Reviewerly/exHarmony huggingface dataset:
Deep learning for gravity and magnetic data interpolation

Deep learning has achieved a great success in various fields in recent years. As a powerful tool, deep learning is used to interpolate the gravity and magnetic data with block lack of data in this paper. We firstly designed a deep convolution neural network (CNN), and mask dataset was created to simulate the block lack in the data. The network was trained by using the earth’s gravity anomalies dataset (bouguer, isostatic and surface free-air) and the earth magnetic anomaly dataset. We demonstrated the feasibility of this approach by numerical experiments, which indicated that this method is able to effectively recover the gravity and magnetic data with block lack. The experiments also demonstrated that CNN gains competitive performance compared with traditional interpolation methods. Presentation Date: Wednesday, September 18, 2019 Session Start Time: 9:20 AM Presentation Time: 10:35 AM Location: Poster Station 1 Presentation Type: Poster
Novel Method to Address Wafer Surface Condition

Minimal wafer loss is the goal of companies particularly in the cut throat semi-conductor industry. Unwanted moisture on the wafer surface at front end of the line can lead to defects in subsequent process steps. Typically, wafer surface exposed to moisture such as high humidity conditions can lead to wafer surface contamination. The moisture effect is addressed at a step which is after the initial processing steps thus minimizing the addition of any new steps affecting cycle time and cost of additional processing. Pre-clean, pad oxide and pad nitride are the typical initial steps of most of the technology nodes in semiconductor industry. Most of the work is done on wafer surface prior to introduction in the production line. This project focuses on addressing it at pad nitride step after the wafer is already processed in pre clean and pad oxide. Initially paper looks at the failure mode on incoming wafer surface, then methodology to eliminate or mitigate the incoming impact and finally optimize the step conditions for minimal impact.
Behavior-Based Compression for Convolutional Neural Networks

This paper presents a method for reducing the redundancy in both fully connected layers and convolutional layers of trained neural network models. The proposed method for fully connected layers consists of two steps, (1) Neuro-Coding: to encode the behavior of each neuron by a vector composed of its outputs corresponding to actual inputs and (2) Neuro-Unification: to unify the neurons having the similar behavioral vectors. Instead of just removing one of the similar neurons, the proposed method let the remaining neuron emulate the behavior of the removed one. Therefore, the proposed method can reduce the number of neurons with small sacrifice of accuracy without retraining. For convolutional layers, we propose Channel-Coding and Channel-Unification based on the same idea of Neuro-Coding and Neuro-Unification. In the convolutional layers, the behavior of each channel is encoded by its output feature maps, and channels with similar behaviors are unified. Through experiments with original and fine-tuned VGG16s, we confirmed that the proposed method performs better than the existing methods.
Affine Variational Autoencoders

Variational autoencoders (VAEs) have in recent years become one of the most powerful approaches to learning useful latent representations of data in an unsupervised manner. However, a major challenge with VAEs is that they have tremendous difficulty in generalizing to data that deviate from the training set (e.g., perturbed image variants). Normally data augmentation is leveraged to overcome this limitation; however, this is not only computational expensive but also necessitates the construction of more complex models. In this study, we introduce the notion of affine variational autoencoders (AVAEs), which extends upon the conventional VAE architecture through the introduction of affine layers. More specifically, within the AVAE architecture an affine layer perturbs the input image prior to the encoder, and a second affine layer performs an inverse perturbation to the output of the decoder. The parameters of the affine layers are learned to enable the AVAE to encode images at canonical perturbations, resulting in a better reconstruction and a disentangled latent space without the need for data augmentation or the use of more complex models. Experimental results demonstrate the efficacy of the proposed VAE architecture for generalizing to images in the MNIST validation set under affine perturbations without the need for data augmentation, demonstrating significantly reduced loss when compared to conventional VAEs.
Elastic Window for Multiple Face Detection and Tracking from Video

This paper deals with an efficient method for the detection and tracking of multiple moving faces from a video sequence. Appropriate detection of multiple faces from a video sequence is a challenging task due to the different combination of noise, illuminations, pose, and locations of the human face which is likely to differ from one frame to another. This paper presents a unique technique for multiple face detection from a video sequence. In this study, our major objective is to detect and track locations of multiple faces from video using elastic window. Additionally, the face tracking system includes the tracking of face motion. Firstly, for each pixel, local entropy is calculated by considering a $$ 3 \times 3 $$ window for detecting the face edges. Subsequently, Gaussian filtering technique is used to eliminate the undesired edges. In this context, it may be noted that a video frame passes through a number of preprocessing steps in order to eliminate the background noise to realize the thin binary image consisting of face boundaries. The human face from video sequences can be tracked by calculating the scalar and vector distances of four corner points between two adjacent frames. The movement of corner points represents the position and location change of the face in the upcoming frame. The presented method has been tasted on several video database and obtained efficient detection and tracking of multiple faces from the video sequences.
Verifying Randomised Social Choice

