Reviewerly/exHarmony huggingface dataset:
Static and dynamic vector semantics for lambda calculus models of natural language

Vector models of language are based on the contextual aspects of language, the distributions of words and how they co-occur in text. Truth conditional models focus on the logical aspects of language, compositional properties of words and how they compose to form sentences. In the truth conditional approach, the denotation of a sentence determines its truth conditions, which can be taken to be a truth value, a set of possible worlds, a context change potential, or similar. In the vector models, the degree of co-occurrence of words in context determines how similar the meanings of words are. In this paper, we put these two models together and develop a vector semantics for language based on the simply typed lambda calculus models of natural language. We provide two types of vector semantics: a static one that uses techniques familiar from the truth conditional tradition and a dynamic one based on a form of dynamic interpretation inspired by Heim’s context change potentials. We show how the dynamic model can be applied to entailment between a corpus and a sentence and provide examples.
Data Augmentation Based on Adversarial Autoencoder Handling Imbalance for Learning to Rank

Data imbalance is a key limiting factor for Learning to Rank (LTR) models in information retrieval. Resampling methods and ensemble methods cannot handle the imbalance problem well since none of them incorporate more informative data into the training procedure of LTR models. We propose a data generation model based on Adversarial Autoencoder (AAE) for tackling the data imbalance in LTR via informative data augmentation. This model can be utilized for handling two types of data imbalance, namely, imbalance regarding relevance levels for a particular query and imbalance regarding the amount of relevance judgements in different queries. In the proposed model, relevance information is disentangled from the latent representations in this AAE-based model in order to reconstruct data with specific relevance levels. The semantic information of queries, derived from word embeddings, is incorporated in the adversarial training stage for regularizing the distribution of the latent representation. Two informative data augmentation strategies suitable for LTR are designed utilizing the proposed data generation model. Experiments on benchmark LTR datasets demonstrate that our proposed framework can significantly improve the performance of LTR models.
A Generalized Language Model in Tensor Space

In the literature, tensors have been effectively used for capturing the context information in language models. However, the existing methods usually adopt relatively-low order tensors, which have limited expressive power in modeling language. Developing a higher-order tensor representation is challenging, in terms of deriving an effective solution and showing its generality. In this paper, we propose a language model named Tensor Space Language Model (TSLM), by utilizing tensor networks and tensor decomposition. In TSLM, we build a high-dimensional semantic space constructed by the tensor product of word vectors. Theoretically, we prove that such tensor representation is a generalization of the n-gram language model. We further show that this high-order tensor representation can be decomposed to a recursive calculation of conditional probability for language modeling. The experimental results on Penn Tree Bank (PTB) dataset and WikiText benchmark demonstrate the effectiveness of TSLM.
Applying Graph Centrality Metrics in Visual Analytics of Scientific Standard Datasets

Graphs are often used to model data with a relational structure and graphs are usually visualised into node-link diagrams for a better understanding of the underlying data. Node-link diagrams represent not only data entries in a graph, but also the relations among the data entries. Further, many graph drawing algorithms and graph centrality metrics have been successfully applied in visual analytics of various graph datasets, yet little attention has been paid to analytics of scientific standard data. This study attempts to adopt graph drawing methods (force-directed algorithms) to visualise scientific standard data and provide information with importance ‘ranking’ based on graph centrality metrics such as Weighted Degree, PageRank, Eigenvector, Betweenness and Closeness factors. The outcomes show that our method can produce clear graph layouts of scientific standard for visual analytics, along with the importance ‘ranking’ factors (represent via node colour, size etc.). Our method may assist users with tracking various relationships while understanding scientific standards with fewer relation issues (missing/wrong connection etc.) through focusing on higher priority standards.
Moving Object Detection Using an Object Motion Reflection Model of Motion Vectors

Moving object detection task can be solved by the background subtraction algorithm if the camera is fixed. However, because the background moves, detecting moving objects in a moving car is a difficult problem. There were attempts to detect moving objects using LiDAR or stereo cameras, but when the car moved, the detection rate decreased. We propose a moving object detection algorithm using an object motion reflection model of motion vectors. The proposed method first obtains the disparity map by searching the corresponding region between stereo images. Then, we estimate road by applying v-disparity method to the disparity map. The optical flow is used to acquire the motion vectors of symmetric pixels between adjacent frames where the road has been removed. We designed a probability model of how much the local motion is reflected in the motion vector to determine if the object is moving. We have experimented with the proposed method on two datasets, and confirmed that the proposed method detects moving objects with higher accuracy than other methods.
Distinctive accuracy measurement of binary descriptors in mobile augmented reality

