Miracle-dz/UniFAR-FTUs huggingface dataset:
A Hybrid Machine Learning Method for Intrusion Detection

Data security is an important area of concern for every computer system owner. An intrusion detection system is a device or software application that monitors a network or systems for malicious activity or policy violations. Already various techniques of artificial intelligence have been used for intrusion detection. The main challenge in this area is the running speed of the available implementations. In this research work, we present a hybrid approach which is based on the “linear discernment analysis” and the “extreme learning machine” to build a tool for intrusion detection. In the proposed method, the linear discernment analysis is used to reduce the dimensions of data and the extreme learning machine neural network is used for data classification. This idea allowed us to benefit from the advantages of both methods. We implemented the proposed method on a microcomputer with core i5 1.6 GHz processor by using machine learning toolbox. In order to evaluate the performance of the proposed method, we run it on a comprehensive data set concerning intrusion detection. The data set is called KDD, which is a version of the data set DARPA presented by MIT Lincoln Labs. The experimental results were organized in related tables and charts. Analysis of the results show meaningful improvements in intrusion detection. In general, compared to the existing methods, the proposed approach works faster with higher accuracy.
Relationship between atmospheric blocking and warm‐season thunderstorms over western and central Europe

A statistically significant link is presented between atmospheric blocking located over the eastern North Atlantic and northern Europe and warm‐season thunderstorm activity over western and central Europe. Lightning data from 2001 to 2014 were used to identify thunderstorm days and blocking events were extracted from the ERA‐Interim reanalysis using an objective identification algorithm. The statistical link between the two phenomena is established through odds ratio analysis. Two areas – one over the eastern part of the North Atlantic and one over the Baltic Sea – were identified as locations where blocking influences the occurrence of deep moist convection in parts of western and central Europe. Based on the mean ambient conditions on days with blocking in these two areas, well‐known dynamic and thermodynamic mechanisms supporting or suppressing the development of thunderstorms were confirmed. The anticyclonic circulation of a block over the eastern part of the North Atlantic leads to a northerly to northwesterly advection of dry and stable air masses into Europe on the eastern flank of the block. In addition, these environmental conditions are on average associated with large‐scale subsidence of air masses (convection‐inhibiting conditions). In contrast, the southerly to southwesterly advection of warm, moist and unstable air masses on the western flank of a block over the Baltic Sea results in convection‐favouring conditions over western and central Europe. Both blocking situations are on average associated with weak wind speeds at mid‐tropospheric levels and with weak wind shear. As a consequence, thunderstorms related to atmospheric blocking over the Baltic Sea tend to be on average less organised.
Modeling lifetime data with the odd generalized exponentiated inverse Lomax distribution

The Lomax distribution also called “Pareto type II” is a special case of the generalized beta distribution of the second kind [1], and can be seen in many application areas, such as actuarial science, economics, biological sciences, engineering, lifetime and reliability modeling and so on [2]. This heavy-duty distribution is considered useful as an alternative distribution to survival problems and lifetesting in engineering and survival analysis [3]. Inverse Lomax distribution is a member of the inverted family of distributions and discovered to be very flexible in analyzing situations with a realized non-monotonic failure rate [4]. The probability density function and cumulative density function of the Inverted Lomax distribution are given by [5]:
Testing for selectivity bias in panel data models

The authors discuss several tests to check for the presence of selectivity bias in estimators based on panel data. One approach to test for selectivity bias is to specify the selection mechanism explicitly and estimate it jointly with the model of interest. Alternatively, one can derive the asymptotically efficient Lagrange multiplier test. Both approaches are computationally demanding. In this paper, the authors propose the use of simple variable addition and (quasi-) Hausman tests for selectivity bias that do not require any knowledge of the response process. They compare the power of these tests with the asymptotically efficient test using Monte Carlo methods. Copyright 1992 by Economics Department of the University of Pennsylvania and the Osaka University Institute of Social and Economic Research Association.
Leis de Escala nos gastos com saneamento básico: dados do SIOP e DOU

