Miracle-dz/UniFAR-FTUs huggingface dataset:
MovieOcean: Assessment of a Personality-based Recommender System

: This research effort explores the incorporation of personality treats into user-user collaborative ﬁltering algorithms. To explore the performance of such a method, MovieOcean, a movie recommender system that uses a questionnaire based on the Big Five model to generate personality proﬁles, was implemented. These personality proﬁles are used to precompute personality-based neighborhoods, which are then used to predict movie ratings and generate recommendations. In an ofﬂine analysis, the root mean square error metric is computed to analyze the accuracy of the predicted ratings and the F1-score to assess the relevance of the recommendations for the personality-based and a standard-rating-based approach. The obtained results showed that the root mean square error of the personality-based recommender system improves when the personality has a higher weight than the information about the user ratings. A subsequent t-test was conducted for the proposed personality-based approach underperformed based on the root mean square error metric. Furthermore, interviews with users suggested that including aspects of personality when computing recommendations is well-perceived and can indeed help improve current recommendation methods.
Based on the Continuum Topology Optimization of the Large Base Lightweight Design

Topology optimization could give the optimized distribution of material when given constrains and boundary conditions.Lightweight design of large base using results of topology optimization of continuum structure is investigated.The theory of continuum topology optimization is introduced,and mathematical model of topology optimization is established.Then make topology optimization design to the base of a large theodolite and obtain its optimal material distribution.Based on the features of material distribution,design welded truss base using square steel tube.Results of finite element analysis to the truss base show that,comparing to the traditional cast structure,the lightweight base can lose 357.4kg and lightweight ration is 27.8% without changing the static displacement.
PV Cell Fed DC-AC Buck and Buck-Boost Inverter

In this concept, two types of DC-DC converters with modified structures and different control methods for generating AC output voltage are proposed. In the modified cascaded dual buck inverter, absence of split capacitors leads to increased reliability of the inverter. Also, a new structure is proposed for integrated dual buck-boost inverter. The proposed inverters are capable of obtaining maximum power from solar cells by themselves. To evaluate operations of the proposed inverters, they have been modeled and simulated in PSCAD/EMTDC for different modulation indexes. Simulation results confirm validity and effectiveness of the proposed inverters. AMONG various multilevel voltage-source inverters, the most commonly used and commercially available ones are the neutral-point-c1amped, flying capacitor and cascade H-bridge inverters. The cascaded inverters are capable of reaching higher output voltage level by using commercially standard lower voltage devices and components. They also feature a modular design concept which lessens the maintenance process and the cascade inverters are well suited for utility interface of various renewable energy sources, such as photovoltaic systems, fuel cells, battery energy storage, and electric vehicle drives, where separate DC Sources naturally exist. However, since most of cascaded inverters are based on a series connection of several single voltage source inverters (VSI) with two active devices in one leg, they suffer from shoot-through problems, which is the most dominating failure (drawback) of VSIs. In addition, for the hard-switched cascaded inverters operating at higher DC bus voltages, cause them to lose the benefit of using power MOSFETs as the active switching devices for efficiency improvement and fast switching speed when they are available at certain voltage and power level.The proposed concept is implemented to PV cell fed using matlab/simulink software.
Surrogate Models-based Multi-Objective Optimization of High-Speed PM Synchronous Machine: Construction and Comparison

This paper employs surrogate models for large-scale high-speed (HS) electrical machine optimization to reduce heavy computational burden caused by finite element method (FEM) based on non-dominated sorting genetic algorithm II. Three artificial neural networks, namely, multilayer perceptron (MLP), support vector regression (SVR), and generalized regression neural network (GRNN) and the classical Kriging model are developed based on k-fold crossing validation method. To find out the most suitable surrogate model, a HS permanent magnet synchronous machine (PMSM) applied in electrically assisted turbocharger (EATC) considering its multi-physics characteristics is optimized by different models. A detailed comparison is provided in terms of prediction accuracy and modeling time consumption. The optimal design is verified to guarantee the accuracy of optimization results. The key contributions of this paper include an automatic HSPMSM optimization process is proposed, where complete modeling and tuning method as well as a reasonable comprehensive evaluation metric of different surrogate models are conducted. It is found that invalid samples from initial dataset contain useful information to improve prediction accuracy. Hence, an entire surrogate process should benefit from a pre-trained classifier. Also, it reveals that MLP, SVR, and Kriging model can greatly improve the design optimization effectiveness with high accuracy whereas GRNN is not suitable for this specific optimization scenario.
Review: Use of EEG on Measuring Stress Levels When Painting and Programming

