Reviewerly/exHarmony huggingface dataset:
A Survey of Development Strategies for Collaborative Systems

The development of software systems that support socio-technical activities, also known as collaborative or groupware systems, has historically been carried out in an ad hoc manner given its particular nature. However, the scientific literature and practical experience have been demanding for more systematization of these developments, due to the increasing need to count on these systems in several application domains, like education, healthcare, tourism, disaster relief and productive activities. In order to shed light on this matter, this paper presents a survey on literature of software strategies to support the development of collaborative systems. The study analyzes, classifies and compares these strategies considering several criteria. The results indicate that there is no development method or strategy that covers the whole lifecycle of these applications, but various of them are complementary, opening thus an opportunity to advance the state-of-the-art in this study domain.
Pixel N-Grams Representation for Medical Image Classification

Image classification has wide applications in many fields including medical imaging. A major aspect of classification is to extract features that can correctly represent important variations in an image. Global image features commonly used for classification include Intensity Histograms, Haralick’s features based on Gray-level co-occurrence matrix, Local Binary Patterns and Gabor filters. A novel feature extraction and image representation technique ‘Pixel N-grams’ inspired from ‘Character N-grams’ concept in text categorization is described in this chapter. The classification performance of Pixel N-grams is tested on the various datasets including UIUC texture dataset, binary shapes dataset, miniMIAS dataset of mammography, and real-world high-resolution mammography dataset provided by an Australian radiology practice. The results are compared with other feature extraction techniques such as co-occurrence matrix features, intensity histogram, and bag of visual words. The results demonstrate promising classification accuracy in addition to reduced computational costs, enabling a new way for mammographic classification on low resource computers.
QoE-Aware Real-Time Multimedia Streaming in SD-WANs

The exponential increase in bandwidth-sensitive multimedia traffic on the net has given rise to new challenges and services. There is a need to have quality management measures to serve the high needs of efficient transmission and delivery in time-constrained environments over IP networks. Quality of Experience is one of the major techniques introduced to achieve the goals of application efficiency and user satisfaction from an end-user perspective. By utilizing crowdsourcing techniques, QoE becomes more cost-efficient and easier to measure. In this paper, we propose a framework that takes real time QoE feedback and forwards it to SD-WAN controllers in order to enhance streaming routes based on realtime user quality perceptions. We analyze how QoE can be affected by different streaming protocols and which streaming protocols perform better with imposing QoS quality changes.
Improved Text-Independent Speaker Identification and Verification with Gaussian Mixture Models

Speaker recognition systems proposed in the literature are conditioned by many factors such as the features used, the type of data to be processed, data duration and the approach to be used. This work presents a novel speaker recognition application using a new dimensional cepstral feature vector for building Gaussian Mixture Models (GMM) for speaker identification and verification systems. Recognition results obtained with the proposed system are compared to the state of the art systems requiring high dimensional feature vectors. We experimentally evaluate this work with speakers taken from TIMIT database. The new system gave substantial improvements even when trained with limited data, which explain the effectiveness of the new feature vectors with this approach which can be comparable to the use additional algorithms or approaches.
Simple Is Better: A Global Semantic Consistency Based End-to-End Framework for Effective Zero-Shot Learning

In image recognition, there are many cases where training samples cannot cover all target classes. Zero-shot learning (ZSL) addresses such cases by classifying the samples of unseen categories that have no corresponding samples contained in the training set via class semantic information. In this paper, we propose a novel and simple end-to-end framework, called Global Semantic Consistency Network (GSC-Net for short), which makes complete use of the semantic information of both seen and unseen classes to support effective zero-shot learning. We also employ a soft label embedding loss to further exploit the semantic relationships among classes and use a seen-class weight regularization to balance attribute learning. Moreover, to adapt GSC-Net to the setting of Generalized Zero-shot Learning (GZSL), we introduce a parametric novelty detection mechanism. Experiments on all the three widely-used ZSL datasets show that GSC-Net performs better than most existing methods under both ZSL and GZSL settings. Especially, GSC-Net achieves the state of the art performance on two datasets (AWA2 and CUB). We explain the effectiveness of GSC-Net from the perspectives of class attribute learning and visual feature learning, and discover that the validation accuracy of seen classes can serve as an indicator of ZSL performance.
Velodyne LiDAR and monocular camera data fusion for depth map and 3D reconstruction

