Reviewerly/exHarmony huggingface dataset:
Active Learning for Image Classification: A Deep Reinforcement Learning Approach

Active learning aims to select “worthy” data for annotation such that the model could achieve better performance using as less labeled data as possible. Previous research works mainly use heuristic selection methods to solve this problem. Since the process of data selection and model training is separated, these methods have limitations in effectiveness. This paper proposes a new active learning framework, which uses deep reinforcement learning as the data selection strategy. Instead of choosing “worthy” data through heuristic algorithms, we use the deep reinforcement learning algorithm explicitly learning a data selection policy. The deep convolutional neural network is used to extract images' features which serve as “state” in the reinforcement learning algorithm. And we use deep Q-learning algorithm to train a Q-network. According to the output of the Q-network to decide taking which “action”, i.e. annotate the data or not. The framework proposed in this paper can be trained by an end-to-end manner. Comprehensive experimental evaluations on CIFAR-10, CIFAR-100 and SVHN datasets with VGG-16 model and four different depth ResNet models demonstrate that the proposed method outperforms those state-of-art active learning methods for the task of image classification.
MP Remix

to create an integrated space. We consider an MR configuration in which collocated collaborators work around a tabletop display, while remote collaborators wear an HMD to interact with a connected virtual environment that gives a 3D perspective, and consider the impact of varying degrees of view congruence with their collaborators. In a within-subjects study with 18 groups of 3, groups completed task scenarios involving 3D object manipulation around a physical-virtual mapped tabletop. We compare a synchronized Tabletop display baseline and two MR conditions with different levels of view congruence: Fishtank and Hover. Fishtank has a high degree of congruence as it shares a top-down perspective of the 3D objects with the tabletop collaborators. The Hover condition has less view congruence since 3D content hovers front of the remote collaborator above the table. The MR conditions yielded higher self-reported awareness and co-presence than the Tabletop condition for both collocated and remote participants. Remote collaborators significantly preferred the MR conditions for manipulating shared 3D models and communicating with their collaborators. Our findings illustrate strengths and weaknesses of both MR techniques but show that more participants preferred the less-congruent Hover condition overall. Reasons include that it facilitated interaction and viewing 3D objects.
TMG-BoBI: Generating Back-of-the-Book Indexes with the Text-to-Matrix-Generator

A common editorial task when preparing scientific monographs, books, even long articles such as surveys, is to prepare a Back-of-the-Book index (BoBI). In this paper we study the automation of this task and propose a system for facilitating authors. We leverage text mining techniques and specifically Automated Keyword Extraction (AKE), the Text to Matrix Generator (TMG) which is a MATLAB toolbox for text mining, and the indexing tools of J4T_{E}X. From our experiments using publicly available datasets, our system appears to be capable of generating satisfactory indexes that can be used after only little user intervention.
Addressing the ‘Tower of Babel’ of pesticide regulations: an ontology for supporting pest-control decisions

Abstract Farmers, who have to decide which pesticide to use against a particular crop-damaging pest, need to take into account country-specific regulations (e.g. permitted levels of pesticide residues), application instructions and financial considerations. The fact that these data are stored in different locations, sometimes using different terminology or different languages, makes it difficult to gather these data and requires that farmers are familiar with the variety of terms used, which consequently hampers the efficiency and effectiveness of the decision process. To overcome these challenges, a Web application for pest control is proposed to facilitate the integration of information coming from different Internet sources and representing different terminologies by using an ontology. The application is based on a pest-control ontology (formal representations of domain knowledge that can be interpreted by computers) that accounts for various pesticide regulations of different countries to which the crop is exported. In recent years, ontologies have become a major tool for domain knowledge representation and a core component of many knowledge management systems, decision support systems and other intelligent systems, inter alia , in the context of agriculture. The pest-control ontology developed in the current research includes pest-control concepts that have yet to be covered by existing ontologies. It is demonstrated in the specific case of pepper in Israel. The ontology is expressed using Web Ontology Language (OWL) and thus can be shared on the Web and reused by other ontologies and systems. In addition, a comprehensive method for developing and evaluating agricultural ontologies is presented.
Image denoising using 2D orthogonal locality preserving discriminant projection

