interest is highlighted with a bounding box to minimize human intervention in video stream processing. At recognition time, the network generates an alarm for the presence of an anomaly to notify of the identification of potentially suspicious actions. Finally, the complete system is validated upon several benchmark datasets and proved to be robust for complex video anomaly detection. The (AUC) average area under the curve for the frame‐level evaluation for all benchmarks is 94.94%. The best improvement ratio of AUC between the proposed system and state‐of‐the‐art methods is 7.7%.
‘As the title implies’: How readers talk about titles in Amazon book reviews

Most stylistic analyses of literary texts begin with the text proper, largely ignoring the paratextual elements that precede it. The extent of this lacuna within stylistics is so great that a search through the back catalogue of Language and Literature, stylistics’ flagship journal, returns no results for contributions whose titles contain the terms ‘title’ or ‘paratext’. In what follows we underline the implications of this neglect through findings which point to the import of titles for readers’ interpretive processes. Drawing on our analysis of a 58-million-word corpus of book reviews from the online retailer Amazon.com, the research reported on here provides evidence for what many theorists have claimed but for which they have often provided no empirical support, namely, that titles contribute to the establishment of reader expectations about a text.
A Reduced Variable Neighbourhood Search for the Beam Angle Optimisation Problem

Intensity Modulated Radiation Therapy (IMRT) is a widely used radiation therapy technique to treat cancer. The main goal in IMRT is to obtain a treatment plan that eliminates cancer cells from the tumour and, at the same time, damages as little as possible the Organs at Risk (OAR) around the tumour. To this end, we first need to seek the best possible set of beam angles, called beam angle configuration (BAC), to irradiate from. In this paper, we propose a reduced Variable Neighbourhood Search (rVNS) algorithm that explores the search space employing two different local search movements. Unlike traditional VNS algorithms, the rVNS we implement here does not need any transition rule to be implemented, as it includes both neighbourhoods moves at each iteration the rVNS we implement here does not need any transition rule to be implemented, as it includes two types of movements at each iteration. The first movement replaces each beam angle in the BAC by a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\pm 5^{\circ }$</tex-math></inline-formula> beam angle, while the second movement replaces each beam angle in the BAC by a randomly chosen beam angle. We try our approach We test our approach on a set of clinical prostate cases from a hospital in Chile. Results show that the rVNS produces better results than both steepest descent and next descent local search algorithms using the same neighbourhood definitions. The rVNS is showed shown to be faster than the Local Search algorithms and quite competitive w.r.t. the obtained treatment plans.
Semantic Analysis of a Linear Temporal Extension of Quantum Logic and Its Dynamic Aspect

Although various dynamic or temporal logics have been proposed to verify quantum protocols and systems, these two viewpoints have not been studied comprehensively enough. We propose Linear Temporal Quantum Logic (LTQL), a linear temporal extension of quantum logic with a quantum implication, and extend it to Dynamic Linear Temporal Quantum Logic (DLTQL). This logic has temporal operators to express transitions by unitary operators (quantum gates) and dynamic ones to express those by projections (projective measurement). We then prove some logical properties of the relationship between these two transitions expressed by LTQL and DLTQL. A drawback in applying LTQL to the verification of quantum protocols is that these logics cannot express the future operator in linear temporal logic. We propose a way to mitigate this drawback by using a translation from (D)LTQL to Linear Temporal Modal Logic (LTML) and a simulation. This translation reduces the satisfiability problem of (D)LTQL formulas to that of LTML with the classical semantics over quantum states.
Uncertainty Based Optimal Sample Selection for Big Data

In Machine learning and pattern recognition, building a better predictive model is one of the key problems in the presence of big or massive data; especially, if that data contains noisy and unrepresentative data samples.These types of samples adversely affect the learning model and may degrade its performance.To alleviate this problem, sometimes, it becomes necessary to sample the data after eliminating unnecessary instances by maintaining the underlying distribution intact.This process is called sampling or instance selection (IS).However, in this process, a substantial computational cost is involved.This paper discusses an uncertainty based optimal sample selection (UBOSS) method which can select a subset of optimal samples efficiently.Our proposed work comprises three main steps; initially, it uses an IS method to identify the patterns of representative and unrepresentative samples from the original data set; then, an uncertainty-based selector is designed to obtain fuzziness (i.e., a type of uncertainty) of those samples using a classifier whose output is a membership or fuzzy vector; this process further utilizes the divide-and-conquer strategy to obtain a subset of representative samples.Experiments are conducted on six datasets to evaluate the performance of the proposed IS method.Results show that our proposed methodology outperforms when compared with the selection performance (i.e., optimum samples) of the baseline methods (i.e., CNN, IB3, and DROP3).
Development of a novel V-frame octocopter: design, kinematic analysis and simulation using PID controllers with Ziegler Nichols tuning method