The autonomous vehicles are required to perceive the environment to take a correct driving decision. The sensors which have been commonly used by autonomous vehicles are the camera and the Light Detection and Ranging (LiDAR). In this work, we have integrated the LiDAR data with the image captured by the camera to assign the color information to the point cloud which resulted in a 3D model and to assign depth information to the image pixels which resulted in a depth map. The LiDAR data is sparse and the resolution of the image is much greater than that of the LiDAR data. In order to match the resolution of the LiDAR data and image data, we had utilized Gaussian Process Regression (GPR) to interpolate the depth map but it was not able to completely fill the empty locations in the depth map. In this paper, we have proposed a method to interpolate the 2D depth map data to completely fill the empty locations in the depth map. In this study, we have used Velodyne VLP-16 LiDAR and a monocular camera. Our method is based on the covariance matrix in which the depth value assigned to the empty locations in depth map is decided according to the value of covariance function in the covariance matrix. Our method surpassed the GPR in run time and interpolation result. This shows that our approach is fast enough in real-time for autonomous vehicles.
Elliptic Curve Cryptography and LSB Steganography for Securing Identity Data

Data theft isBudianto, Christofer Derian a growing phenomenon in order to gain useful information for theWicaksana, Arya attackers own benefits over the victims sensitive data. TheHansun, Seng loss caused by this fraudulent act is severe as what happened to the Equifax data breach in 2017. Here cryptography and steganography are applied to secure identity data contained in the electronic Indonesian identity card (e-KTP). Cryptography has been used for decades to secure data in storage. Steganography is often added after the cryptography process to provide two layers of protection. The elliptic curve cryptography (ECC) is an approach to public-key cryptography based on the algebraic structure of elliptic curves over finite fields. The ECC is used here to secure the Indonesia identity card data. Least significant bit steganography is used after the ECC to embed the information (cipher text) into the person picture. Thus the only stored information in the application is a collection of photos. The testing and evaluation show that the implementation is successful with the PSNR value above 79 dB.
Care of Archaeological Materials Begins in the Field

Abstract Care of archaeological materials should begin when recovered in the field. Care and stabilization of objects in the field will greatly increase their research and exhibit potential. Identifying problems and understanding basic solutions to object care and stabilization is an important part of training for all potential object handlers. Proper care and stabilization of objects can and should be a priority for all object users—excavators, lab analysts, museum staff, and researchers. Constant dialogue and communication between repository specialists and archaeologists can be the most useful source for care of all archaeological objects.
Robustness of Keystroke Dynamics Identification Algorithms Against Brain-Wave Variations Associated with Emotional Variations

Keystroke dynamics facilitates the identification of a person by the way they type. This article focuses on analysing the robustness of keystroke dynamics algorithms against variations in biometric records through electroencephalography, using waves associated with states of relaxation and excitement and a self-report questionnaire. An experiment was conducted to capture keystroke patterns in different affective states. The results suggested specific classification distances such as A and R metrics, Canberra distance and two Minkowski-based distances have their accuracy slightly and negatively influenced by changing moods. Euclidean distance seemed to be the least affected.
Binary Tomography Using Variants of Local Binary Patterns as Texture Priors

In this paper, we propose a novel approach for binary image reconstruction from few projections. The binary reconstruction problem can be highly underdetermined and one way to reduce the search space of feasible solutions is to exploit some prior knowledge of the image to be reconstructed. We use texture information extracted from sample image patches as prior knowledge. Experimental results show that this approach can retain the structure of the image even if just a very few number of projections are used for the reconstruction.
Time-Guided High-Order Attention Model of Longitudinal Heterogeneous Healthcare Data

Due to potential applications in chronic disease management and personalized healthcare, the EHRs data analysis has attracted much attentions of both researchers and practitioners. There are three main challenges in modeling longitudinal and heterogeneous EHRs data: heterogeneity, irregular temporality and interpretability. A series of deep learning methods have made remarkable progress in resolving these challenges. Nevertheless, most of existing attention models rely on capturing the 1-order temporal dependencies or 2-order multimodal relationships among feature elements. In this paper, we propose a time-guided high-order attention (TGHOA) model. The proposed method has three major advantages. (1) It can model longitudinal heterogeneous EHRs data via capturing the 3-order correlations of different modalities and the irregular temporal impact of historical events. (2) It can be used to identify the potential concerns of medical features to explain the reasoning process of healthcare model. (3) It can be easily expanded into cases with more modalities and flexibly applied in different prediction tasks. We evaluate the proposed method in two tasks of mortality prediction and disease ranking on two real world EHRs datasets. Extensive experimental results show the effectiveness of the proposed model.
A Formalisation of Nominal C-Matching through Unification with Protected Variables

