Reviewerly/exHarmony huggingface dataset:
Comprehensive competency assessment of workers and proportion forecast of top workers in the manufacturing enterprise

In this paper, the comprehensive competency assessment of workers and the proportion forecast of top workers in the enterprise are studied. Firstly, the comprehensive competency assessment system is established based on nine indicators, and the RBF (Radial Basis Function) neural network is used to assess the comprehensive ability of workers. Aiming at the changing trend of top workers in the manufacturing enterprise, a Grey Verhulst Model is established to forecast the proportion of top workers.
The concept of pedagogical innovations in the formation of chastity

Valorization of the learning process is currently of key importance for the formation of the academic and political foundations of pedagogical innovations in the education system. This article presents the results of a study aimed at defining the concept of modern pedagogical innovations focused on the formation of adolescent chastity. Analysis of the data obtained allowed us to identify seven different concepts of the concept of pedagogical innovation, formulate an updated definition and propose a cycle of pedagogical innovation concept. The article analyzes the factors that contribute to the successful implementation of innovative pedagogical practices in the system of measures aimed at the formation of chastity. The questions of configuration and intensity level of the influence of various factors involved in the implementation of innovative teaching practices, the relationship between the level of intensity of the identified factors affecting the dynamics of educational innovation and change level of the education system as a whole.
Evaluation and speciation of cobalt, copper, and zinc in saline soil by<scp>microwave‐assisted</scp>single extraction

Abstract The present research assessed the chemical fractionation of Cobalt (Co), Copper (Cu), and Zinc (Zn), detected in saline soil samples of Hyderabad Pakistan. The partitioning of Co, Cu, and Zn in soil samples was carried by single‐step extraction using microwave irradiation energy. The validity of the methods was confirmed using the same operating conditions applied and checked as in BCR sequential extraction fractionation using certified reference material BCR‐701 and the standard method of addition (recoveries 96%–102%). Atomic absorption spectrometry was used to test extractable Co, Cu, and Zn contents obtained by the comparative method. Using compromised microwave conditions steps 1–3 of the sequential extraction (excluding hydrogen peroxide digestion in step 3) could be accomplished with an ultrasonic bath between 15 and 30 min, whereas 60–120 s were needed for microwave single‐extraction (MSE). The total concentrations of Co, Cu, and Zn extractable metals produced by three separate extractions ranged from 1.02–3.30, 5.02–9.95, and 3.97–8.01 μg/g respectively. Consequently, the use of single‐step extraction with the aid of microwave and ultrasound energy to reduce the time required for single‐step extraction based on the BCR sequential extraction method. However, with the aid of developed single‐step extraction methods, the obtained results were comparable to those values obtained from BCR sequential extraction method. For further study, the MSE can be frequently used to know the metal‐bound intensity with a different chemical fraction on environmental solid samples.
Tokai University Student Project Center and the “Four Abilities”

Since 2006, we have established the “Student Project Center” as a place where students can cultivate practical abilities. The “four abilities” that Tokai University will nurture in order to increase abilities of our degree and the practical abilities of our student. There are “The ability to think independently”, “The ability to connect with others”, “The ability to tackle challenges”, “The ability to accomplish goals”. In 2019, almost 2000 students have participated in 39 projects. A progress report on generic skill (PROG) was able to measure their “four abilities”.
The Effect of Small and Medium Enterprises in Employment Creation and Income Generation in a case of Kebridahar Town: Qorahe Zone, Somali Region State, Ethiopia.

This study were look at the effect of small and medium enterprises in employment creation and income generation Somali Regional State in kebridahar town using primary data collected depth interview, questionnaire, and focus group discussions. It was examine use of International and Ethiopian Experience literature in general, and in Ethiopia in particular. To obtain data needed to undertake the study primary data were collected to appropriate data collection tools. In order to confirm that the designed data collection tool is accurate, pilot survey were also being conducted. Moreover, the question of sampling techniques and sample size determination has been dealt with. Both descriptive and quantitative methods were employed for analysis purpose. Descriptive statistical tools such as tables and percentages were use. Also, Mean, maximum, minimum were used to classify the respondents annual income and number of employees per Small and medium Enterprises in to the mean, below the mean, the minimum and the maximum.
Requirement Consistency and Integrity Verification Method based on Natural Language Processing

