Miracle-dz/UniFAR-FTUs huggingface dataset:
Precision image recognition system based on laser microscopic anti-counterfeiting features

With the gradual miniaturization of microelectronic products, the requirements of enterprises for code encoding and decoding of their products continue to increase. This paper takes the micro anti-counterfeiting code as the research object and designs a set of micro-feature recognition equipment based on machine vision technology and an automatic threshold algorithm via different light intensity and interference. The equipment is composed of industrial cameras, auxiliary light sources, and three-axis fine-tuning modules. In order to collect typical micrographs with characteristic size in the range of 50-200μm. The algorithm uses the local gray features of the anti-counterfeiting code to perform a bilateral threshold to remove the false and inferior edges and eliminate the light intensity influence on the image threshold by fitting the relationship formula between the pixel value and the gamma transformation. Based on the images collected in actual production, the applicable recognition rate based on 5335 samples is as high as 99.5%, with a particular application value in microscopic anti-counterfeiting detection.
Perspectives on Odor-Based Control of Tsetse Flies in Africa

Tsetse-transmitted trypanosomiases are among the most neglected tropical diseases in sub-Sahara Africa. Although all tsetse species are susceptible to trypanosome infections, their differential attraction/feeding preferences for different wildlife, domestic animals, and/or humans constitute critical determinants of trypanosomes species they predominantly transmit. Artificial bait technologies, based on long-range tsetse olfactory responses to natural cues emitted by preferred hosts and blends of synthetic versions that mimic these cues, have successfully been applied in attractant-odor-based (“pull” tactic) reduction of field populations of some tsetse species. Olfactory attribute associated with active avoidance of tsetse-refractory non-hosts has similarly been exploited in design of repellent-odor-based (“push” tactic) protection of livestock. These tactics have opened possibility of spatially strategic deployment of the two sets of odor baits in “push-pull” tactics. Possibility of developing blends with enhanced attraction and repellence compared with those associated with savannah tsetse fly hosts and non-hosts, respectively, have been explored, where structure activity and blends of different components generated two novel blends. The studies evaluated structure activity and blends of different components. One based on attractive constituents associated with buffalo (Syncerus caffer) comprised of ε-nonalactone, nonanoic acid, 2-nonanone (in 1:3:2 proportion) delivered together with acetone, which showed significantly better attractancy on savannah tsetse fly than the standard blend comprised of 3-propylphenol, octenol, p-cresol, and acetone (POCA). The other blend comprised of δ-nonalactone, heptanoic acid, 4-methylguaiacol and geranylacetone (in 6:4:2:1 proportion) was significantly more repellent than previously characterized blend based on tsetse fly refractory waterbuck (Kobus defassa) constituents (δ-octalactone, pentanoic acid, guaiacol and geranylacetone). So far, no effective attractants or repellents of riverine tsetse fly species have been characterized. Optimized attractant and repellent blends for savannah tsetse flies lay down useful groundwork for future development of the “push-pull” deployment tactic for area-wide control of tsetse flies. Better understanding of the physiological, cellular, and molecular basis of response in the tsetse fly to odors can potentially augment the current tsetse fly-control interventions.
Exploring the Impact of Leadership Characteristics on Subordinates’ Counterproductive Work Behavior: From the Organizational Cultural Psychology Perspective

Counterproductive work behavior (CWB) is extremely detrimental to an organization and its stakeholders as they impact economic efficiency and damage the atmosphere within the organization. The culture and personality of leaders can affect their behavior, psychology and ability. Leaders are in a position of authority, have resources and decision-making power, and their words and actions are noticed and imitated by employees. From a leadership perspective, an effective way to avoid CWB is to seek ways to reduce in its occurrence and escalation. First, we conducted a grounded theory study on the leadership characteristics which are the antecedent variable of CWB, and the leadership characteristics were divided into three categories: psychological, behavioral, and ability. These characteristics impact subordinates’ CWB. Second, based on the conservation of resource theory, we conducted an ecological validation of the mechanism through which leadership characteristics affect subordinates’ CWB, explored the role of work resources and personal resources in it. The results indicate that all three types of leadership characteristics have a negative effect on subordinates’ CWB, among them, the mediating effect of work resources was established, and the mediating effect of personal resources was established in some cases. Therefore, different characteristics of leaders will affect the cognitive differences of subordinates to resources, and then trigger behavioral responses of subordinates. By an advantage analysis of the three leadership characteristics on subordinates’ CWB, it is found that the three leadership characteristics are of similar importance on interpersonal CWB. However, in the effect of organizational CWB, the characteristics of leadership have obvious advantages.
Teaching Facilitation of Family Participation in Educational Institutions