Mobile Augmented Reality (MAR) requires a descriptor that is robust to changes in viewing conditions in real time application. Many different descriptors had been proposed in the literature for example floating-point descriptors (SIFT and SURF) and binary descriptors (BRIEF, ORB, BRISK and FREAK). According to literature, floating-point descriptors are not suitable for real-time application because its operating speed does not satisfy real-time constraints. Binary descriptors have been developed with compact sizes and lower computation requirements. However, it is unclear which binary descriptors are more appropriate for MAR. Hence, a distinctive and efficient accuracy measurement of four state-of-the-art binary descriptors, namely, BRIEF, ORB, BRISK and FREAK were performed using the Mikolajczyk dataset and ALOI dataset to identify the most appropriate descriptor for MAR in terms of computation time and robustness to brightness, scale and rotation changes. The obtained results showed that FREAK is the most appropriate descriptor for MAR application as it able to produce an application that are efficient (shortest computation time) and robust towards scale, rotation and brightness changes.
Beyond opinion classification: Extracting facts, opinions and experiences from health forums

Surveys indicate that patients, particularly those suffering from chronic conditions, strongly benefit from the information found in social networks and online forums. One challenge in accessing online health information is to differentiate between factual and more subjective information. In this work, we evaluate the feasibility of exploiting lexical, syntactic, semantic, network-based and emotional properties of texts to automatically classify patient-generated contents into three types: "experiences", "facts" and "opinions", using machine learning algorithms. In this context, our goal is to develop automatic methods that will make online health information more easily accessible and useful for patients, professionals and researchers.We work with a set of 3000 posts to online health forums in breast cancer, morbus crohn and different allergies. Each sentence in a post is manually labeled as "experience", "fact" or "opinion". Using this data, we train a support vector machine algorithm to perform classification. The results are evaluated in a 10-fold cross validation procedure.Overall, we find that it is possible to predict the type of information contained in a forum post with a very high accuracy (over 80 percent) using simple text representations such as word embeddings and bags of words. We also analyze more complex features such as those based on the network properties, the polarity of words and the verbal tense of the sentences and show that, when combined with the previous ones, they can boost the results.
Deep Mean Functions for Meta-Learning in Gaussian Processes.

Fitting machine learning models in the low-data limit is challenging. The main challenge is to obtain suitable prior knowledge and encode it into the model, for instance in the form of a Gaussian process prior. Recent advances in meta-learning offer powerful methods for extracting such prior knowledge from data acquired in related tasks. When it comes to meta-learning in Gaussian process models, approaches in this setting have mostly focused on learning the kernel function of the prior, but not on learning its mean function. In this work, we propose to parameterize the mean function of a Gaussian process with a deep neural network and train it with a meta-learning procedure. We present analytical and empirical evidence that mean function learning can be superior to kernel learning alone, particularly if data is scarce.
Low Contrast Image Enhancement Using Convolutional Neural Network with Simple Reflection Model

Low contrast images degrade the performance of image processing system.To solve the issue, plenty of image enhancement methods have been proposed.But the methods work properly on the fixed environment or specific images.The methods dependent on fixed image conditions cannot perform image enhancement properly and perspective of smart device users, algorithms including iterative calculations are inconvenient for users.To avoid these issues, we propose a locally adaptive contrast enhancement method using CNN and simple reflection model.The experimental results show that the proposed method reduces over-enhancement, while recovering the details of the low contrast regions.
An Efficient FPGA Accelerator Design for Optimized CNNs Using OpenCL

Convolutional Neural Networks (CNNs) require highly parallel Hardware (HW) accelerators in the form of Graphical Processing Units (GPUs), Application Specific Integrated Circuits (ASICs) or Field Programmable Gate Arrays (FPGAs) to build low latency solutions necessary for implementing image processing applications. FPGAs have the ability to provide a right balance between flexibility, performance and energy efficiency. The design of FPGA based accelerator design traditionally required a tedious Register Transfer Level (RTL) design flow process. To improve design productivity, the proposed work uses High-Level Synthesis (HLS), described in OpenCL, to generate the FPGA bitstream for the CNN model. The 2D Winograd transformation is integrated in the pipeline to reduce the overall number of Multiply and Accumulate (MAC) operations in the CNN. Instead of increasing the batch size to improve the throughput, this work discusses a mixed precision approach which can counter the limited memory bandwidth issue within the CNN. The obtained results are competitive against other FPGA based implementations proposed in literature. The proposed accelerator can achieve more than $$1.9 \times $$ higher energy efficiency compared to an embedded Nvidia Jetson TX1 implementation of VGG-16.
Saccadic selection does not eliminate attribute amnesia.