This work describes the formalisation of a recent result from Randomised Social Choice Theory in Isabelle/HOL. The original result had been obtained through the use of linear programming, an unverified Java program, and SMT solvers; at the time that the formalisation effort began, no human-readable proof was available. Thus, the formalisation with Isabelle eventually served as both independent rigorous confirmation of the original result and led to human-readable proofs both in Isabelle and on paper. This presentation focuses on the process of the formalisation itself, the domain-specific tooling that was developed for it in Isabelle, and how the structured human-readable proof was constructed from the SMT proof. It also briefly discusses how the formalisation uncovered a serious flaw in a second peer-reviewed publication.
Automating Complex News Stories by Capturing News Events as Data

KEYWORDS: Automated journalismcomputational journalismstructured journalismstructured eventsstructured narrativesnatural language generationcomputational narrativedata journalism
In Method We Trust: Towards an Open Method Kit for Characterizing Spot Cloud Service Pricing

Based on market-driven mechanisms that can improve utilization of idle compute resources at dynamic prices, spot cloud services are becoming increasingly popular to reach a win-win situation of providers' revenue maximization and consumers' budget optimization. Nevertheless, unlike fixed pricing schemes, the spot pricing scheme is both psychologically and practically sophisticated for people to understand and employ. As such, characterizing spot cloud pricing has been identified to be crucial and beneficial for various purposes ranging from facilitating service procurement to addressing service interruptions. In addition, it is also noteworthy that the spot cloud market is inherently volatile, and the providers' (e.g., Amazon's) pricing policies can change from time to time. Consequently, the previous analysis results can quickly be out of date, and the previous analysis methods can barely be reusable if their details are not specified. Therefore, we decided to develop a domain-specific method kit and make it open to improve the repeatability, replicability, and reproducibility of spot pricing characterization studies. This paper reports the typical content of this method kit, including a group of central tendency analysis methods and two types of distribution modeling analysis methods. In a generic sense, we particularly argue that open methods act as a higher-level strategy over open-source tools and open-access data for scientific studies in any research domain.
Combining Global Sparse Gradients with Local Gradients in Distributed Neural Network Training

One way to reduce network traffic in multinode data-parallel stochastic gradient descent is to only exchange the largest gradients.However, doing so damages the gradient and degrades the model's performance.Tranformer models degrade dramatically while the impact on RNNs is smaller.We restore gradient quality by combining the compressed global gradient with the node's locally computed uncompressed gradient.Neural machine translation experiments show that Transformer convergence is restored while RNNs converge faster.With our method, training on 4 nodes converges up to 1.5x as fast as with uncompressed gradients and scales 3.5x relative to singlenode training.
XP 2019 Panel: Agile Manifesto – Impacts on Culture, Education, and Software Practices

Manifestos are often a vehicle to trigger change by catalyzing discussion around a core group of ideas and values – and there is no doubt that the publication of the “Agile Manifesto” in 2001 increased visibility for an emergent breed of lightweight software practices. The panel discussed how the Agile Manifesto has impacted academic and industry software professionals in the areas of culture, education, and software practices.
Data Security Using 512 Bits Symmetric Key Based Cryptography In Cloud Computing System

Cloud Computing is a very new and advance technology that is growing very fast today.It is also belongs to distributed architecture that have centralized data space (Server) to store the data.In this paper, we mainly focus on data security.There are so many security levels like browser security, information security, virtual machine security, data storage security.Data access or information transmission issues and attacks are more related with data security that is provided to the users when they are handling the data for different issue.There are lots of security issues for cloud computing to handling the data including more technologies of networks, databases system or storage, resource handling schedule, operating systems, controlling, load balancing, and virtualization.The network system including cloud concept must have secure technology for mapping from virtual machines to the physical machines.There is proposed an algorithm for Data security including data encryption technology and used for data sharing or data transmission.
Label-Driven Time-Frequency Masking for Robust Speech Command Recognition

Speech enhancement driven robust Automatic Speech Recognition (ASR) systems typically require a parallel corpus with noisy and clean speech utterances for training. Moreover, many studies have reported that such front-ends, though improve speech quality, do not translate into improved recognition performance. On the other hand, multi-condition training of ASR systems has little visualization or interpretability capabilities of how these systems achieve robustness. In this paper, we propose a novel neural architecture with unified enhancement and sequence classification block, that is trained in an end-to-end manner only using noisy speech without having any knowledge of clean speech. The enhancement block is a fully convolutional network that is designed to perform Time Frequency (T-F) masking like operation, followed by an LSTM sequence classification block. The T-F masking formulation enables visualization of learned mask and helps us to analyse the T-F points that are important for classification of a speech command. Experiments performed on Google Speech Command dataset show that the proposed network achieves better results than the model without an enhancement front-end.
An Ontology-Powered Dialogue Engine For Patient Communication of Vaccines.

