Reviewerly/exHarmony huggingface dataset:
CAG: Compliance Adherence and Governance in Software Delivery Using Blockchain

The software development life cycle (SDLC) starts with business and functional specifications signed with a client. In addition to this, the specifications also capture policy / procedure / contractual / regulatory / legislation / standard compliances with respect to a given client industry. The SDLC must adhere to service level agreements (SLAs) while being compliant to development activities, processes, tools, frameworks, and reuse of open-source software components. In today's world, global software development happens across geographically distributed (autonomous) teams consuming extraordinary amounts of open source components drawn from a variety of disparate sources. Although this is helping organizations deal with technical and economic challenges, it is also increasing unintended risks, e.g., use of a non-complaint license software might lead to copyright issues and litigations, use of a library with vulnerabilities pose security risks etc. Mitigation of such risks and remedial measures is a challenge due to lack of visibility and transparency of activities across these distributed teams as they mostly operate in silos. We believe a unified model that non-invasively monitors and analyzes the activities of distributed teams will help a long way in building software that adhere to various compliances. In this paper, we propose a decentralized CAG - Compliance Adherence and Governance framework using blockchain technologies. Our framework (i) enables the capturing of required data points based on compliance specifications, (ii) analyzes the events for non-conformant behavior through smart contracts, (iii) provides real-time alerts, and (iv) records and maintains an immutable audit trail of various activities.
Spatio-Temporal Measures Of Naturalness

Today, a wide variety of casual users of diverse videographic skills and styles capture a large portion of all videos, often equipped with uncertain hands and operating under difficult lighting conditions. These videos are taken with many types of camera devices having different characteristics, resulting in a wide range and diversity of video qualities. These are the kinds of videos shared on YouTube, Snapchat, and Face-book. Being able to predict the quality of these videos is an important goal for a variety of invested practitioners, including camera designers, cloud engineers, and users who could be directed to recapture videos of poor quality. In nearly every instance, a high quality reference video is not available, hence blind video quality predictors are of the greatest interest. Towards advancing this area, we have studied the spatiotemporal statistic of a wide variety of natural videos, constructed new directional temporal statistical models of videos, and studied whether measures of directional spatio-temporal naturalness can be developed that are predictive of quality.
Chaos based 3D color image encryption

Nowadays, multimedia encryption represents one of the most important blocks inside communication systems. More encryption techniques are introduced every day; however, specific algorithms are suitable for optical encryption due to the optical nature of an image. There are main problems that troubled these techniques; the robustness against severe attacks and handling 3D color images. In this paper, a novel technique for optical encryption will be introduced. Depending on the proposed confusion and diffusion processes, the chaotic keys can efficiently encrypt the color images. To provide better shuffling effect plus increasing the unfamiliarity, image transformation using Discrete Cosine Transform (DCT) also are embedding through the proposed confusion stage with higher robustness against various types of attacks. By intensive comparative study versus traditional and related techniques, it has been found that the proposed system achieved better performance than conventional ones especially in the case of severe composite attacks in a fast way.
Sharing Emotion by Displaying a Partner Near the Gaze Point in a Telepresence System

In this paper, we explore the effect of showing a remote partner close to user gaze point in a teleconferencing system. We implemented a gaze following function in a teleconferencing system and investigate if this improves the user's feeling of emotional interdependence. We developed a prototype system that shows a remote partner close to the user's current gaze point and conducted a user study comparing it to a condition displaying the partner fixed in the corner of a screen. Our results showed that showing a partner close to their gaze point helped users feel a higher level of emotional interdependence. In addition, we compared the effect of our method between small and big displays, but there was no significant difference in the users' feeling of emotional interdependence even though the big display was preferred.
An efficient and secure data storage in cloud computing using modified RSA public key cryptosystem