: For years, brain activity, stress level during programming and painting have been analyzed separately. As the world gets more digital and human life gets more dependent on technology, it has become more important to analyse the relationship between programming, software developers’ brain activity, creative practices (i.e painting) and stress level. In this paper, we present the results of a systematic literature review whereby the research questions are centred around analysing the relationship between stress levels and brain activity when a person is painting or writing a piece of software. The search for relevant studies was done on google scholar and IEEE Xplore. The results of our review show that: (1) EEG can be used to accurately measure stress levels, (2) there is limited research in the analysis of stress level pattern of the stress level when people paint depending on different situations and styles of painting. In light of the systematic literature review result, using EEG we plan to conduct experiments to measure the stress level when a person is painting a picture or programming.
Image measures in the evaluation of ATR algorithm performance

Image measure is a very important part of automatic target recognition algorithm performance evaluation. Whether the image measure is tightly related with algorithm performance affects directly the evaluation works. The current image measures are introduced, and the deficiency in the target to background contrast(TBC) measure which is the representative of current general measures is analyzed. A new texture-based image clutter measure(TIC) is proposed to solve the deficiency in TBC. Experimental result of testing two measures TBC and TIC shows that TIC has very good monotonic relation with the segmentation algorithm performance in both of the given conditions,which proves TIC as a robust indicator of segmentation algorithm performance and gives better performance than TBC.
Machine Learning Techniques for Intrusion Detection Systems in SDN-Recent Advances, Challenges and Future Directions

Software-Defined Networking (SDN) enables flexibility in developing security tools that can effectively and efficiently analyze and detect malicious network traffic for detecting intrusions. Recently Machine Learning (ML) techniques have attracted lots of attention from researchers and industry for developing intrusion detection systems (IDSs) considering logically centralized control and global view of the network provided by SDN. Many IDSs have developed using advances in machine learning and deep learning. This study presents a comprehensive review of recent work of ML-based IDS in context to SDN. It presents a comprehensive study of the existing review papers in the field. It is followed by introducing intrusion detection, ML techniques and their types. Specifically, we present a systematic study of recent works, discuss ongoing research challenges for effective implementation of ML-based intrusion detection in SDN, and promising future works in this field.
Visual Affective Stimulus Database: A Validated Set of Short Videos

Two hundred and ninety-nine videos representing four categories (people, animals, objects, and scenes) were standardized using Adobe Premiere Pro CC 2018, with a unified duration of 3 s, a resolution of 1080 pixels/inch, and a size of 1920 × 1080 mm2. One-hundred and sixteen participants (mean age 22.60 ± 2.06 years; 51 males) assessed the videos by scoring, on a self-reported 9-point scale, three dimensions of emotion: valence, arousal, and dominance. The content was attributed a specific valence (positive, neutral, or negative) if more than 60% of the participants identified it with an emotion category. Results: In total, 242 short videos, including 112 positive videos, 47 neutral videos, and 83 negative videos, were retained in the video stimuli database. In the three-dimensional degree of emotion, the distribution relationship between them conformed to the fundamental characteristics of emotion. The internal consistency reliability coefficient for valence, arousal, and dominance was 0.968, 0.984, and 0.970. The internal consistency reliability of the emotional dimensions for people and faces, animals, objects, and scenes ranged between 0.799 and 0.968. Conclusions: The emotion short-video system contains multi-scene dynamic stimuli with good reliability and scoring distribution and is applicable in emotion and emotion-related research.
How to Accurately Monitor the Weld Penetration From Dynamic Weld Pool Serial Images Using CNN-LSTM Deep Learning Model?

This letter illustrates how a challenging problem be solved by assuring the adequacy of the raw information and using an appropriate deep learning network to extract the relevant information. The problem concerned is accurate monitoring of the penetration, in a fully penetrated weld pool, as quantified by the width of the weld on the back-side of the workpiece. This is challenging as the penetration occurs below the workpiece surface and is not visible. A popular method is to use a weld pool image to derive it. Analysis of the physical process suggests that a single weld pool does not contain adequate information but most recent serial weld pools may. As such, although a deep learning model may extract information that is already there, the raw information may not be sufficient. Hence, a model that is capable of extracting information from dynamic serial weld pool images is needed. To this end, a CNN-LSTM (convolutional neural network combined with long-short term memory one) model is proposed. Dynamic weld pools are experimentally generated by changing the welding current and welding speed randomly. The weld pools are imaged using an HDR camera during experiments. Images are also captured from the back-side surface of the workpiece to provide the ground truth for training, validation, and testing. It is found that the highly dynamically changing weld pool can be accurately predicted using serial weld pool images at 0.3 mm for its back-side bead width. Comparison has been made with results from comparative studies to verify the effectiveness of and the contribution from the information adequacy (by using serial images) and the feature extracting capability (by using deep learning).
Obtaining and Validating High-Density Coffee Yield Data