Image denoising using a combination of non-local self-similarity and transformed domain techniques has become popular in past few years. Instead of working on independent pixels, patches extracted from the noisy image are grouped together based on structural similarity and noise elimination is performed in transformed domain. Orthogonal locality preserving projection and its variant that processes the images directly in matrix format have been used for image denoising recently. Locality preserving nature of these techniques takes care of similarity within image patches while learning the basis, hence reducing the task of grouping patches explicitly. Non-local self-similarity based image denoising approaches perform patch grouping based on structural similarity. Discriminant information, if considered can play pivotal role in achieving superior clustering of data and thereby is expected to enhance the quality of denoising. With this aim in mind, two-dimensional (2D) orthogonal locality preserving discriminant projection is formulated in this study. While learning the basis, along with the similarity, proposed approach also takes into account dissimilarity between patches. A global basis thus learnt from the noisy image is used for denoising and comparable denoising performance is shown relative to the state-of-the-art methods.
Research on DCT Image Compression Algorithm Based on Dynamic Energy Analysis

With the rapid development of Internet technology and communication technology, image compression has become one of the key technologies in the field of modern multimedia and communication. Discrete Cosine Transform (DCT), as the core part of the international standards on many kinds of image coding, is considered to be one of the more practical image transform compression coding, but there is also the problem that uniform operation of each pixel block does not ensure better retain details and discard redundancy. This article is based on the conventional DCT compression principle, and proposes a Dynamic Energy Analysis Discrete Cosine Transform (DEADCT) image compression algorithm. The DEADCT introduces a new concept of "compression radius" and partition the image, then adjusts and quantifies the compression radius dynamically according to energy spectrum of different pixel blocks. In this way, it is possible to preserve more detailed image features while ensuring the compression effect. Experiment proves that the DEADCT block analysis compression algorithm can have higher performance than DCT, DFT and DWT compression algorithms.
Super-Resolution on Degraded Low-Resolution Images Using Convolutional Neural Networks

Single Image Super-Resolution (SISR) has witnessed a dramatic improvement in recent years through the use of deep learning and, in particular, convolutional neural networks (CNN).In this work we address reconstruction from low-resolution images and consider as well degrading factors in images such as blurring.To address this challenging problem, we propose a new architecture to tackle blur with the down-sampling of images by extending the DBSRCNN architecture [1].We validate our new architecture (DBSR) experimentally against several state of the art super-resolution techniques.
Knowledge-aware Textual Entailment with Graph Attention Network

Textual entailment is a central problem of language variability, which has been attracting a lot of interest and it poses significant issues in front of systems aimed at natural language understanding. Recently, various frameworks have been proposed for textual entailment recognition, ranging from traditional computational linguistics techniques to deep learning model based methods. However, recent deep neural networks that achieve the state of the art on textual entailment task only consider the context information of the given sentences rather than the real-world background information and knowledge beyond the context. In the paper, we propose a Knowledge-Context Interactive Textual Entailment Network (KCI-TEN) that learns graph level sentence representations by harnessing external knowledge graph with graph attention network. We further propose a text-graph interaction mechanism for neural based entailment matching learning, which endows the redundancy and noise with less importance and put emphasis on the informative representations. Experiments on the SciTail dataset demonstrate that KCI-TEN outperforms the state-of-the-art methods.
Emotion predictions in geo-textual data using spatial statistics and recommendation systems

Microblogs are used by millions of users to express their emotions, such as joy, surprise and anger, on a plethora of different topics. For the same topic, different places may exhibit different emotions for identical topics. The goal of this work is to learn, model and predict emotions on various topics and in different cities. For this purpose, we propose a hybrid approach which combines spatial statistics (kriging) and recommendation system (matrix factorization-based). Our experimental evaluations, using millions of tweets across the United States, show that our hybrid approach outperforms individual approaches based on matrix factorization and Kriging alone. This case study shows the potential of combining spatial statistics methods such as Kriging with machine learning solutions to support knowledge discovery on spatial data.
Implementation of Data Mining to Classify the Consumer’s Complaints of Electricity Usage Based on Consumer’s Locations Using Clustering Method

Abstract Data collected by PLN staff on customer’s complaints in the usage of electricity are very huge and accumulate. Previously, there was no information about these kinds of complaints in all sub-districts included inside the company, PT. PLN Binjai. On the other hand, each year company experienced problems to classify all complaints in order to obtain customers with information to later be used as a basis for making policy/decision. Data mining used clustering method and K-Means algorithm to classify the data so that important information is obtained from customer’s complaints on the electricity usage, using variable: types of complaints, power using by consumer and consumer’s region. Data was analyzed using Matlab to produce cluster centers and obtained a relationship between variables obtained with groups with high number of complaints. Results revealed that from 500 customers who have complained, cluster 1 had 218 complaints namely NT Fuse Putus with power used was 1300 watt and was located in Binjai.
Novel updatable identity-based hash proof system and its applications