This work adapts a formalisation in Coq of a nominal C-unification algorithm to include “protected variables” that cannot be instantiated during the unification process. By introducing protected variables we are able to reuse the C-unification algorithm to solve nominal C-matching (as well as equality check) problems. From the algorithmic point of view having this extension would be enough to adapt the C-unification algorithm for dealing with equational check as well as with C-matching problems, but the resulting algorithms would not be formally checked by simple reuse of the original formalisation. This paper describes the additional effort necessary in order to adapt the specification and reuse previous formalisations.
Texture Description for Classification of Fine Needle Aspirates

In breast cancer diagnosis, fine needle aspiration biopsy is an important diagnostic tool. It is used to estimate cancer malignancy grade that is further required for treatment determination. In this paper we describe a scheme based on pattern recognition and image processing techniques for automatic breast cancer malignancy grading from cytological slides of fine needle aspiration biopsies. To determine a malignancy classification of the slide we propose to extract textural features of nuclei with an application of local binary patterns. Based on texture determination, we present an improved classification system for cancer malignancy grading.
Analysis of Mood Tags for Multimedia Content Recommendation in Social Networks

The propensity of Web information purchasers is changing from cost effectiveness that emphasizes price with performance to cost satisfaction that emphasizes purchaser's psychological satisfaction. One of the methods to improve user's cost satisfaction in recommending multimedia contents is to use mood inherent in multimedia contents. SNS services based on mood folksonomy are applications using such a method. However, in the applications based on folksonomy, the problem caused by synonyms exists. This paper suggests a cost-satisfactive multimedia contents recommendation method to solve the problem on synonym. To this end, we utilizes the value of Arousal & Valence which express mood of multimedia content as its internal tag. A method that defines the relation between AV values of multimedia contents and AV values of mood tags is suggested by considering synonym and the correlation between them is analyzed. From the analysis, it is shown that the AV values of multimedia contents with a mood tag and its synonyms reside in the area of the mood tag in the Thayor model.
Co-Compressing and Unifying Deep CNN Models for Efficient Human Face and Speaker Recognition

Deep CNN models have become state-of-the-art techniques in many application, e.g., face recognition, speaker recognition, and image classification. Although many studies address on speedup or compression of individual models, very few studies focus on co-compressing and unifying models from different modalities. In this work, to joint and compress face and speaker recognition models, a shared-codebook approach is adopted to reduce the redundancy of the combined model. Despite the modality of the inputs of these two CNN models are quite different, the shared codebook can support two CNN models of sound and image for speaker and face recognition. Experiments show the promising results of unified and co-compressing heterogeneous models for efficient inference.
Two-Stage Session-Based Recommendations with Candidate Rank Embeddings

Session-based recommender systems have gained attention recently due to their potential for providing real-time personalized recommendations with high recall, especially when compared to traditional methods like matrix factorization and item-based collaborative filtering. Two recent methods are Short-Term Attention/Memory Priority Model for Session-based Recommendation (STAMP) and Neural Attentive Session-based Recommendation (NARM). However, when we applied these two methods to the similar-item recommendation dataset of Zalando, they did not outperform a simple collaborative filtering baseline. Aiming for improving similar-item recommendation, in this work we propose to re-rank a list of generated candidates, by employing the user session information encoded by an attention network. We confirm the efficacy of this strategy when using a novel Candidate Rank Embedding that encodes the global ranking information of each candidate in the re-ranking process. Offline and online experiments show significant improvements over the baseline in terms of recall and MRR, as well as improvements in click-through rate. Additionally, we evaluate the potential of this method on the next click prediction problem, where, when applied to STAMP and NARM, it improves recall and MRR on two publicly available real-world datasets.
Cache attack detection in virtualized environments

Cache Side Channel (CSC) attacks are prominent security threats in modern day processors. CSC attacks have caused a large impact in security and widespread concern in information security community. The objective of CSC attacks is to crack the confidentiality of a computer system and extract sensitive secrets through CPU caches. CSC attacks do not require any specific permission and just need access to a shared cache. CSC attacks are subtle, powerful and much more feasible in virtualized environments than in non-virtualized environments. To help protect security in virtualization environments, CSC attacks detection methods have become critical and important. The Flush+Flush cache attack technique, as the most recent CSC attack variant demonstrated that the state-of-the-art discovery methods can be simply sidestepped. In this paper, we introduce novel approach that make the detection of Flush+Flush cache attack in virtualized environments.
Mitigating the Curse of Dimensionality in Data Anonymization