Abstract In the iterative process of the update of the information service system, the continuous improvement of the system is restricted due to problems such as non-standard, incomplete, and strong ambiguity in the description of requirements. It makes it difficult to verify the consistency and integrity of requirements. In this paper, we focus on the huge requirements of complex information service system, and propose research on requirement itemized extraction and structured description technology based on natural language processing and requirement item keyword extraction and standardized expression technology. The library management system is used as the verification object to verify the effectiveness of the method proposed in this paper.
Outlier Detection for Load Monitoring Data of Transformer Based on Kernel-Density-Fluctuation Outlier Factor

In this paper, an outlier detection method based on kernel-density-fluctuation is proposed, which is suitable for preprocessing transformer load monitoring data and can improve the quality of data sets used for data mining tasks. At first, some basic concepts involved in this newly proposed algorithm are introduced, and then the definition and calculation method of the k-density-fluctuation outlier factor are illustrated, which is used to quantify the possibility that this object is an anomaly. Finally, the algorithm rearranges the objects in the origin data set according to the value of their k-density-fluctuation outlier factors to discover the outliers. The simulation results of the algorithm running on the artificial data set and the natural data set to verify that the performance of the proposed algorithm in detecting isolated points is more ideal than the current used algorithms such as LOF in preprocessing transformer load monitoring data.
A New Algorithm for Privacy-Preserving Horizontally Partitioned Linear Programs

In a linear programming for horizontally partitioned data, the equality constraint matrix is divided into groups of rows. Each group of the matrix rows and the corresponding right-hand side vector are owned by different entities, and these entities are reluctant to disclose their own groups of rows or right-hand side vectors. To calculate the optimal solution for the linear programming in this case, Mangasarian used a random matrix of full rank with probability 1, but an event with probability 1 is not a certain event, so a random matrix of full rank with probability 1 does not certainly happen. In this way, the solution of the original linear programming is not equal to the solution of the secure linear programming. We used an invertible random matrix for this shortcoming. The invertible random matrix converted the original linear programming problem to a secure linear program problem. This secure linear programming will not reveal any of the privately held data.
Real-time Visualization on Actual Object of Measured Displacement Distribution Using Three-dimensional Shape Measurement Projection Mapping

The demand for non-contact inspection methods is increasing in various fields. However, there is a problem that it takes a great deal of effort for the operator to recognize the defect areas on the actual object from the measurement results displayed on the monitor. Authors have already proposed a three-dimensional shape measurement projection mapping system to reduce the problem. In this study, the prototype was applied to visualize the displacement distribution on an actual object. The measured displacement distributions images of flat and bent steel sheets due to small thermal local deformation, which was difficult to recognize with the naked eyes, were projected onto the actual object in real-time. The projected displacements in time-series was almost same the value shown by the laser displacement meter. The effectiveness of the projection of the distribution of evaluated values obtained from measured object was confirmed.
A Muti-detection Method of Covert Timing Channel Based on Perceptual Hashing

Covert Timing Channel (CTC) is a process of covert information transmission using existing network resources for information hiding and distribution of secret and sensitive date. By exploiting Inter-packet delays (IPDs) of legitimate network traffic, which is a network resources that were not designed for the purpose of communication, they have the ability that traditional security strategies, such as firewalls and intrusion detection systems, cannot effectively distinguish and disrupt them. In this paper, we propose a novel approach, CTC Multi-Threshold Detection (CTCMTD) to detect different CTCs and legitimate traffic. We extract four features of traffic samples for muti-classification by analysis its perceptual discrimination and robustness. We have shown that the method based on perceptual hashing has great potential to muti-classificate CTCs blindly.
LODMF: A Linked Open Data Maintenance Framework

RDF triples have been extensively deployed for the interlinking of data in a structured way. Links between web resources described by RDF models are at the core of the Semantic Web. By exploring a variety of computation methods, semi-automatic algorithms are used to build connections among RDF datasets. However, RDF assertions are subject to constant changes, which can affect existing links. Interconnected open data demands (semi-)automatic methods and tools to maintain their consistency over time. This paper presents a framework for supporting the maintenance of “external” links, which connect different Linked Open Data (LOD) datasets. The LODMF framework is suited to detect change operations on RDF triples as well as deal with affected links to other repositories. We define link maintenance actions, which are applied based on triple change operations to keep links up-to-date. This study illustrates how the framework works in practical scenarios. Our contribution paves the way for the development of (semi-)automatic mechanisms for link evolution in LOD.Web of Data evolution; Link evolution; Change Operations; Link changes; Link Repairment.
Method for sharing test data based on blackboard model

