ANALIZA POGREŠAKA U PREVOĐENJU U APLIKACIJI TREAT: WINDOWS APLIKACIJI KOJA SE TEMELJI NA MQM OKVIRU

The aim of this research paper is to conduct a thorough analysis of inter-annotator agreement in the process of error analysis, which is well-known for its subjectivity and low level of agreement. Since the process is tiresome in its nature and the available user interfaces are pretty distinct from what the average annotator is accustomed to, a user-friendly Windows 10 application offering a more attractive user interface is developed with the aim to simplify the process of error analysis. Translations are performed with Google Translate engine and English-Croatian is selected as the language pair. Since there has been a lot of dispute on inter-annotator agreement and the need for guidelines has been often been emphasized as crucial, the annotators are given a very detailed introduction into the process of error analysis itself. They are given a presentation with a list of the MQM guidelines enriched with tricky cases. All annotators are native speakers of Croatian as the target language and have a linguistic background. The results demonstrate that a stronger agreement indicates more similar backgrounds and that the task of selecting annotators should be conducted more carefully. Furthermore, a training phase on a similar test set is deemed necessary in order to gain a stronger agreement.Cilj rada je izvršiti temeljitu analizu slaganja među označivačima u postupku analize pogrešaka koji je poznat po svojoj subjektivnosti i niskoj razini slaganja. Budući da je sam postupak po prirodi zamoran, a sučelja dostupnih alata i usluga poprilično se razlikuju od onog na što je prosječni označivač naviknut, u svrhu pojednostavljenja samog postupka analize pogrešaka razvijena je Windows 10 aplikacija s poznatim i atraktivnim korisničkim sučeljem. Englesko-hrvatski prijevodi preuzeti su s usluge Google Translate. Budući da je slaganje među označivačima čest predmet rasprave i da je od neospornog značaja istaknuta potreba za smjernicama, označivačima je dan vrlo detaljan uvid u postupak analize pogrešaka. Također, popis MQM smjernica uz primjere potencijalnih pogrešaka uobličen je u prezentaciju i dan označivačima na raspolaganje. Označivačima je ciljni, tj. hrvatski jezik materinski, a svi imaju određenu razinu lingvističke pozadine. Rezultati otkrivaju da veća razina slaganja ukazuje na sličnije formalno obrazovanje i da proces odabira označivača treba biti pažljivo osmišljen. Štoviše, testiranje na sličnom skupu podataka trebalo bi prethoditi odabiru označivača kako bi se postigla veća razina slaganja

Uvid u automatsko izlučivanje metaforičkih kolokacija

Collocations have been the subject of much scientific research over the years. The focus of this research is on a subset of collocations, namely metaphorical collocations. In metaphorical collocations, a semantic shift has taken place in one of the components, i.e., one of the components takes on a transferred meaning. The main goal of this paper is to review the existing literature and provide a systematic overview of the existing research on collocation extraction, as well as the overview of existing methods, measures, and resources. The existing research is classified according to the approach (statistical, hybrid, and distributional semantics) and presented in three separate sections. The insights gained from existing research serve as a first step in exploring the possibility of developing a method for automatic extraction of metaphorical collocations. The methods, tools, and resources that may prove useful for future work are highlighted.Kolokacije su već dugi niz godina tema mnogih znanstvenih istraživanja. U fokusu ovoga istraživanja podskupina je kolokacija koju čine metaforičke kolokacije. Kod metaforičkih je kolokacija kod jedne od sastavnica došlo do semantičkoga pomaka, tj. jedna od sastavnica poprima preneseno značenje. Glavni su ciljevi ovoga rada istražiti postojeću literaturu te dati sustavan pregled postojećih istraživanja na temu izlučivanja kolokacija i postojećih metoda, mjera i resursa. Postojeća istraživanja opisana su i klasificirana prema različitim pristupima (statistički, hibridni i zasnovani na distribucijskoj semantici). Također su opisane različite asocijativne mjere i postojeći načini procjene rezultata automatskoga izlučivanja kolokacija. Metode, alati i resursi koji su korišteni u prethodnim istraživanjima, a mogli bi biti korisni za naš budući rad posebno su istaknuti. Stečeni uvidi u postojeća istraživanja čine prvi korak u razmatranju mogućnosti razvijanja postupka za automatsko izlučivanje metaforičkih kolokacija

