Miracle-dz/UniFAR-FTUs huggingface dataset:
Better OOV Translation with Bilingual Terminology Mining

Unseen words, also called out-of-vocabulary words (OOVs), are difficult for machine translation. In neural machine translation, byte-pair encoding can be used to represent OOVs, but they are still often incorrectly translated. We improve the translation of OOVs in NMT using easy-to-obtain monolingual data. We look for OOVs in the text to be translated and translate them using simple-to-construct bilingual word embeddings (BWEs). In our MT experiments we take the 5-best candidates, which is motivated by intrinsic mining experiments. Using all five of the proposed target language words as queries we mine target-language sentences. We then back-translate, forcing the back-translation of each of the five proposed target-language OOV-translation-candidates to be the original source-language OOV. We show that by using this synthetic data to fine-tune our system the translation of OOVs can be dramatically improved. In our experiments we use a system trained on Europarl and mine sentences containing medical terms from monolingual data.
A comparison of two object-oriented programming environments for novices

Teaching Object Oriented Programming (OOP) to novices is quite problematic. In order to overcome the difficulties various teaching approaches have been proposed. In this paper, we present our findings regarding two teaching approaches based on two different environments: (i) teaching with the Educational Programming Environment of BlueJ; (ii) teaching with the Programming Microworld of objectKarel. The presented results are based on students' answers to a questionnaire that was given to students at the last lesson of the two studies, and refer exclusively to the comprehension of the most basic concepts of OOP: objects and classes. The analysis of the results shows that the combined use of the two environments seems to constitute a better didactic strategy from just using one of the two environments.
Endless Forams: >34,000 Modern Planktonic Foraminiferal Images for Taxonomic Training and Automated Species Recognition Using Convolutional Neural Networks

Planktonic foraminiferal species identification is central to many paleoceanographic studies, from selecting species for geochemical research to elucidating the biotic dynamics of microfossil communities relevant to physical oceanographic processes and interconnected phenomena such as climate change. However, few resources exist to train students in the difficult task of discerning amongst closely related species, resulting in diverging taxonomic schools that differ in species concepts and boundaries. This problem is exacerbated by the limited number of taxonomic experts. Here we document our initial progress toward removing these confounding and/or rate‐limiting factors by generating the first extensive image library of modern planktonic foraminifera, providing digital taxonomic training tools and resources, and automating species‐level taxonomic identification of planktonic foraminifera via machine learning using convolution neural networks. Experts identified 34,640 images of modern (extant) planktonic foraminifera to the species level. These images are served as species exemplars through the online portal Endless Forams (endlessforams.org) and a taxonomic training portal hosted on the citizen science platform Zooniverse (zooniverse.org/projects/ahsiang/endless‐forams/). A supervised machine learning classifier was then trained with ~27,000 images of these identified planktonic foraminifera. The best‐performing model provided the correct species name for an image in the validation set 87.4% of the time and included the correct name in its top three guesses 97.7% of the time. Together, these resources provide a rigorous set of training tools in modern planktonic foraminiferal taxonomy and a means of rapidly generating assemblage data via machine learning in future studies for applications such as paleotemperature reconstruction.
Origin-Destination Matrix Prediction via Graph Convolution: a New Perspective of Passenger Demand Modeling

Ride-hailing applications are becoming more and more popular for providing drivers and passengers with convenient ride services, especially in metropolises like Beijing or New York. To obtain the passengers' mobility patterns, the online platforms of ride services need to predict the number of passenger demands from one region to another in advance. We formulate this problem as an Origin-Destination Matrix Prediction (ODMP) problem. Though this problem is essential to large-scale providers of ride services for helping them make decisions and some providers have already put it forward in public, existing studies have not solved this problem well. One of the main reasons is that the ODMP problem is more challenging than the common demand prediction. Besides the number of demands in a region, it also requires the model to predict the destinations of them. In addition, data sparsity is a severe issue. To solve the problem effectively, we propose a unified model, Grid-Embedding based Multi-task Learning (GEML) which consists of two components focusing on spatial and temporal information respectively. The Grid-Embedding part is designed to model the spatial mobility patterns of passengers and neighboring relationships of different areas, the pre-weighted aggregator of which aims to sense the sparsity and range of data. The Multi-task Learning framework focuses on modeling temporal attributes and capturing several objectives of the ODMP problem. The evaluation of our model is conducted on real operational datasets from UCAR and Didi. The experimental results demonstrate the superiority of our GEML against the state-of-the-art approaches.
Algorithm for detecting deforestation and forest degradation using vegetation indices

