Reviewerly/exHarmony huggingface dataset:
Image Synthesis From Reconfigurable Layout and Style

Despite remarkable recent progress on both unconditional and conditional image synthesis, it remains a long-standing problem to learn generative models that are capable of synthesizing realistic and sharp images from reconfigurable spatial layout (i.e., bounding boxes + class labels in an image lattice) and style (i.e., structural and appearance variations encoded by latent vectors), especially at high resolution. By reconfigurable, it means that a model can preserve the intrinsic one-to-many mapping from a given layout to multiple plausible images with different styles, and is adaptive with respect to perturbations of a layout and style latent code. In this paper, we present a layout- and style-based architecture for generative adversarial networks (termed LostGANs) that can be trained end-to-end to generate images from reconfigurable layout and style. Inspired by the vanilla StyleGAN, the proposed LostGAN consists of two new components: (i) learning fine-grained mask maps in a weakly-supervised manner to bridge the gap between layouts and images, and (ii) learning object instance-specific layout-aware feature normalization (ISLA-Norm) in the generator to realize multi-object style generation. In experiments, the proposed method is tested on the COCO-Stuff dataset and the Visual Genome dataset with state-of-the-art performance obtained. The code and pretrained models are available at \url{https://github.com/iVMCL/LostGANs}.
KLASIFIKASI SPAM E-MAIL MENGGUNAKAN METODE TRANSFORMED COMPLEMENT NAÏVE BAYES (TCNB)

Classification is one of the ways to organize text so that the texts with the same contents can be grouped in the same category. One of the famous text classification methods is the Naive Bayes Method. Naive Bayes has efficient computation and good prediction result however the performance of Naive Bayes is not really good in classifying unbalanced dataset. This Naive Bayes method is then modified to overcome the weakness, this modified method is then known as Transformed Complement Naive Bayes (TCNB) method. In this research, TCNB method was used to the spam e-mails whose dataset were unbalanced and were consisted of 481 dataset in spam e-mail class, and 2412 dataset in legitimate e-mail class (in total, there are 2893 dataset). The classification was done with and without cross validation. The classification with cross validation was done starting from k=2 until k=10. The classification without cross validation was done by dividing the training data by 80% and testing data by 20%. The result showed that the classification by using TCNB with cross validation had its best accuracy level on k=10 by 93,917% and the classification without cross validation had its best accuracy by 92,760%. Thus it can be concluded that TCNB can handle unbalanced dataset with good prediction accuracy.
Vision Based Real-time Recognition of Hand Gestures for Indian Sign Language using Histogram of Oriented Gradients Features

A sign language is the method of communication used by the deaf people where gestures are used to express meaning. Due to illiteracy of sign language by normal people, there exists communication gap between normal people and deaf people. Very little work has been done for recognition of Indian Sign Language due to lack of standardization and complexity of hand gestures. This resulted in need of automatic system that can recognize Indian Sign Language. Such system, developed via use of image processing and computer vision techniques, will help deaf peoples to communicate with normal people thus filling the communication gap. This paper presents vision based system for real time recognition of hand gesture for Indian Sign Language. The developed system is first trained using training data set. For this, from all the images of dataset, hand region is cropped by performing segmentation using thresholding in YCbCr color space. Histogram of Oriented Gradients (HOG) features of these cropped images are then computed and used to train the classifier. During testing, features of hand region of frames from real time video are presented to classifier for classification. Recognition rates of different classifiers like Support Vector Machine (SVM), K-Nearest Neighbors (KNN) and Linear Discriminant Analysis (LDA) are discussed.
Application of Human Machine Interface and Augmented Reality Technology to Flight Operation

The primary objective of this paper is to introduce the application of Human-Machine Interface (HMI) and Augmented Reality (AR) technologies in flight operations.These include: self-check-in, baggage handling, airport security and surveillance, airport operations monitoring, In-Flight Entertainment and Connectivity (IFEC), cockpit design, and cabin crew support.This paper investigates the application status and development trends of HMI and AR technologies for airports and aircraft.These technologies can provide more efficient in-flight passenger service and experience by using AR devices.This paper also discusses the developments such as; the Integrated Control Application (ICA) for the IFEC interface, AR flight simulation training program using the fixed-based simulator, and the AR aircraft cabin interior concept test program.These applications present how HMI and AR techniques can be utilized in actual flight operations.The developed programs in this paper can be applied to their purpose within aircraft interiors and services to enhance efficiency, comfort, and experience.
Investigation of Kırıkkale City Drilling Results on Neighborhood Bases and Improvement of Consolidation Values by Fly Ash Contribution

