Reviewerly/exHarmony huggingface dataset:
Meta-IP: An Imbalanced Processing Model Based on Meta-Learning for IT Project Extension Forecasts

With increasing developments in information technology, IT projects have received widespread attention. However, the success rate of large information technology projects is extremely low. Most current extension forecast models are designed based on a balanced number of samples and require a large amount of training data to achieve an acceptable prediction result. Constructing an effective extension forecast model with a small number of actual training samples and imbalanced data remains a challenge. This paper proposes a Meta-IP model based on transferable knowledge bases with few-shot learning and a model-agnostic meta-learning improvement algorithm to solve the problems of sample scarcity and data imbalance. The experimental results show that Meta-IP not only outperforms many current imbalance processing strategies but also resolves the problem of having too few samples. This provides a new direction for IT project extension forecasts.
Effect of Reading Activities on Children’s Mental Health under the Environment of Artificial Intelligence and Deep Learning

The illustrations in picture books are lovely and emotional; pictures and text work together to express a theme and weave into one another to create a seamless and comprehensive visual image in picture books. Reading helps children improve for life and is an unending source of wealth in one’s life. Picture book reading has a positive effect on children’s psychological well-being since it allows them to develop different intelligences and their spiritual worlds in addition to reading stories and learning facts. This thesis investigates, against the backdrop of artificial intelligence (AI) and deep learning, the impact of reading activities on children’s psychological health. Deep learning and AI are the tools used where students can engage in a more in-depth conversation with the article and comprehend the author’s mindset and background after completing the basic reading. This can help students’ analytical, thinking, writing, and other application skills, as well as give them the opportunity to learn from and build upon their first reading accumulation. Research has shown that this method produces impressive results, and it is suited for widespread use because its reasonable strength has improved by 17.24% when compared to the conventional method.
Flood Detection and Prevention System

Flood is the most damaging natural disaster that happens everywhere in the world. The government would spend billions of ringgits to tackle it by creating public info flood website. The website could only be accessed by the user with an internet connection. To tackle this the researcher, develop a Flood Detection and Prevention System to sends a flood SMS alert directly to the user without the use of an internet connection. FDAP hardware consists of one microcontroller to process input and output, three sensors to detect rain, water level, and temperatures, and One GSM module to send SMS to the user directly to their phone. Development of the system will use Rapid Prototyping that focused on creating multiple prototypes until a finished product is developed. The product then will be analyzed for its usage on flood detection and prevention of flooding. A fully functional system will be developed including an SMS feature and a Web Server. Test involved functionality and acceptance test which uses a simulation environment that is created using a physical model and replica of flood phenomenon. FDAP system developed will be useful to rural villages to detect flood earlier and prevent it from harming live or properties. The finding of this study is expected to gain informative data for future flood analysis and study.
FD2Foremer: Thinking Face Forgery Detection in Midfrequency Geometry Details

Face forgery by DeepFake has caused widespread concern in community because of the synthesized media’s risks to the society. However, advances in recent years have been able to produce synthetic images indistinguishable from real images in the RGB space. Extracting midfrequency facial geometry details, including person-specific details and dynamic expression-dependent ones on facial geometry surfaces, is a promising way to highlight forgery clues during face forgery detection. In this paper, we use 3D face reconstruction to generate the displacement map from a single input face image, which is able to represent middle and fine scale details by indicating signed distance from the point in UV space. The cropped face images can also provide eyes and mouse information, so we use face image and its displacement map to extract the image features. Besides, we save the computation cost and maintain competitive performance using a universal transformer architecture and introduce a manifold distillation strategy to train our model from a more complex transformer backbone. Extensive experiments on various public DeepFake datasets indicate the effectiveness of the extracted facial geometry details, and proposed method achieves competitive performance.
The Problem of Personal Security: An Analysis of Basic Concepts and Approaches to Modeling

