Reviewerly/exHarmony huggingface dataset:
A Sound and Complete Logic for Algebraic Effects

Abstract This work investigates three notions of program equivalence for a higher-order functional language with recursion and general algebraic effects, in which programs are written in continuation-passing style. Our main contribution is the following: we define a logic whose formulas express program properties and show that, under certain conditions which we identify, the induced program equivalence coincides with a contextual equivalence. Moreover, we show that this logical equivalence also coincides with an applicative bisimilarity. We exemplify our general results with the nondeterminism, probabilistic choice, global store and I/O effects.
Curls &amp; Whey: Boosting Black-Box Adversarial Attacks

Image classifiers based on deep neural networks suffer from harassment caused by adversarial examples. Two defects exist in black-box iterative attacks that generate adversarial examples by incrementally adjusting the noise-adding direction for each step. On the one hand, existing iterative attacks add noises monotonically along the direction of gradient ascent, resulting in a lack of diversity and adaptability of the generated iterative trajectories. On the other hand, it is trivial to perform adversarial attack by adding excessive noises, but currently there is no refinement mechanism to squeeze redundant noises. In this work, we propose Curls & Whey black-box attack to fix the above two defects. During Curls iteration, by combining gradient ascent and descent, we `curl' up iterative trajectories to integrate more diversity and transferability into adversarial examples. Curls iteration also alleviates the diminishing marginal effect in existing iterative attacks. The Whey optimization further squeezes the `whey' of noises by exploiting the robustness of adversarial perturbation. Extensive experiments on Imagenet and Tiny-Imagenet demonstrate that our approach achieves impressive decrease on noise magnitude in l2 norm. Curls & Whey attack also shows promising transferability against ensemble models as well as adversarially trained models. In addition, we extend our attack to the targeted misclassification, effectively reducing the difficulty of targeted attacks under black-box condition.
Translation Equivalence of Boolean Functions Expressed by Primitive Element

In recent years, algebraic attacks and fast algebraic attacks have received a lot of attention in the cryptographic community. There are three Boolean functions achieving optimal algebraic immunity based on primitive element of F2n. The support of Boolean functions in [1]-[3] have the same parameter s, which makes us have a large number of Boolean functions with good properties. However, we prove that the Boolean functions are affine equivalence when s takes different values.
Exploiting a More Global Context for Event Detection Through Bootstrapping

Over the last few years, neural models for event extraction have obtained interesting results. However, their application is generally limited to sentences, which can be an insufficient scope for disambiguating some occurrences of events. In this article, we propose to integrate into a convolutional neural network the representation of contexts beyond the sentence level. This representation is built following a bootstrapping approach by exploiting an intra-sentential convolutional model. Within the evaluation framework of TAC 2017, we show that our global model significantly outperforms the intra-sentential model while the two models are competitive with the results obtained by TAC 2017 participants.
The iWildCam 2018 Challenge Dataset

Camera traps are a valuable tool for studying biodiversity, but research using this data is limited by the speed of human annotation. With the vast amounts of data now available it is imperative that we develop automatic solutions for annotating camera trap data in order to allow this research to scale. A promising approach is based on deep networks trained on human-annotated images. We provide a challenge dataset to explore whether such solutions generalize to novel locations, since systems that are trained once and may be deployed to operate automatically in new locations would be most useful.
Voice Trigger Detection from Lvcsr Hypothesis Lattices Using Bidirectional Lattice Recurrent Neural Networks

We propose a method to reduce false voice triggers of a speech-enabled personal assistant by post-processing the hypothesis lattice of a server-side large-vocabulary continuous speech recognizer (LVCSR) via a neural network. We first discuss how an estimate of the posterior probability of the trigger phrase can be obtained from the hypothesis lattice using known techniques to perform detection, then investigate a statistical model that processes the lattice in a more explicitly data-driven, discriminative manner. We propose using a Bidirectional Lattice Recurrent Neural Network (LatticeRNN) for the task, and show that it can significantly improve detection accuracy over using the 1-best result or the posterior.
Variance Preserving Initialization for Training Deep Neuromorphic Photonic Networks with Sinusoidal Activations

