Reviewerly/exHarmony huggingface dataset:
Neural Cryptanalysis: Metrics, Methodology, and Applications in CPS Ciphers

Many real-world cyber-physical systems (CPS) use proprietary cipher algorithms. In this work, we describe an easy-to-use black-box security evaluation approach to measure the strength of proprietary ciphers without having to know the algorithms. We quantify the strength of a cipher by measuring how difficult it is for a neural network to mimic the cipher algorithm. We define new metrics (e.g., cipher match rate, training data complexity and training time complexity) that are computed from neural networks to quantitatively represent the cipher strength. This measurement approach allows us to directly compare the security of ciphers. Our experimental demonstration utilizes fully connected neural networks with multiple parallel binary classifiers at the output layer. The results show that when compared with round-reduced DES, the security strength of Hitag2 (a popular stream cipher used in the keyless entry of modern cars) is weaker than 3-round DES.
Immersive wave propagation experiments in a two-dimensional acoustic waveguide

The physical implementation of immersive boundary conditions (IBCs) allows acoustic or elastic waves to propagate seamlessly between a physical domain, such as a wave propagation laboratory, and a numerical simulation virtually enclosing the physical domain. IBCs correctly account for all wavefield interactions between both domains, including higher-order long-range scattering. In this contribution, IBCs are physically implemented in a two-dimensional (2-D) acoustic waveguide. The boundary surrounding the waveguide is densely populated with hundreds of loudspeakers that apply the necessary boundary conditions. The required signals to be injected at the boundary are predicted in real-time by (1) measuring the pressure field and its gradient on two acoustically transparent auxiliary surfaces of microphones inside the waveguide and (2) extrapolating the wavefields to the boundary by evaluating a Kirchhoff-Helmholtz integral using a low-latency, FPGA-enabled data acquisition, computation and control system. Here, we demonstrate the first real-time, 2-D physical immersive wave propagation experiments. We present the setup, as well as a suite of experiments that demonstrate the ability of IBCs to actively suppress broadband incident fields at the boundary of the waveguide and to correctly reproduce all orders of wavefield scattering between the physical experiment and the numerical simulation.
Sequence-to-Set Semantic Tagging: End-to-End Multi-label Prediction using Neural Attention for Complex Query Reformulation and Automated Text Categorization

Novel contexts may often arise in complex querying scenarios such as in evidence-based medicine (EBM) involving biomedical literature, that may not explicitly refer to entities or canonical concept forms occurring in any fact- or rule-based knowledge source such as an ontology like the UMLS. Moreover, hidden associations between candidate concepts meaningful in the current context, may not exist within a single document, but within the collection, via alternate lexical forms. Therefore, inspired by the recent success of sequence-to-sequence neural models in delivering the state-of-the-art in a wide range of NLP tasks, we develop a novel sequence-to-set framework with neural attention for learning document representations that can effect term transfer within the corpus, for semantically tagging a large collection of documents. We demonstrate that our proposed method can be effective in both a supervised multi-label classification setup for text categorization, as well as in a unique unsupervised setting with no human-annotated document labels that uses no external knowledge resources and only corpus-derived term statistics to drive the training. Further, we show that semi-supervised training using our architecture on large amounts of unlabeled data can augment performance on the text categorization task when limited labeled data is available. Our approach to generate document encodings employing our sequence-to-set models for inference of semantic tags, gives to the best of our knowledge, the state-of-the-art for both, the unsupervised query expansion task for the TREC CDS 2016 challenge dataset when evaluated on an Okapi BM25--based document retrieval system; and also over the MLTM baseline (Soleimani et al, 2016), for both supervised and semi-supervised multi-label prediction tasks on the del.icio.us and Ohsumed datasets. We will make our code and data publicly available.
SISTEM PENDUKUNG KEPUTUSAN PENENTUAN LOKASI PEMBANGUNAN PETERNAKAN AYAM MENGGUNAKAN METODE MOORA

This study aims to help beginner farmers to determine the location of the chicken farm construction using the MOORA method. The location factor for determining chicken farms is one of the important factors, because if the farm location is close to the highway, then the chicken will be exposed to transportation noise and easily stressed, but vice versa if the chicken farm is close to the cutting or sales location such as the market, the transportation costs will be relatively cheaper. The MOORA method has simple and stable mathematical calculations. MOORA method has the right mathematical calculation results in making a decision. The MOORA method has advantages over other methods, which are simpler and easier to implement. The level of selection in the MOORA method which is quite good can decide on an alternative solution in a problem.
Generalization Bounds with Integral Probability Metrics