The participation of families in schools where their children study is a recurring research topic. This field tends to address the perception of parents or teaching staff. This work is novel in that it considers what teachers, and not families, do to facilitate this participation. The purpose of this work has been to contrast a theoretical model with a multidimensional questionnaire designed to obtain information on the assistance provided by teachers to improve parental involvement in schools. It will allow us to lay the foundations for the content necessary for the initial and permanent training of teachers. Then, an initial questionnaire was created and, after being subjected to expert judgment, it was applied to 225 Spanish teachers, using a quantitative and a non-experimental methodology. After calculating the exploratory and confirmatory factor analysis and applying the structural equation model, a questionnaire (QFIS-TP) was obtained that had satisfactory construct validity and reliability.
Monitoring Negative Sentiment-Related Events in Open Source Software Projects

Open source software (OSS) development is a highly collaborative process where individuals, groups and organizations interact to develop, operate and maintain software and related artifacts. The developers' sentiment in this process can have an impact on their working willingness and efficiency. Monitoring sentiment factors can help to improve OSS development and management. However, no method has been proposed to dynamically monitor the sentiment phenomena during the OSS development process. In this paper, an approach to detect Negative Sentiment-related Events (NSE) is proposed. It consists of two steps. The first step is to identify the burst interval of negative comments from open source projects, which corresponds to a NSE. The second step is to annotate this NSE with its event type. To support this approach, the types of NSEs in OSS projects are defined through an empirical study and classifiers are trained to annotate event types automatically. Moreover, conversation disentanglement techniques are employed to make the comments extracted more complete. Finally, the factors that have an influence on NSEs in the OSS project are studied.
Multichannel Blind Music Source Separation Using Directivity-Aware MNMF With Harmonicity Constraints

In this paper we present a harmonic constrained Multichannel Non-Negative Matrix Factorization (MNMF) method for the task of blind music source separation. In this model, the mixing filter encodes the spatial information in terms of magnitude and phase differences between channels whereas the source variances are modelled using a harmonic constrained NMF structure. In this work, the spatial covariance matrix is obtained from the constant-Q transform to account for the frequency logarithmic scale inherent in music signals and reduce the dimensionality of the parameters. Moreover, to mitigate the strong sensitivity to parameter initialization, we propose to initialize the spatial weights with the output of the steered response power (SRP) with the phase transform (PHAT) algorithm. The proposed method has been evaluated for the task of music source separation using a multichannel classical chamber music dataset with several polyphony and reverberation setups. Furthermore, a comparison with other state-of-the-art signal decomposition methods has been accomplished showing reliable results in terms of BSS_EVAL metrics.
PNPEq: Verification of Scheduled Conditional Behavior in Embedded Software using Petri Nets

Software for embedded systems goes through a scheduling phase where it is subjected to optimizing transformations. In such a scenario, validating the preservation of semantics across the transformation is essential. In this paper, we present PNPEq (Petri Net Program Equivalence), an ongoing work on a novel translation validation technique to handle various schedule-time conditional optimizations among others. The method makes use of a reduced size Petri net model integrating SMT solvers for validating arithmetic transformations. The approach is illustrated with a simple program and its translation, and further validated with a preliminary example suite.
Crash Distribution Dataset: Development and Validation for the Undivided Rural Roads in Oromia, Ethiopia

Abstract Predicting the number of crashes that may occur as a result of specific highway features is critical in evaluating different treatment or design alternatives. Since different highway geometric characteristics can influence crash distribution datasets, Highway Safety Manual’s (HSM’s) predictive method encourages users to predict crashes based on their severity and collision type proportions. This study used crash data from rural two-way two-lane road segments in the Oromia region over seven years to develop Oromia’s fixed crash distribution dataset on Interactive Highway Safety Design Model (IHSDM) software. The crash distribution dataset has two parts; the crash severity proportions and the collision type percentages. The developed Oromia’s fixed crash distribution dataset was compared and validated against the default HSM crash configuration. As a result, the Crash Prediction Model (CPM) evaluation results confirmed that the developed crash severity proportion (the first part of the crash distribution dataset) estimates are more accurate and closer to the observed values. Furthermore, the findings show that crashes in the Oromia region are severer than in the states where the HSM crash configuration was developed. According to the second part of the crash distribution dataset evaluation (collision type percentage), the developed fixed crash distribution dataset outperforms the default HSM configuration in most collision type proportions, but not in all. For instance, from the ten collision type proportions developed, Right-Angle and sides-wipe collision proportions are predicted more precisely by the default HSM configuration. This points to the need for developing collision type proportion (the second part of the crash distribution dataset) as a function rather than a fixed configuration for a better result, based on the availability of complete crash data (i.e. crash location). In general, the study revealed that in order to exploit the full potential of HSM’s predictive approach, researchers must develop a jurisdiction crash distribution dataset using local crash data. The methodology demonstrated in this study to develop the jurisdiction’s crash distribution dataset has been validated as true thus, safety practitioners are encouraged to adopt it.
Modeling of Mobile and Fixed Broadband Subscriptions of Countries with Fractional Calculus

