Miracle-dz/UniFAR-FTUs huggingface dataset:
Map-Reduce-Join-Locate: a Data Processing Framework for Decreasing the Processing Cost on Large Data

The related retrieval operation of cloud database is often very time-consuming, takes up a lot of storage and network transmission cost. MapReduce model provides processing framework for cloud database connection operation, but the processing performance should be further optimized. Based on the analysis of the MapReduce processing framework, this paper proposes a Map-Reduce-Join-Locate processing framework. The framework consists of four phases, Map, Reduce, Join and Locate. The new framework can be deployed on the original MapReduce framework without additional modification. Experiments show that the proposed framework enhances the performance of the associated cloud database retrieval in time and space. The framework also applies to the star-connected operation of the data warehouse and the associative retrieval operation of the application secondary indexes.
Leveraging Privacy In Data Analysis

Data analysis is inherently adaptive, where previous results may influence which tests are carried out on a single dataset as part of a series of exploratory analyses. Unfortunately, classical statistical tools break down once the choice of analysis may depend on the dataset, which leads to overfitting and spurious conclusions. In this dissertation we put constraints on what type of analyses can be used adaptively on the same dataset in order to ensure valid conclusions are made. Following a line of work initiated from Dwork et al. [2015], we focus on extending the connection between differential privacy and adaptive data analysis. Our first contribution follows work presented in Rogers et al. [2016]. We generalize and unify previous works in the area by showing that the generalization properties of (approximately) differentially private algorithms can be used to give valid p-value corrections in adaptive hypothesis testing while recovering results for statistical and low-sensitivity queries. One of the main benefits of differential privacy is that it composes, i.e. the combination of several differentially private algorithms is itself differentially private and the privacy parameters degrade sublinearly. However, we can only apply the composition theorems when the privacy parameters are all fixed up front. Our second contribution then presents a framework for obtaining composition theorems when the privacy parameters, along with the number of procedures that are to be used, need not be fixed up front and can be adjusted adaptively Rogers et al. [2016]. These contributions are only useful if there actually exists some differentially private procedures that a data analyst would want to use. Hence, we present differentially private hypothesis tests for categorical data based on the classical chi-square hypothesis tests (Gaboardi et al. [2016], Kifer Rogers [2017]). Degree Type Dissertation Degree Name Doctor of Philosophy (PhD) Graduate Group Applied Mathematics First Advisor Michael Kearns Second Advisor Aaron Roth
Dynamic Pattern Utilization for Automatic UAV Control Support

Within the research done at Fraunhofer IOSB in Karlsruhe in the area of civil security, various types of sensor/sensor systems, ground vehicles and Unmanned Aerial Vehicles (UAVs), have been used in the project AMFIS for some years. When it comes to aerial situation overview and reconnaissance, the research is focused primarily on electrically operated Vertical Takeoff and Landing (VTOL) systems which can be operated easily by police or rescue forces on account of the simple use and the good maneuverability even during applications in urban areas. One of the main research intentions of AMFIS is the further reduction of workload for the operator in scenarios where multiple and complex networks of different sensors and sensor carriers are used. That leads directly to the need for a high level of automation of the single sensor carriers. To further improve this automation the use of a dynamic and adaptable ground pattern as well as the detection and extraction of the information content of the displayed ground pattern onboard a flying vehicle is examined. The central objective of this investigation is the technical advancement of the dynamic ground pattern and the evaluation of the present test results as well as the use limits and the possibilities of the presented
Design for the Next: Integration of Path to Sustained Usage Model into Design Process

The aim of this chapter is to evaluate and further discuss the integration of the “Path to Sustained Usage” model into design process. To achieve this, this chapter explains the details and the outcomes of Engage! Workshop in which the model was tested with “backwards-designing” approach. The paper ends with further suggestions for application of the model into design process.
CHEETAH: circuit-switched high-speed end-to-end transport architecture

