Reviewerly/exHarmony huggingface dataset:
Strengthening the School Literacy Movement: The Utilization of Literacycloud.com Technology to Improve Students' Interest in Reading

A great nation is a nation that not only utilizes its natural resources but also develops other potentials such as reading and writing as a bridge of civilization from one generation to another. Nowadays, technology is commonly used in all fields, including education, to increase the reading interest among students that are still very low. The School Literacy Movement (Gerakan Literasi Sekolah or GLS) is part of the implementation of the Regulation of the Minister of Education and Culture Number 23 the Year 2015 concerning "Business Character Development." It aims to foster a literacy culture and increase interest in reading, critical thinking, and problem-solving. This study aims to reveal the use of technology to support the policy of the school literacy movement using Literacycloud.com at SDN Nglumber II Kepohbaru Bojonegoro. The method used is descriptive qualitative with data sources in the form of interviews, literature reviews, field notes, and observations. The results showed that using Literacycloud technology synergized with the GLS policy program at SDN Nglumber II Kepohbaru Bojonegoro could improve students' reading interest. In addition, there are also inhibiting factors for the GLS program at SDN Nglumber II Kepohbaru Bojonegoro, including inadequate books and library space, the absence of a GLS bureaucratic structure, and low student awareness of reading.
Mitigating disparate impact on model accuracy in differentially private learning

The techniques based on the theory of differential privacy (DP) have become a standard building block in the machine learning community. The DP training mechanisms offer strong guarantees that an adversary cannot determine with high confidence about the training data based on analyzing the released model, let alone any details of the instances. However, DP may disproportionately affect the underrepresented and relatively complicated classes, which means that the reduction in utility (i.e., model’s accuracy) is unequal for each class. Existing work neglects the adverse impact of DP or omits the influence of hyperparameters on the private learning procedure. This paper proposes a fair differential privacy algorithm (FairDP) to mitigate the disparate impact on each class’s model accuracy. We cast the learning procedure as a bilevel programming problem, which could integrate differential privacy with fairness. FairDP establishes a self-adaptive DP mechanism and dynamically adjusts instance influence in each class depending on the theoretical bias-variance bound with privacy guarantees simultaneously. Our experimental evaluation shows the effectiveness of FairDP in mitigating the disparate impact on model accuracy among the classes on several benchmark datasets and scenarios ranging from text to vision and achieving state-of-the-art accuracy and fairness.
The Formation of Philologocentric Culture Approaching the Principle of Global Axiology: From Special Theory and Methodology to the General Theory and Methodology of Creativity

The formation of philologocentric culture in the alternation of "multiple intelligences"/ J.P. Guilford - "multiple graces"/ L.Botezatu is mobilized in the entire content of the present to demonstrate the effectiveness of the integrationist- associationist correlation: Theory and Special Methodology of Creativity - General Theory and Methodology of Creativity to the revalorization of the evolutionary-self- evaluative potential, opposite the Methodology of Formative Potentials Vehicle "6 to 6" (Annex 3) - Methodology of Evidence of Axiological Completions (Annex 4). Thus, at the head of the table of preoccupational efforts, there is an ever more insistent place: a) that "plan to guide education"/J.Dewey; b) a principle of complete divination, opposite the 10 theories of cutting-edge creativity (Appendices 1 and 2); c) I.Druță "pastoral staff"/ the authenticating spirit connected to the "sovereign inner principle"/M.Cimpoi; c) The uniqueness of A.Einstein's wisdom: "Science without religion is lame, religion without science is blind"; d) The Principle of Global Axiology - the backbone of the entire human race.
Assessment of age estimation methods for forensic applications using non-occluded and synthetic occluded facial images

Age estimation is a valuable forensic tool for criminal investigators since it helps to identify minors or possible offenders in Child Sexual Exploitation Materials (CSEM). Nowadays, Deep Learning methods are considered state-of-the-art for general age estimation. However, they have low performance in predicting the age of minors and older adults because of the few examples of these age groups in the existing datasets. Moreover, facial occlusion is used by offenders in certain CSEM, trying to hide the identity of the victims, which may also affect the performance of age estimators. In this work, we assess the performance of six deep-learning-based age estimators on non-occluded and occluded facial images. We selected FG-Net and APPA-REAL datasets to evaluate the models under non-occluded conditions. To assess the models under occluded conditions, we created synthetically occluded versions of the non-occluded datasets by drawing eye and mouth black masks to simulate the conditions observed in some CSEM images. Experimental results showed that the evaluated age estimators are affected more by eye occlusion than by mouth occlusion. Also, facial occlusion affects more the accuracy of the age estimation of minors and the elderly compared to other age groups. We expect that this study could become an initial benchmark for age estimation under non-occluded and occluded conditions, especially for forensic applications like victim profiling on CSEM where age estimation is essential.
Research and Application of Data Fusion and Data Mining in Early Warning Detection Intelligence Processing System