In this study, we introduce an ontology-driven software engine to provide dialogue interaction functionality for a conversational agent for HPV vaccine counseling. Currently, the HPV vaccination rates are low that risks unprotected individuals at being infected with HPV, a virus that leads to life-threatening cancers. In addition, we developed a question answering subsystem to support the dialogue engine. In this paper, we discuss our design and development of an ontology-driven dialogue engine that uses the Patient Health Information Dialogue Ontology, an ontology that we previously developed, and a question answering subsystem based on various previous methods to supplement the dialogue engine's interaction with the user. Our next step is to test the functional ability of the ontology-driven software components and deploy the engine in a live environment to be integrated with a speech interface.
Development and Application of Online Tourism English Teaching Platform

In the age of Internet+, the integration of information technology and vocational English course teaching is bound to bring about the reformation of ESP teaching. On the basis of introducing the main idea of "flipped classroom" teaching model reform in tourism English course, this paper mainly discusses the development and application of online tourism English teaching platform including the designing goals, framework, modules, and database design of the platform. It has been found that the tourism English teaching platform designed in this paper can offer effective learning scaffolding to college students majoring in tourism and tourism practitioners, meeting their needs of learning and practicing at anytime and anywhere. At the same time, teachers can handle the learning progress and quality of students so as to realize the "flipped classroom" teaching model of tourism English course.
Strong Equivalence for Argumentation Frameworks with Collective Attacks

Argumentation frameworks with collective attacks are a prominent extension of Dung’s abstract argumentation frameworks, where an attack can be drawn from a set of arguments to another argument. These frameworks are often abbreviated as SETAFs. Although SETAFs have received increasing interest recently, the notion of strong equivalence, which is fundamental in nonmonotonic formalisms to characterize equivalent replacements, has not yet been investigated. In this paper, we study how strong equivalence between SETAFs can be decided with respect to the most important semantics and also consider variants of strong equivalence.
An Imprecise Probability Approach for Abstract Argumentation Based on Credal Sets

Some abstract argumentation approaches consider that arguments have a degree of uncertainty, which impacts on the degree of uncertainty of the extensions obtained from a abstract argumentation framework (AAF) under a semantics. In these approaches, both the uncertainty of the arguments and of the extensions are modeled by means of precise probability values. However, in many real life situations the exact probabilities values are unknown and sometimes there is a need for aggregating the probability values of different sources. In this paper, we tackle the problem of calculating the degree of uncertainty of the extensions considering that the probability values of the arguments are imprecise. We use credal sets to model the uncertainty values of arguments and from these credal sets, we calculate the lower and upper bounds of the extensions. We study some properties of the suggested approach and illustrate it with an scenario of decision making.
Implementasi Algoritma Apriori Terhadap Data Penjualan Oli Mobil Berbasis Sistem Pencarian Aturan Asosiasi

Data mining techniques are used to design effective sales or marketing strategies by utilizing sales transaction data that is already available in the company. The problem in the company is that there are many data transactions that occur unknown, causing an accumulation of data unknown sales most in each month &amp; year, unknown brands of car oil are often sold or demanded by customers. So this association search uses a priori algorithm as a place to store data using pattern recognition techniques such as static and mathematical techniques from a set of relationships (associations) between items obtained, it is expected that can help developers in designing marketing strategies for goods in the company. Software testing results that have been made have found the most sold oil brand products if you buy Shell Hx7, it will buy Toyota Motor Oil with 50% support and 66.7% confidence. If you buy Toyota Motor Oil, you will buy Shell Hx 7 with 50% support and 85.7% confidence.
A Cryptographic System Based on a New Class of Binary Error-Correcting Codes

Abstract In this paper we introduce a new cryptographic system which is based on the idea of encryption due to [McEliece, R. J. A public-key cryptosystem based on algebraic coding theory , DSN Progress Report. 44 , 1978, 114–116]. We use the McEliece encryption system with a new linear error-correcting code, which was constructed in [Hannusch, C.—Lakatos, P.: Construction of self-dual binary 2 2 k, 2 2 k −1, 2 k -codes , Algebra and Discrete Math. 21 (2016), no. 1, 59–68]. We show how encryption and decryption work within this cryptosystem and we give the parameters for key generation. Further, we explain why this cryptosystem is a promising post-quantum candidate.
Formalizing and Proving Privacy Properties of Voting Protocols Using Alpha-Beta Privacy