Leveraging the dominance of Ethernet in LANs and SONET/SDH in MANs and WANs, we propose a service called CHEETAH (Circuit-switched High-speed End-to-End Transport ArcHitecture). The service concept is to provide end hosts with high-speed, end-to-end circuit connectivity on a call-by-call shared basis, where a "circuit" consists of Ethernet segments at the ends that are mapped into Ethernet-over-SONET long-distance circuits. This paper focuses on the file-transfer application for such circuits. For this application, the CHEETAH service is proposed as an add-on to the primary Internet access service already in place for enterprise hosts. This allows an end host that is sending a file to first attempt setting up an end-to-end Ethernet/EoS circuit, and if rejected, fall back to the TCP/IP path. If the circuit setup is successful, the end host will enjoy a much shorter file-transfer delay than on the TCP/IP path. To determine the conditions under which an end host with access to the CHEETAH service should attempt circuit setup, we analyze mean file-transfer delays as a function of call blocking probability in the circuit-switched network, probability of packet loss in the IP network, round-trip times, link rates, and so on.
Constrained Bayesian Optimization for Automatic Chemical Design

Automatic Chemical Design is a framework for generating novel molecules with optimized properties. The original scheme, featuring Bayesian optimization over the latent space of a variational autoencoder, suffers from the pathology that it tends to produce invalid molecular structures. First, we demonstrate empirically that this pathology arises when the Bayesian optimization scheme queries latent points far away from the data on which the variational autoencoder has been trained. Secondly, by reformulating the search procedure as a constrained Bayesian optimization problem, we show that the effects of this pathology can be mitigated, yielding marked improvements in the validity of the generated molecules. We posit that constrained Bayesian optimization is a good approach for solving this class of training set mismatch in many generative tasks involving Bayesian optimization over the latent space of a variational autoencoder.
METODE DATA MINING UNTUK SELEKSI CALON MAHASISWA PADA PENERIMAAN MAHASISWA BARU DI UNIVERSITAS PAMULANG

Universitas Pamulang berusaha memberikan pendidikan tinggi dengan biaya yang terjangkau oleh kalangan bawah. Tetapi mahasiswanya banyak yang keluar di tiap semester, sehingga menyebabkan rasio jumlah mahasiswa baru dengan jumlah yang lulus tidak seimbang. Selain itu banyak mahasiswa yang tidak lulus tepat waktu, hal ini mengakibatkan rasio dosen dan mahasiswa tidak seimbang. Kedua hal ini akan mengurangi penilaian pada saat akreditasi. Penyebab keluarnya mahasiswa tanpa menyelesaikan pendidikannya, atau tidak dapat menyelesaikan pendidikannya tepat waktu belum dapat dideteksi dengan sistem seleksi saat ini. Pada penelitian ini diusulkan penggunaan teknik data mining untuk memprediksi ketepatan waktu lulus calon mahasiswa. Teknik data mining dan machine learning dapat digunakan untuk memprediksi berdasarkan data-data masa lalu. Metode data mining yang digunakan untuk memprediksi adalah klasifikasi, yaitu Naive Bayes (NB), k-Nearest Neighbor (k-NN), Random Forest (RF), Decision Stump (DS), Decision Tree (DT), Rule Induction (RI), Linear Regression (LR), Linear Discriminant Analysis (LDA), Neural Network (NN), dan Support Vector Machine (SVM). Berdasarkan hasil implementasi dan pengukuran algoritma/model yang diusulkan diperoleh algoritma/model terbaik, yaitu Support Vector Machine (SVM) dengan akurasi 65.00%. Tetapi akurasi ini masih jauh dari nilai excellent (sangat baik).
A competition for WEC control systems