As a key technology of modern intelligence systems, Intelligence processing is a famous research area, and plays a basic role in the economy of the country especially in the early warning detection intelligence processing system. The basic theory of data fusion and date mining is comprehensively expounded, and real-time requirements of multi-target track data fusion, as well as offline demodulation timely information on characteristics of data mining, is deeply studied. Firstly, this paper discusses roundly the theory of association rules, which is an important research method of early warning intelligence data mining, proposes a new weighted probabilistic data association algorithm and improves joint probabilistic data association algorithm. Secondly, the knowledge of early warning intelligence data warehouse is detailed, and Law of cosines-text clustering algorithm based on distance is highlighted and extended. Finally, through the integration of real-time data integration and off-line data mining, the practical problems in information processing are effectively solved.
Path planning algorithm of mobile robot based on combination of fuzzy inference and reinforcement learning

This paper combines fuzzy inference algorithm with the simulated annealing modified Q (&lambda;) learning algorithm to solve the problem of robot who avoids obstacles in unknown environments. The system learns autonomously by itself without supervision or any prior training data. One of the popular methods for path planning algorithm is improved simulated annealing Q (λ)-learning. The fuzzy logic control is utilized to solve the generalization of continuous space. The combination of the two algorithms can solve the generalization of the continuous state space. Another advantage is that it can reach the balance of the exploration and utilization of the action strategy. The Simulation results exhibit the effectiveness of the proposed method compared with the original method.
Two-dimensional signature of images and texture classification

We introduce a proper notion of two-dimensional signature for images. This object is inspired by the so-called rough paths theory, and it captures many essential features of a two-dimensional object such as an image. It thus serves as a low-dimensional feature for pattern classification. Here, we implement a simple procedure for texture classification. In this context, we show that a low-dimensional set of features based on signatures produces an excellent accuracy.
Criminal Law Proposal on Protection and Compensation for Public Interest Reporters of Corrupt Crime

Corruption, which is prevalent in Korean society, is having a significant negative impact on national competitiveness as well as on the happiness index of citizens. Although our society has made great efforts to eradicate such corruption, it is not easy to find tangible results indicating that corruption is decreasing in our society. This is because it is difficult to detect corruption crimes because they are done secretly among insiders. In addition, even if corruption crimes occur due to the culture of loyalty or paternalistic and closed organizational selfishness in Korean society, they try to hide it from within the organization. In order to overcome the secrecy and concealment of corruption crimes, this study intends to suggest ways to improve criminal law by examining the Public Interest Reporter Protection Act that protects and supports whistleblowers reporting corruption crimes within the organization and its problems. First, under the current law, there is a possibility that public interest reporters and collaborators may be subject to criminal punishment, disciplinary action, or administrative sanctions, so it is not possible to provide a realistic incentive to lead them to public interest reporting. This is because the current law selects a voluntary reduction or exemption, stating that “the punishment can be reduced or exempted” when criminal acts by the public interest reporter and collaborators are found in relation to public interest reporting. In order to move forward to a more active public interest report, it will need to be amended to reduce the sentence as necessary. Public interest whistleblowers and collaborators go out as witnesses in the investigation process or trial process. It is necessary to establish an explicit regulation to protect public interest reporters and collaborators who appear as witnesses in investigation or trial procedures, and it is necessary to establish a procedure for investigating evidence that can harmonize the protection of public interest whistleblowers, etc. and the guarantee of the defendant's right to cross-examination. Under the Public Interest Whistleblower Protection Act, personal protection measures can be applied only when serious harm to life or body has been suffered or is likely to be sustained. It is unclear what the obvious harm is, and there may be cases where there is a delay in proving it, resulting in serious harm. As in the Anti-Corruption Act, it should be changed to a case where there is a risk of serious harm to life or body. In addition, without delay, apply for personal protection measures to the Anti-Corruption and Civil Rights Commission, and immediately apply to the police agency, which is specialized in identity protection, so that they can receive protection measures promptly. In addition, the compensation system such as compensation, bounty, and relief money under the current law is not compensated enough for public interest whistleblowers to actively report to public interest because the amount is low in reality. should be raised significantly.
Adaptation Of Novel The Witches By Roald Dahl To Graphic Novel By Penelope Bagieu

