Reviewerly/exHarmony huggingface dataset:
Improving Neural Non-Maximum Suppression for Object Detection by Exploiting Interest-Point Detectors

Non-maximum suppression (NMS) is a post-processing step in almost every visual object detector. Its goal is to drastically prune the number of overlapping detected candidate regions-of-interest (ROIs) and replace them with a single, more spatially accurate detection. The default algorithm (Greedy NMS) is fairly simple and suffers from drawbacks, due to its need for manual tuning. Recently, NMS has been improved using deep neural networks that learn how to solve a spatial overlap-based detections rescoring task in a supervised manner, where only ROI coordinates are exploited as input. In this paper, neural NMS performance is augmented by feeding the network additional information extracted from the appearance of each candidate ROI. This information captures statistical properties regarding the spatial distribution of interest-points detected within the corresponding image region. Thus, the deviation in 2D distribution between the interest-points detected inside a ROI that encloses the actual object entirely, and within one that only captures it partially, is exploited as a discriminant factor, with the NMS network being implicitly forced to also learn how to solve an additional, appearance-based binary classification task (complete vs partial object silhouettes). The empirical evaluation on three public person detection datasets leads to state-of-the-art results, at a small computational overhead.
Rpair: Rescaling RePair with Rsync

Data compression is a powerful tool for managing massive but repetitive datasets, especially schemes such as grammar-based compression that support computation over the data without decompressing it. In the best case such a scheme takes a dataset so big that it must be stored on disk and shrinks it enough that it can be stored and processed in internal memory. Even then, however, the scheme is essentially useless unless it can be built on the original dataset reasonably quickly while keeping the dataset on disk. In this paper we show how we can preprocess such datasets with context-triggered piecewise hashing such that afterwards we can apply RePair and other grammar-based compressors more easily. We first give our algorithm, then show how a variant of it can be used to approximate the LZ77 parse, then leverage that to prove theoretical bounds on compression, and finally give experimental evidence that our approach is competitive in practice.
Online Anomaly Detection in Multivariate Settings

This paper considers the real-time and nonparametric detection of anomalies in high-dimensional systems. The goal is to detect anomalies quickly and accurately such that the appropriate countermeasures could be taken before any possible harm is caused by the anomalous event. We propose a k NN-based sequential anomaly detection method in both semi-supervised and supervised settings. We prove that the proposed method is asymptotically optimum in the minimax sense under certain conditions in terms of minimizing the average detection delay for a given false alarm constraint. The proposed method is shown to be capable of multivariate anomaly detection and also scalable to high-dimensional datasets. We further propose an online learning scheme that combines the desirable properties of our semi-supervised and supervised methods.
SentiLR: Linguistic Knowledge Enhanced Language Representation for Sentiment Analysis.

Most of the existing pre-trained language representation models neglect toconsider the linguistic knowledge of texts, whereas we argue that suchknowledge can promote language understanding in various NLP tasks. In thispaper, we propose a novel language representation model called SentiLR, whichintroduces word-level linguistic knowledge including part-of-speech tag andprior sentiment polarity from SentiWordNet to benefit the downstream tasks insentiment analysis. During pre-training, we first acquire the prior sentimentpolarity of each word by querying the SentiWordNet dictionary with itspart-of-speech tag. Then, we devise a new pre-training task called label-awaremasked language model (LA-MLM) consisting of two subtasks: 1) word knowledgerecovering given the sentence-level label; 2) sentence-level label predictionwith linguistic knowledge enhanced context. Experiments show that SentiLRachieves state-of-the-art performance on several sentence-level / aspect-levelsentiment analysis tasks by fine-tuning, and also obtain comparative results ongeneral language understanding tasks.
An Empirical Study of Information Flows in Real-World JavaScript

Information flow analysis prevents secret or untrusted data from flowing into public or trusted sinks. Existing mechanisms cover a wide array of options, ranging from lightweight taint analysis to heavyweight information flow control that also considers implicit flows. Dynamic analysis, which is particularly popular for languages such as JavaScript, faces the question whether to invest in analyzing flows caused by not executing a particular branch, so-called hidden implicit flows. This paper addresses the questions how common different kinds of flows are in real-world programs, how important these flows are to enforce security policies, and how costly it is to consider these flows. We address these questions in an empirical study that analyzes 56 real-world JavaScript programs that suffer from various security problems, such as code injection vulnerabilities, denial of service vulnerabilities, memory leaks, and privacy leaks. The study is based on a state-of-the-art dynamic information flow analysis and a formalization of its core. We find that implicit flows are expensive to track in terms of permissiveness, label creep, and runtime overhead. We find a lightweight taint analysis to be sufficient for most of the studied security problems, while for some privacy-related code, observable tracking is sometimes required. In contrast, we do not find any evidence that tracking hidden implicit flows reveals otherwise missed security problems. Our results help security analysts and analysis designers to understand the cost-benefit tradeoffs of information flow analysis and provide empirical evidence that analyzing information flows in a cost-effective way is a relevant problem.
ODIN: ODE-Informed Regression for Parameter and State Inference in Time-Continuous Dynamical Systems