Photonic neuromorphic hardware can provide significant performance benefits for Deep Learning (DL) applications by accelerating and reducing the energy requirements of DL models. However, photonic neuromorphic architectures employ different activation elements than those traditionally used in DL, slowing down the convergence of the training process for such architectures. An initialization scheme that can be used to efficiently train deep photonic networks that employ quadratic sinusoidal activation functions is proposed in this paper. The proposed initialization scheme can overcome these limitations, leading to faster and more stable training of deep photonic neural networks. The ability of the proposed method to improve the convergence of the training process is experimentally demonstrated using two different DL architectures and two datasets.
Sentiment Analysis of Movie Reviews using Machine Learning Classifiers

In today's world, it has become customary to collect opinions and reviews from people through various surveys, polls, social media platform and analyse them in order to understand the preferences of customers.So, in order to understand the sentiments of customers and their view on the services offered by producers, there comes the need for an accurate and canonical mechanism for speculating and anticipating sentiments which possess the ability to fabricate a positive or negative impact in the market and thus making this kind of analysis important for the pair of producers and consumers.In this paper, the main focus is to anatomize the reviews conveyed by viewers on various movies and to use this analysis to understand the customers' sentiments and market behaviour for better customer experience.This paper intends to analyse the reviews of customers on various movies by implementing three algorithms namely K Nearest Neighbours, Logistic Regression and Naive Bayes and provides conclusive remarks.
Educational Model as an Important Factor Influencing on Conjugation Between Higher Education and Labor Sphere

The article presents the results of the analysis of possible mechanisms of conjugation between the higher education sphere and labor market in the context of various educational models. The liberal, professional and mixed educational models are used for the identification of optimal conditions for conjugation between the higher education system and labor sphere. In addition, these educational models are used for detection of essential differences in the educational system’s response to the requirement of compliance of educational standards to professional standards. The analysis of these components of the considered educational models has revealed the additional requirements. The implementation of these requirements is necessary for the formation of more effective mechanisms, which allow for educational management and conjugation of the higher education system with the labor sphere. It is shown that the erroneous identification of the educational model is associated with certain risks. These risks include the discrepancy between the choice of the educational model and the priorities of the state educational policy; underestimation of the influence of the features of the economic structure of the country and the mechanisms of the labor sphere formation on the higher education system; overstatement of the possibilities of the educational model for the successful solution of the problems of accessibility, quality and efficiency of higher education. The discrepancy between the choice of the definite educational model and the current educational reality can generate destructive trends in the system of higher education.
Effect of Mobile Gaming on Mathematical Achievement among 4th Graders

This study aimed at identifying the effect of the mobile gaming in the mathematical achievement among the 4th graders. The study instrument was a math achievement test consisted of (30) items of the multiple choice type (4 alternatives). The sample consisted of (66) students of the 4th grade in the City of Zarqa (Jordan), which was distributed over two groups: experimental groups (n=34), and control group (n=32). The researcher employed the experimental method on both groups. The experimental group was taught using the educational mobile games, and the control group was taught by the conventional method. The study found that the use of mobile games is an effective practice for providing educational support to the students in mathematics. In addition, the size of the effect of using the mobile games was significant, which supports the effectiveness of the mobile games in math achievement among the 4th graders.
Semantic query for Quranic ontology

The automatic processing of natural languages knew a remarkable growth in terms of techniques, methods and the variety of its potential applications. For instance, to automatize the processing of the Arabic language with its particular features, we selected the holy Quran as a study case. The latter has represented a challenge for the artificial intelligence. The main objective of the present paper is to design the semantic search engine for the text of the Quran using Quranic ontology. To determine the semantic fields of the words of the holy Quran, the Quranic ontology was developed, that presents the meaning of words and their relations. This method is used for each concept in order to enrich the query.
State estimation for stochastic time‐varying multisensor systems with multiplicative noises: Centralized and decentralized data fusion

Abstract In this paper, the state estimation problems, including filtering and one‐step prediction, are solved for uncertain stochastic time‐varying multisensor systems by using centralized and decentralized data fusion methods. Uncertainties are considered in all parts of the state space model as multiplicative noises. For the first time, both centralized and decentralized estimators are designed based on the regularized least‐squares method. To design the proposed centralized fusion estimator, observation equations are first rewritten as a stacked observation. Then, an optimal estimator is obtained from a regularized least‐squares problem. In addition, for decentralized data fusion, first, optimal local estimators are designed, and then fusion rule is achieved by solving a least‐squares problem. Two recursive equations are also obtained to compute the unknown covariance matrices of the filtering and prediction errors. Finally, a three‐sensor target‐tracking system is employed to demonstrate the effectiveness and performance of the proposed estimation approaches.
Ngarai Sianok as Physics Education’s Edupark