Penelope Bagieu adapted the children's novel The Witches by Roald Dahl into a graphic novel. This novel tells of the struggle of a little boy who has been transformed into a mouse with his grandmother against a group of evil witches. Adapting a literary work into a literary work is a creative process consisting of adding, subtracting, removing, and developing so that it becomes a new work. This study aims to reveal the similarities and differences and the changes that occur. The method used is to analyze the storytelling elements between the two works by comparing the two.
Electroweak renormalization based on gauge-invariant vacuum expectation values

We briefly review a recently proposed scheme for a gauge-invariant treatment of tadpole corrections in spontaneously broken gauge theories called Gauge-Invariant Vacuum expectation value Scheme (GIVS).The tadpole scheme matters in higher-order predictions of observables if not all free parameters are fixed by renormalization conditions based on S-matrix elements, such as in MS renormalization.In contrast to previously used tadpole schemes, the GIVS unifies the properties of gauge invariance and perturbative stability.The application of the GIVS to the Standard Model, for instance, leads to very moderate electroweak corrections in the conversion of on-shellrenormalized to MS-renormalized masses.Moreover, in models with extended Higgs sectors, the GIVS is less prone to perturbative instabilities in the MS renormalization of Higgs mixing angles than observed for the traditional gauge-independent tadpole treatment.We illustrate this by considering the next-to-leading-order (electroweak and QCD) corrections to the decay processes h/H → WW/ZZ → 4 fermions of the CP-even neutral Higgs bosons h and H in a singlet Higgs extension of the Standard Model and in the Two-Higgs-Doublet Model.
Basic Knowledge of Network Systems

AbstractIn the information network system, there are unified rules, standards or conventions that constrain all the connected devices and user terminals involved in communication. The set of these rules, standards or conventions is called protocol. For example, when a terminal user and an operator of a large server communicate in a network, they cannot understand each other’s commands because of the different character sets used by their respective devices, so the communication would not be successful. In order to eliminate this communication barrier, a network device or network terminal is required to convert the characters in its private character set to those in the public standard character set before network transmission, and then further convert to the characters in the private character set of the destination terminal after arrival.
A NOVEL MODEL FOR NETWORK INTRUSION DETECTION BY USING STATISTICAL LEARNING APPROACH

Network intrusion detection is the pressing need of every communication network.Network Intrusion Detection Systems (NIDSes) play an important role in security operations to detect and defend against cyberattacks.As artificial intelligence (AI)-powered NIDSes are adaptive to various kinds of attacks by exploring the knowledge presented in the data, they are in high demand to treat the cyber-attacks nowadays with increasing diversity and intensity.This research works finds that the efficient learning to overcome these issues.The NBMU model performs well and it holds an accuracy 89.44%.The NBM shows worst output which as 65.80%.The NBMU and NB models perform well and it have same precision value 0.89.The NBM model has poor performance which as 0.66.The NBMU and NB models perform well and it have same recall value 0.89.The NBM model has poor performance which as 0.66.The NBMU and NB models perform well and it have same F-Measure value 0.89.The NBM model has poor performance which as 0.64.The NBMU has ROC level 0.90 which as highest performance compare with other models.The NBM model has shown the worst outcome which as 0.59.The NBMU has highest PRC level 0.90.The NBM model has not satisfactory which as 0.59 of PRC value.The NBMU and NB has the highest MCC 0.88.The NBM model has poor performance which as 0.36 of MCC value.The NBMU and NB has same kappa 0.88.The NBM model has poor performance which as 0.32 of kappa value.The NBMU and NB has the least MAE 0.09.The NBM model has poor performance which as 0.32 of MAE value.The NBM has poor performance which as 0.89.The NB has the least RAE 10.52%.The NBU modes has poor performance as well same RAE which as 116.64%.The NBU modes has poor performance as well same RRSE which as 132.51%.The NBMU has poor performance for making its model which as 20.02 seconds.
Authority is constructed and created … but not by you

This paper engages in a critical review of information literacy practices as manifest through materials shared publicly on the ACRL Sandbox. Focusing primarily on the frames “Authority is Contested and Constructed,” and “Scholarship as Conversation,” we analyze lesson plans shared on the ACRL Sandbox that address those frames, including the positionality of the students for whom the lessons were created. Preliminary analysis finds that most of the lessons shared position students exclusively as information consumers, learning how to find and evaluate existing published research, rather than emphasizing their potential roles as producers. This perspective fails to capitalize on students as creators (e.g., from formal undergraduate research to social media). We conclude by presenting some ways in which information literacy instruction can be adjusted to prepare future librarians to recognize and respect students’ existing knowledge and build upon that knowledge.
Prediction of fertilizer price based on bidirectional LSTM