In forestry sector, the remote sensing technology hold a key role on forest inventory and monitoring their changes. This paper describes the algorithm for detecting deforestation and forest degradation using high resolution satellite imageries with knowledge-based approach. The main objective of the study is to develop a practical technique for monitoring deforestation and forest degradation occurred within the mangrove and swamp forest ecosystem. The SPOT 4, 5, and 6 images acquired in 2007, 2012 and 2014 were transformed into three vegetation indices, i.e., Normalized Difference Vegetation Index (NDVI), Green-Normalized Difference Vegetation index (GNDVI) and Normalized Green-Red Vegetation index (NRGI). The study found that deforestation was well detected and identified using the NDVI and GNDVI, however the forest degradation could be well detected using NRGI, better than NDVI and GNDVI. The study concludes that the strategy for monitoring deforestation, biomass-based forest degradation as well as forest growth could be done by combining the use of NDVI, GNDVI and NRGI respectively.
Energy-efficient discovery process for mMTC applications

Reducing energy consumption is fundamental to address the requirements of Massive Machine-Type Communication (mMTC) applications. In this sense, D2D relaying could play an important role in some scenarios. D2D discovery process is a key phase of D2D relaying since in this phase the MTC device (MTD) has to select an optimal relay. This process should ensure low energy consumption and low complexity while taking into account the specific constraints of the MTDs.In this paper, we analyze the total (i.e., during the discovery phase and the data transmission phase) energy consumed by the MTD when it uses a D2D relaying mechanism. We propose a simple distributed discovery protocol, which allows reducing the MTD energy consumption. Our numerical and simulation results confirm that when using the proposed discovery mechanism, the total MTD energy consumption can be significantly reduced depending on the data packet size and the location of the MTD with respect to the base station.
A quantitative study into the errors in measuring an automotive vehicle radiated emissions signature using the CISPR 12 method

This thesis presents a quantitative study into the errors in measuring automotive vehicle radiated emissions using the CISPR~12 method. This method is based upon a limited set of tests, when compared to that used for many other commercial electrical devices. This research, details the errors introduced in recording the maximum amplitude of the radiated electric field from the vehicle by using the simplified method. Two key differences between the CISPR~12 setup and other radiated emissions test methods were researched, in order to quantify the errors. These were: not scanning the receive antenna in height, and not rotating the vehicle through 360 degrees in the azimuth plane whilst the E-field is measured. It was concluded that the current CISPR12 method has the potential to under-estimate the maximum amplitude of the E-field by up to 30~dB. Research was then performed to investigate alternative methods to those defined in CISPR~12. A number of approaches were considered before being subsequently discounted. The final alternative method considered was the `Test Wire Method', which was originally designed for performing tests on large, industrial machines. The Test Wire Method, TWM, was initially used as a `proof of concept' that the approach could be used for testing a much smaller device than it was originally designed for. Once the method had been successfully used, and a reduction in the error compared to the CISPR~12 method confirmed, the TWM was then further developed into a novel, new measurement system, designated the MicroStrip Method. A small, near-field probe was designed which, along with the use of a calibration factor, allows for a closer approximation of the maximum amplitude of the radiated E-field to be recorded. Measurements performed on a range of vehicles, resulted in a reduction in the mean error of over 10~dB, compared to using the CISPR~12 method.
A Classroom Experiment on Effort Allocation Under Relative Grading

Grading on the curve is a form of relative evaluation similar to an all-pay auction or rank-order tournament. The distribution of students drawn into the class from the population is predictably linked to the size of the class. Increasing the class size draws students' percentile ranks closer to their population percentiles. Since grades are awarded based on percentile ranks in the class, this reallocates incentives for effort between students with different abilities. The predicted aggregate effort and the predicted effort from high-ability students increases while the predicted effort from low-ability students decreases. Andreoni and Brownback (2017) find that the size of a contest has a causal impact on the aggregate effort from participants and the distribution of effort among heterogeneous agents. In this paper, I randomly assign "class sizes" to quizzes in an economics course to test these predictions in a real-stakes environment. My within-subjects design controls for student, classroom, and time confounds and finds that the lower variance of larger classes elicits greater effort from all but the lowest-ability students, significantly increasing aggregate effort.
Packed E-Cell (PEC) Converter Topology Operation and Experimental Validation