Parameter inference in ordinary differential equations is an important problem in many applied sciences and in engineering, especially in a data-scarce setting. In this work, we introduce a novel generative modeling approach based on constrained Gaussian processes and leverage it to build a computationally and data efficient algorithm for state and parameter inference. In an extensive set of experiments, our approach outperforms the current state of the art for parameter inference both in terms of accuracy and computational cost. It also shows promising results for the much more challenging problem of model selection.
The Persistence of Weapons: Global Evidence

This article investigates persistence (or hysteresis) in weapons using a panel of 163 countries for the period 2010‐15. The following are some main findings: (1) compared to countries that are landlocked, persistence in heavy weapons is more apparent in nations that are open to the sea; (2) relative to the Middle East and North Africa, heavy weapons are more persistent in the East Asia and the Pacific countries. This tendency is consistent with “weapons imports;” (3) evidence of persistence is not very apparent in “weapons imports” with the exception of the fact that it is higher in low‐income countries, compared to their high‐income counterparts. Hence, there is less hysteresis in weapons exports when compared with heavy weapons exports; and (4) the determinants of persistence employed in the conditioning information set are contingent on fundamental characteristics and panels. Policy implications are discussed. Related Articles Beal, Amanda L., and Leah Graham. 2014. “Foundations for Change: Rule of Law, Development, and Democratization.” Politics &amp; Policy 42 (3): 311‐345. https://doi.org/10.1111/polp.12077 Fails, Matthew D. 2014. “Leader Turnover, Volatility, and Political Risk.” Politics &amp; Policy 42 (3): 369‐399. https://doi.org/10.1111/polp.12078 Jakubow, Alexander. 2014. “State Intervention and Life Satisfaction Reconsidered: The Role of Governance Quality and Resource Misallocation.” Politics &amp; Policy 42 (1): 3‐36. https://doi.org/10.1111/polp.12057 Peev, Evgeni, and Dennis C. Mueller. 2013. “Democracy, Economic Freedom and Growth in Transition Economies.” Kyklos 65 (3): 371‐407. http://onlinelibrary.wiley.com/doi/10.1111/j.1467-6435.2012.00543.x/abstract Siddiqui, Danish A., and Qazi M. Ahmed. 2013. “The Effect of Institutions on Economic Growth: A Global Analysis Based on GMM Dynamic Panel Estimation.” Structural Change and Economic Dynamics 24: 18‐33. http://www.sciencedirect.com/science/article/pii/S0954349X12000902
Bayesian inference for dynamical systems

Bayesian inference is a common method for conducting parameter estimation for dynamical systems. Despite the prevalent use of Bayesian inference for performing parameter estimation for dynamical systems, there is a need for a formalized and detailed methodology. This paper presents a comprehensive methodology for dynamical system parameter estimation using Bayesian inference and it covers utilizing different distributions, Markov Chain Monte Carlo (MCMC) sampling, obtaining credible intervals for parameters, and prediction intervals for solutions. A logistic growth example is given to illustrate the methodology.
Genuine temporal correlations can certify the quantum dimension

Abstract Temporal correlations in quantum mechanics are the origin of several non-classical phenomena, but they depend on the dimension of the underlying quantum system. This allows one to use such correlations for the certification of a minimal Hilbert space dimension. Here we provide a theoretical proposal and an experimental implementation of a device-independent dimension test, using temporal correlations observed on a single trapped 171 Yb + ion. Our test goes beyond the prepare-and-measure scheme of previous approaches, demonstrating the advantage of genuine temporal correlations.
AQuA: ASP-Based Visual Question Answering

AQuA (ASP-based Question Answering) is an Answer Set Programming (ASP) based visual question answering framework that truly “understands” an input picture and answers natural language questions about that picture. The knowledge contained in the picture is extracted using YOLO, a neural network-based object detection technique, and represented as an answer set program. Natural language processing is performed on the question to transform it into an ASP query. Semantic relations are extracted in the process for deeper understanding and to answer more complex questions. The resulting knowledge-base—with additional commonsense knowledge imported—can be used to perform reasoning using an ASP system, allowing it to answer questions about the picture, just like a human. This framework achieves 93.7% accuracy on CLEVR dataset, which exceeds human baseline performance. What is significant is that AQuA translates a question into an ASP query without requiring any training. Our framework for Visual Question Answering is quite general and closely simulates the way humans operate. In contrast to existing purely machine learning-based methods, our framework provides an explanation for the answer it computes, while maintaining high accuracy.
Pashto isolated digits recognition using deep convolutional neural network

