Reviewerly/exHarmony huggingface dataset:
Construction and capability analysis of air-space-ground integrated early warning and detection system

In order to effectively realize the early warning and interception of hypersonic weapons, an air-space-ground integrated early-warning and detection system is proposed to build. By cooperating with the early warning and detection subsystems in the whole range of land, sea, air and sky, the launch alarm, detection identification and continuous tracking of hypersonic targets are realized. Detection models of different subsystems are constructed, and the detection capabilities are analysed by using the models. The key factors and influence trends that affect the detection capabilities of each sub-system of the air-space-ground integrated early warning and detection system are clarified, and theoretical reference and data support are provided for further equipment demand analysis and demonstration.
Design of Portable Cartesian 3D Printer Using Arduino Mega 2560

This research aims to design and build a 3D printer machine using Arduino Mega 2560 and compare it to a standard 3D printer using a 3D printer module and bed temperature. The design simulation between the two machines is compared from the von Mises stress, displacement, and safety factor values. In contrast, testing machine performance by comparing the tensile strength and dimensional accuracy of the printing results. A cost comparison is also presented to complete the analysis. From a design perspective, the von Mises stress ratio is 10.94: 2.24; displacements 0.04: 0.03; and the same safety factor, which is 15 for the newly designed 3D printer and the standard one respectively. In terms of dimensional accuracy and power, the two machines are equivalent. While the new-designed machine can save costs of Rp.1.639.000 or 40% off. It is concluded that the machine with the Arduino Mega 2580 deserves to be used as an alternative to 3D printer machines at a lower cost.
Layer-Specific Repair of Neural Network Classifiers

Deep neural networks (DNNs) become increasingly popular. However, the vulnerability of DNNs can lead to a performance decrease when they cannot correctly predict the given samples. We propose a repair method for DNN-based classifiers to solve this problem, such that the accuracy can be improved by modifying the parameters of a DNN. First, we transform the DNN repair problem into a linear programming model, by encoding the constraints and the objective in linear programming. Second, to reduce the scale of the LP model, we repair the DNN by considering the parameters in the last layer. Third, to enhance the accuracy on the previously wrongly predicted samples without sacrificing the accuracy on the previously correctly predicted samples, we adopt these two types of samples in the optimization process. The evaluation on two popular datasets shows that our method outperforms the state-of-the-art methods and improves the accuracies by $$25.4\%$$ points in the adversarial attacking scenario and $$67.6\%$$ points in the backdooring attacking scenario. Meanwhile, our method can avoid obvious accuracy decreasing on standard test sets, which is at most 0.5%. The extensive experimentation demonstrates that the proposed method is effective and efficient in repairing DNN based classifiers.
DETERMINATION OF EVAPOTRANSPIRATION BASING ON SOLAR RADIATION IN HOA BINH PROVINCE

In the management, planning of agro-forestry and water resources, the determination of evapotranspiration is extremely important, evapotranspiration (ET) is a parameter that needs to be determined in many practical applications. The content of the article presents 4 methods to determine the amount of evapotranspiration: Turn (1961), McGuinness and Bordne (1972), Makkink (1957), Doorenbos and Pruitt (1977). All methods use meteorological data directly measured at hydro-meteorological stations in Hoa Binh province to calculate the value of solar radiation energy to estimate the average daily evapotranspiration. The results of evapotranspiration calculation by the methods are compared and the accuracy is evaluated based on direct measurement data at hydro-meteorological stations in the province. Applying the formula to calculate ET according to the following methods: Turn (1961), McGuinness and Bordne (1972), Makkink (1957), Doorenbos and Pruitt (1977), obtained ET values ​​of 6.2 mm, 6.3 mm, 8.1 mm, 9.3 mm, respectively. Mean square error of evapotranspiration at meteorological stations calculated by the methods of Turn (1961), McGuinness and Bordne (1972), Makkink (1957), Doorenbos and Pruitt (1977), compared with the average evapotranspiration at meteorological stations measured directly is 2.1 mm, 1.9 mm, 1.3 mm, 1.9 mm respectively. The study shows that Makkink (1957) method with coefficients a = 0.9, b = 0 is suitable for calculating evapotranspiration at Hoa Binh area.
CONTRASTIVE ANALYSIS BETWEEN ENGLISH AND BATAK TOBA LANGUAGE IN QUESTION WORD