Attribute amnesia (Chen & Wyble, 2015, 2016) demonstrates that we may not always be able to spontaneously retrieve a simple attribute of a visual object (e.g., its color) for conscious report, even though the object had just been the target in a visual task. Attribute amnesia has been suggested to reflect a lack of consolidation of the task-irrelevant attribute in visual working memory. Here we tested whether saccadic selection eliminates or attenuates attribute amnesia. Saccade targets have been shown to be preferentially encoded into visual working memory and may therefore be spared. We used simple color pop-out displays, asking participants to indicate the location of the color singleton letter target on each trial either by keypress or by making a saccade toward it. After a couple of trials and unannounced to the participants, we asked for the color and identity of the last target letter on a surprise trial. We found that saccade targets were not spared from attribute amnesia: Participants were as bad in correctly reporting the color in the saccade as in the keypress condition. For letter identity, the effect was attenuated but not abolished when the target was foveated for a short period of time. We argue that the current results do not refute an obligatory coupling between saccadic selection and encoding in visual working memory. However, the encoded information may not necessarily be stored in a manner that is robust enough to persist in the face of a surprise question. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
An improved gray-scale transformation method for pseudo-color image enhancement

Image enhancement is a very important process of image preprocessing and it plays a critical role in the improvement of image quality and the follow-up image analysis, which makes the research of image enhancement algorithm a hot research field. Image enhancement not only needs to strengthen image determination and recognition, but also needs to avoid the consequential color distortion. Pseudo-color enhancement is the technique to map different gray scales of a black-and-white image into a color image. As humans have extremely strong ability in distinguishing different colors visually and relatively weak capacity in discriminating gray scales, so, color the gray-scale changes which cannot be differentiated by human eyes so that they can tell them apart. The mapping function in conventional gray-scale transform method is not working well in dark and low-contrast images. So, this paper comes up with an improved gray-scale transformation algorithm. This algorithm can achieve the enhancement, preserve the image colors, process dark and low-contrast images, reinforce the enhancement and improve the blocking effect. The experiment proves that the enhanced image obtained by the method of this paper can have improved average brightness, natural colors and more detail information and it has good application value.
Characterization of a Big Data Storage Workload in the Cloud

The proliferation of big data processing platforms has led to radically different system designs, such as MapReduce and the newer Spark. Understanding the workloads of such systems facilitates tuning and could foster new designs. However, whereas MapReduce workloads have been characterized extensively, relatively little public knowledge exists about the characteristics of Spark workloads in representative environments. To address this problem, in this work we collect and analyze a 6-month Spark workload from a major provider of big data processing services, Databricks. Our analysis focuses on a number of key features, such as the long-term trends of reads and modifications, the statistical properties of reads, and the popularity of clusters and of file formats. Overall, we present numerous findings that could form the basis of new systems studies and designs. Our quantitative evidence and its analysis suggest the existence of daily and weekly load imbalances, of heavy-tailed and bursty behaviour, of the relative rarity of modifications, and of proliferation of big data specific formats.
Multi-party ring quantum digital signatures

Quantum digital signatures (QDSs), which are unforgeable, non-repudiable, and transferable, are widely used in quantum communication networks. Unfortunately, previous protocols have often relied on three-party models for security analysis and experimental verification. As the number of parties increases, the number of quantum links required increases exponentially, undoubtedly leading to high channel losses and cost for multi-party QDSs. This study proposes a multi-party ring QDS scheme that can effectively deal with the issue of redundant quantum links. Furthermore, compared with the previously proposed QDS protocols, our protocol can prevent repudiation attacks without symmetry operation or swap test. Hence, it also can reduce the possibility of forging attacks by eliminating the possibility of forgery using illegal strings.
Incorporating Ceteris Paribus Preferences in Multiobjective Virtual Machine Placement

Proper handling of preferences in multiobjective evolutionary algorithms is essential for the algorithms' success in real-life applications. While there has been a tremendous work addressing preferences in evolutionary algorithms, the issue of the exact interpretation of decision maker's (DM) preferences and how it affects the performance of evolutionary algorithms has received little attention. One interpretation of preferences that has received significant attention lately by the AI community and is believed to be exercised naturally by decision makers is the Ceteris Paribus (all else being equal) interpretation. In this paper, we adopt the notion of Ceteris Paribus as an interpretation for the DM preferences and incorporate it in a constrained multiobjective problem known as virtual machine placement (VMP). VMP is an essential multiobjective problem in the design and operation of cloud data centers concerned about placing each virtual machine to a physical machine (a server) in the data center. We propose a variant of the NSGA-II that promotes Ceteris Paribus preferred solutions and evaluate its applicability. Our experiment results show that this variant was able to return preferred solutions at almost no extra cost when compared to NSGA-II.
Hybrid-FL: Cooperative Learning Mechanism Using Non-IID Data in Wireless Networks.