Qualitative Modelling and Reasoning About Behavior of Objects in the Dynamic Vision System

U radu je predstavljen sustav LABAQM koji je razvijen za analizu ponašanja laboratorijske životinje. Sustav se temelji na kvalitativnom modeliranju kretanja životinje. Bavimo se fazom raspoznavanja, koja predstavlja višu razinu analize ponašanja laboratorijske životinje, tijekom farmakoloških eksperimenata. Ulazni podaci sustava su kvantitativni podaci koje dobiva od aplikacije za praćenje, te nepotpuno znanje eksperta. Sustav LABAQM radi u dvije glavne faze: faza učenja ponašanja i faza analize ponašanja. Faza učenja i faza analize se temelje na nizovima simbola dobivenim transformacijom kvantitativnih podataka. Faza učenja uključuje postupak nadgledanog učenja, postupak nenadgledanog učenja i njihovu kombinaciju. Pokazali smo da kvalitativne modele ponašanja možemo modelirati kao skrivene Markovljev modele (SMM-e). Rezultati fuzije postupaka nadgledanog i nenadgledanog učenja su robustniji modeli karakterističnih ponašanja, koji se koriste u fazi analize ponašanja.The paper presents the LABAQM system developed for the analysis of laboratory animal behaviours. It is based on qualitative modelling of animal motions. We are dealing with the cognitive phase of the laboratory animal behaviour analysis as a part of the pharmacological experiments. The system is based on the quantitative data from the tracking application and incomplete domain background knowledge. The LABAQM system operates in two main phases: behaviour learning and behaviour analysis. The behaviour learning and behaviour analysis phase are based on symbol sequences, obtained by the transformation of the quantitative data. Behaviour learning phase includes supervised learning procedure, unsupervised learning procedure and their combination. We have shown that the qualitative model of behaviour can be modelled by hidden Markov models. The fusion of supervised and unsupervised learning procedures produces more robust models of characteristic behaviours, which are used in the behaviour analysis phase

Qualitative Modelling and Reasoning About Behavior of Objects in the Dynamic Vision System

U radu je predstavljen sustav LABAQM koji je razvijen za analizu ponašanja laboratorijske životinje. Sustav se temelji na kvalitativnom modeliranju kretanja životinje. Bavimo se fazom raspoznavanja, koja predstavlja višu razinu analize ponašanja laboratorijske životinje, tijekom farmakoloških eksperimenata. Ulazni podaci sustava su kvantitativni podaci koje dobiva od aplikacije za praćenje, te nepotpuno znanje eksperta. Sustav LABAQM radi u dvije glavne faze: faza učenja ponašanja i faza analize ponašanja. Faza učenja i faza analize se temelje na nizovima simbola dobivenim transformacijom kvantitativnih podataka. Faza učenja uključuje postupak nadgledanog učenja, postupak nenadgledanog učenja i njihovu kombinaciju. Pokazali smo da kvalitativne modele ponašanja možemo modelirati kao skrivene Markovljev modele (SMM-e). Rezultati fuzije postupaka nadgledanog i nenadgledanog učenja su robustniji modeli karakterističnih ponašanja, koji se koriste u fazi analize ponašanja.The paper presents the LABAQM system developed for the analysis of laboratory animal behaviours. It is based on qualitative modelling of animal motions. We are dealing with the cognitive phase of the laboratory animal behaviour analysis as a part of the pharmacological experiments. The system is based on the quantitative data from the tracking application and incomplete domain background knowledge. The LABAQM system operates in two main phases: behaviour learning and behaviour analysis. The behaviour learning and behaviour analysis phase are based on symbol sequences, obtained by the transformation of the quantitative data. Behaviour learning phase includes supervised learning procedure, unsupervised learning procedure and their combination. We have shown that the qualitative model of behaviour can be modelled by hidden Markov models. The fusion of supervised and unsupervised learning procedures produces more robust models of characteristic behaviours, which are used in the behaviour analysis phase

Qualitative Modelling and Reasoning About Behavior of Objects in the Dynamic Vision System