This paper outlines a proposed open competition which will compare energy-maximising controllers for wave energy converters (WECs), both in simulation, and in real time, using a scale device in a tank test situation. To date, a wide variety of WEC control algorithms have been proposed, but have been difficult to compare due to differences in the simulation/scale models they are evaluated on, the range of incident sea states employed, and the reliance to a greater or lesser extent on wave or excitation force forecasts. In addition, most WEC control algorithms have been evaluated only in simulation, which masks the real-time computational capability, as well as the degree to which the model-based controllers are robust to WEC modelling errors, since the controllers are predominantly evaluated with a WEC simulation model identical to that upon which the controller is based. This paper describes the format of a proposed WEC control competition, detailing the scale target device, the open-source WEC-Sim simulation platform, along with likely performance metrics and range of sea states under which the assessment will be performed. The paper serves the purpose both as an announcement of the competition and indicates the nominal schedule, as well as soliciting feedback at this stage of the process.
In Depth Observational Studies of Professional Intelligence Analysis

Our goal is to produce metrics for measuring the effectiveness of software tools and environments produced for the intelligence community. To this end we need to understand the analytic process and to determine which data need to be captured to meaningfully measure process and effectiveness. In this paper we compare data from observational studies of professional intelligence analysts with data collected from an instrumented environment. We discuss some findings and their implications for possible metrics and for additional data needed to compute potential measures.
SF-12® Health Survey (Version 1.0)

The SF-12 Health Survey includes 12 questions covering the physical function; role limitations due to physical health problems; pain; general health perceptions; extent of vitality (energy/fatigue); social function; 2, role limitations because of emotional problems; and general mental health (psychological distress and psychological well-being).
Principle Components Analysis and Multi Layer Perceptron Based Intrusion Detection System

Security has become an important issue for networks. Intrusion detection technology is an effective approach in dealing with the problems of network security. In this paper, we present an intrusion detection model based on PCA and MLP. The key idea is to take advantage of different feature of NSL-KDD data set and choose the best feature of data, and using neural network for classification of intrusion detection. The new model has ability to recognize an attack from normal connections. Training and testing data were obtained from the complete NSL-KDD intrusion detection evaluation data set.
Unleashing the power of semantic text analysis: a complex systems approach

In the present information era, a huge amount of machine-readable data is available regarding scientific publications. Such unprecedented wealth of data offers the opportunity to investigate science itself as a complex interacting system by means of quantitative approaches. These kind of studies have the potential to provide new insights on the large-scale organization of science and the driving mechanisms underlying its evolution. A particularly important aspect of these data is the semantic information present within publications as it grants access to the concepts used by scientists to describe their findings. Nevertheless, the presence of the so-called buzzwords, i.e. terms that are not specific and are used indistinctly in many contexts, hinders the emerging of the thematic organization of scientific articles. In this Thesis, I resume my original contribution to the problem of leveraging the semantic information contained in a corpus of documents. Specifically, I have developed an informationtheoretic measure, based on the maximum entropy principle, to quantify the information content of scientific concepts. This measure provides an objective and powerful way to identify generic concepts acting as buzzwords, which increase the noise present in the semantic similarity between articles. I prove that the removal of generic concepts is beneficial in terms of the sparsity of the similarity network, thus allowing the detection of communities of articles that are related to more specific themes. The same effect is observed when describing the corpus of articles in terms of topics, namely clusters of concepts that compose the papers as a mixture. Moreover, I applied the method to a collection of web documents obtaining a similar effect despite their differences with scientific articles. Regarding the scientific knowledge, another important aspect I examine is the temporal evolution of the concept generality, as it may potentially describe typical patterns in the evolution of concepts that can highlight the way in which they are consumed over time.
Interspecies comparison of sea star adhesive proteins