Speech recognition has become one of the most significant parts of human-computer interaction due to emergence of new technologies such as smartphone, smart watch and many modern technologies, therefore the need of an ASR for local languages is felt. The basic aim of this paper is to develop an isolated digits recognition for Pashto language, using deep CNN. The database of Pashto digits from 0 to 9 with 50 utterance for each digits is used. Twenty MFCC features extracted for each isolated digit and fed as input to CNN. The network has been used for the proposed system is deep up to 4 convolutional layers, followed by ReLU and max-pooling layers. The network has been trained on the 50% of data and the rest of the data was used for testing. The total average of 84.17% accuracy was achieved for testing which show 7.32% better performance as compared to existing similar works.
Identification of hydrothermal altered/weathered and clay minerals through airborne AVIRIS-NG hyperspectral data in Jahajpur, India

Advanced visible infrared imaging spectrometer-new generation (AVIRIS-NG) airborne Hyperspectral data has 5nm spectral resolution which allows us to identify characteristic spectral signatures of the different altered and weathered mineral assemblage. In this study Airborne AVIRIS-NG hyperspectral data were used to identify the different altered, weathered and clay group of minerals in the Jahajpur, Bhilwara, India. In the study area, different hydrothermal minerals such as Montmorillonite, Smectite and Talc were identified. Apart from this, Goethite/Limonite mineral spectral signatures were identified using the AVIRIS-NG data in the VNIR (visible and near infrared) region of the electromagnetic spectrum. Minerals thus identified were verified by the conventional geological analysis viz. petrography and XRD of the field samples collected from the study area. The results of the conventional geological methods and spectroscopy were in good confirmation with the results found through the analysis of the AVIRIS-NG data. Identified minerals show a good indication of the advance argillic alteration in the study area which stand in confirmation with the geology of the area. Spectral analysis of the AVIRIS-NG data reveals that the reflectance spectra of the airborne AVIRIS-NG Hyperspectral data found promising for mineral identification and mapping.
Second Order Optimization Made Practical.

Optimization in machine learning, both theoretical and applied, is presently dominated by first-order gradient methods such as stochastic gradient descent. Second-order optimization methods that involve second-order derivatives and/or second-order statistics of the data have become far less prevalent despite strong theoretical properties, due to their prohibitive computation, memory and communication costs. In an attempt to bridge this gap between theoretical and practical optimization, we present a proof-of-concept distributed system implementation of a second-order preconditioned method (specifically, a variant of full-matrix Adagrad), that along with a few yet critical algorithmic and numerical improvements, provides significant practical gains in convergence on state-of-the-art deep models and gives rise to actual wall-time improvements in practice compared to conventional first-order methods. Our design effectively utilizes the prevalent heterogeneous hardware architecture for training deep models which consists of a multicore CPU coupled with multiple accelerator units. We demonstrate superior performance on very large learning problems in machine translation where our distributed implementation runs considerably faster than existing gradient-based methods.
UAV Sensor Spoofing Detection Algorithm Based on GPS and Optical Flow Fusion

The UAV security problem is becoming more and more serious, and the security of sensors is receiving much attention. UAV's operation depends on the data collected by sensors, and spoofed data is prone to lead UAV making wrong decisions. In order to detect sensor spoofing, we propose a drone sensor spoofing detection (SSDGOF) algorithm based on GPS and optical flow fusion. In SSDGOF, the fusion model of GPS and optical flow raw data is compared with the model of fusion data in the normal state of the drone, and the difference between them is used to determine whether the sensor was attacked. The SSDGOF is evaluated through GPS deception experiments, and the results show that this method is superior to other methods in detection efficiency.
A Model of Text-Enhanced Knowledge Graph Representation Learning With Mutual Attention

Recently, it has gained lots of interests to jointly learn the embeddings of knowledge graph (KG) and text information.However, previous work fails to incorporate the complex structural signals (from structure representation) and semantic signals (from text representation).This paper proposes a novel text-enhanced knowledge graph representation model, which can utilize textual information to enhance the knowledge representations.Especially, a mutual attention mechanism between KG and text is proposed to learn more accurate textual representations for further improving knowledge graph representation, within a unified parameter sharing semantic space.Different from conventional joint models, no complicated linguistic analysis or strict alignments between KG and text are required to train our model.Besides, the proposed model could fully incorporate the multi-direction signals.Experimental results show that the proposed model achieves the state-of-the-art performance on both link prediction and triple classification tasks, and significantly outperforms previous text-enhanced knowledge representation models.
Correlation signatures for a coherent three-photon scattering in waveguide quantum electrodynamics