This paper proposes a cooperative mechanism for mitigating the performance degradation due to non-independent-and-identically-distributed (non-IID) data in collaborative machine learning (ML), namely federated learning (FL), which trains an ML model using the rich data and computational resources of mobile clients without gathering their data to central systems. The data of mobile clients is typically non-IID owing to diversity among mobile clients' interests and usage, and FL with non-IID data could degrade the model performance. Therefore, to mitigate the degradation induced by non-IID data, we assume that a limited number (e.g., less than 1%) of clients allow their data to be uploaded to a server, and we propose a hybrid learning mechanism referred to as Hybrid-FL, wherein the server updates the model using the data gathered from the clients and aggregates the model with the models trained by clients. The Hybrid-FL solves both client- and data-selection problems via heuristic algorithms, which try to select the optimal sets of clients who train models with their own data, clients who upload their data to the server, and data uploaded to the server. The algorithms increase the number of clients participating in FL and make more data gather in the server IID, thereby improving the prediction accuracy of the aggregated model. Evaluations, which consist of network simulations and ML experiments, demonstrate that the proposed scheme achieves a 13.5% higher classification accuracy than those of the previously proposed schemes for the non-IID case.
PVSS: A Progressive Vehicle Search System for Video Surveillance Networks

This paper is focused on the task of searching for a specific vehicle that appears in the surveillance networks. Existing methods usually assume the vehicle images are well cropped from the surveillance videos, and then use visual attributes, like colors and types, or license plate numbers to match the target vehicle in the image set. However, a complete vehicle search system should consider the problems of vehicle detection, representation, indexing, storage, matching, and so on. Besides, it is very difficult for attribute-based search to accurately find the same vehicle due to intra-instance changes in different cameras and the extremely uncertain environment. Moreover, the license plates may be mis-recognized in surveillance scenes due to the low resolution and noise. In this paper, a progressive vehicle search system, named as PVSS, is designed to solve the above problems. PVSS is constituted of three modules: the crawler, the indexer, and the searcher. The vehicle crawler aims to detect and track vehicles in surveillance videos and transfer the captured vehicle images, metadata and contextual information to the server or cloud. Then multi-grained attributes, such as the visual features and license plate fingerprints, are extracted and indexed by the vehicle indexer. At last, a query triplet with an input vehicle image, the time range, and the spatial scope is taken as the input by the vehicle searcher. The target vehicle will be searched in the database by a progressive process. Extensive experiments on the public dataset from a real surveillance network validate the effectiveness of PVSS.
A Semi-Supervised Learning Approach for Tackling Twitter Spam Drift

Twitter has changed the way people get information by allowing them to express their opinion and comments on the daily tweets. Unfortunately, due to the high popularity of Twitter, it has become very attractive to spammers. Unlike other types of spam, Twitter spam has become a serious issue in the last few years. The large number of users and the high amount of information being shared on Twitter play an important role in accelerating the spread of spam. In order to protect the users, Twitter and the research community have been developing different spam detection systems by applying different machine-learning techniques. However, a recent study showed that the current machine learning-based detection systems are not able to detect spam accurately because spam tweet characteristics vary over time. This issue is called “Twitter Spam Drift”. In this paper, a semi-supervised learning approach (SSLA) has been proposed to tackle this. The new approach uses the unlabeled data to learn the structure of the domain. Different experiments were performed on English and Arabic datasets to test and evaluate the proposed approach and the results show that the proposed SSLA can reduce the effect of Twitter spam drift and outperform the existing techniques.
Smart School Guidance and Vocational Guidance System Through the Internet of Things

In the current era of scholar orientation in Morocco, social inequalities in school and professional pathways remain blatant. A referral process which consists of distributing students among the options, according to processes defined by the government and essentially based on academic performance. Vocational guidance in schools is a very necessary aspect that cannot be ignored throughout secondary education, where a bad orientation is often associated with failure or dropping out of school. This is indicated in the Moroccan Strategic Vision 2015-2030 which aims to improve the quality of the school and resume the system of educational and vocational guidance with a view to developing it and guarantee equal opportunities for students. In the context of vocational guidance, the article discuses and examines a smart school guidance system called "ETC guidance system", its role is to reduce failure orientation rate and create an environment conducive to the successful adaptation of school guidance according to students' cognitive trends, using Internet of things (IoT) and mobile devices. The system is easy to use and an innovative solution to guide students from third world countries, to choose the best professional career.