Abstract With the accelerated development of the equipment under test towards informatization and systemization, higher requirements are put forward for the construction of the test capacity of the equipment under test. It is a good way to maximize the value of test data as a core resource through building a test data distributing and sharing platform. A test data distributing and sharing method based on the blackboard model is proposed in this paper. Then a data sharing model is established including data management service center, data demanders, and operating mechanism to meet the needs of remote storage and global sharing of test data.
Single-Trace Side-Channel Analysis on Polynomial-Based MAC Schemes

This paper presents the first side-channel analysis (SCA) on polynomial-based message authentication code (MAC) schemes which is applicable to Poly1305. Typical SCAs (e.g., simple power analysis (SPA) and differential power analysis (DPA)) and conventional attacks on GCM/GMAC that focus on the first multiplication result in the universal hashing (i.e., polynomial evaluation) cannot be applied to Poly1305 owing to one-time keys and the structure of prime-field multiplication. On the other hand, the proposed attack retrieves the hash key from a single side-channel trace (e.g., a power/EM trace given by one execution) with a non-negligible probability and is applicable to polynomial-based MAC schemes implemented on an 8-bit micro-controller. The proposed attack allows the attacker to forge the authentication tag even if the hash key is a one-time key. The basic idea of the proposed attack is to exploit the addition in polynomial-based MAC schemes. Since the output or one input of the addition in these MAC schemes is known, we can efficiently estimate the unknown operands of addition, and then retrieve the hash key by the polynomial factorizations with the estimated candidates. This study also shows a cost-effective countermeasure for ChaCha20-Poly1305 using a combination of a lightweight masked Poly1305 and first-order mask conversion from Boolean to arithmetic.
Towards a System for Automatic Media Transcription in Macedonian

This paper presents the design and evaluation of a system for Automatic Speech Recognition used for automatic speech transcription of media in Macedonian. The system is based on the powerful Kaldi Speech Recognition Toolkit. We use several methods and algorithms to train, align and implement different models. A comparison between the models is made to analyze their performance and discuss their pros and cons in the specific situation where a relatively small dataset is used. The results show that the delta model outperforms even the more complex models. Finally, we compare the impact of the language model on the overall system performance.
An Adaptive Multi-level Block-wise Encryption based Reversible Data Hiding Scheme

The design and development of reversible data hiding algorithms got a lot of consideration in recent years because of its wide applicability in medical image transmission and cloud computing. In this paper, we propose a new reversible data hiding scheme though a block-wise multi-level image encryption process to hide lengthy secret messages in an image. In the proposed technique, the data hider will use two additional encryption keys K <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> and K <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> for reversible data hiding purpose other than the image encryption key K. During the process of data hiding, an encrypted image will be taken as the input and produces a final modified encrypted image with hidden data. For the extraction of data and recovery of image, the receiver needs the image decryption key K and the additional decryption keys K <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> and K <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> . The naturalness property of the image blocks is analyzed to recover the image blocks through a multi-level decryption process. The standard images from the USC-SIPI dataset controlled by the University of Southern California are used to conduct the experimental study.
StackVault: Protection from Untrusted Functions

Data exfiltration attacks have led to huge data breaches. Recently, the Equifax attack affected 147M users and a third party library - Apache Struts - was alleged to be responsible for it. These attacks often exploit the fact that sensitive data are stored unencrypted in process memory and can be accessed by any function executing within the same process, including untrusted third party library functions. This paper presents StackVault, a kernel-based system to prevent sensitive stack-based data from being accessed in an unauthorized manner by intra-process functions. Stack-based data includes data on stack as well as data pointed to by pointer variables on stack. StackVault consists of three components: (1) a set of programming APIs to allow users to specify which data needs to be protected, (2) a kernel module which uses unforgeable function identities to reliably carry out the sensitive data protection, and (3) an LLVM compiler extension that enables transparent placement of stack protection operations. The StackVault system automatically enforces stack protection through spatial and temporal access monitoring and control over both sensitive stack data and untrusted functions. We implemented StackVault and evaluated it using a number of popular real world applications, including gRPC. The results show that StackVault is effective and efficient, incurring only up to 2.4% runtime overhead.
Research And Implementation Of Facialnet Based On Convolutional Neural Network