Coffee producers are ever more interested in understanding the dynamics of coffee’s spatial and temporal variability. However, it is necessary to obtain high-density yield data for decision-making. The objective of this study is to evaluate the quality of yield data obtained through a yield monitor onboard a coffee harvester, as well as to evaluate the potential of the data collected over three harvests. The yield monitor validation data showed a high correlation (above R2 0.968) when compared with the data obtained by a wagon instrumented with load cells. It was also possible to obtain yield maps for three consecutive seasons, allowing the identification of their internal variability, as well as classifying regions that show alternating yield patterns between years as the expression of the biennial yield behavior manifested inside and along the field, in addition to the spatial variability. This result indicates that, in addition to knowing the spatial yield variability, the biennial variance information must also be considered in the strategies for site-specific management. Regions that presented high yield variance should be alternated according to the productive year (high and low yield) and not only in consideration of their yield variability as on the regions with more stable yield behavior over time. The use of yield data can help the producer make more assertive decisions for crop and farm management.
Unusual slow energy relaxation induced by mobile discrete breathers in one-dimensional lattices with next-nearest-neighbor coupling

We study the energy relaxation process in one-dimensional (1D) lattices with next-nearest-neighbor (NNN) couplings. This relaxation is produced by adding damping (absorbing conditions) to the boundary (free-end) of the lattice. Compared to the 1D lattices with on-site potentials, the properties of discrete breathers (DBs) that are spatially localized intrinsic modes are quite unusual with the NNN couplings included, i.e. these DBs are mobile, and thus they can interact with both the phonons and the boundaries of the lattice. For the interparticle interactions of harmonic and Fermi–Pasta–Ulam–Tsingou-β (FPUT-β) types, we find two crossovers of relaxation in general, i.e. a first crossover from the stretched-exponential to the regular exponential relaxation occurring in a short timescale, and a further crossover from the exponential to the power-law relaxation taking place in a long timescale. The first and second relaxations are universal, but the final power-law relaxation is strongly influenced by the properties of DBs, e.g. the scattering processes of DBs with phonons and boundaries in the FPUT-β type systems make the power-law decay relatively faster than that in the counterparts of the harmonic type systems under the same coupling. Our results present new information and insights for understanding the slow energy relaxation in cooling the lattices.
RECENT ADVANCES IN NTC THICK FILM THERMISTOR PROPERTIES AND APPLICATIONS

An introduction to thermal sensors and thermistor materials is given in brief. After that novel electrical components such as thick film thermistors and thermal sensors based on them are described: Custom designed NTC thermistor pastes based on nickel manganite NiM 2 O 4 micro/nanostructured powder were composed and new planar cell-based (segmented) constructions were printed on alumina. The thick film segmented thermistors were used in novel thermal sensors such as anemometers, water flow meters, gradient temperature sensor of the ground, and other applications. The advances achieved are the consequence of previous improvements of thermistor material based on nickel manganite and modified nickel manganite such as Cu 0.2 Ni 0.5 Zn 1.0 Mn 1.3 O 4 and optimization of thick film thermistor geometries for sensor applications. The thermistor powders where produced by a solid state reaction of MnCO 3 , NiO, CuO, ZnO powders mixed in proper weight ratio. After calcination the obtained thermistor materials were milled in planetary ball mils, agate mills and finally sieved by 400 mesh sieve. The powders were characterized by XRD and SEM. The new thick film pastes where composed of the powders achieved, an organic vehicle and glass frit. The pastes were printed on alumina, dried and sintered and characterized again by XRD, SEM and electrical measurements. Different thick film thermistor constructions such as rectangular, sandwich, interdigitated and segmented were printed of new thermistor pastes. Their properties such as electrical resistance of the thermistor samples where mutually compared. The electrode effect was measured for all mentioned constructions and surface resistance was determined. It was used for modeling and realizations of high, medium and low ohmic thermistors with different power dissipation and heat loss. Finally all the results obtained lead to thermal sensors based on heat loss for measuring the air flow, water flow, temperature gradient and heat transfer from the air to the ground.
Factors Influencing Students' Selection to the Certificate In Food Processing and Quality Control Program at Sik Community College, Ministry Of Higher Education (MOHE), Malaysia

Selection of field of study is an important matter for every student when furthering their studies in higher education. Each individual has different interests. In determining the success of one's life, the selection of the right course is important, especially in determining a career upon graduation and entering the industry. Individual talent and abilities towards courses offered by higher education institutions are also important to ensure that every individual who chooses a field of study at the Higher Education level achieves excellence in studies. This is a qualitative study that employs a semi-structured interview tool. The goal of this research was to identify the factors that influence students' decisions to enroll in the Certificate in Food Processing and Quality Control Certificate program at Sik Community College. The research was carried out at Sik Community College, Ministry of Higher Education Malaysia. Five informants were chosen at random for the interview session based on sampling. Based on the results of interviews with informants, the study's findings were recorded and reviewed. According to the study's findings, the factor of continuing their studies to a higher level is the main factor for students to choose to take this course, along with job opportunities and family influence factors that also play a role in course selection. As a result, some suggestions for improvement have been made in order to increase student enrollment and the institution may also define student interest to implement promotion for intake to College's Certificate in Food Processing and Quality Control program.
Supporting users in data disclosure scenarios in agriculture through transparency