Most common formulations of privacy-type properties for security protocols are specified as bisimilarity of processes in applied- $$\pi $$ calculus. For instance, voting privacy is specified as the bisimilarity between two processes that differ only by a swap of two votes. Similar methods are applied to formalize receipt-freeness. We believe that there exists a gap between these technical encodings and an intuitive understanding of these properties. We use $$(\alpha ,\beta )$$ -privacy to formalize privacy goals in a different way, namely as a reachability problem. Every state consists of a pair of formulae: $$\alpha $$ expresses the publicly released information (like the result of the vote) and $$\beta $$ expresses the additional information available to the intruder (like observed messages). Privacy holds in a state if every model of $$\alpha $$ can be extended to a model of $$\beta $$ , i.e., the intruder cannot make any deductions beyond what was deliberately released; and privacy of a protocol holds if privacy holds in every reachable state. This allows us to give formulations of voting privacy and receipt-freeness that are more declarative than the common bisimilarity based formulations, since we reason about models that are consistent with all observations like interaction with coerced (but potentially lying) voters. Also, we show a relation between the goals: receipt-freeness implies voting privacy. Finally, the logical approach also allows for declarative manual proofs (as opposed to long machine-generated proofs) like reasoning about a permutation of votes and the intruder’s knowledge about that permutation.
Incorporating Case-Based Reasoning for Radiation Therapy Knowledge Modeling: A Pelvic Case Study

Knowledge models in radiotherapy capture the relation between patient anatomy and dosimetry to provide treatment planning guidance. When treatment schemes evolve, existing models struggle to predict accurately. We propose a case-based reasoning framework designed to handle novel anatomies that are of same type but vary beyond original training samples. A total of 105 pelvic intensity-modulated radiotherapy cases were analyzed. Eighty cases were prostate cases while the other 25 were prostate-plus-lymph-node cases. We simulated 4 scenarios: Scarce scenario, Semiscarce scenario, Semiample scenario, and Ample scenario. For the Scarce scenario, a multiple stepwise regression model was trained using 85 cases (80 prostate, 5 prostate-plus-lymph-node). The proposed workflow started with evaluating the feature novelty of new cases against 5 training prostate-plus-lymph-node cases using leverage statistic. The case database was composed of a 5-case dose atlas. Case-based dose prediction was compared against the regression model prediction using sum of squared residual. Mean sum of squared residual of case-based and regression predictions for the bladder of 13 identified outliers were 0.174 ± 0.166 and 0.459 ± 0.508, respectively (P = .0326). For the rectum, the respective mean sum of squared residuals were 0.103 ± 0.120 and 0.150 ± 0.171 for case-based and regression prediction (P = .1972). By retaining novel cases, under the Ample scenario, significant statistical improvement was observed over the Scarce scenario (P = .0398) for the bladder model. We expect that the incorporation of case-based reasoning that judiciously applies appropriate predictive models could improve overall prediction accuracy and robustness in clinical practice.
Evaluation of Five Sentence Similarity Models on Electronic Medical Records

Capturing the semantic similarity between sentences plays a vital role in several primary applications in biomedical and clinical domains: biomedical sentence search, evidence attribution, question-answering and text summarization. In this pilot study, we evaluated the effectiveness of five representative sentence similarity models, ranging from traditional machine learning methods to the latest bidirectional transformers in the clinical domain. The evaluation was performed on a dataset consisting of over 1K sentence pairs from EMRs - the largest public dataset in this domain by far. The results show that embeddings on large biomedical corpora are the most effective methods. It also demonstrates that CNN and BERT are effective to capture sentence similarity under relatively small datasets.
iperfTZ: Understanding Network Bottlenecks for TrustZone-Based Trusted Applications

The growing availability of hardware-based trusted execution environments (TEEs) in commodity processors has recently advanced support (i.e., design, implementation and deployment frameworks) for network-based secure services. Examples of such TEEs include ARM TrustZone or Intel SGX, largely available in embedded, mobile and server-grade processors. TEEs shield services from compromised hosts, malicious users or powerful attackers. TEE-enabled devices are largely being deployed on the edge of the network, paving the way for large-scale deployments of trusted applications. These applications allow processing and disseminating sensitive data without having to trust cloud providers. However, uncovering network performance limitations of such trusted applications is difficult and currently lacking, despite the interest and reliance by developers and system deployers. iperfTZ is an open-source tool to uncover network performance bottlenecks rooted at the design and implementation of trusted applications for ARM TrustZone and underlying runtime systems. Our evaluation based on micro-benchmarks shows current trade-offs for trusted applications, both from a network as well as an energy perspective; an often overlooked yet relevant aspect for edge-based deployments.