Purpose The main purpose of the study was mechanical designing, simulation and manufacturing process for a new model of octocopter V-frame and to achieve simple manufacturing with 3D printing technology. Moreover, the octocopter PID controller was simulated on the Simulink environment to get performance on the roll and pitch angle control. Design/methodology/approach Octocopter is one kind of multirotor vehicle (a rotorcraft with more than two rotors), that has lately gained a lot of attention for both the scientific and commercial spheres. With a greater number of rotors, the multirotor is very maneuverable and robust. Multi-copter makes an important contribution to the technological revolution in the military, industry, transportation, mapping and especially agriculture. Nowadays, we are heading to the four-industrial revolutions as well as the new technological application in the agricultural field such as precision agriculture, mapping and surveillance. Due to recently advanced technology about sensors, electronics, 3D printing, battery with high performance, multi-copter can be manufactured at low cost. Findings The V-frame octocopter was chosen to design in this paper; it had better performance scores including high redundancy rotors, high payload capability and affordable cost than another multi-copter family. The V-frame octocopter increasing freedom field of view of the camera was considered to place the camera position in the front of the drone. Research limitations/implications For the future aspects, the mechanical structure of the octocopter could be improved by using more advanced metal 3D printing to produce the aluminum or titan alloy materials for lighter and more rigid compared with ABS material, and finally the assembly to the real test. Originality/value The study shows the new platform of the V-frame octocopter kinematics analysis, designed on the CAD software, with some important mechanical parts using FEM analysis to find the highest stress and displacement under high load applied, the result of all connecting the joints 3D printing part is completely safe. Mechanical parts were manufactured by using 3D printing technology and CNC milling. Moreover, the study has shown V-frame octocopter simulation based on Simulink using the second method Ziegler- Nichols to find suitable parameters of the PID controller for roll and pitch angle. Using the block simulation is good for implementing and fast checking the new algorithm when building the new platform of the robot.
DFLER: Drone Flight Log Entity Recognizer to support forensic investigation on drone device

DFLER is an open-source CLI-based tool developed using Python and supported by a fine-tuned BERT model to perform named entity recognition on drone flight log data, specifically the log messages. This model is hosted on the HuggingFace platform to make it publicly available and accessible. The tool expects decrypted DJI flight log files as input and generates a forensic report in a PDF containing a forensic timeline highlighting parts of the mentioned entities in the log messages. The generated file is an attachment to a complete forensic report and helps forensic investigators pinpoint critical events on the constructed forensic timeline.
Non-standard linear recurring sequence subgroups in finite fields and automorphisms of cyclic codes I

A pair (n,q) with q a prime power and n a positive integer for which gcd⁡(n,q)=1 is non-standard if one of the following properties holds, which we will show to be equivalent. − The group Un,q of n-th roots-of-unity in the splitting field Fqm of xn−1 over Fq is fixed set-wise by an Fq-linear map on Fqm that is not of the form x⟶αxqj. − There is a non-cyclic linear recurring sequence s of period n (so with s not of the form si=αξi for i≥0) with associated characteristic polynomial being irreducible over Fq, such that Un,q={s0,…,sn−1}. In that case, the group Un,q is called non-standard by Brison and Nogueira, who studied this phenomenon in a sequence of papers. − A q-ary irreducible cyclic code of length n has “extra” permutation automorphisms (some well-known examples are the (duals of the) Golay codes and binary simplex codes for n≥7). We will refer to such codes as non-standard irreducible cyclic codes or NSIC-codes. We first investigate non-standard linear recurring sequence subgroups and establish the equivalence between the first and second of the above properties. Then we investigate permutation automorphisms of general (repeated-root) cyclic codes and irreducible cyclic codes and establish the equivalence between the first and third of these properties. We also introduce a notion of non-standardness for general cyclic codes, and relate it to our earlier definition of NSIC-codes. This paper is the first part of an expanded version of the arXiv paper [28].
Siamese labels auxiliary learning

In deep learning, auxiliary modules for model training have become increasingly popular, such as Deep Mutual Learning (DML) and Multi-Scale Dense Convolutional Networks (MSDNet), which can maximize the performance of the model without increasing the amount of test computation. Nevertheless, current research does not fully exploit the knowledge between different auxiliary modules. This paper proposes a new model training technique–Siamese Labels Auxiliary (SiLA) Learning, in which the SiLA module is designed to concatenate the outputs of the modules to get auxiliary information. In addition, the knowledge learned from different modules can be fully exploited by auxiliary information. Our experiments show that SiLA Learning effectively improves the performance of standard models and achieves convincing experimental results on image classification tasks without increasing the amount of test computation. Moreover, the experiments show that SiLA Learning can be easily combined with methods such as DML and MSDNet to exploit the knowledge fully and perform best.
Maximum likelihood FIR filter for systems with Bernoulli distributed random delays and measurement failures

A new solution for the state estimation problems for the state-space model with random time delay and measurement failures is proposed, and the batch and iterative versions of the finite impulse response (FIR) filter are constructed based on the theorem of maximum likelihood in this paper. First, the state model is addressed with the maximum likelihood in the situation of random delays and measurement failures. Then the state-space model is expressed as a linear probability function following a random Bernoulli distribution. Finally, the proposed algorithm is compared to an adaptive Kalman filter and the UFIR filter using a drifting stochastic resonator and rotary flexible joint simulation analysis. It shows that the proposed maximum likelihood FIR method has improved estimation performance and robustness in coping with random delays and measurement failures compared to some commonly used algorithms.
Assessment of Digital Teaching Skills—A Pilot Study Based on the Precepts of Digcompedu

This study aimed to evaluate the level of digital competence of a group of 17 teachers by applying a questionnaire of the European Digital Competence Framework for Educators DigCompEdu [1]. With this information, we aimed to evaluate which are the areas of mastery and use of digital technologies in which there is less development of skills and use of these technologies when these professionals work in the classroom. This diagnostic will be part of a pilot study that will be the materials and methods of a study of bigger proportions, with a larger number of teachers. Therefore, the participants accessed the questionnaire on the DigCompEdu platform, and we collected their answers. The answers were analyzed and the teachers’ scores allowed to classify them into six levels of digital competence based on the metrics proposed by DigCompEdu questionnaire. The results allowed us to identify that most teachers present intermediary levels of digital competence development, being classified with the general score B2 and present a good comprehension of the use of technological resources in the classroom. However, they not always show the necessary criticism and history of experimentation to dare to implement different tools. Finally, it was possible to identify the existing gaps in the digital competence of teachers, making it possible to map the points that need to be worked on, thus allowing these teachers to make autonomous and assertive decisions regarding their professional training.