Introduction. The problem of security is extremely relevant for humanity as a whole and for the individual in particular. It is closely connected, for a person, with the main concepts – “life” and “death”, is interdisciplinary and the key to all arts and a huge number of sciences. However, the largely isolated development of their thesauri and model tools led to the formation of very eclectic conceptual and model apparatuses, exacerbated the problem of generalization (conceptual synthesis) and reduction in the dimension of the problem field. The purpose of the work is to analyze the basic concepts and model tools of the problem field of personal security. Materials and Methods. Systematic and interdisciplinary approaches and methods were applied: analysis and conceptual synthesis; analogy and transfer of knowledge; generalized method of qualitative structures; modeling, including the idea of multimodel descriptions and model games when used for these purposes, adequate to the reality of the collision of opposing models. In the case of insufficient evidence to confirm the hypotheses put forward at specific levels of the system hierarchy, we carried out the transition to the super system, as required by the conclusion following from the proofs of Kurt Gödel’s well-known theorems “On incompleteness”. Results. When understanding the problem of the security of the personality of a multidimensional person, the benefits of the transfer of knowledge and meaningful procedures, according to generalizations of concepts. Using the concept of “personality” as an example, the effectiveness of using the GMQS by I. M. Kalinauskas – B. V. Shmakov for the purposes of conceptual synthesis. The prospects of a multi-model description of the vast problem field of personal security by opposing models and model games of the type of “Life Strategy of a Creative Personality” by G. S. Altshuller – I. M. Vertkin. It is hypothesized that the current problem of “identity depersonalization” in thesauri and modeling, as well as the design and construction of multi-aspect systems for ensuring the personal safety of people in the future, can be removed in the process of creating their individual model “digital twins”. Discussion and Conclusion. The conducted research contributes to the development of ideas about the scale and depth of the problematic field of personal security. The materials of the article will be useful to a wide range of researchers and practitioners engaged in research and solving not only the aggravated “eternalˮ problems, but also new problems of ensuring the security of an individual that arise in the conditions of digitalization of the life of modern society.
Multi-feature stacking order impact on speech emotion recognition performance

One of the biggest challenges in implementing SER is to produce a model that performs well and is lightweight. One of the ways is using one-dimensional convolutional neural network (1D CNN) and combining some handcrafted features. 1D CNN is mostly used for time series data. In time series data, the order of information plays an important role. In this case, the order of stacked features also plays an important role. In this work, the impact of changing the order is analyzed. This work proposes to brute force all possible combinations of feature orders from five features: Mel-frequency cepstral coefficient (MFCC), Mel-spectrogram, chromagram, spectral contrast, and tonnetz, then uses 1D CNN as the model architecture and benchmarking the model's performance on the Ryerson audio-visual database of emotional speech and song (RAVDESS) dataset. The results show that changing the order of features can impact overall classification accuracy, specific emotion accuracy, and model size. The best model has an accuracy of 79.17% for classifying 8 emotion classes with the following order: spectral contrast, tonnetz, chromagram, Mel-spectrogram, and MFCC. Finding a suitable order can increase the accuracy up to 16.05% and reduce the model size up to 96%.
Privacy Amplification Strategies in Sequential Secret Key Distillation Protocols Based on Machine Learning

It is well known that Renyi’s entropy of order 2 determines the maximum possible length of the distilled secret keys in sequential secret key distillation protocols so that no information is leaked to the eavesdropper. There have been no attempts to estimate this key quantity based on information available to the legitimate parties to this protocol in the literature. We propose a new machine learning system, which estimates the lower bound of conditional Renyi entropy with high accuracy, based on 13 characteristics locally measured on the side of legitimate participants. The system is based on a prediction intervals deep neural network, trained for a given source of common randomness. We experimentally evaluated this result for two different sources, namely 14 and 6-dimensional EEG signals, of 50 participants, with varying advantage distillation and information reconciliation strategies with and without additional lossless compression block. Across all proposed systems and analyzed sources on average, the best machine learning strategy, called the hybrid strategy, increases the quantity of generated keys 2.77 times compared to the classical strategy. By introducing the Huffman lossless coder before the PA block, the loss of potential source randomness was reduced from 68.48% to a negligible 0.75%, while the leakage rate per one bit remains in the order of magnitude 10−4.
Research on the layout of primary control network and new measurement method for the HIAF survey and alignment