This paper proposes a novel single-dc-source multilevel inverter called Packed E-Cell (PEC) topology to achieve nine levels with noticeably reduced components count, while dc capacitors are actively balanced. The nine-level PEC (PEC9) is composed of seven active switches and two dc capacitors that are shunted by a four-quadrant switch to from the E-cell, and it makes use of a single dc link. With the proper design of the corresponding PEC9 switching states, the dc capacitors are balanced using the redundant charging/discharging states. Since the shunted capacitors are horizontally extended, both capacitors are simultaneously charged or discharged with the redundant states, so only the auxiliary dc-link voltage needs to be sensed and regulated to half of the input dc source voltage, and consequently, dc capacitors’ voltages are inherently balanced to one quarter of the dc bus voltage. To this end, an active capacitor voltage balancing integrated to the level-shifted half-parabola carrier PWM technique has been designed based on the redundant charging/discharging states to regulate the dc capacitors voltages of PEC9. Furthermore, using the E-cell not only reduces components count but also the proposed topology permits multi ac terminal operation. Thus, five-level inverter operation can be achieved during the four-quadrant switch fault, which confers to the structure high reliability. The theoretical analysis as well as the experimental results are presented and discussed, showing the basic operation, multi-functionality, as well as the superior performance of the proposed novel PEC9 inverter topology.
Codesign of ${K}$ a-Band Integrated GaAs PIN Diodes Limiter and Low Noise Amplifier

In this paper, a novel concurrent design of integrated PIN-diode based limiter and low noise amplifier (LNA) is presented for <italic>Ka</italic>-band MMICs fabricated using a combined PIN/0.15-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula>-pHEMT technology. To improve the small-signal performance and the power-handling capability of the limiter-LNA, the improvement of the PIN-limiter circuit structure and the survivability of the LNA network are proposed. In addition, the total chip area is <inline-formula> <tex-math notation="LaTeX">$2.5\,\,\text {mm} \times 1.2\,\,\text {mm}$ </tex-math></inline-formula> with an equalizer integrated on chip behind the limiter-LNA to improve the bandwidth with a minimum impact on overall NF. The measurements show that the proposed limiter-LNA with only two-stage limiter structure tolerates up to 38 dBm continuous wave (CW) input power without failure, and the average gain and the noise figure for the limiter-LNA are 17 dB and 2.2 – 2.6 dB, respectively, on the 30– 38 GHz frequency bandwidth.
Leveraging psychological framing of warning messages to improve user security on smartphones

OF THE THESIS Leveraging Psychological Framing of Warning Messages to Improve User Security on Smartphones by David Lambropoulos Thesis Director: Janne Lindqvist The current way in which Android warns users about the capabilities their apps request, do not effectively warn users about their security risks. This work focused on the design, implementation and evaluation of smartphone warning system. We also utilized psychological framing for design of warning messages. These warning messages were delivered to the user in a field study (N = 41) using their personal smartphones. We present effective strategies for warning smartphone users. These resulted in average longer viewing/interaction times, higher on average rate at which they consider making changes to their permission settings and were more likely to view apps requesting their permissions.
Bridging the Edge-Cloud Barrier for Real-time Advanced Vision Analytics

Advanced vision analytics plays a key role in a plethora of real-world applications. Unfortunately, many of these applications fail to leverage the abundant compute resource in cloud services, because they require high computing resources and high-quality video input, but the network connections between visual sensors (cameras) and the cloud servers do not always provide sufﬁcient and stable bandwidth to stream high-ﬁdelity video data in real time. This paper presents CloudSeg, an edge-to-cloud framework for advanced vision analytics that co-designs the cloud-side inference with real-time video streaming, to achieve both low latency and high inference accuracy. The core idea is to send the video stream in low resolution, but recover the high-resolution frames from the low-resolution stream via a super-resolution procedure tailored for the actual analytics tasks. In essence, CloudSeg trades additional cloud-side computation (super-resolution) for signiﬁcantly reduced network bandwidth. Our initial evaluation shows that compared to pre-vious work, CloudSeg can reduce bandwidth consumption by ∼ 6.8 × with negligible drop in accuracy.
Diagnosing Gender Bias in Image Recognition Systems

Image recognition systems offer the promise to learn from images at scale without requiring expert knowledge. However, past research suggests that machine learning systems often produce biased output. In this article, we evaluate potential gender biases of commercial image recognition platforms using photographs of U.S. members of Congress and a large number of Twitter images posted by these politicians. Our crowdsourced validation shows that commercial image recognition systems can produce labels that are correct and biased at the same time as they selectively report a subset of many possible true labels. We find that images of women received three times more annotations related to physical appearance. Moreover, women in images are recognized at substantially lower rates in comparison with men. We discuss how encoded biases such as these affect the visibility of women, reinforce harmful gender stereotypes, and limit the validity of the insights that can be gathered from such data.
Figuring out the User in a Few Steps: Bayesian Multifidelity Active Search with Cokriging

Can a system discover what a user wants without the user explicitly issuing a query? A recommender system proposes items of potential interest based on past user history. On the other hand, active search incites, and learns from, user feedback, in order to recommend items that meet a user's current tacit interests, hence promises to offer up-to-date recommendations going beyond those of a recommender system. Yet extant active search methods require an overwhelming amount of user input, relying solely on such input for each item they pick. In this paper, we propose MF-ASC, a novel active search mechanism that performs well with minimal user input. MF-ASC combines cheap, low-fidelity evaluations in the style of a recommender system with the user's high-fidelity input, using Gaussian process regression with multiple target variables (cokriging). To our knowledge, this is the first application of cokriging to active search. Our empirical study with synthetic and real-world data shows that MF-ASC outperforms the state of the art in terms of result relevance within a budget of interactions.
Enhanced FMEA: An integrative approach of fuzzy logic-based FMEA and collective process capability analysis