Abstract Today, operators have become the resource of telecommunications data. Therefore, knowledge about subscriptions is easily available for most countries. In recent years, high-speed mobile internet access subscriptions are also increasing rapidly. There are two important subscriptions reviewed by The International Telecommunication Union (ITU): mobile broadband (MBB) subscriptions and Fixed broadband (FBB) subscriptions. In this study, we proposed an original mathematical model employing the fractional analysis theory and evaluate its validity by modeling the mobile broadband and fixed broadband subscriptions of six countries including France, Italy, Turkey, Germany, Spain, and the U.K. Later, we compared the Fractional Model we developed with the Polynomial Model. The results show that the Fractional Model is superior to the conventional Polynomial Model in modeling broadband subscriptions. For all selected countries, our proposed Fractional approach outperforms conventional polynomial regression. For all investigation categories, on average, the fractional approach works better by at least 10% and at most 30%.
Structural Design and Aeroelastic Analysis of an Oscillating Airfoil for Flapping Wing Propulsion

To investigate the aeroelastic eects, the design as well as the numerical analysis of a flexible and oscillating airfoil is described in this contribution. Due to the interaction of the fluid flow with the structural system, a multiphysical approach is employed here, were firstly the airfoil shape for low speed range based on a bird’s hand foil is designed and secondly the structural subsystem is developed including the interaction eects of the fluid flow. Further, in this paper the investigation of low-Reynolds-number flows past this flexible and flapping airfoil is presented. Transition takes place along a laminar separation bubble. To predict the point of transition, a linear stability solver fully coupled to an unsteady Reynolds-averaged Navier-Stokes flow analysis code is utilized. Results of the simulation of the airfoil’s flapping motion in air are presented for specific parameters and discussed in detail.
Detecting Duplicate Questions in Stack Overflow via Semantic and Relevance Approaches

Stack Overflow is a popular online Q&A website related to programming. Although Stack Overflow has detailed questioning guidance, duplicate questions still appear frequently, and a large number of duplicate questions make the quality of the community degraded. To solve this problem, Stack Overflow allows users with high reputations to manually mark duplicate questions. However, this method is inefficient and causes many duplicate questions to remain undiscovered. Therefore, this paper proposes a duplicate questions detection model based on semantic and relevance. The model employs Siamese BiLSTM to encode question pairs and captures the semantic interaction information of title and body through soft align attention and inference composition. The soft term match captures the relevance information in the title. We evaluate the effectiveness of the model in six question groups on Stack Overflow. Compared with the latest deep learning model, the F1-Score and ACC of our model increased by 9.401% and 8.901%, respectively. Experimental results show that our model outperforms the baselines and achieves competitive performance.
Empirical Evaluation of Minority Oversampling Techniques in the Context of Android Malware Detection

In Android malware classification, the distribution of training data among classes is often imbalanced. This causes the learning algorithm to bias towards the dominant classes, resulting in mis-classification of minority classes. One effective way to improve the performance of classifiers is the synthetic generation of minority instances. One pioneer technique in this area is Synthetic Minority Oversampling Technique (SMOTE) and since its publication in 2002, several variants of SMOTE have been proposed and evaluated on various imbalanced datasets. However, these techniques have not been evaluated in the context of Android malware detection. Studies have shown that the performance of SMOTE and its variants can vary across different application domains. In this paper, we conduct a large scale empirical evaluation of SMOTE and its variants on six different datasets that reflect six types of features commonly used in Android malware detection. The datasets are extracted from a benchmark of 4,572 benign apps and 2,399 malicious Android apps, used in our previous study. Through extensive experiments, we set a new baseline in the field of Android malware detection, and provide guidance to practitioners on the application of different SMOTE variants to Android malware detection.
Electrical submersible pump complex model for sensorless parameters observing