U radu je predstavljen sustav LABAQM koji je razvijen za analizu ponašanja laboratorijske životinje. Sustav se temelji na kvalitativnom modeliranju kretanja životinje. Bavimo se fazom raspoznavanja, koja predstavlja višu razinu analize ponašanja laboratorijske životinje, tijekom farmakoloških eksperimenata. Ulazni podaci sustava su kvantitativni podaci koje dobiva od aplikacije za praćenje, te nepotpuno znanje eksperta. Sustav LABAQM radi u dvije glavne faze: faza učenja ponašanja i faza analize ponašanja. Faza učenja i faza analize se temelje na nizovima simbola dobivenim transformacijom kvantitativnih podataka. Faza učenja uključuje postupak nadgledanog učenja, postupak nenadgledanog učenja i njihovu kombinaciju. Pokazali smo da kvalitativne modele ponašanja možemo modelirati kao skrivene Markovljev modele (SMM-e). Rezultati fuzije postupaka nadgledanog i nenadgledanog učenja su robustniji modeli karakterističnih ponašanja, koji se koriste u fazi analize ponašanja.The paper presents the LABAQM system developed for the analysis of laboratory animal behaviours. It is based on qualitative modelling of animal motions. We are dealing with the cognitive phase of the laboratory animal behaviour analysis as a part of the pharmacological experiments. The system is based on the quantitative data from the tracking application and incomplete domain background knowledge. The LABAQM system operates in two main phases: behaviour learning and behaviour analysis. The behaviour learning and behaviour analysis phase are based on symbol sequences, obtained by the transformation of the quantitative data. Behaviour learning phase includes supervised learning procedure, unsupervised learning procedure and their combination. We have shown that the qualitative model of behaviour can be modelled by hidden Markov models. The fusion of supervised and unsupervised learning procedures produces more robust models of characteristic behaviours, which are used in the behaviour analysis phase

LABAQM - A SYSTEM FOR QUALITATIVE MODELLING AND ANALYSIS OF ANIMAL BEHAVIOUR

Tracking of a laboratory animal and its behaviour interpretation based on frame sequence analysis have been traditionally quantitative and typically generates large amounts of temporally evolving data. In our work we are dealing with higher-level approaches such as conceptual clustering and qualitative modelling in order to represent data obtained by tracking. We present the LABAQM system developed for the analysis of laboratory animal behaviours. It is based on qualitative modelling of animal motions. We are dealing with the cognitive phase of the laboratory animal behaviour analysis as a part of the pharmacological experiments. The system is based on the quantitative data from the tracking application and incomplete domain background knowledge. The LABAQM system operates in two main phases: behaviour learning and behaviour analysis. The behaviour learning and behaviour analysis phase are based on symbol sequences, obtained by the transformation of the quantitative data. Behaviour learning phase includes supervised learning procedure, unsupervised learning procedure and their combination. The fusion of supervised and unsupervised learning procedures produces more robust models of characteristic behaviours, which are used in the behaviour analysis phase

Postharvest Quality Monitoring and Variance Analysis of Peach and Nectarine Cold Chain with Multi-Sensors Technology

Fresh peaches and nectarines are very popular for their high nutritional and therapeutic value. Unfortunately, they are prone to rapid deterioration after harvest, especially if the cold chain is not well maintained. The objective of this work is to study the environmental fluctuation and the quality change of fresh peaches and nectarines in cold chain. The temperature, relative humidity, and CO2 level were real-time monitored by sensor nodes with a wireless sensor network (WSN). The cold chain lasted for 16.8 h and consisted of six segments. The dynamic change of temperature, relative humidity, and CO2 level were real-time monitored and analyzed in detail in each of the six stages. The fruit quality index (fruit weight, fruit firmness, and soluble solids concentration (SSC)) were detected and analyzed immediately before the first stage (S1) and at the beginning of the last stage (S6). The results show that without good temperature control fruit softening is the most significant problem, even in a short chain; the WSN node can provide complete and accurate temperature, humidity, and gas monitoring information for cold chains, and can be used to further improve quality and safety assurance for peach fruit cold chains

Utilization of Explainable Machine Learning Algorithms for Determination of Important Features in ‘Suncrest’ Peach Maturity Prediction