In previous chapters, we presented several seminal results regarding the generalization bounds for domain adaptation and the impossibility theorems for some of them. We have shown that the basic shape of generalization bounds in the context of domain adaptation remains more or less the same and principally differs only in the divergence used to measure the distance between the source and the target marginal distributions. To this end, we showed that the first original bound proposed by Ben-David et al. is restricted to a particular 0 − 1 loss function and a symmetric hypothesis class: a drawback that was tackled by Mansour et al with the introduction of the discrepancy distance. Despite the differences between the two, however, they both have several common features, which is an explicit dependence on the considered hypothesis class and the computational issues related to their computation. Indeed, the computation of the ℋΔℋ-divergence presents an intractable problem, while the minimization of the discrepancy distance has a prohibitive computational complexity. To this end, a natural quest for other metrics with some attractive properties suitable for quantifying the divergence between the two domains arises. In this chapter, we consider a large family of metrics on the space of probability measure called integral probability metrics (IPMs) that present a well-studied topic in probability theory. We particularly show that depending on the chosen functional class, some instances of IPMs can have interesting properties that are completely different from those exhibited by both ℋΔℋ-divergence and discrepancy distance.
On New Rebalancing Algorithm

This paper proposes a new algorithm for rebalancing a binary search tree, called qBalance, by a certain analogy. The running time of this algorithm is proportional to the number of keys in the tree. To compare qBalance with existing rebalancing algorithms, several versions of the algorithm are implemented. We compare qBalance with the well-known DSW algorithm on binary search trees (BSTs), with the Sedgewick algorithm on an ordered tree, with the modified Sedgewick algorithm on Red-Black (RB) trees with the specific structure of the node. For RB trees whose nodes have a standard (typical) structure, qBalance is implemented using asynchronous mode. This version of the algorithm is considerably complicated, but it is twice as fast as the serial implementation. The results of numerical experiments confirm the advantage of the new algorithm compared to DSW. The Sedgewick algorithm and its modification retain advantage by 30% in terms of running time on ordered trees. On red-black trees, the advantage of the new algorithm is significant. This especially applies to the asynchronous version of the algorithm.
Upstream: Exposing Performance Information from Cloud Providers to Tenants

Infrastructure-as-a-Service (IaaS) is a widely adopted cloud computing paradigm due to its flexibility and competitive prices. To improve resource efficiency, most IaaS providers consolidate several tenants in the same virtualization server, which usually incurs variable performance experiences. In this paper, we evaluate the CPU time received by tenants’ virtual machines (VMs). We present a model that estimates the probability of a VM to receive, at least, a determined fraction of CPU time using limited information about the host and VMs running on it. We constructed this model using a series of experiments with different numbers of CPU cores and co-located VMs.
A Contextual Hierarchical Graph Model for Generating Random Sequences of Objects with Application to Music Playlists

Recommending the right content in large scale multimedia streaming services is an important and challenging problem that has received much attention in the past decade. A key ingredient for successful recommendations is an effective similarity metric between two objects, and models that leverage the current context to constrain the recommendations. This work proposes a model for random object generation that introduces two key novel elements: (i) a similarity metric based on the distance between objects in a given object sequence, that is also used to measure similarity between meta-data associated with the objects, such as artists and genres; (ii) a hierarchical graph model with different graphs each associated with a different meta-data. A biased random walk in each graph that are coupled and synchronized dictate the random generation of objects, leveraging the current context to constrain randomness. The proposed model is fully parameterized from sequences of objects, requiring no external parameters or tuning. The model is applied to a large music dataset with over 1 million playlists generating a hierarchy with three layers (genre, artist, track). Results indicate its superiority in generating actual full playlists against two baseline models.
CartoonRenderer: An Instance-based Multi-Style Cartoon Image Translator

Instance based photo cartoonization is one of the challenging image stylization tasks which aim at transforming realistic photos into cartoon style images while preserving the semantic contents of the photos. State-of-the-art Deep Neural Networks (DNNs) methods still fail to produce satisfactory results with input photos in the wild, especially for photos which have high contrast and full of rich textures. This is due to that: cartoon style images tend to have smooth color regions and emphasized edges which are contradict to realistic photos which require clear semantic contents, i.e., textures, shapes etc. Previous methods have difficulty in satisfying cartoon style textures and preserving semantic contents at the same time. In this work, we propose a novel "CartoonRenderer" framework which utilizing a single trained model to generate multiple cartoon styles. In a nutshell, our method maps photo into a feature model and renders the feature model back into image space. In particular, cartoonization is achieved by conducting some transformation manipulation in the feature space with our proposed Soft-AdaIN. Extensive experimental results show our method produces higher quality cartoon style images than prior arts, with accurate semantic content preservation. In addition, due to the decoupling of whole generating process into "Modeling-Coordinating-Rendering" parts, our method could easily process higher resolution photos, which is intractable for existing methods.
Euskarazko elkarrizketa sistema automatikoa sare neuronalen bidez