Swelling of soil is known as a volume expansion under various climatic effects. The swelling potential of the clay ground causes significant deformations on the constructions. The city, economically hosting many military factories, refineries, natural gas conversion plants and hydroelectric power plants. It is also a transit point for trips with more than forty cities. When it is added to the population of more than two hundred tousand which is hosted, it is almost necessary for our country to examine the ground character of Kirikkale in a healthy way. In the study, high plastice clay grounds (CH) were investigated in Kirikkale province in 2014-2015-2016-2017 years. Considering the data from the drilling work, the neighborhood list will be prepared and the results of the treatment with fly ash additives will be investigated in order to reduce the swelling potential in the determined locations.
Binary Neural Networks for Object Detection

In the past few years, convolutional neural networks (CNNs) have been widely utilized and shown state-of-the-art performances on computer vision tasks. However, CNN based approaches usually require a large amount of storage, run-time memory, as well as computation power in both training and inference time, which are usually used on GPU based machines to ensure the speed for inferences. But they are usually insufficient to be deployed on low-power applications. Although many approaches were proposed to compress and accelerate the CNN models, most of them were only evaluated on relatively simple problems (e.g. image classification), which only support limited real-world applications. Especially, among those methods, binary quantization can achieve very high model compression, but only a few works have been observed to utilize it on more complex tasks. Therefore, the exploration and evaluations of applying binary quantization on more complex tasks like object detection are worthwhile, which can be used in much more applications like autonomous driving and face detection. In this project, we apply and evaluate two different binary quantization approaches, named ABC-Net and PA-Net on object detection tasks. Also, we specify the exact implementation details for the binary convolutional operations in this project. As a result, we can achieve maximally 6.1× (around 16% of the full-precision model) compression, and minimal 2.5% accuracy reduction for weight quantization. The weight quantized models were able to outperform some existing real-time detectors in terms of both accuracy and storage size. Although large accuracy reduction was observed for input quantization, the quantized model could still maintain an acceptable accuracy compared to existing real-time object detectors.
Cross-modality Knowledge Transfer for Prostate Segmentation from CT Scans

Creating large scale high-quality annotations is a known challenge in medical imaging. In this work, based on the CycleGAN algorithm, we propose leveraging annotations from one modality to be useful in other modalities. More specifically, the proposed algorithm creates highly realistic synthetic CT images (SynCT) from prostate MR images using unpaired data sets. By using SynCT images (without segmentation labels) and MR images (with segmentation labels available), we have trained a deep segmentation network for precise delineation of prostate from real CT scans. For the generator in our CycleGAN, the cycle consistency term is used to guarantee that SynCT shares the identical manually-drawn, high-quality masks originally delineated on MR images. Further, we introduce a cost function based on structural similarity index (SSIM) to improve the anatomical similarity between real and synthetic images. For segmentation followed by the SynCT generation from CycleGAN, automatic delineation is achieved through a 2.5D Residual U-Net. Quantitative evaluation demonstrates comparable segmentation results between our SynCT and radiologist drawn masks for real CT images, solving an important problem in medical image segmentation field when ground truth annotations are not available for the modality of interest.
Device-Level Security

In the life cycle of an attack, there are different stages an attacker goes through. We have already discussed in detail the first stage of an attack in our review of e-mail security in Chapter 2 . If an attacker manages to get into a user’s system as a result of things like human error or a successful social engineering attempt, we need to have the device’s defenses up and running to stop the intruder from getting further into the network.
Students’ Motivations in Choosing English as Additional Course in SMAN 1 Bengkulu

The type of this research is Descriptive Quantitative and Qualitative research. It is aimed to find out the motivation which make students in XII Social 6 choose English as an additional subject course and to find out the highest motivation which make students in XII Social 6 choose English as an additional subject course. The subject of this study is the students in class XII social 6 SMAN 1 Kota Bengkulu. In this research, the researcher collected the data by using questionnaire. Then, the data obtained from the questionnaires will be analyzed using SPSS program. The data concerning subjects’ general background as well as their comments will be calculated and presented in percentage. A Likert scale will be used to measure the level and type of subjects’ learning motivation. According to table that the researcher used to analyze extrinsic and intrinsic motivation of students in choosing English as additional subject, the researcher got the result that show the extrinsic motivation is the highest reason chosen by the students when they chose English as their additional subject course at school than intrinsic motivation. It can be seen from the calculation scores of students intrinsic and extrinsic motivation that showed the total of mean score for extrinsic motivation is 47.5 and the total mean score for intrinsic motivation is 46.62. There are several extrinsic motivation from the students, for example the students prepare to learn English because they want to continue the study or get the works, go abroad and want to try to communicate with the foreigner.
Seismic High Amplitude Noise Attenuation Based on the Deep Learning Method