Alwen et al. in Eurocrypt 2010, showed that an identity-based hash proof system (IB-HPS), where IB-HPS generalizes the notion of hash proof system (HPS) to the identity-based setting, almost immediately yields an identity-based encryption (IBE) scheme which is secure against partial leakage of the target identity's decryption key. That is, an IBE scheme with bounded leakage resilience can be naturally created from an IB-HPS. However, in the real world, the leakage is unbounded, and any adversary can break the security of cryptography shceme by performing continuous leakage attacks. To further increase the practicability, a cryptography scheme must hold the claimed security in the continuous leakage setting. Dodis et al. in FOCS 2010, showed a generic method how to create a cryptography shceme with continuous leakage resilience from the bounded leakage-resilient cryptosystem by performing an additional key update algorithm while the public parameters keep unchanged. To construct a continuous leakage-resilient cryptography scheme, a new primitive, called it updatable identity-based hash proof system (U-IB-HPS), is proposed, which is an improved IB-HPS. In particular, the improved system has an additional key update algorithm, which can push some new randomness into the private key of user (or the master secret key), the updated results are random in the adversary's view, and the leakage of previous private key of user (or the master secret key) does not work for the updated results. However, the previous instantiations of U-IB-HPS cannot achieve the claimed security. To solve the above problems, in this paper, two instantiations of U-IB-HPS with better performance are created, and the security of proposed system is proved, in the standard model, based on the classic decisional bilinear Diffie-Hellman assumption. The corresponding IBE scheme created with our U-IB-HPS allows continuous leakage of multiple keys, i.e., continuous leakage of the master secret key and the private key of user. Additionally, our U-IB-HPS can also be employed as an underlying basic tool to build the generic construction of continuous leakage-amplified public-key encryption scheme, continuous leakage-resilient identity-based authenticated key exchange protocol, and continuous leakage-resilient public-key encryption scheme with keyword search, etc.
Feature Matching with Intra-Group Sparse Model

Feature matching is a fundamental problem in computer vision area. In many real applications, one can usually obtain some potential (candidate) matches C by using some discriminative feature descriptors, such as SIFT descriptor. Then, the feature matching problem can be formulated as the problem of trying to select the correct matches S from the potential match set C. In this paper, we propose to solve matches selection by developing a novel intra-group sparse matching (IGSM) model. Our IGSM is motivated by a simple observation that the potential match set C can be divided into several non-overlapping groups Ci, among which the correct matches S are uniformly distributed. We thus develop an intra-group selection model to conduct matches selection at the intra-group level to incorporate the one-to-one matching constraint more in matches selection process. Our IGSM model has three main advantages: (1) The selection mechanism is parameter-free; (2) it generates an intra-group sparse solution which better maintains the one-to-one matching constraint in nature; (3) a simple yet effective update algorithm has been derived to solve IGSM model. The optimality and convergence of the algorithm are theoretically guaranteed. Experimental results on several image feature matching datasets show the effectiveness and efficiency of the proposed IGSM matching method.
Instructional Strategies for a Smart Learning System

Nowadays educational environment presents a set of needs different than years ago and which are no constant anymore. We must assume a learning prepared for those changes and adapted to the student's learning needs and features. This can be achieved with the use of Information Technologies creating a real adaptive learning, a smart learning system. This kind of system will characterize each learner by their learning features and then follow different strategies, composed by a set of decisions made by the teacher, to achieve the learning outcomes for each leaner. This is what this research aims, to propose possible instructional strategies to be selected by the teacher to fulfill their objectives and the variables the smart learning system will consider.
Tracy–Singh Product and Genetic Whale Optimization Algorithm for Retrievable Data Perturbation for Privacy Preserved Data Publishing in Cloud Computing

Abstract This paper proposes a retrievable data perturbation model for overcoming the challenges in cloud computing. Initially, genetic whale optimization algorithm (genetic WOA) is developed by integrating genetic algorithm (GA) and WOA for generating the optimized secret key. Then, the input data and the optimized secret key are given to the Tracy–Singh product-based model for transforming the original database into perturbed database. Finally, the perturbed database can be retrieved by the client, if and only if the client knows the secret key. The performance of the proposed model is analyzed using three databases, namely, chess, T10I4D100K and retail databases from the FIMI data set based on the performance metrics, privacy and utility. Also, the proposed model is compared with the existing methods, such as Retrievable General Additive Data Perturbation, GA and WOA, for the key values 128 and 256. For the key value 128, the proposed model has the better privacy and utility of 0.18 and 0.83 while using the chess database. For the key value 256, the proposed model has the better privacy and utility of 0.18 and 0.85, using retail database. From the analysis, it can be shown that the proposed model has better privacy and utility values than the existing models.
An attentive RNN model for session-based and context-aware recommendations