In general, just suppressing identifiers from released microdata is insufficient for privacy protection. It has been shown that the risk of re-identification increases with the dimensionality of the released records. Hence, sound anonymization procedures are needed to anonymize high-dimensional records. Unfortunately, most privacy models yield very poor utility if enforced on data sets with many attributes. In this paper, we propose a method based on principal component analysis (PCA) to mitigate the curse of dimensionality in anonymization. Our aim is to reduce dimensionality without incurring large utility losses. We instantiate our approach with anonymization based on differential privacy. Empirical work shows that using differential privacy on the PCA-transformed and dimensionality-reduced data set yields less information loss than directly using differential privacy on the original data set.
Improving Named Entity Recognition with Commonsense Knowledge Pre-training

Commonsense can be vital in some applications like Natural Language Understanding, where it is often required to resolve ambiguity arising from implicit knowledge and under-specification. In spite of the remarkable success of neural network approaches on a variety of Natural Language Processing tasks, many of them struggle to react effectively in cases that require commonsense knowledge. In the present research paper, we take advantage of the availability of the open multilingual knowledge graph ConceptNet, by using it as an additional external resource in a Named Entity Recognition system (NER). Our proposed architecture involves BiLSTM layers combined with a CRF layer that was augmented with some features such as pre-trained word embedding layers and dropout layers. Moreover, apart from using word representations, we used also character-based representation to capture the morphological and the orthographic information. Our experiments and evaluations showed an improvement in the overall performance with +2.86 in the F1-measure. To the best of our knowledge, there is no study relating the integration of a commonsense knowledge base in NER.
Center-Emphasized Gradient-Feature Based Quality Assessment Method for Screen Content Images

Screen content images (SCI) are composed of texts, graphics, images, emails, web pages, and other computergenerated components which make it different from the pure natural images. As a result, image quality assessment (IQA) methods designed for natural images are not always suitable for screen content images. However, as time goes, many screen contents are being generated which share several properties of natural images. As a result, IQA methods for SCI need to be more generic while giving priority to the prominent SCI features. The image gradient is a very important feature for all kinds of images, especially for SCI. In this paper, we proposed a novel image gradient based similarity index (CGSI) for screen content images where the gradient is used both as a quality map and extractor for another feature map. Our previous studies show that the center part of an image is visually the most important part and HVS is more sensitive to any distortion in the middle area. To address this issue, the center area of both gradient and feature similarity maps are raised by element-wise squaring. Eventually, the final quality score is calculated using a summation of standard deviation based pooling strategy. We evaluated our proposed method on three large scale SCI databases and compared with 8 other state-of-the-art IQA methods designed for both natural and screen content images. Results show that the proposed approach provides competitive performance and defeats all other methods with a large gap in overall performance. Also, the relatively faster running time makes it suitable for most of the real-time applications.
A new optimal control of obstacle avoidance for safer autonomous driving

The autonomous vehicle is one of the greatest challenges in modern vehicle design. This paper proposes a new method of control named FLOP, Feedback Local Optimality Principle, recently proposed by the authors. The method, starting from the Pontryagin's theory, introduces a new optimality principle that minimizes a sequence of individual functionals with the chance of a direct feedback control. The theory is applied to the steering and traction control of a two-wheeled vehicle, showing the ability of tracking a given trajectory, obstacles avoidance in a complex environment and measuring the benefits for driving safety.
When a dollar makes a BWT

The Burrows-Wheeler-Transform (BWT) is a reversible string transformation which plays a central role in text compression and is fundamental in many modern bioinformatics applications. The BWT is a permutation of the characters, which is in general better compressible and allows to answer several different query types more efficiently than the original string. It is easy to see that not every string is a BWT image, and exact characterizations of BWT images are known. We investigate a related combinatorial question. In many applications, a sentinel character $ is added to mark the end of the string, and thus the BWT of a string ending with $ contains exactly one $-character. Given a string w, we ask in which positions, if any, the $-character can be inserted to turn w into the BWT image of a word ending with $. We show that this depends only on the standard permutation of w and present a O(nlog⁡n)-time algorithm for identifying all such positions, improving on the naive quadratic time algorithm. We also give a combinatorial characterization of such positions and develop bounds on their number and value. This is an extended version of [Giuliani et al. ICTCS 2019].