Summary The road traffic noise is a special kind of high amplitude noise in the land seismic acquisition around a road network. We propose a noise detection and attenuation convolutional neural network (NDA-CNN) method, which the conventional detection step with CNN learning method and scale the noisy traces down by further using the standard deviation of the learning result. CNN learning mainly help us to get the complex distribution of high amplitude noises from the noisy data through learning the training data. The distribution includes the mask of noise position and scaler information for next attenuation step, which is more correct than the conventional automatic threshold-determination technique. The real data tests show that NDA-CNN method can get good results for high amplitude noise attenuation, and it is practical and efficient.
Research on Predicting the Tamping Cycle of Heavy Haul Railway for Different Large Machine Tamping Modes

An important research direction for heavy haul railway maintenance is to use track condition inspection data. This paper analyses the MTQI (Machine Tamping Quality Index) before and after tamping for different large machine..tamping modes (tamping machine type, single/double tamping, stabilizing vehicle, line type) using inspection and repair data and establishes a tamping effect model. A prediction model of the MTQI index using gross weight is established with grey GM (1, 1) prediction technology. When combined with the tamping effect model, it can be used to compare and select maintenance modes and predict the tamping cycle under different tamping modes. Using an example from a heavy haul railway, the paper shows the proposed prediction model will help the track management department select the best tamping maintenance mode. It can provide a scientific and economic decision-making plan for tamping maintenance and facilitate preventive maintenance.
Standard Gravity Inversion Modeling FDI Per Capita GDP in the Ethiopian economy Environment from 1998 to 2017

The reality of FDI in the Ethiopian economy environment as well as the standard and analytical modeling of the reflection of this type of investment on the economic growth during the period (1998-2017) based on the hypothesis that the policy of attracting foreign investments in the Ethiopian economy has a positive effect in general And per capita GDP in particular. The analytical and standard study concluded with a number of results, including: (1) The Ethiopian economy's qualitative leap coincided with the entry of foreign direct investment into Ethiopia until Ethiopia reached Africa's fastest growing economy in return for the second most attractive country for foreign investment in the world after Angola in 2016. (2) The strength of foreign direct investment (FDI) has been reflected in the size of the GDP, but it has not been reflected in the level required for the welfare of the Ethiopian individual. (3) The standard study indicates that there is a direct correlation between FDI and GDP per capita, meaning that any increase in the volume of FDI in the Ethiopian economy will lead to an increase in per capita GDP.
Deep Floor Plan Recognition Using a Multi-Task Network with Room-Boundary-Guided Attention

This paper presents a new approach to recognize elements in floor plan layouts. Besides walls and rooms, we aim to recognize diverse floor plan elements, such as doors, windows and different types of rooms, in the floor layouts. To this end, we model a hierarchy of floor plan elements and design a deep multi-task neural network with two tasks: one to learn to predict room-boundary elements, and the other to predict rooms with types. More importantly, we formulate the room-boundary-guided attention mechanism in our spatial contextual module to carefully take room-boundary features into account to enhance the room-type predictions. Furthermore, we design a cross-and-within-task weighted loss to balance the multi-label tasks and prepare two new datasets for floor plan recognition. Experimental results demonstrate the superiority and effectiveness of our network over the state-of-the-art methods.
Text steganography in Letter of Credit (LC) using synonym substitution based algorithm

Text steganography uses text files as carrier files to hide confidential information. The goal of text steganography is to make the hidden data undetectable except the sender and receiver. This paper focuses on semantic-based text steganography. It hides confidential information by replacing words with their synonyms (same meanings). In international trading processes, products are imported and exported between foreign countries, in this case, the sellers and the buyers do not know each other. Therefore, the seller has a number of risks such as credit risk and legal risk caused by the distance. Letter of credit (LC) has been used to solve these risks. In the modern age, LC information has been sent online. Therefore, the security of LC information has become an important role. Most of the bank’s LC information is sent via SWIFT message, email or message formats. Using these types of message does not secure the LC information. This paper implements synonym substitution based algorithm to make confidential LC information more secure. This system is implemented by using Java software development kit (J2SE 7).
Fast measurement of chromatic BSDF and its application to LED lighting

Bidirectional scattering distribution function (BSDF) represents the scattered light distribution in forward and backward directions. BSDF is an important function in precise lighting design, because surface scattering is difficult to determine and including it in simulations. However, to measure a whole field BSDF is time consuming, as it can take as long as a day. In this paper, we propose and demonstrate a new way to measure BSDF. We utilize a screen that is illuminated by the scattered light, and a camera that captures the image on the screen. A complex calibration between the grey level of the camera and intensity is performed to make sure the measurement is valid. Through continuously imaging the screen for various scattered light distributions, an image fusion is performed to present the final BSDF. We call this instrument as screen image synthesis (SIS) BSDF meter. In this paper, two generations of SIS BSDF meter was developed, and is shown in details.
LibGuides: Introduction to Academic Writing for Graduate Students: Finding Articles