This research intends to establish the similarities and differences in question words when using English and the Toba Batak language. It used a qualitative descriptive research design. Documentation techniques were used to collect the data. The researcher used descriptive analysis to analyze the data. The question words in English consist of 9 question words, which are as follows: "What", "Who", "Whose", "When", "Where", "Which", "Why," and "How." And in the Batak Toba language, consists of 11 question words, which are as follows: "Aha", "Ise", "Andigan", "Nandigan", "Dia", "Boasa/Beasa", "Boha", "Piga", "Sadia", "Mahua," and "Marhua." The results show that there are six similarities or equations between English and Toba Batak language in question words. However, there are also six differences between English and Toba Batak language in question words. Question words in English can occur and be put in the beginning and in the middle of interrogative sentence, whereas question words in Batak Toba language can be put in the beginning, in the middle, and at the end of interrogative sentence. English functioning question words similarly to Batak Toba, requesting the following: a person, an object, place, a manner, an activity, time, reason, and possession/ownership.
The Hierarchy of Case Priority (HiCaP):- A proposed method for case prioritisation in digital forensic laboratories

The need for digital forensic science (DFS) services has grown due to widespread and consistent engagement with technology by members of society. Whilst digital evidence often plays an important role in many inquiries, available investigative resources have failed to keep pace with such demand for them. As a result, the use case prioritisation models for backlog/workload management are of increasing importance to ensure the effective deployment of laboratory resources. This work focuses on the concept of ​​case prioritisation in a digital forensic laboratory setting, following the submission of exhibits for examination, where this workflow is described. The challenges of case management and prioritisation in laboratories are discussed, with both ‘case acceptance’ and ‘case prioritisation’ procedures explained. Finally, the ‘Hierarchy of Case Priority’ (HiCaP) - a transparent, risk-based approach for the prioritisation of cases for examination, is proposed and described using examples.
ANALISIS PENCEMARAN KUALITAS KIMIA AIR SUMUR DANGKAL DI PERMUKIMAN KUMUH RW IV KELURAHAN BANGKA JAKARTA SELATAN

Use of groundwater as a water source in South Jakarta is quite high with a usage value of 3,768,226 m3. Most of the shallow groundwater in RW IV Bangka village are at a depth of &lt;25 mbgl, so they have the potential to be polluted.. Moreover that village categorized as slums area, where there is high risk to be polluted by poor environment. . This study aims to analyze the level quality of Fe and CaCO3 pollution and their correlation with the risk factors in shallow well water in RW IV Bangka Village. This type of research is analytic observational with a cross sectional approach. The research population was the total number of wells in the residential area of RW IV, with the research sample using purposive random sampling of 30 shallow wells. The results showed that 100% of the samples met the requirements for pH (x̄= 8,0) and CaCO3 (x̄= 253 mg/l) levels. Meanwhile, for Fe (x̄= 0,29 mg/l) value , there were 7 samples (23,2%) that did not meet the requirements. Chi-square statistical analysis showed that there was no relationship between latrine distance (p=0,666), distance of other pollutants (p=0,675), standing water around the well (p=1,00), and SPAL conditions (p=0,304) with the Fe quality in shallow well water. However, based on the prevalence ratio (PR), the highest risk was found in other pollutant distance factors compare to others (PR=1,524). The conclusion that, although the levels of Fe and CaCO3 in shallow well water are quite high, statistical analysis shows that there is no correlation from environment risk factors for these two value levels by reason, most of well type for sampling is bore well type which is low for contamination.
Investment Decision Analysis Based on NPV, IRR, and the Fisher Separation Theorem

In today's society, investment decision-making has become one of the main decisions of every large-scale company.The management and shareholders of the company conduct reasonable analysis and vote for high-quality investment plans.The paper mainly shows the impact of economic models on project decision-making, and mainly summarizes the relationship between the economic model NPV and IRR and its application loopholes in actual situations.The final results of the paper show that the economic models NPV and IRR have their own advantages and disadvantages, and they need to be used reasonably according to the situation.Second, investors do not have to follow economic models to make decisions.When faced with investment decisions, they should adapt to the current situation.
An Empirical Study on Markowitz and Single Index Model

In this paper, the Markowitz Model and the Single-Index Model were used to study U.S. stocks.Four industries of U.S. stocks were selected to empirically analyze the data from 2002 to 2021, and the combination selection results given by the two models under five restrictions were compared.The results turned out that with all five constraints, the Sharpe ratio of the optimal portfolio provided by the Index Model, 1.432, 1.523, 1.597, 1.279, 1.333, respectively, was higher than that by the Markowitz Model,1.416,1.503, 1.540, 1.256, 1.329, respectively, which showed that although both models can give investors investment suggestions for portfolio selection, the single-index model performs better.
Priority ideas for the development of the pedagogical science