Lan honetan sare neuronalen bidez euskaraz hitz egiten ikasten duen elkarrizketa sistema automatiko bat aurkezten dugu. Horretarako, turingen testaren ideia era konputazionalean inplementatzen duten sare neuronal sortzaile aurkariak erabili ditugu. Normalean erabiltzen diren ingelesezko corpusak baino bi magnitude ordena txikiagoa den euskarazko corpus batekin halako sareak doitzea badagoela frogatzen dugu. Amaitzeko, euskararen morfologia kontuan hartzen duen aurreprozesamendua erabiltzea komenigarria dela erakusten dugu. Sare neuronaletan oinarrituta dagoen euskarazko lehen elkarrizketa sistema aurkezten dugu.
KNU-HYUNDAI’s NMT system for Scientific Paper and Patent Tasks onWAT 2019

In this paper, we describe the neural machine translation (NMT) system submitted by the Kangwon National University and HYUNDAI (KNU-HYUNDAI) team to the translation tasks of the 6th workshop on Asian Translation (WAT 2019).We participated in all tasks of ASPEC and JPC2, which included those of Chinese-Japanese, English-Japanese, and Korean→Japanese.We submitted our transformer-based NMT system with built using the following methods: a) relative positioning method for pairwise relationships between the input elements, b) back-translation and multi-source translation for data augmentation, c) right-to-left (r2l)-reranking model robust against error propagation in autoregressive architectures such as decoders, and d) checkpoint ensemble models, which selected the top three models with the best validation bilingual evaluation understudy (BLEU) .We have reported the translation results on the two aforementioned tasks.We performed well in both the tasks and were ranked first in terms of the BLEU scores in all the JPC2 subtasks we participated in.
UPAYA PEMBERDAYAAN MASYARAKAT MELALUI PELAYANAN SOSIAL BAHASA INGGRIS

English language institutes make various empowerment efforts to encourage community participation so they have the ability to achieve a prosperous standard of living. Empowerment efforts undertaken by English language course institutions in Pelem Village have made the community aware of the potential, have the ability to access welfare and change existing barriers. The description is the main attraction to examine how efforts to empower the community through social service institutions English courses in the Village of Pelem, Pare District, Kediri Regency. The purpose of this research is to find out, describe and analyze efforts to empower the community through social service institutions of English courses in Pelem Village, Pare District, Kediri Regency. This research uses a case study approach with qualitative research type. The technique of determining the informant using the subjectivity of researchers and the technique of determining the location using purposive.Data collection techniques using observation, interviews and documentation. Data analysis techniques include data collection, data reduction, data display and verification. The results showed that the role of the course institutions could improve the welfare of the people of Pelem Village.Keywords: Empowerment Efforts, Institutions and community
KFHE-HOMER: A multi-label ensemble classification algorithm exploiting sensor fusion properties of the Kalman filter

Multi-label classification allows a datapoint to be labelled with more than one class at the same time. In spite of their success in multi-class classification problems, ensemble methods based on approaches other than bagging have not been widely explored for multi-label classification problems. The Kalman Filter-based Heuristic Ensemble (KFHE) is a recent ensemble method that exploits the sensor fusion properties of the Kalman filter to combine several classifier models, and that has been shown to be very effective. This article proposes KFHE-HOMER, an extension of the KFHE ensemble approach to the multi-label domain. KFHE-HOMER sequentially trains multiple HOMER multi-label classifiers and aggregates their outputs using the sensor fusion properties of the Kalman filter. Experiments described in this article show that KFHE-HOMER performs consistently better than existing multi-label methods including existing approaches based on ensembles.
Towards Design Methodology of Efficient Fast Algorithms for Accelerating Generative Adversarial Networks on FPGAs