RIBEIRO, Ludmila Deute. Scaling Patterns in Basic Sanitation Expenditure: data from SIOP and DOU. 2019. 148 p. Dissertation (Master of Science) – School of Arts, Sciences and Humanities, University of São Paulo, São Paulo, 2019. Starting in the late 20th century, the Brazilian federal government created several programs to increase the access to water and sanitation. However, although these programs made improvements in water access, sanitation was generally overlooked. While water supply, and waste collection are available in the majority of the Brazilian municipalities, the sewage system is still spatially concentrated in the Southeast region and in the most urbanized areas. In order to explain this spatially concentrated pattern it is frequently assumed that the size of cities does really matter for sanitation services provision, specially for sewage collection. As a matter of fact, as cities grow in size, one should expect economies of scale in sanitation infrastructure volume. Economies of scale in sanitation infrastructure means a decrease in basic sanitation costs, proportional to the city size, leading also to a (expected) power law relationship between the expenditure on sanitation and city size.Using population, N(t), as the measure of city size at time t, power law scaling for infrastructure takes the form Y (t) = Y0N(t) β where β ≈ 0.8 < 1, Y denotes infrastructure volume and Y0 is a constant. Many diverse properties of cities from patent production and personal income to electrical cable length are shown to be power law functions of population size with scaling exponents, β, that fall into distinct universality classes. Quantities reflecting wealth creation and innovation have β ≈ 1.2 > 1 (increasing returns), whereas those accounting for infrastructure display β ≈ 0.8 < 1 (economies of scale). We verified this relationship using data from federal government databases, called Integrated Planning and Budgeting System, known as SIOP. SIOP data refers only to grants, funds given to municipalities by the federal government to run programs within defined guidelines. Preliminary results from SIOP show decrease in Federal Grants to Brazilian Municipalities, proportional to the city size. For the initial budget allocation, β was found to be roughly 0.63 for municipalities above twenty thousand inhabitants; to be roughly 0.92 for municipalities above twenty thousand inhabitants; and to be roughly 1.18 for municipalities above fifty thousand inhabitants. The second data source is DOU, government journal for publishing official acts. DOU data should give us information not only about grants, but also about FGTS funds for basic sanitation loans. In order to extract data from DOU we have applied Natural Language Processing (NLP) tools. These techniques often work better when the algorithms are provided with annotations— metadata that provides additional information about the text. In particular, we fed a database with annotations into a bidirectional LSTM model applied to POS Tagging and Named-entity Recognition. Preliminary results are reported in the paper.
Interaction of Learning Mathematics and Constructive Attitudes of Students on Triangle Materials Through STAD Cooperative Learning Model

Abstrak Jenis penelitian ini adalah penelitian deskriptif dengan pendekatan kualitatif. Penelitian ini dilaksanakan di kelas VII-A SMPN 2 Balongbendo semester genap tahun ajaran 2018/2019 dengan jumlah siswa 33 orang. Data interaksi belajar siswa dalam kelompok diperoleh dengan lembar observasi, sikap konstruktif siswa diperoleh dari angket. Berdasarkan analisis data, dapat disimpulkan bahwa interaksi belajar matematika siswa dalam pembelajaran kooperatif yang terjadi dalam menyampaikan ide atau pendapat yaitu sebesar 53,8% untuk kelompok 1 dan 46,7% untuk kelompok 2, memberi bantuan yang disertai dengan penjelasan sebesar 15,4% untuk kelompok 1 dan 20,0% untuk kelompok 2, memberi bantuan tanpa disertai pejelasan sebesar 15,4% untuk kelompok 1 dan 13,3% untuk kelompok 2, dan interaksi belajar matematika siswa dalam meminta bantuan sebesar 15,4% untuk kelompok 1 dan 20,0% untuk kelompok 2. Sikap konstriktis siswa dengan menerapkan model pembelajaran kooperatif tipe STAD menunjukkan mayoritas siswa mempunyai sikap konstruktif terhadap matematika yang cukup bagus yaitu sebanyak 24 siswa dari 33 siswa Abstract The type of this research is descriptive research with qualitative approach. This research was done in class of VII-A SMPN 2 Balongbendo in second semester 2018/2019. There are thirty three students in that class. The data of students learning interaction were got from observation sheet and note and students’ constructive attitudes were got from questionnaire. Based on data analysis can be concluded that the interaction of students Mathematics learning in cooperative learning in case of delivering idea or opinion is 53,8% for first group and 46,7% for second group. In case of giving a help with some explanations is 15,4% for first group and 20,0% for second group. In case of giving a help without some explanations is 15,4% for first group and 13,3% for second group. In case of asking a help is 15,4% for first group and 20,0% for second group. Students’ constructive attitudes by using STAD type of cooperative learning show that most of students have quite good constructive attitudes about Mathematics that is twenty four of thirty three students.
Wavelet Packet Based Video Watermarking and Extraction Using Independent Component Analysis