The article reveals priority ideas of the development of the pedagogical science and peculiarities of the worldview formation in the future teacher, which emphasize the recognition of humanistic and democratic processes in education as the basis for the formation of value orientations, expansion of a person’s spiritual world, establishment of relations with nationality, and discovery of the spiritual content of national traditions. On the basis of the analysis of modern state of higher education, its compliance with the social order and European standards, the article characterizes changes in the qualitative construction of educational content, which allows us to implement the main guidelines for the qualitative training of the future specialist, embody characteristics of humanization as self-humanization, which will necessarily affect the formation of pedagogical consciousness in future specialists, their independence in decision-making processes, activity and creativity in finding ways to solve problems. Emphasis is made on the independent participation of the individual in his/her formation. In this connection, organization of students’ independent work and development of their independence are seen as priority directions for improving the educational process. The main educational goals are achieved due to effective use of classroom and extracurricular activities, use of innovative leaning methods, ensuring high organization of independent creative work.
A Highly Reliable Data Storage Method for Power Data Centers with Hyper-Converged Architectures

The data center suffers from poor reliability and unbalanced load. This is caused by the selection of single-round storage nodes that are too close to each other. This paper proposes a data storage method based on deep reinforcement learning, which includes two parts: the node reliability judgment module and the data placement module. Firstly, we use multiple neural networks to build a node reliability judgment module to evaluate the reliability degree of server nodes. Based on this, a distributed parallel data storage method is proposed to select server nodes. The experimental results show that the data storage method in this paper has high reliability and can achieve load balancing in the data center.
Structure-Preserving Video Super Resolution with Multi-Scale Convolution

Video super resolution technology refers to the reconstruction of low resolution video into high resolution frames. In recent years, the application of deep learning to super-resolution technology has attracted extensive attention. However, the reconstruction effect of the existing model still has some problems such as double shadow, structural loss, and the solutions of problems are relatively rare. In this paper, we propose a new idea to use gradient extraction branches to guide the reconstruction of high resolution frames in backbone networks. The loss function is improved by combining gradient loss with pixel loss to improve convergence ability. Multi-scale convolution is introduced into the alignment module to enlarge the receptive field and improve the performance of the model to extract large motion features. Experimental results show that the model has good performance on REDS4 and Vid4 data sets.
Distinguishing the Types of Coordinated Verbs with a Shared Argument by Means of New ZeugBERT Language Model and ZeugmaDataset

Sentences where two verbs share a single argument represent a complex and highly ambiguous syntactic phenomenon. The argument sharing relations must be considered during the detection process from both a syntactic and semantic perspective. Such expressions can represent ungrammatical constructions, denoted as zeugma, or idiomatic elliptical phrase combinations. Rule-based classification methods prove ineffective because of the necessity to reflect meaning relations of the analyzed sentence constituents. This paper presents the development and evaluation of ZeugBERT, a language model tuned for the sentence classification task using a pre-trained Czech transformer model for language representation. The model was trained with a newly prepared dataset, which is also published with this paper, of 7,849 Czech sentences to classify Czech syntactic structures containing coordinated verbs that share a valency argument (or an optional adjunct) in the context of coordination. ZeugBERT here reaches 88% of test set accuracy. The text describes the process of the new dataset creation and annotation, and it offers a detailed error analysis of the developed classification model.
The Future of Information Architecture

The role of the Information Architect is to sit at the nexus of business, audience and technology, and to negotiate the interests, needs and expectations of each group. That is – to communicate a shared understanding that translates the esoteric language of an organisation’s inner workings into the common parlance of everyday people. This dialogue is mediated by the capabilities (and constraints) of the technology platform that serves as the interface between the two.
Novel Insights on Induced Sparsity in Multi-Time Attention Networks

Current deep learning approaches for dealing with sparse irregularly sampled time-series data do not exploit the extent of sparsity of the input data. Our work is inspired by the sparse and irregularly sampled nature of physiological time series data in electronic health records. We explore the effect of inducing varying degrees of sparsity on the predictive performance of Multi-Time Attention Networks (mTAN) [1]. Our methodology is to induce sparsity by first sub-sampling the time-series before feeding it to the mTAN network. We conduct empirical experiments with sub-sampling ranging from 10 to 90 %. We investigate the performance of our methodology on the Human Activity dataset and Physionet 2012 mortality prediction task. Our results demonstrate that our proposed time-point sub-sampling coupled with mTAN improves the performance by 2 % on the Human Activity dataset with 80 % lesser time-points for training. On the Physionet dataset, our approach achieves comparable performance as baseline with 30 % lesser time-points. Our experiments reveal that time-series data could be further coarsely acquired when used in tandem with state-of-the-art networks capable of handling sparse data (mTAN). This could be of immense help for various applications where data acquisition and labeling is a significant challenge.
IEEE Transactions on Cognitive Communications and Networking Publication Information