Peaches (Prunus persica (L.) Batsch) are a popular fruit in Europe and Croatia. Maturity at harvest has a crucial influence on peach fruit quality, storage life, and consequently consumer acceptance. The main goal of this study is to develop a machine learning model that will detect the most important features for predicting peach maturity by first training models and then using the importance ratings of these models to detect nonlinear (and linear) relationships. Thus, the most important peach features at a given stage of its ripening could be revealed. To date, this method has not been used for this purpose, and at the same time, it has the potential to be applied to other similar peach varieties. A total of 33 fruit features are measured on the harvested peaches, and three imbalanced datasets are created using firmness thresholds of 1.84, 3.57, and 4.59 kg·cm−2. These datasets are balanced using the SMOTE and ROSE techniques, and the Random Forest machine learning model is trained on them. Permutation Feature Importance (PFI), Variable Importance (VI), and LIME interpretability methods are used to detect variables that most influence predictions in the given machine learning models. PFI shows that the h° and a* ground color parameters, COL ground color index, SSC/TA, and TA inner quality parameters are among the top ten most contributing variables in all three models. Meanwhile, VI shows that this is the case for the a* ground color parameter, COL and CCL ground color indexes, and the SSC/TA inner quality parameter. The fruit flesh ratio is highly positioned (among the top three according to PFI) in two models, but it is not even among the top ten in the third

Assessment of Various Machine Learning Models for Peach Maturity Prediction Using Non-Destructive Sensor Data

To date, many machine learning models have been used for peach maturity prediction using non-destructive data, but no performance comparison of the models on these datasets has been conducted. In this study, eight machine learning models were trained on a dataset containing data from 180 ‘Suncrest’ peaches. Before the models were trained, the dataset was subjected to dimensionality reduction using the least absolute shrinkage and selection operator (LASSO) regularization, and 8 input variables (out of 29) were chosen. At the same time, a subgroup consisting of the peach ground color measurements was singled out by dividing the set of variables into three subgroups and by using group LASSO regularization. This type of variable subgroup selection provided valuable information on the contribution of specific groups of peach traits to the maturity prediction. The area under the receiver operating characteristic curve (AUC) values of the selected models were compared, and the artificial neural network (ANN) model achieved the best performance, with an average AUC of 0.782. The second-best machine learning model was linear discriminant analysis with an AUC of 0.766, followed by logistic regression, gradient boosting machine, random forest, support vector machines, a classification and regression trees model, and k-nearest neighbors. Although the primary parameter used to determine the performance of the model was AUC, accuracy, F1 score, and kappa served as control parameters and ultimately confirmed the obtained results. By outperforming other models, ANN proved to be the most accurate model for peach maturity prediction on the given dataset

Utilization of Explainable Machine Learning Algorithms for Determination of Important Features in ‘Suncrest’ Peach Maturity Prediction

Peaches (Prunus persica (L.) Batsch) are a popular fruit in Europe and Croatia. Maturity at harvest has a crucial influence on peach fruit quality, storage life, and consequently consumer acceptance. The main goal of this study is to develop a machine learning model that will detect the most important features for predicting peach maturity by first training models and then using the importance ratings of these models to detect nonlinear (and linear) relationships. Thus, the most important peach features at a given stage of its ripening could be revealed. To date, this method has not been used for this purpose, and at the same time, it has the potential to be applied to other similar peach varieties. A total of 33 fruit features are measured on the harvested peaches, and three imbalanced datasets are created using firmness thresholds of 1.84, 3.57, and 4.59 kg·cm−2. These datasets are balanced using the SMOTE and ROSE techniques, and the Random Forest machine learning model is trained on them. Permutation Feature Importance (PFI), Variable Importance (VI), and LIME interpretability methods are used to detect variables that most influence predictions in the given machine learning models. PFI shows that the h° and a* ground color parameters, COL ground color index, SSC/TA, and TA inner quality parameters are among the top ten most contributing variables in all three models. Meanwhile, VI shows that this is the case for the a* ground color parameter, COL and CCL ground color indexes, and the SSC/TA inner quality parameter. The fruit flesh ratio is highly positioned (among the top three according to PFI) in two models, but it is not even among the top ten in the third