Due to the widespread use of digital media applications, multimedia refuge and the copyright protection has grown incredible important. Digital watermarking is a technology used for the copyright protection of digital application. Many literatures have reported about Discrete Wavelet Transform (DWT) based watermarking techniques for data security. However, DWT based watermarking schemes are found to be less robust against video processing attacks. Hence in this paper, an attempt is made to develop a video watermarking scheme based on Wavelet Packet Transform (WPT) and extraction using Independent Component Analysis (ICA). The real time multimedia video sequence is converted into video frames, each frame is decomposed into various sub-bands using WPT. The watermark is embedded in a particular wavelet packet sub-band based on the value of Peak Signal to Noise Ratio (PSNR) that is calculated between watermarked and original image. Embedding in WPT subbands enhanced the performance of the video watermarking technique than DWT. The proposed scheme generates high PSNR value even in the presence of salt & pepper noise and rotation. An intelligent blind technique namely, ICA is implemented for extracting the watermark. From the simulation results, it is revealed that wavelet packet transform performs better when compared to DWT. 
Composable, Distributed-state Models for High-dimensional Time Series

In this thesis we develop a class of nonlinear generative models for high-dimensional time series. The first key property of these models is their distributed, or “componential” latent state, which is characterized by binary stochastic variables which interact to explain the data. The second key property is the use of an undirected graphical model to represent the relationship between latent state (features) and observations. The final key property is composability: the proposed class of models can form the building blocks of deep networks by successively training each model on the features extracted by the previous one. We first propose a model based on the Restricted Boltzmann Machine (RBM) that uses an undirected model with binary latent variables and real-valued “visible” variables. The latent and visible variables at each time step receive directed connections from the visible variables at the last few timesteps. This “conditional” RBM (CRBM) makes on-line inference efficient and allows us to use a simple approximate learning procedure. We demonstrate the power of our approach by synthesizing various motion sequences and by performing on-line filling in of data lost during motion capture. We also explore CRBMs as priors in the context of Bayesian filtering applied to multi-view and monocular 3D person tracking. We extend the CRBM in a way that preserves its most important computational properties and introduces multiplicative three-way interactions that allow the effective interaction weight between two variables to be modulated by the dynamic state of a third variable. We introduce a factoring of the implied three-way weight tensor to permit a more compact parameterization. The resulting model can capture diverse styles of motion with a single set of parameters, and the three-way interactions greatly improve its ability to blend motion styles or to transition smoothly among them. In separate but related work, we revisit Products of Hidden Markov Models (PoHMMs). We show how the partition function can be estimated reliably via Annealed Importance Sampling. This enables us to demonstrate that PoHMMs outperform various flavours of HMMs on a variety of tasks and metrics, including log likelihood.
Sensemaking with shared visualizations: investigating the effects of visualizations in remote collaborative analysis

Increasingly, collaborators are separated geographically and are also faced with large quantities of information that can complicate collaboration. Visualizing information can help collaborators sort through large quantities of data, but visualizations help only when they promote effective problem-solving behaviors such as division of labor and open communication. This dissertation explores the impact of network visualizations on collaborative problem solving by examining three laboratory studies. Using the "detective mystery" as an experimental paradigm, remote collaborators worked synchronously via instant messenger to identify a serial killer who was hidden within multiple crime reports. In the first study, the evidence was divided between the pair of collaborators. When collaborators were given a network visualization tool that showed them how the evidence was linked, they performed better than those without a visualization. The visualization also fostered discussion between partners. The second study looked at whether the visualization would help collaborators if they already had full access to all the evidence. Whereas the visualization tool improved performance for collaborators with half the evidence, the same visualization tool did not improve performance when each collaborator had access to all the evidence. Collaborators seemed to be overwhelmed and did not approach the task systematically. Unlike their counterparts, who each had half the evidence, collaborators with all the evidence talked less, discussed fewer hypotheses, and did not divide the labor. The final study asked whether interpersonal and task-oriented discussion-prompt interventions encourage collaborators to adopt problem-solving strategies necessary for success. Discussion-prompt interventions helped pairs improve their search and analysis process. This dissertation suggests that visualization tools may prompt collaborations to be more systematic, but this depends on collaborators effectively using the visualization, finding relevant patterns within the visualization, and ultimately using these findings to direct their analysis.
Correcting collocation errors in learners’ writing based on probability of syntactic links

The paper describes a novel method for automatic collocation error correction in NL texts written by language learners or translated from another NL with the aid of machine translators. We assume that the main cause of collocation errors is the strategy of word-by-word translation used by authors of the texts or by machine translators, so the errors essentially depend on the source language. While processing a sentence from the text, the method considers as potential correcting variants all its paraphrases that have the same syntactic structure and are built by replacing all words of the sentence by their substitutes. Substitutes are automatically generated using word translation equivalents taken from a translation dictionary. To detect an error in the sentence, we propose a relevance degree function computed from the probability of the word’s syntactic links and applied to the sentence and its paraphrases. If the function value for the sentence is lower than for some of its paraphrases, our method signals an error, then it is corrected by an appropriate sentence paraphrase. The method was evaluated by correcting collocation errors in English texts written by Russian speakers. Stanford Parser and an English text collection were used to gather statistics and compute the probability of English word syntactic links. Within certain limitation, the experiments gave promising results: our method detected about 80% of collocation errors (with words of various POS) and 87% of proposed correcting paraphrases contained a proper correction.
Directional relative total variation for structure-texture decomposition