Generative adversarial networks (GANs) have shown excellent performance in image and speech applications. GANs create impressive data primarily through a new type of operator called deconvolution (DeConv) or transposed convolution (Conv). To implement the DeConv layer in hardware, the state-of-the-art accelerator reduces the high computational complexity via the DeConv-to-Conv conversion and achieves the same results. However, there is a problem that the number of filters increases due to this conversion. Recently, Winograd minimal filtering has been recognized as an effective solution to improve the arithmetic complexity and resource efficiency of the Conv layer. In this paper, we propose an efficient Winograd DeConv accelerator that combines these two orthogonal approaches on FPGAs. Firstly, we introduce a new class of fast algorithm for DeConv layers using Winograd minimal filtering. Since there are regular sparse patterns in Winograd filters, we further amortize the computational complexity by skipping zero weights. Secondly, we propose a new dataflow to prevent resource underutilization by reorganizing the filter layout in the Winograd domain. Finally, we propose an efficient architecture for implementing Winograd DeConv by designing the line buffer and exploring the design space. Experimental results on various GANs show that our accelerator achieves up to 1.78x~8.38x speedup over the state-of-the-art DeConv accelerators.
Accurate Uncertainty Estimation and Decomposition in Ensemble Learning.

Ensemble learning is a standard approach to building machine learning systems that capture complex phenomena in real-world data. An important aspect of these systems is the complete and valid quantification of model uncertainty. We introduce a Bayesian nonparametric ensemble (BNE) approach that augments an existing ensemble model to account for different sources of model uncertainty. BNE augments a model's prediction and distribution functions using Bayesian nonparametric machinery. It has a theoretical guarantee in that it robustly estimates the uncertainty patterns in the data distribution, and can decompose its overall predictive uncertainty into distinct components that are due to different sources of noise and error. We show that our method achieves accurate uncertainty estimates under complex observational noise, and illustrate its real-world utility in terms of uncertainty decomposition and model bias detection for an ensemble in predict air pollution exposures in Eastern Massachusetts, USA.
Detecting cutaneous basal cell carcinomas in ultra-high resolution and weakly labelled histopathological images

Diagnosing basal cell carcinomas (BCC), one of the most common cutaneous malignancies in humans, is a task regularly performed by pathologists and dermato-pathologists. Improving histological diagnosis by providing diagnosis suggestions, i.e. computer-assisted diagnoses is actively researched to improve safety, quality and efficiency. Increasingly, machine learning methods are applied due to their superior performance. However, typical images obtained by scanning histological sections often have a resolution that is prohibitive for processing with current state-of-the-art neural networks. Furthermore, the data pose a problem of weak labels, since only a tiny fraction of the image is indicative of the disease class, whereas a large fraction of the image is highly similar to the non-disease class. The aim of this study is to evaluate whether it is possible to detect basal cell carcinomas in histological sections using attention-based deep learning models and to overcome the ultra-high resolution and the weak labels of whole slide images. We demonstrate that attention-based models can indeed yield almost perfect classification performance with an AUC of 0.99.
LIDA: Lightweight Interactive Dialogue Annotator

Dialogue systems have the potential to change how people interact with machines but are highly dependent on the quality of the data used to train them. It is therefore important to develop good dialogue annotation tools which can improve the speed and quality of dialogue data annotation. With this in mind, we introduce LIDA, an annotation tool designed specifically for conversation data. As far as we know, LIDA is the first dialogue annotation system that handles the entire dialogue annotation pipeline from raw text, as may be the output of transcription services, to structured conversation data. Furthermore it supports the integration of arbitrary machine learning models as annotation recommenders and also has a dedicated interface to resolve inter-annotator disagreements such as after crowdsourcing annotations for a dataset. LIDA is fully open source, documented and publicly available [ https://github.com/Wluper/lida ]
Effects of Cooperative Learning on High School Students’ English Reading Ability and the Key Competencies of the 2015 Revised National Curriculum

The 2015 revised national curriculum is implemented to the 10th and 11th graders in 2019. According to the curriculum, 6 key competencies need to be promoted during the whole school education in Korea. The purpose of the present study is verifying the effects of cooperative learning on key competencies of the 2015 revised national curriculum and English reading competence in EFL context in Korea. A total of 101 high school students participated in this study. They were divided into two groups(49 for experimental group, 52 for controlled one). To verify the effectiveness of the study, SCI(Student Competence Index) developed by KEDI(Korean Educational Development Institute) and 2 reading comprehension test were conducted before and after the experiment. Cooperative learning was adapted for the experimental group, whereas individual learning for controlled group. The experiment was operated for 14 weeks(2 hours every week). The result of the study revealed that both group showed significant improvements on reading ability but no significant differences were found between the groups. Whereas experimental group showed improvements on key competencies.