Research and library resources for students taking ESL/Wr 107, Wr 109 and SEC 111 at Caltech.
Sparse, Low-bias, and Scalable Estimation of High Dimensional Vector Autoregressive Models via Union of Intersections

Vector autoregressive (VAR) models are widely used for causal discovery and forecasting in multivariate time series analyses in fields as diverse as neuroscience, environmental science, and econometrics. In the high-dimensional setting, model parameters are typically estimated by L1-regularized maximum likelihood; yet, when applied to VAR models, this technique produces a sizable trade-off between sparsity and bias with the choice of the regularization hyperparameter, and thus between causal discovery and prediction. That is, low-bias estimation entails dense parameter selection, and sparse selection entails increased bias; the former is useful in forecasting but less likely to yield scientific insight leading to discovery of causal influences, and conversely for the latter. This paper presents a scalable algorithm for simultaneous low-bias and low-variance estimation (hence good prediction) with sparse selection for high-dimensional VAR models. The method leverages the recently developed Union of Intersections (UoI) algorithmic framework for flexible, modular, and scalable feature selection and estimation that allows control of false discovery and false omission in feature selection while maintaining low bias and low variance. This paper demonstrates the superior performance of the UoI-VAR algorithm compared with other methods in simulation studies, exhibits its application in data analysis, and illustrates its good algorithmic scalability in multi-node distributed memory implementations.
Prediksi Harga Saham Perusahaan Perbankan Menggunakan Regresi Linear Studi Kasus Bank BCA Tahun 2015-2017

Stock investment is important for financial development in a company. Moreover, the stock price displayed by the company can be known by the people and the local economy because the company has gone public on the Indonesia Economic Exchange (IDX) at www.idx.co.id. There are several fundamental factors that influence the stock market price in a listed company and as a result the number of stock investors in Indonesia is very small. This cause made it difficult for the community to predict the stock price of banking companies at inconsistent prices. The method to be used in this paper is Linear Regression using Excel tools to perform calculations and SPSS 16.0 as a data mining tool. The research data taken is historical data of banking companies for 3 periods as a whole in the form of excel that has been downloaded from the Yahoo Finance website. The final results are in the form of MAPE charts in 3 years period, and Average error chart in 3 years period.
Development of Sine Cosine Toolbox for LabVIEW

LabVIEW is one of the prominent programming platforms due to its simplicity and robustness towards hardware design. It finds application in different fields of engineering such as industrial automation, signal processing, instrumentation, control system design and so forth. LabVIEW has a rich ecosystem of toolboxes and libraries for simplifying the improvement in numerous zones of engineering, however, it lacks in optimization toolbox for advanced meta-heuristic algorithms. This is a serious deficiency as there is an astonishing improvement in the field of meta-heuristic algorithm in recent years. Thus, in this article, a potentially acclaimed meta-heuristic named as sine cosine algorithm (SCA) is designed in LabVIEW environment. The designed algorithm is benchmarked on a testbed of six non-trivial functions and compared to existing differential evolution (DE) optimizer in LabVIEW. Statistical analysis of results depicts superior accuracy and stability of SCA in comparison to DE. Further, SCA based PID optimizer is also implemented to show the applicability of the designed algorithm in solving real-world optimization problems.
Universal Adversarial Triggers for Attacking and Analyzing NLP

Adversarial examples highlight model vulnerabilities and are useful for evaluation and interpretation. We define universal adversarial triggers: input-agnostic sequences of tokens that trigger a model produce a specific prediction when concatenated any input from a dataset. We propose a gradient-guided search over tokens which finds short trigger sequences (e.g., one word for classification and four words for language modeling) that successfully trigger the target prediction. For example, triggers cause SNLI entailment accuracy drop from 89.94% 0.55%, 72% of why questions in SQuAD be answered to kill american people, and the GPT-2 language model spew racist output even when conditioned on non-racial contexts. Furthermore, although the triggers are optimized using white-box access a specific model, they transfer other models for all tasks we consider. Finally, since triggers are input-agnostic, they provide an analysis of global model behavior. For instance, they confirm that SNLI models exploit dataset biases and help diagnose heuristics learned by reading comprehension models.
A Short Course on Error-Correcting Codes

When digital data are transmitted over a noisy channel, it is important to have a mechanism allowing recovery against a limited number of errors. Normally, a user string of 0's and 1's, called bits, is encoded by adding a number of redundant bits to it. When the receiver attempts to reconstruct the original message sent, it starts by examining a possibly corrupted version of the encoded message, and then makes a decision. This process is called the decoding. The purpose of this course is giving an introduction to the theory and practice of error-correcting codes.