In the RecSys Challenge 2019 the participants were asked to predict which items, from a presented list of items/accommodations of a search result on trivago, had been clicked-on during the last part of a user's session. Here we present the 7th place solution1. It consists of a neural network designed to learn interactions between session, context, sequence features, and the features of the displayed items at the time of a click. Our approach uses well established deep learning techniques, such as Recurrent Neural Networks, Attention and self-Attention mechanisms to deal with the different aspects of the information available, and it predicts a (categorical) probability distribution over the list of presented items. In addition to the model structure we also describe the somewhat heavy feature engineering, data augmentation and other decisions/observations made a long the way.
Deep Learning Based Mass Detection in Mammograms

Mammogram is the primary imaging technique for breast cancer screening, the leading type of cancer in women worldwide. While the clinical effectiveness of mammogram has been well demonstrated, the mammographic characteristics of breast masses are quite complex. As a result, radiologists certified for reading mammography are lacking, which limits the accessibility of mammography for more population. In this paper, we propose a Computer Aided Detection (CADe) method to automatically detect masses in mammography. Our method combines Faster R-CNN with Feature Pyramid Network, Focal Loss, NonLocal Neural Network to achieve the optimal mass detection performance. We comprehensively evaluated our method and baseline methods on three public datasets combined, namely the Digital Database for Screening Mammography (DDSM), INbreast, and Breast Cancer Digital repository (BCD). Our results demonstrate that our method outperforms the baselines by a large margin, reporting an Average Precision of 0.805, and a recall of 0.977, respectively.
Epoch Detection Using Hilbert Envelope for Glottal Excitation Enhancement and Maximum-Sum Subarray for Epoch Marking

A technique is presented for detecting glottal excitation epochs in the speech signal, using Hilbert envelope for enhancing the glottal excitation and maximum-sum subarray for marking the epochs. The processing comprises the steps of dynamic range compression, Hilbert envelope calculation, saliency enhancing, and epoch marking. The dynamic range compression reduces the amplitude variation of the signal. The Hilbert envelope enhances the glottal excitation. The saliency enhancing further enhances the instants of significant excitation by reducing the residual ripples related to the vocal tract filter, by using a dynamic peak detector, a nonlinear smoother, and a differentiator. The epoch marking locates the peak of the maximum-sum subarray as the instant of significant glottal excitation. Evaluation of the proposed technique showed its performance measures to be similar to those of the state-of-the-art techniques for normal speech, telephone-quality speech, and highpass filtered speech and better than or similar to them for pathological speech. The averaged accuracy-weighted identification rates with the proposed technique for normal speech, telephone-quality speech, and pathological speech were 79.46%, 77.04%, and 71.52%, respectively. The proposed technique employs single-pass processing and may find applications in speech training aids, diagnosis of speech disorders, and voice conversion of speech with voice disorders.
An Initial Dot Encoding Scheme with Significantly Improved Robustness and Numbers

Coded mark point, as an important part of automatic matching, is widely used in stereo vision and photogrammetry. An encoding scheme named the Initial Dot encoding scheme, has been designed based on the programme of concentric rings, and which is able to solve restrictions on the number of codes and promote the stability of decoding. The designed coding pattern includes three functional points: locating point, start point and coded point. The maximum coding number of the Initial Dot scheme can reach 262,144 when the number of code bits is 18. The recognition experiments show that the Initial Dot encoding scheme can achieve 100% recognition rate at a 75 degree angle, which is very difficult for other coding schemes. By comparing experiments with the Schneider encoding scheme for a large flexible panel, the recognition rate of the proposed scheme is 99%, which is higher than the recognition rate of 90% of the Schneider scheme. The experimental results indicate that the Initial Dot encoding scheme can be successfully applied on flexible solar panels under a 1000 mm × 500 mm field of view.
Increasing Native Speakers' Awareness of the Need to Slow Down in Multilingual Conversations Using a Real-Time Speech Speedometer

Collaborating using a common language can be challenging for non-native speakers (NNS). These challenges can be reduced when native speakers (NS) adjust their speech behavior for NNS, for example by speaking more slowly. In this study, we examined whether the use of real-time speech rate feedback (a speech speedometer) would help NS monitor their speaking speed and adjust for NNS accordingly. We conducted a laboratory experiment with 20 triads of 2 NS and 1 NNS. NS in half of the groups were given the speech speedometer. We found that NS with the speech speedometer were significantly more motivated to slow down their speech but they did not actually speak more slowly, although they made other speech adjustments. Furthermore, NNS perceived the speech of NS with the speedometer less clear, and they felt less accommodated. The results highlight the need for tools that create scaffolding to help NS make speech accommodations. We conclude with some design ideas for these scaffolding tools.