HIAF (High Intensity heavy ion Accelerator Facility) is one of the major national science and technology infrastructure projects in the “12th Five-Year Plan” undertaken by the Institute of Modern Physics, CAS. The primary control network, as the basic framework of the local coordinate system control of the particle accelerator, is the basis for calculating the coordinates in the process of accelerator construction and implementation, and plays a very important role in the survey and alignment during the construction period and the maintenance and monitoring during the operation period. Based on the multi-dissimilar design structure of HIAF and the unique geological conditions of the construction area, a new high-density layout method of the primary control network and a high-precision 3D(three-dimensional) measurement method were studied, and were successfully applied to the survey and alignment of HIAF. Inside and outside the accelerator tunnel, a high-density 3D primary control network that can form a full envelope for the accelerator beam is respectively laid out. All primary control networks are embedded in the bedrock layer with stainless steel reference inner cylinder and outer protective cylinder, and special waterproof filling layer is set up. All primary control network points in the accelerator tunnel are equipped with measuring intervisible holes and measurement platforms that can directly perform 3D measurements on the ground. By setting up the laser tracker on the horizontal attitude of the measurement platform, the 3D coordinates of the primary control network points inside and outside the accelerator tunnel can be measured directly and with high precision, and the simultaneous adjustment of the measurement data can be completed. The HIAF primary control network layout and measurement method proposed in this paper overcomes the problems of heavy pile foundation, poor crack resistance and poor water seepage resistance of the primary control network laid by the traditional method and solves the problem that the traditional primary control network must separate the plane network and the elevation network for independent measurement. The traditional method of tunnel point and forced centering measurement is avoided. By using a laser tracker to directly measure the 3D coordinates of the primary control network points inside and outside the tunnel, the measurement efficiency and measurement accuracy of the primary control network are improved, and the reliability of primary control network measurements is increased. The feasibility and correctness of the HIAF primary control network layout and measurement method are verified by comparing the actual multi-system measurement results in the field. This method has a certain reference value for the layout of the primary control network of the same type of accelerator.
Spoken language identification on 4 Indonesian local languages using deep learning

Language identification is at the forefront of assistance in many applications, including multilingual speech systems, spoken language translation, multilingual speech recognition, and human-machine interaction via voice. The identification of indonesian local languages using spoken language identification technology has enormous potential to advance tourism potential and digital content in Indonesia. The goal of this study is to identify four Indonesian local languages: Javanese, Sundanese, Minangkabau, and Buginese, utilizing deep learning classification techniques such as artificial neural network (ANN), convolutional neural network (CNN), and long-term short memory (LSTM). The selected extraction feature for audio data extraction employs mel-frequency cepstral coefficient (MFCC). The results showed that the LSTM model had the highest accuracy for each speech duration (3 s, 10 s, and 30 s), followed by the CNN and ANN models.
Critical success factors for smart-professional disruptor in university

&lt;span lang="EN-US"&gt;Current and emerging technologies have changed a lot. Consequently, every year, Gartner Technology has made many new changes in accordance with global developments. For example, in terms of artificial intelligence (AI), mixed reality (MR), extended reality (XR), collaboration platforms, online learning, distributed cloud, internet of behaviors (IoB), and cybersecurity. Due to changes in technology, disruptors have to constantly learn new technology in order to be up to date in the transfer of knowledge to learners. Therefore, in this research, critical success factors (CSFs) have been studied, which help them become highly skilled professionals by developing their own skills with technology to be a successful disruptor at university. The study found the CSFs, which were derived from the synthesis of international research papers. Disruptors' success consists of 12 internal and 10 external success factors. Smart-professional disruptors in universities were assessed using a focus group method with eight experts. Focus group results found that there were seven important internal factors for smart-professional disruptors in universities and seven minor internal factors. Including all internal factors, smart-professional disruptors have 14 factors; external factors are the most important ones for smart-professional disruptors in universities. In total, smart-professional disruptors have a total of 11 external factors.&lt;/span&gt;
DeViT

This paper proposes a novel video inpainting method. We make three main contributions: First, we extended previous Transformers with patch alignment by introducing Deformed Patch-based Homography (DePtH), which improves patch-level feature alignments without additional supervision and benefits challenging scenes with various deformation. Second, we introduce Mask Pruning-based Patch Attention (MPPA) to improve patch-wised feature matching by pruning out less essential features and using saliency map. MPPA enhances matching accuracy between warped tokens with invalid pixels. Third, we introduce a Spatial-Temporal weighting Adaptor (STA) module to obtain accurate attention to spatial-temporal tokens under the guidance of the Deformation Factor learned from DePtH, especially for videos with agile motions. Experimental results demonstrate that our method outperforms recent methods qualitatively and quantitatively and achieves a new state-of-the-art.
DISTANCE LEARNING AND FACE-TO-FACE LEARNING: STUDENT PERCEPTION OF QUALITY ASSURANCE AND PROSPECTS FOR IMPROVEMENT IN EDUCATION MANAGEMENT TECHNOLOGIES