ABSTRACT Business collaboration in the era of digital transformation requires the exchange of operational data. Since data are hardly controllable once they have been published or shared with others, it is highly important that users are clearly informed about who has access to which data and how certain settings can prevent the disclosure of sensitive data. However, giving end users more control over their data through increased transparency could also lead to information overload. This is particularly true in the field of agriculture, where tight schedules put pressure on employees of small enterprises. We conduct an empirical prestudy with 52 German farmers to investigate current data sharing scenarios. From these insights, we derive requirements and a concept for data sharing solutions providing data flow transparency for users. To investigate the behavior of users and the effects of transparent UI controls, we evaluate a prototype with 18 persons. Our evaluation shows that farmers demand flexible and secure tools that adjust to their workflows. Also, data should be stored and processed locally, granting farmers data sovereignty. Although the controls require additional effort, the evaluated transparent controls for data disclosure are easy to use and raise user awareness.
Components of Active Music Interventions in Therapeutic Settings—Present and Future Applications

Musical interventions in therapy have become increasingly relevant for rehabilitation in many clinics. What was long known for physiotherapy training—that the agency of the participant is crucial and moving is much more efficient for rehabilitation success than being moved—has over recent years also been shown to be true for music therapy. Accumulating evidence suggests that active musical interventions are especially efficient at helping rehabilitation success. Here, we review various approaches to active music therapy. Furthermore, we present several components that allow for manipulating musical expressiveness and physical engagement during active musical interventions, applying a technology-based music feedback paradigm. This paper will allow for a transfer of insights to other domains of music-based therapeutic interventions.
Ragam Bahasa Gaul dalam Caption Akun Instagram Beauty Influencer @cindercella dan Dampaknya terhadap Eksistensi Bahasa Indonesia

Bahasa gaul yang digunakan oleh influencer pada media sosial sangatlah berpengaruh bagi masyarakat, utamanya kalangan remaja. Tujuan dari penelitian ini yakni untuk mendeskripsikan bentuk-bentuk bahasa gaul yang ditemukan pada caption instagram @cindercella dan dampak penggunaannya terhadap eksistensi bahasa Indonesia. Pendekatan yang digunakan yakni dengan metode deskriptif kualitiatif. Teknik pengumpulan data dalam penelitian ini menggunakan teknik dokumentasi. Teknik analisis data menggunakan model interaktif yang terdiri atas beberapa tahapan yakni pengumpulan data, reduksi data, penyajian data, dan penarikan kesimpulan. Hasil penelitian yang dilakukan yakni terdapat beberapa bentuk bahasa gaul yang ditemukan, diantaranya: 1) bentuk akronim 1 data, 2) bentuk abreviasi 6 data, 3) bentuk penggunaan bahasa asing 3 data, 4) penggantian fonem vokal 5 data, 5) penggantian fonem konsonan 4 data, 6) bentuk kontraksi 8 data, 7) pelesapan fonem vokal 3 data, 8) kosakata khas 4 data, dan 9) penggantian diftong 1 data. Selain itu, dampak dari penggunaan bahasa gaul bagi pengguna media sosial instagram yang disebabkan oleh influencer cukup memberikan dampak yang negatif bagi eksistensi bahasa Indonesia saat ini.
Effective User Clustering and Power Control for Multiantenna Uplink NOMA Transmission

This paper investigates the user clustering and power control in the uplink multiple-input single-output non-orthogonal multiple access (MISO-NOMA) networks. A joint optimization problem is formulated to minimize the system transmit power. The formulated optimization problem is prohibitively complicated, especially when the number of users is large. Alternatively, a two-step user clustering and power control algorithm is proposed. First, a K-means-based algorithm is proposed for user clustering, where both channel gain and channel correlation among users are taken into account for the distance measurement to reduce the intra- and inter-cluster interference. Then, a semi-orthogonal user selection (SUS) algorithm is designed, with which the optimal cluster number and cluster centers can be dynamically obtained. Further, the closed-form expression of the optimal intra-cluster power control is derived, and the resulting inter-cluster power control problem is solved by designing an efficient iterative algorithm. Simulation results show that the proposed K-means-based iterative power control scheme outperforms other reference methods, and can approach the optimal performance in terms of power consumption and energy efficiency at a much lower computational complexity.