We study the correlation signatures of a coherent three-photon scattering process by computationally investigating scattering photon wavefunctions, correlation functions, and nonlinear conditional phases. It is shown that both the three-photon bound states and the hybrid states, which consist of two-photon bound states and one unbound photon, contribute to all the correlation signatures. All computational correlation signatures are in good agreement with a recent experiment, wherein a weak coherent photonic state propagates through a quantum nonlinear Rydberg medium [Liang et al. , Science 359 , 783 ( 2018 ) SCIEAS 0036-8075 10.1126/science.aao7293 ].
Low-Latency Hardware Masking with Application to AES

During the past two decades there has been a great deal of research published on masked hardware implementations of AES and other cryptographic primitives. Unfortunately, many hardware masking techniques can lead to increased latency compared to unprotected circuits for algorithms such as AES, due to the high-degree of nonlinear functions in their designs. In this paper, we present a hardware masking technique which does not increase the latency for such algorithms. It is based on the LUT-based Masked Dual-Rail with Pre-charge Logic (LMDPL) technique presented at CHES 2014. First, we show 1-glitch extended strong noninterference of a nonlinear LMDPL gadget under the 1-glitch extended probing model. We then use this knowledge to design an AES implementation which computes a full AES-128 operation in 10 cycles and a full AES-256 operation in 14 cycles. We perform practical side-channel analysis of our implementation using the Test Vector Leakage Assessment (TVLA) methodology and analyze univariate as well as bivariate t-statistics to demonstrate its DPA resistance level.
A Multi-Level Tourism Destination Recommender System

There are multiple factors that play a significant role in determining a tourist choice of a vacation destination such as affordability, availability of activities, popularity, and safety. Despite the mass of content available on the World Wide Web, the efficiency of utilizing it to find a destination that meets all the criteria of a potential traveler is always questionable. Therefore, travel and tourism software tools tend to incorporate a recommender system component to enhance the quality of the service they provide. In this paper, we propose a simple multi-level tourism recommender system framework to assist potential travelers find the destination that best matches their preferences and requirements. The system incorporates two recommendation procedures: providing the user with a set of destinations liked by similar users to allow constructing a list of potential destinations. Then the system ranks the destinations on this list based on the user preferences and constraints.
Comparative Determination of Mass Fractions of Elements with Variable Chalcophile Affinities in Geological Reference Materials with and without HF‐desilicification

Desilicification elevates extraction of Re and platinum‐group elements (PGEs) from many geological reference materials (RMs), but the extent to which it affects less chalcophile elements has been investigated rarely. To further evaluate the effect of desilicification, mass fractions of elements with variable chalcophile affinities (In, Cd, Cu, Ag, S, Se, Te, Re and PGEs) in different RMs were obtained by isotope dilution and digestion procedures involving HF‐HNO 3 in bombs versus HNO 3 ‐HCl in Carius tubes. The results show that the extraction efficiencies of HF‐desilicification vary in different RMs and for different elements. HF‐desilicification led to a significant increase (30–70%) for In and Cd mass fractions in all analysed RMs, but it played a negligible role in other strongly chalcophile elements in many samples (e.g., UB‐N and WGB‐1). Noticeably, desilicification led to a 10–30% increase in the mass fractions of Cu, Ag, S, Se and Te in BHVO‐2 and BIR‐1a, but less so in BCR‐2. These results could be attributed mainly to the variable chalcophile affinities of elements and their relative budget in sulfides, alloys and silicates. Desilicification should thus be preferred to determine chalcophile elements for most samples, except in cases where they are negligibly hosted in silicates.
A novel multi-stream method for violent interaction detection using deep learning

Violent interaction detection is a hot topic in computer vision. However, the recent research works on violent interaction detection mainly focus on the traditional hand-craft features, and does not make full use of the research results of deep learning in computer vision. In this paper, we propose a new robust violent interaction detection framework based on multi-stream deep learning in surveillance scene. The proposed approach enhances the recognition performance of violent action in video by fusing three different streams: attention-based spatial RGB stream, temporal stream, and local spatial stream. The attention-based spatial RGB stream learns the spatial attention regions of persons that have high probability to be action region through soft-attention mechanism. The temporal stream employs optical flow as input to extract temporal features. The local spatial stream learns spatial local features using block images as input. Experimental results demonstrate the effectiveness and reliability of the proposed method on three violent interactive datasets: hockey fights, movies, violent interaction. We also verify the proposed method on our own elevator surveillance video dataset and the performance of the proposed method is satisfied.
A framework for end-to-end deep learning-based anomaly detection in transportation networks

We develop an end-to-end deep learning-based anomaly detection model for temporal data in transportation networks. The proposed EVT-LSTM model is derived from the popular LSTM (Long Short-Term Memory) network and adopts an objective function that is based on fundamental results from EVT (Extreme Value Theory). We compare the EVT-LSTM model with some established statistical, machine learning, and hybrid deep learning baselines. Experiments on seven diverse real-world data sets demonstrate the superior anomaly detection performance of our proposed model over the other models considered in the comparison study.