Deep learning, artificial intelligence and other cutting-edge technologies are constantly being integrated into people's daily lives. Even small vending machines that can be seen everywhere in life have begun to use facial payment methods. The detection and recognition of face images is no longer unattainable, but the analysis and recognition of face information and characteristics (gender, age, race, etc.) is still not fully mature, in order to improve the accuracy of face information recognition In this paper, a face information recognition model is designed. The feature extraction part uses an eight-layer convolutional neural network, and then uses two fully connected modules as the classifiers for gender recognition and age recognition. The experimental results show that the model uses the advantages of the convolutional neural network so that the model can predict the gender and age of the face more accurately.
Learning 3D Faces from Photo-Realistic Facial Synthesis

We present an approach to efficiently learn an accurate and complete 3D face model from a single image. Previous methods heavily rely on 3D Morphable Models to populate the facial shape space as well as an over-simplified shading model for image formulation. By contrast, our method directly augments a large set of 3D faces from a compact collection of facial scans and employs a high-quality rendering engine to synthesize the corresponding photo-realistic facial images. We first use a deep neural network to regress vertex coordinates from the given image and then refine them by a non-rigid deformation process to more accurately capture local shape similarity. We have conducted extensive experiments to demonstrate the superiority of the proposed approach on 2D-to-3D facial shape inference, especially its excellent generalization property on real-world selfie images.
Who Distributes It? Privacy-Preserving Image Sharing Scheme with Illegal Distribution Detection

In image sharing schemes, the privacy of shared images can be protected by sending encrypted images to the receiver. However, the decrypted image cannot avoid being illegally distributed by receivers for their own benefits. Some methods have been proposed to solve this problem by embedding query user’s identification into the image. However, spatial domain embedding are usually vulnerable to attacks, such as Least Significant Bit (LSB) replacement attacks. We propose a privacy-preserving image sharing scheme for detecting illegal distributors based on edge detection and Arnold transformation. This scheme can adaptively embed the query user’s authentication information into 1 to 5 LSBs of each pixel of the image according to the embedded position map. After receiving the scrambled encrypted image and embedded position map, the cloud server will generate the embedded value map and embed in the encrypted domain. The image owner can identify the distributor’s identification with the help of cloud server when a suspected illegally distributed image is found. The scheme is robust under different attacks as shown by experiments.
Small-size Unmanned Control Algorithm of an Underwater Complex

Purpose of research: The creation of water bodies monitoring systems makes it possible to evaluate environmental situation in various points of monitoring area quickly. One of the main stages of water quality research is sampling, which is currently carried out at stationary posts, which makes it impossible to ensure operational control in various areas of an observation site. The problem can be solved by using mobile robotic platforms. The purpose of this study is to create mathematical model and algorithm for controlling autonomous movement of an underwater robotic device for collecting water samples in a reservoir. Methods. The following problems were solved for this purpose: the structure of the device was developed, which consists of a power frame unit, units of screw electric drives, units of depth and direction rudders. On-board power supply unit, sensor unit providing interaction of underwater vehicle with environment were also developed. Control tasks are formulated. A scheme of device movement in a reservoir has been developed for this purpose. Results. Modular method of trajectory planning is proposed. It is based on a single motion cycle concept, which consists of 2 turns and 2 straight-line stages of 2R2P. A model of control algorithm is also proposed and reactions of apparatus to external disturbances are studied. General dynamics theorems, method of algorithm synthesizing according to inverse dynamics problem were applied when solving problems. Conclusion: As a result of conducted studies, the system response to disturbances acting in longitudinal direction is described, disturbance diagrams are also described, random type with normal distribution law and mass center deviation s from the given position are given.
Image-based Transmission Line Detection on Finite Element Line Segments

Detection of transmission line is an important and challenging topic in low altitude flight. Different from the traditional transmission line detection method, in which the transmission line is detected based on the straight line, in order to extract the transmission line more completely, this paper proposes a new algorithm of transmission curve detection based on the morphological relation of finite element under the sky background. In the process of image processing, firstly, the whole sky background is extracted, and the line segment of the image under the sky background is extracted. Secondly, in the detection process, according to the idea of finite element, multiple groups of transmission line targets with certain curvature changes are regarded as composed of multiple groups of local line segment pairs. Using the morphological characteristics of the finite element line segment pairs of transmission line, different from other linear targets, line segment screening is carried out to remove the interference of pseudo linear targets. Finally, the finite element line pairs with obvious collinear characteristics are connected by collinear conditions to get the complete extracted transmission line. The experimental results show that this algorithm can detect the transmission line in the field of view and extract them completely.