Structure–texture decomposition is one of the fundamental branches of computer vision and image processing. It is not only beneficial to image understanding but also to subsequent object recognition and tracking. However, diversity and complexity of textures impede structure extraction. In this study, a simple yet effective structure–texture decomposition method is proposed based on directional relative total variation. More concretely, the directionality of relative total variation is taken into account by using the directional gradient operator instead of partial derivatives both in x and y directions. In addition, in order to explore the directional information on different scales, the non-subsampled pyramid filter bank is employed to obtain the multi-scale properties. Experiments on a wide variety of images reveal the efficacy of the proposed method and show its superiority over several state-of-the-art methods. Besides, the applicability of the proposed method to several image processing tasks is also demonstrated, such as edge extraction, defect detection, and so on.
New Knowledge In Human Values

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Research on Embedded Multi-joint Servo Controller of Quadruped Bionic Robot

The principle of DSP chip TMS320LF2407 is introduced,and a multi-joint servo controller based on CAN bus is presented.In view of the application of quadruped bionic robot joint control,we adopt a streamline sampling strategy and the adaptive fuzzy control rules with modifiable function in DSP software.The experiment shows that these methods can effectively overcome such control defects as coupling,nonlinear moment,etc.in robot joint trajectory tracking control.The integration of control and driving increases the speed and load volume of mobile robots.
Capturing anharmonic and anisotropic natures in the thermotics and mechanics of Bi2Te3 thermoelectric material through an accurate and efficient potential

Force-field-(FF)-based molecular simulation is essential but challenging in the theoretical research of complex thermoelectric (TE) materials. As they are general and crucial in TE semiconductors, the structural natures of anharmonicity and anisotropy can help us understand the inherent relation between thermal and mechanical behavior, and therefore the reliability of FF studies can be assessed. In this paper, given prior knowledge of the structural, mechanical and thermal properties as well as the limitations and necessary approximations of the FF method, a feasible and detailed FF modeling scheme and simulation has been designed for Bi2Te3, which is a typical high-performance TE material. Using the complementary approach combining quasi-harmonic lattice and molecular dynamics, the obtained potential is systematically confirmed to be accurate and efficient for the prediction of anharmonic and anisotropic behavior in thermotics and mechanics over a wide temperature range compared with the present Bi2Te3 models. This reveals that the intrinsic anisotropy and anharmonicity can measure the asymmetry of crystal lattices and the interatomic force in the current state. In addition, the significant distinction of temperature-dependent anharmonic effects in different directions of Bi2Te3 stems from its layered hierarchical structure, in which weak van der Waals bonding will probably be the key structural factor in comprehensively improving performance for mass production and wearable application. This prior-knowledge-based FF study is also suggested as a bridge between the theoretical understanding of micro-mechanisms and the experimental measurement of TE material properties, leading to a general framework of molecular simulation for other complex energy materials.
Not Too Small to be Strategic: The State of Academic Program Review Guidelines and Instrumentation in Public Institutions

In higher educational settings, the academic program review process allows academic units to solicit feedback, make data-driven decisions regarding effectiveness and sustainability, and report to stakeholders. In order to discover trends and best practices in the current state of academic program review in higher education, examined was a sample of 53 processes representing a range of small-to-large public institutions in the United States in the categories of associate-degree granting, bachelors-degree granting, and masters-degree granting. Using a content analysis methodology, outcomes assessment and the use of data results featured prominently with 86.7% of the sample requiring their incorporation in academic program review, followed closely by judgments of resource adequacy and demonstration of program necessity and efficiency.
Method of Driving Object Model with Real-Time Data and Its Implementation

After introducing the general architecture of the object model with real-time data drive, in this paper, how to build the vivid 3D object profile and make the inside structure visualized by computer language is analyzed. And the key data of the real object are collected by sensors. Then through the net, the data are loaded on the virtual 3D model to drive the model in order to visualize the object’s inside structure, real-time pose and position, and even the process of transmission. So the real object could be studied, analyzed and monitored. In the end, a sample is put forward to discuss the feasibility, the details ,and the significance of this method.