Sea stars use adhesive secretions to attach their numerous tube feet strongly and temporarily to diverse surfaces. After detachment of the tube feet, the adhesive material stays bound to the substrate as so-called ‘footprints’. In the common sea star species Asterias rubens, the adhesive material has been studied extensively and the first sea star footprint protein (Sfp1) has been characterized. We identified Sfp1-like sequences in 17 additional sea star species, representing different taxa and tube foot morphologies, and analysed the evolutionary conservation of this protein. In A. rubens, we confirmed the expression of 34 footprint proteins in the tube foot adhesive epidermis, with 22 being exclusively expressed in secretory cells of the adhesive epidermis and 12 showing an additional expression in the stem epidermis. The sequences were used for BLAST searches in seven asteroid transcriptomes providing a first insight in the conservation of footprint proteins among sea stars. Our results highlighted a high conservation of the large proteins making up the structural core of the footprints, whereas smaller, potential surface-binding proteins might be more variable among sea star species. This article is part of the theme issue ‘Transdisciplinary approaches to the study of adhesion and adhesives in biological systems’.
Heterogeneous Mobile Platform Characterization and Accelerator Design

Due to power constraints, growing sections of a chip need to remain passive. This is often referred to as the “dark silicon” problem. This problem is more severe on mobile platforms, especially on emerging IoT and wearable devices, due to their strict power envelope and energy budget. Heterogeneous architecture with specialized hardware is an effective way to utilize the limited active transistor count. To guide heterogeneous mobile platform design, analyzing current and near-future mobile workloads and building specialized hardware based on the analysis is essential. To this end, this dissertation starts with a quantitative analysis of current mobile applications on smartphones, followed by an accelerator-based solution for efficient execution of wearable workloads, with an emphasis on machine learning and signal processing kernels. The first part of the thesis focuses on a study of mobile device utilization, where the results argue against using many (more than 4) cores in mobile devices, and suggest a flexible heterogeneous architecture to accommodate utilization variations. To meet the increasing computational need of mobile devices, many vendors try to increase the number of CPU cores in mobile device SoCs. However, it does not translate proportionally into performance gain and power reduction. This work analyzes the behavior of a wide range of commonly used mobile applications, using Thread Level Parallelism (TLP) as a major metric. The results demonstrate that mobile applications have TLP less than 2 on average, and we observe a small return on utilization when the number of cores is increased. Further analysis shows that TLP tends to appear in peaks and valleys, which gives opportunity for using flexible, heterogeneous mobile architecture for better energy efficiency.
High-resolution spectroscopic analysis of four new chemically peculiar stars

We present detailed chemical compositions of four stars on the first-ascent red giant branch that are classified as chemically peculiar, but lack comprehensive analyses at high spectral resolution. For BD+03{\deg}2688, HE 0457-1805, HE 1255-2324, and HE 2207-1746, we derived metallicities [Fe/H] $=-1.21$, $-0.19$, $-0.31$, and $-0.55$, respectively, indicating a range in Galactic population membership. In addition to atmospheric parameters, we extracted elemental abundances for 28 elements, including the evolutionary-sensitive CNO group and $^{12}$C/$^{13}$C ratios. Novel results are also presented for the heavy elements tungsten and thallium. All four stars have very large enhancements of neutron-capture elements, with high [La/Eu] ratios indicating enrichments from the slow neutron capture ($s$-process). To interpret these abundances, all indicative of [$s$/Fe] $>1.0$, we compared our results with data from literature, as well as with predictions from the Monash and FRUITY $s$-process nucleosynthesis models. BD+03{\deg}2688, HE 1255-2324, and HE 2207-1746 show C/O $>1$, while HE 0457-1805 has C/O $<1$. Since HE 0457-1805 and HE 1255-2324 are binary stars, their peculiarities are attributable to mass transfer. We identified HE 0457-1805 as a new barium giant star, and HE 1255-2324 as a new CH star, in fact a higher metallicity analogue CEMP-$r/s$ star; the single object reported in literature so far with similar characteristics is the barium star HD 100503 ([Fe/H] $= -0.72$). A systematic monitoring is needed to confirm the binary nature of BD+03{\deg}2688 and HE 2207-1746, which are probably CH stars.
The political economy of teacher hiring across German states, 1992–2004