As an important agricultural product, the market price of chemical fertilizer is regulated by various factors such as raw material price, exchange rate policy, supply, etc., and the fluctuation of chemical fertilizer price has a significant impact on the national economy. It can be seen that if the price of chemical fertilizer can be accurately predicted, its impact on agriculture and even the national economy can be minimized. Therefore, in order to accurately predict the price of fertilizers, this paper proposes a bidirectional long-short term memory neural network model based on the attention mechanism(A-Bi-LSTM). The model makes full use of the contextual relationship between the forward and backward directions on the time series, which can effectively adopt the long distance information for the sequence data relying on. Combined with the trend data of fertilizer transaction prices in recent years, this paper performs regression fitting on the data to generate a training model which is used to predict the price of chemical fertilizer. Finally, the root mean square error on the test set is 0.011, and good prediction results are obtained.
NL2SQL enhancement through semantic representation aggregation

Natural Language to SQL (NL2SQL) is a technology that converts natural language into executable SQL statements. For single table tasks, the dominant approach is a slot-filling-based model which actually performs multi-objective prediction. However, in existing multi-objective models, it is difficult to optimize multiple objectives simultaneously. Low correlation or conflict between various objectives can result in the model not being able to learn the parameters effectively. To effectively address this problem, this paper considers redesigning the sharing mechanism between multiple tasks and the specific network structure of individual tasks through a semantic aggregation approach in order to alleviate the above phenomenon.
Students’ Effort for Independent Learning Through Voluntary Extensive Reading

This study was aimed at analyzing students’ effort for independent learning through voluntary extensive reading in which it was viewed from students’ reading motives and materials. This study was designed in the form of qualitative study by involving 30 junior high school students and the data were gained through observation, interview, and survey. The instruments were the researcher, observation sheet, interview guide, and questionnaire. The collected data were analyzed by using qualitative data analysis. The findings revealed that; 1) the English reading materials read by the students are the materials related to the school materials, fiction (story, novel), and news, 2) students have their motives in reading extensively, namely to gain experience and skills in English language, to learn new things, to be confident and to improve ability in using the language. This study suggested that teachers to encourage extensive reading in classroom.
Research on prediction model of dewatering and solidification of river and lake sediment based on machine learning

In order to study the effects of mud moisture content, dosage and sludge specific resistance of river and lake sediment on dehydration and solidification of river and lake sediment, a prediction model between filter cake moisture content expressed by mud moisture content, dosage and sludge specific resistance was established by using machine learning (BP neural network and symbolic regression). The results showed that the prediction models obtained by the two machine learning methods had good correlation accuracy. Based on the comparison of four commonly used error evaluation indexes, the accuracy of BP neural network prediction results was better, and the contribution of mud moisture content and sludge specific resistance in the input parameters of the two models to the final filter cake moisture content was similar and large. The established correlation model provided a reliable prediction and analysis tool for the dehydration and solidification of river and lake sediment.
Kriging Based Weighting Model for Network RTK Coverage Expansion

Network RTK is a navigation technology that can secure cm-level position accuracy using carrier phase measurements. The coverage of network RTK is known to be 50-70 km. The reason why the coverage of network RTK is limited is that the user's residual error cannot satisfy the integer ambiguity determinable level as the network expands. If the user's residual error can be maintained at the integer ambiguity determinable level, the coverage can be expanded. Network RTK user’s residual error mainly consists of ionospheric delay error and tropospheric delay error. Therefore, even if the network is expanded, if the ionospheric delay error and the tropospheric delay error can be effectively eliminated, the coverage of the network RTK can be expanded. Therefore, in this study, the possibility of expanding the coverage of network RTK was analyzed by suggesting the use of the Kriging based weight model and the determination of the integer ambiguity using the ionospheric free combination measurements. In order to test the effectiveness of the proposed method, the conventional 50-70km network and the expanded 100-150km network were constructed using the CORS reference stations, and the network RTK performance on sunny day and rainy days was analyzed. As a result, when the conventional methods were used, the integer ambiguity determination rate of less than 50% was confirmed in the expanded network, and the position error of 10 cm or more occurred because the integer ambiguities were incorrectly determined. In particular, on the day with precipitation, it was not possible to secure network RTK availability because the integer ambiguity determination rate was decreased and position error were further increased. On the other hand, using the proposed method, it was confirmed that the integer ambiguity determination rate of 80% or more can be secured in the extended network regardless of the troposphere environment. Moreover, it was determined that the coverage of network RTK can be expanded by checking the cm level position accuracy as all integer ambiguities were corretly determined.