Ngarai Sianok is a natural tourist destination in Bukittinggi. Ngarai Sianok tourism attraction is always crowded with visitors who enjoy the beautiful nature. The activities carried out by visitors are taking photos, running marathons, enjoying culinary, camping, and also doing low impact outbound. Physics learning activities can also be held in Ngarai Sianok. Utilizing Ngarai Sianok as one of the learning resources of physics was named "edupark". To support Ngarai Sianok as a Physics Edupark, it is necessary to study physics learning material that can be learned through the Ngarai Sianok tourism destination. The purpose of this study was to analyze the basic competencies of physics based on the 2013 revised curriculum which can be learned through the media of Edupark Ngarai Sianok. The method used is descriptive analysis by integrating the Ngarai Sianok tourism destination in physics concepts using Concepts Fitting Technique so that it is known as edupark for physics learning. Data collection is done through observation and literature studies. The results showed that physics learning according to the 2016 revised curriculum in 2016 could be carried out in Edupark Ngarai Sianok. The results of this review will be used as a theoretical reference for Ngarai Sianok As Physics Education's Edupark.
A portable grammar-based NLG system for verbalization of structured data

In this paper, we report on adapting a Natural Language Generation system to Semantic Web datasets, and on the results obtained in two triple verbalization challenges.
Deep multi-Wasserstein unsupervised domain adaptation

In unsupervised domain adaptation (DA), 1 aims at learning from labeled source data and fully unlabeled target examples a model with a low error on the target domain. In this setting, standard generalization bounds prompt us to minimize the sum of three terms: (a) the source true risk, (b) the divergence between the source and target domains, and (c) the combined error of the ideal joint hypothesis over the two domains. Many DA methods – especially those using deep neural networks – have focused on the first two terms by using different divergence measures to align the source and target distributions on a shared latent feature space, while ignoring the third term, assuming it is negligible to perform the adaptation. However, it has been shown that purely aligning the two distributions while minimizing the source error may lead to so-called negative transfer. In this paper, we address this issue with a new deep unsupervised DA method – called MCDA – minimizing the first two terms while controlling the third one. MCDA benefits from highly-confident target samples (using softmax predictions) to minimize class-wise Wasserstein distances and efficiently approximate the ideal joint hypothesis. Empirical results show that our approach outperforms state of the art methods.
Balanced Homodyne Detector With Independent Phase Control and Noise Detection Branches

Squeezed state light is a fundamental resource for many quantum optics experiments. Important applications like quantum-enhanced magnetometer and quantum key distribution, require the squeezing level as high as possible. In balanced homodyne detection, the detector output is directly proportional to the noise of the squeezed beam. The detected quadrature is dependent on the relative phase between the squeezed beam and local oscillator. Here, we present a balanced homodyne detector (BHD) with independent phase control and noise detection branches. The independence between the phase control and noise detection branches is experimentally verified, the two branches are independently optimized according to the practical requirements. With the BHD, a bright squeezed light at 1550 nm with quantum noise reduction of 10.3 dB is directly detected. After stabilizing the relative phase between the squeezed states and local oscillator by the phase locking branch of the BHD, the fluctuation of the noise suppression is ±0.2 dB by 1-h testing time.
ANALYSIS OF ARTIFICIAL INTELLIGENCE APPLICATIONS FOR AUTOMATED TESTING OF VIDEO GAMES

Game testing is a software testing process for quality control in video games. Game environments, sometimes called levels or maps, are complex and interactive systems. These environments can include level geometry, interactive entities, player and non-player controllable characters etc. Depending on the number and complexity of levels, testing them by hand may take a considerable effort. This is especially true for video games with procedurally generated levels that are automatically created using a specifically designed algorithm. A single change in a procedural generation algorithm can alter all of the video game levels, and they will have to be retested to ensure they are still completable or meet any other requirements of the game. This task may be suitable for automation, in particular using Artificial Intelligence (AI). The goal of this paper is to explore the most promising and up-to-date research on AI applications for video game testing to serve as a reference for anyone starting in the field.
HAWQ: Hessian AWare Quantization of Neural Networks with Mixed-Precision