Strong institutional constraints and better-informed voters may lead re-election seeking incumbents to shift the use of political business cycle mechanisms away from monetary and fiscal policy towards other policy domains that are more easily manipulable, targetable, and timeable. We investigate teacher employment patterns at the state level in Germany and find strong evidence of cycling mechanisms, in the form of electioneering and honeymooning. Against a backdrop of a continuously shrinking total teachers’ pool, German state-level incumbents accelerate the hiring of new teachers during election periods and partly reverse this during politically safer points in the electoral cycle. Cycles are mediated by issue salience: heightened attention to German public schooling after the notorious PISA-2000 tests further strengthens the manipulation of new teacher hiring for electoral purposes.
Binary Classification on ECoG Signals Using Optimized Extremely Learning Machine

In order to improve the accuracy of brain signal processing and accelerate speed meanwhile, we present an optimal and intelligent method for large dataset classification application in this paper. Optimized Extreme Learning Machine (OELM) is introduced in ElectroCorticoGram (ECoG) feature classification of motor imaginary-based brain-computer interface (BCI) system, with common spatial pattern (CSP) to extract the feature. When comparing it with other conventional classification methods like SVM and ELM, we exploit several metrics to evaluate the performance of all the adopted methods objectively. The accuracy of the proposed BCI system approaches approximately 92.31% when classifying ECoG epochs into left hand little finger or tongue movement, while the highest accuracy obtained by other methods is no more than 81%, which substantiates that OELM is more efficient than SVM, ELM, etc. Moreover, the simulation results also demonstrate that OELM will significantly improve the performance with p-value being far less than 0.001. Hence, the proposed OELM is satisfactory in addressing non-stationarity of ECoG signal.
Masters Thesis: Deep Learning applied to cooperative motion planning in environments shared with humans

Autonomous navigation has become, in the last years, one of the main field of research for both transportation and robotic purposes, employing all kind of terrain, maritime and aerial vehicles. An example of its growing importance is the recent development of autonomous driving solutions that is been carried out by most of the world leader car makers. A common aspect of such applications is the need of involving cooperation with other agents, that might be robots or humans, in order to accomplish the given task. Therefore, having at our disposal effective models that accurately predicts other agents actions becomes crucial in this context. The aim of this work is the design of a cooperative motion planning algorithm that comprises a series of required features in order to be employed in an autonomous navigation scheme. Among these features, we will focus on the ability to reason about neighboring agents and static obstacles, including observable information from other agents and characteristic data of the ego agent, and performing multi-hypothesis estimates. In this work, we have chosen the task of navigating through human crowds in complex environments. Therefore, our goal consists in designing a cooperative motion planning algorithm capable of modeling human motion behavior, including the earlier mentioned characteristics. The developed approach is an extension of a current state-of-the-art method designed by Alexandre Alahi, known as Social LSTM. This algorithm is composed by an artificial neural network that captures cooperative motion behavior by sharing information of the neighboring agents latent representations, and returns unimodal stochastic estimations of the future positions of the agents. This baseline method is going to be extended in order to include the missing mentioned qualities that are desired in a path prediction model within the context of autonomous navigation. These extensions are implemented through simulations of crowded environments in complex scenarios by using the open source crowd simulator Pedsim. The generated datasets, together with some real world recorded scenarios from public datasets, allowed us to test the presented algorithms in order to tune the corresponding hyperparameters of the neural network and design the different employed network architectures. Due to the lack of an official code implementation of the Social LSTM from Alexandre Alahi, it has been considering, as starting point, the Social LSTM implementation publicly available from Anirudh Vemula. Master of Science Thesis Francisco J. Marquez Bonilla
Pedestrian Action Recognition Using Motion Sensor and k-NN Classifier

We focused on the recognition of pedestrian action using motion sensor and machine learning. In this paper, we present our method how to define feature of actions from acquired data, and how to use k nearest neighborhood classifier to data learning and evaluation. Keywords—motion sensor; action recognition; machine learning; feature definition; k nearest neighborhood classifier