In addition to the above, the IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING (TCCN) is committed to timely publishing of high-quality manuscripts that advance the state-of-the-art of cognitive communications and networking research.The journal will focus on "cognitive" behaviors in all aspects of communications and network control, from the PHY functions (including hardware) through the applications (including architecture), and in all kinds of communication networks and systems regardless of type of traffic, transmission media, operating environment, or capabilities of communicating devices.TCCN welcomes papers dealing with the design, analysis, evaluation, experimentation, and testing of cognitive communications and network systems.Interdisciplinary approaches are encouraged.Papers that focus on experimental infrastructures or tools for cognitive communications and networking will also be considered, provided that they contain significant original contributions in the communications or networking areas.For membership and subscription information and pricing,
Retraction: Research on Informatics System and Practice Prospects Based on Artificial Intelligence Mathematical Algorithm (J. Phys.: Conf. Ser. 1865 042076)

This article has been retracted by IOP Publishing following an allegation that raises concerns this article may have been created, manipulated, and/or sold by a commercial entity. In addition, IOP Publishing has seen no evidence that reliable peer review was conducted on this article, despite the clear standards expected of and communicated to conference organisers. The authors of the article have been given opportunity to present evidence that they were the original and genuine creators of the work, however at the time of publication of this notice, IOP Publishing has not received any response. IOP Publishing has analysed the article and agrees there are enough indicators to cause serious doubts over the legitimacy of the work and agree this article should be retracted. The authors are encouraged to contact IOP Publishing Limited if they have any comments on this retraction. Retraction published: 9 September 2022
RegWeight: Adversarial Training based on Dynamically-Adjusted Regularization Weights

The recent studies have evidenced that, for a target deep learning model, the accuracy of classification on adversarial examples will tend to decline while the performance on natural (original) examples becomes better. We find out that the behind reasons of such problem stems from the overfitting phenomenon. To mitigate this predicament, we in this paper propose a regularization-weight learning method RegWeight to dynamically adjust the classification boundaries over different classes. A set of experiments using two commonly-used datasets also validate the effectiveness of our proposed method, the experimental results show that our proposed RegWeight can validly promote the accuracy of classification on adversarial examples while at the same time retaining a little decreasing-level on the accuracy of classification on natural examples.
Towards Hierarchical Categorical Named Entity Recognition via Injective Multi-task Learning Strategy

The Named Entity Recognition (NER) task aims to locate meaningful entities in a text and classify them into pre-defined categories. NER is a fundamental task in the field of natural language processing. In practice, entities often have multiple types at different levels of granularity, and it is essential to identify various levels of categories of entities simultaneously. A deep learning-based approach treats NER as a sequence annotation problem by giving each character a unique entity label, which significantly improves the accuracy of NER. Deep learning-based NER performs well on single-category and coarse-grained NER but does not perform well on hierarchical category NER problems. This paper proposes a multi-task learning-based hierarchical category named entity recognition model to solve this problem. The model uses a multi-task learning mechanism to simultaneously learn multiple levels of information of named entity recognition tasks. Named entity recognition results at higher levels are directly constructed from lower-level recognition structures. Experiments show that both systems can significantly improve the recognition performance at each level compared to the basic multi-tasking model. The model is also compared with each level's traditional named entity recognition method. The hierarchical category named entity recognition model can sig-nificantly improve the performance of named entity recognition tasks at each level.
Retraction: Development and Practice of the Course System of Embedded Technology Specialty in the Context of “Course Certificate Integration” Based on Big Data Analysis (J. Phys.: Conf. Ser. 1992 042009)

This article has been retracted by IOP Publishing following an allegation that raises concerns this article may have been created, manipulated, and/or sold by a commercial entity. In addition, IOP Publishing has seen no evidence that reliable peer review was conducted on this article, despite the clear standards expected of and communicated to conference organisers. The authors of the article have been given opportunity to present evidence that they were the original and genuine creators of the work, however at the time of publication of this notice, IOP Publishing has not received any response. IOP Publishing has analysed the article and agrees there are enough indicators to cause serious doubts over the legitimacy of the work and agree this article should be retracted. The authors are encouraged to contact IOP Publishing Limited if they have any comments on this retraction. Retraction published: 9 September 2022
Retraction: On the Monophthong Features of Cangzhou Dialect (Hebei Province) based on Acoustic Data Analysis in the Big Data Era (J. Phys.: Conf. Ser. 1992 042051)

This article has been retracted by IOP Publishing following an allegation that raises concerns this article may have been created, manipulated, and/or sold by a commercial entity. In addition, IOP Publishing has seen no evidence that reliable peer review was conducted on this article, despite the clear standards expected of and communicated to conference organisers. The authors of the article have been given opportunity to present evidence that they were the original and genuine creators of the work, however at the time of publication of this notice, IOP Publishing has not received any response. IOP Publishing has analysed the article and agrees there are enough indicators to cause serious doubts over the legitimacy of the work and agree this article should be retracted. The authors are encouraged to contact IOP Publishing Limited if they have any comments on this retraction. Retraction published: 9 September 2022