The article discusses the following issues: development and research of an electrical submersible pump complex model. Development and configure of a sensorless parameters observing system for a submersible induction motor. Development of a flow rate observing system for the electrical submersible pump based on a machine learning model. Verification of the developed electrical submersible pump sensorless parameters observing system.
Fuzzy Controller for Efficiency Maximization of Induction Motors Connected to Islanded Network Powered By Wind Turbine Generation Using Optimum Constant Ratios of V/Hz and V/Hz3

In the remote area or offshore electrical installation, efficient power network is required. In many application wind turbine farm is used and operated isolated from the grid. In such cases, the frequency can be fluctuated with high range. This can affect the induction motor torque and efficiency. Typical solution for such problem is to use DC link to control the frequency. This method is costly and consume high power losses. The purpose of this paper is to design fuzzy controller for the induction motor to deliver the motor at the required torque and at highest possible efficiency. For that, combined fuzzy motor controllers based on linear dependency (V/Hz) and cubic dependency (V/Hz3) are developed for LV induction motor. The fuzzy controller receives frequency signal as input from the network and estimate the required firing angle of a full-wave thyristor circuit that receives power from the wind turbine and connected in series with the induction motor to control the voltage at motor terminal. Fluctuation of the power supply frequency during the starting and running condition of the motor are considered in the fuzzy controller design to select the optimum applied voltage to the motor for both cases. MATLAB Simulink is used to simulate and test the controller and the motor operation during the starting and running for the full applicable range of frequency. The output of the simulation results show the designed controller is fast and accurate. Comparison between the obtained results and the DC link method results is provided. From this comparison it was noticeable that at all input power range, the efficiency of the pump-motor system is considerably improved by using the proposed fuzzy controller. The minimum gain in the efficiency is 16.67% occurs at 800-Watt input power.
Prediction of Coupon Usage Behavior Based on Customer Segmentation and XGBoost algorithm

Coupons are an important means for merchants to promote sales. It is necessary to separately perform precision marketing on different types of customers and distribute coupons to customers who are more likely to consume. Therefore, a prediction model of coupon usage behavior based on customer segmentation was constructed in this paper. First, the sensitivity information customer makes on coupons was introduced into the RFM model to propose the improved RFM model, namely the RFS model. Moreover, combined with the K-means algorithm, the RFS model was divided into different categories according to customer value. Then, the XGBoost algorithm was applied to the full data and each category of customers to predict the write-off situation after receiving the coupon. Finally, actual cases were applied to predicting the use of coupons, which were compared with GBDT and LighGBM algorithms. The results showed that the prediction effect of the model proposed in this paper was higher than that of full data and other machine learning algorithms, which provided a reference for the accurate delivery of coupons in real applications.
Improvements on Just-About-Right (JAR) Scales as Product Optimization Tools Using Kano Modeling Concepts

Attribute Kano characteristics are useful in product design to prioritize development efforts. However, attribute Kano characteristics have not been discussed and applied to product optimization when using Just-About-Right (JAR) scales. Product optimizations without identifying attributes Kano characteristics can be misleading. The two objectives in this research were: 1. Determine attribute Kano categories using a modified classic Kano classification methodology. 2. Propose a method to measure attribute performance and identify attribute Kano characteristics to direct product optimization. Two methodologies of attribute Kano classification were investigated. In experiment one, a modified classic Kano methodology was employed to determine attribute Kano categories through an online survey. Orange juice users (n=1072) participated in the survey. Seven orange juice attributes were evaluated. In experiment two, orange juice users (n=100) tested three commercial orange juices. The same attributes used in part one were investigated. Attribute Kano characteristics were investigated using the performance scale and partial least squares regressions. In experiment one, the results show consumers classified orange color as an indifferent attribute, and other attributes, i.e., orange flavor, sweetness, sourness, pulpiness, thickness and freshness, as attractive attributes. The determination on thickness seems weak since consumer responses in the categories of attractive and indifferent are relatively equal, reflecting the potential consumer segmentation. The decision on freshness is firm because there are more than 75% consumer responses in the attractive category. Relatively high consumer responses in the questionable category of sourness and pulpiness indicate low efficiency of this research method. In experiment two, the results show that orange color was an indifferent attribute and the others, i.e., orange flavor, sweetness, sourness, pulpiness, thickness and freshness were identified as one-dimensional attributes. In Minute Maid Original, orange flavor, sweetness, thickness and freshness were not strong enough, and sourness was too strong. For Simple Orange Original, sourness was too strong and orange flavor, pulpiness, thickness and freshness were not enough. No defect was found with Tropicana Pure Premium. By integrating Kano characteristics, modification of these attributes can be prioritized. This thesis is approved for recommendation to the Graduate Council Thesis Director: _______________________________________ Dr. Jean-François Meullenet