Global challenges are an objective factor influencing the development of socio-economic and socio-political subsystems of society. The influence of this factor is not uniform, causing a different impact on every social activity sphere. The actualization of challenge to people’s health risk, determined by the widespread of the COVID-19 infection in 2020, has become the cause of changes in modes of operation for most of social institutes, including that of education. To implement the national governments’ decisions concerning prevention of population’s infecting with COVID-19, the administrations of most European universities have refrained from teaching educational programs in the face-to-face mode and adopted the decision of applying the distance form of training. This forced step by the universities has determined the raise in stakeholders’ interest in the problematics of the quality of education obtained through the distance mode of higher education. In their attempt to provide the answer concerning the potential of the distance form of training in ensuring education quality, the authors of this article have conducted a poll of 544 students from higher education establishments of Eastern Ukraine. The answers obtained were analyzed by the following aspects: comparing education quality depending on the mode of obtaining it; perspectives of applying the distance form of education at each stage of higher education; efficiency of applying the distance training instruments in the training process. On the results of analyzing the respondents’ answers, recommendations and conclusions as to the state of distance education and the perspectives of its development were formulated. Besides, the article offers a classification of distance education instruments and proposes recommendations concerning their application in the training process. The article’s content will be of interest not only for specialists working at agencies that ensure quality of higher education, but also for stakeholders and administrations of the universities that are planning to raise the level of their presence at the education services market on account of implementing the distance training’s potentials.
A Smoothed Matrix Multivariate Elliptical Distribution-Based Projection Method for Feature Extraction

Big data has the traits such as “the curse of dimensionality,” high storage cost, and heavy computation burden. Self-representation-based feature extraction methods cannot effectively deal with the image-level structural noise in the data, so how to character a better relationship of reconstruction representation is very important. Recently, sparse representation with smoothed matrix multivariate elliptical distribution (SMED) using structural information to handle low-rank error images caused by illumination or occlusion has been proposed. Based on SMED, we present a new method named SMEDP for feature extraction. SMEDP firstly utilizes SMED to automatically construct an adjacency graph and then obtains an optimal projection matrix by maximizing the ratio of the local scatter matrix and the total scatter matrix in the PCA subspace. Experiments on the COIL-20 object database, ORL face database, and CMU PIE face database prove that SMEDP works well and can achieve considerable visual and recognition performance than the relevant methods.
Modeling and Analyzing of Breast Tumor Deterioration Process with Petri Nets and Logistic Regression

It is important to understand the process of cancer cell metastasis and some cancer characteristics that increase disease risk. Because the occurrence of the disease is caused by many factors, and the pathogenesis process is also complicated. It is necessary to use interpretable and visual modeling methods to characterize this complex process. Machine learning techniques have demonstrated extraordinary capabilities in identifying models and extracting patterns from data to improve medical prognostic decisions. However, in most cases, it is unexplainable. Using formal methods to model can ensure the correctness and understandability of prediction decisions in a certain extent, and can well visualize the analysis process. Coloured Petri Nets (CPN) is a powerful formal model. This paper presents a modeling approach with CPN and machine learning in breast cancer, which can visualize the process of cancer cell metastasis and the impact of cell characteristics on the risk of disease. By evaluating the performance of several common machine learning algorithms, we finally choose the logistic regression algorithm to analyze the data, and integrate the obtained prediction model into the CPN model. Our method allows us to understand the relations among the cancer cell metastasis and clearly see the quantitative prediction results.
Towards the identification of MARCOS models based on intuitionistic fuzzy score functions

We encounter uncertainty in many areas.In decision-making, it is an aspect that allows for better modeling of real-world problems.However, many methods rely on crisp numbers in their calculations.It makes it necessary to use techniques that perform this conversion.In this paper, we address the problem of score functions assessment regarding their effectiveness and usefulness in the decision-making field.The selected methods were used to convert the intuitionistic fuzzy set matrix into crisp data, then used in the multi-criteria assessment.Managing the theoretical problem showed that the used techniques provide high similarity values.Moreover, they proved to be helpful when dealing with intuitionistic fuzzy sets in the decision-making area.
Pedestrian detection system based on deep learning

&lt;p&gt;Pedestrian detection is a rapidly growing field of computer vision with applications in smart cars, surveillance, automotive safety, and advanced robotics. Most of the success of the last few years has been driven by the rapid growth of deep learning, more efficient tools capable of learning semantic, high-level, deeper features of images are proposed. In this article, we investigated the task of pedestrian detection on roads using models based on convolutional neural networks. We compared the performance of standard state-of-the-art object detectors like Faster region-based convolutional network (R-CNN), single shot detector (SSD), and you only look once, version 3 (YOLOv3). Results show that YOLOv3 is the best object detection model than others for pedestrians in terms of detection and time prediction.&lt;/p&gt;