Model size and inference speed/power have become a major challenge in the deployment of Neural Networks for many applications. A promising approach to address these problems is quantization. However, uniformly quantizing a model to ultra low precision leads to significant accuracy degradation. A novel solution for this is to use mixed-precision quantization, as some parts of the network may allow lower precision as compared to other layers. However, there is no systematic way to determine the precision of different layers. A brute force approach is not feasible for deep networks, as the search space for mixed-precision is exponential in the number of layers. Another challenge is a similar factorial complexity for determining block-wise fine-tuning order when quantizing the model to a target precision. Here, we introduce Hessian AWare Quantization (HAWQ), a novel second-order quantization method to address these problems. HAWQ allows for the automatic selection of the relative quantization precision of each layer, based on the layer's Hessian spectrum. Moreover, HAWQ provides a deterministic fine-tuning order for quantizing layers, based on second-order information. We show the results of our method on Cifar-10 using ResNet20, and on ImageNet using Inception-V3, ResNet50 and SqueezeNext models. Comparing HAWQ with state-of-the-art shows that we can achieve similar/better accuracy with $8\times$ activation compression ratio on ResNet20, as compared to DNAS~\cite{wu2018mixed}, and up to $1\%$ higher accuracy with up to $14\%$ smaller models on ResNet50 and Inception-V3, compared to recently proposed methods of RVQuant~\cite{park2018value} and HAQ~\cite{wang2018haq}. Furthermore, we show that we can quantize SqueezeNext to just 1MB model size while achieving above $68\%$ top1 accuracy on ImageNet.
Video Instance Segmentation

In this paper we present a new computer vision task, named video instance segmentation. The goal of this new task is simultaneous detection, segmentation and tracking of instances in videos. In words, it is the first time that the image instance segmentation problem is extended to the video domain. To facilitate research on this new task, we propose a large-scale benchmark called YouTube-VIS, which consists of 2883 high-resolution YouTube videos, a 40-category label set and 131k high-quality instance masks. In addition, we propose a novel algorithm called MaskTrack R-CNN for this task. Our new method introduces a new tracking branch to Mask R-CNN to jointly perform the detection, segmentation and tracking tasks simultaneously. Finally, we evaluate the proposed method and several strong baselines on our new dataset. Experimental results clearly demonstrate the advantages of the proposed algorithm and reveal insight for future improvement. We believe the video instance segmentation task will motivate the community along the line of research for video understanding.
Graph Queries

We review various graph query language fragments that are both theoretically tractable and practically relevant. We focus on the most expressive one that retains these properties and use it as a stepping stone to examine the underpinnings of graph query evaluation along graph view maintenance. Further broadening the scope of the discussion, we then consider alternative processing techniques for graph queries, based on graph summarization and path query learning. We conclude by pinpointing the open research directions in this emerging area.
Perceptual Video Coding Based on Visual Saliency Modulated Just Noticeable Distortion

To reduce the perceptual redundancy in the video coding process, human visual system (HVS)-based visual attention and visual sensitivity can be utilized due to their intrinsic natures. Just Noticeable Distortion (JND) is one of widely used models to simulate human visual sensitivity, while visual saliency map has been popular for years in image processing to describe the visual attention feature, which has been proved by the ability to enhance the visual sensitivity effect. In this paper, we proposed a perceptual video coding (PVC) scheme with visual saliency modulated JND model to suppress the DCT coefficient without resulting in noteworthy subjective quality degradation. The experimental results show that the PVC scheme with the proposed VS-JND model can save bit rates up to 35.58% in high bit rates case with the similar subjective quality compared with that of HEVC software reference code HM 16.12.
Learning MILP Resolution Outcomes Before Reaching Time-Limit

The resolution of some Mixed-Integer Linear Programming (MILP) problems still presents challenges for state-of-the-art optimization solvers and may require hours of computations, so that a time-limit to the resolution process is typically provided by a user. Nevertheless, it could be useful to get a sense of the optimization trends after only a fraction of the specified total time has passed, and ideally be able to tailor the use of the remaining resolution time accordingly, in a more strategic and flexible way. Looking at the evolution of a partial branch-and-bound tree for a MILP instance, developed up to a certain fraction of the time-limit, we aim to predict whether the problem will be solved to proven optimality before timing out. We exploit machine learning tools, and summarize the development and progress of a MILP resolution process to cast a prediction within a classification framework. Experiments on benchmark instances show that a valuable statistical pattern can indeed be learned during MILP resolution, with key predictive features reflecting the know-how and experience of field’s practitioners.