Abstract The aim of this study is to modify and enhance the quantitative/mathematical features of both computational and analytical aspects of the process failure modes and effects analysis (FMEA). For this purpose, a hybrid approach including the Fuzzy Logic-based FMEA (FFMEA) and collective process capability analysis (CPCA) has been developed in three phases. First, failure modes have been defined based on lack of quality in quality characteristics under investigation, and then, they have been prioritised using FFMEA. Second, the most critical failure has been selected for statistical analysis using CPCA, leading to the corrective actions in the third phase. The proposed approach was investigated in an electrical-equipment-manufacturing company. Findings indicated that the diameter deviation in Insulator A was the most critical failure effect caused by a rightward mean shift of 0.32 cm. In addition, Cpk has been improved from 0.41 to 1.12, and defective products have been reduced from 115,083.09 to 336.98 parts per million.
Mapping tropical forest vegetation from Landsat TM based on knowledge discovering

This paper focuses on mapping the tropical forest vegetation by using Landsat TM and ancillary data such as DEM in Xishuangbanna autonomous prefecture, Yunnan Province, Southwest China. The spectral knowledge of the tropical forest vegetation was obtained by image analysis and field work. The relationship knowledge of the different tropical forest vegetation types with the ancillary data was obtained. A classification method based on knowledge was presented and applied to classify the entire image. It shows that more forest vegetation types with high accuracy can be obtained by the method.
Aplikasi Sistem Informasi Geografis Manajemen Aset Wakaf

Harta benda wakaf adalah aset umat yang harus diselamatkan, dikelola dengan baik, serta dikembangkan untuk kepentingan yang bermanfaat. Tujuan dari penelitian ini adalah untuk mengembangkan aplikasi sistem informasi geografis manajemen aset wakaf di Kota Semarang yang dapat digunakan untuk menyimpan, memproses, mengontrol aset wakaf dalam rangka penyelamatan aset wakaf sampai titik objek wakaf dan menghasilkan informasi baik berupa laporan, dokumen, grafik, gambar peta dan keluaran lainnya yang relevan. Metode penelitian dan pengembangan sistem secara deskriptif kualitatif dimulai dengan pengumpulan data dan identifikasi kebutuhan sistem melalui observasi lapangan, wawancara dan studi pustaka kemudian dilanjutkan dengan melakukan perancangan sistem. Pada tahap implementasi digunakan bahasa pemrograman php, java script dan database MySql. Navigasi dan interaksi sistem aplikasi dengan browser menggunakan Google Maps API untuk menampilkan dan menggambarkan informasi berujuk pada lokasi geografis objek wakaf.
Track and Field Throwing Performance Prediction: Training Intervention, Muscle Architecture Adaptations and Field Tests Explosiveness Ability

Purpose: The purpose of the present study was to investigate whether a) modification in muscle architecture due to training intervention and b) neuromuscular explosive field tests results may predict competition performance in track and field throwing athletes. Methods: Eleven track and field throwers completed 10 weeks of specific training that was performed prior to the summer official national competition. Before and following the 10 weeks of training programe, track and field throwing trials, vastus lateralis muscle architecture, shot put tests, standing long jump, and 40m sprinting were evaluated. Results: Track and field throwing performance, muscle thickness, fascicle length and power-position shot put throw all increased by 5.78 ± 2.82% (P = 0.000), 6.2 ± 7.4% (P = 0.01), 10.5 ± 13.1% (P = 0.02) and 2.8 ± 3.9% (P = 0.04), respectively. Sprinting time in 40m decreased by -1.2 ± 1.0% (P = 0.004), whilst, work power output during the standing long-jump increased by 1.7 ± 2.5% (P = 0.03) when compared between preand post-training intervention. The power-position shot put throwing result was positively correlated with the increase in standing long-jump (r = 0.81, P < 0.01) and with the increase in 40m sprint (r = -0.63, P < 0.05) performances. The increase in muscle thickness was positively correlated with the increase in 40m sprint performance (r = 0.62, P < 0.05). The multiple linear regression analysis, combining the percentage alteration in backward shot put throw and vastus lateralis thickness with the proportional increase in track and field throwing competition performance, explains the 56% increase observed in throwing competition result. Conclusions: These results may suggest that consistent examination of muscle thickness and evaluation of throwing ability using explosive field tests during a training period both may be used to predict the increase in track and field throwing competive performance.