&lt;span lang="EN-US"&gt;Cloud Computing is the ability to improve the utility or train new human resources without investing in new infrastructure, or add capabilities to existence without the latest software licensing. It expanded the capabilities of Information Technology (IT). From the past few years, cloud computing has developed from a good business concept in the best rising sectors of the IT industry. But more information on individuals and companies was put in the cloud, and concerns began to think about how secure the cloud environment was. Despite cloud surrounding structures, enterprise users still do not want to expand their business in the cloud. Security reduces the growth of cloud computing and continues to spread the market with complexity with data privacy and data protection. The security of cloud computing has constantly been an significant aspect of improved quality of service from cloud service providers. Data storage in the cloud has a problem related to data security. However, cloud computing construct many new security challenges which have not been well examine. In order to ensure that the user's data in the cloud is secure, we have proposed an effective mechanism with a distinctive feature of data integrity and privacy. This paper focusing on problems relating to the cloud data storage techniques and security in virtual environment. We recommend a method for providing data storage and security in cloud using public key Cryptosystem, which uses the concept of the modified RSA algorithm to provide better security for the data stored in the cloud. &lt;/span&gt;
DeepWSC: A Novel Framework with Deep Neural Network for Web Service Clustering

Correlative approaches have attempted to cluster web services based on either the explicit information contained in service descriptions or functionality semantic features extracted by probabilistic topic models. However, the implicit contextual information of service descriptions is ignored and has yet to be properly explored and leveraged. To this end, we propose a novel framework with deep neural network, called DeepWSC, which combines the advantages of recurrent neural network and convolutional neural network to cluster web services through automatic feature extraction. The experimental results demonstrate that DeepWSC outperforms state-of-the-art approaches for web service clustering in terms of multiple evaluation metrics.
Incorporating Word and Subword Units in Unsupervised Machine Translation Using Language Model Rescoring

This paper describes CAiRE's submission to the unsupervised machine translation track of the WMT'19 news shared task from German to Czech.We leverage a phrase-based statistical machine translation (PBSMT) model and a pre-trained language model to combine word-level neural machine translation (NMT) and subword-level NMT models without using any parallel data.We propose to solve the morphological richness problem of languages by training byte-pair encoding (BPE) embeddings for German and Czech separately, and they are aligned using MUSE (Conneau et al., 2018).To ensure the fluency and consistency of translations, a rescoring mechanism is proposed that reuses the pre-trained language model to select the translation candidates generated through beam search.Moreover, a series of pre-processing and post-processing approaches are applied to improve the quality of final translations.
Twitter sentiment analysis using adaptive neuro-fuzzy inference system with genetic algorithm

In recent decades, it is very challenging for the researchers to identify the users’ sentiments from the twitter data, due to unstructured nature, misspells, abbreviations, limited size, and slangs. In order to address these problems, a new system is developed in this research study for improving the performance of twitter sentiment analysis. In this research, the proposed system comprises of three major phases; data collection, pre-processing and classification of sentiments from pre-processed tweets. Initially, the twitter sentiment analysis was carried-out by using twitter-sanders-apple 2 dataset. Generally, the raw collected tweets contain more noises by means of positive emoji’s, URLs, stop words, negative emoji’s, which were essentially eliminated to achieve better classification. Finally, a hybrid classifier (Adaptive Neuro-Fuzzy Inference System (ANFIS)-Genetic Algorithm (GA)) was used to classify the twitter sentiment classes as either positive class or negative class. The ANFIS classifier was the fuzzy based ontology that was designed by actualizing and the GA optimizes the fuzzy principles in the ANFIS classifier. The experimental consequence showed that the proposed methodology enhanced the accuracy in sentiment analysis up to 5.5-6% related to the existing methodology.
Multi-Resolution Generative Adversarial Networks for Tiny-Scale Pedestrian Detection

Although pedestrian detection techniques have achieved great success recently, the accurate tiny-scale pedestrian detection remains challenging with low resolution. The existing CNN based detection methods are limited in terms of in-line mechanism when encountering tiny-scale pedestrian detection, because the embedded convolution and pooling operations tend to weaken the features' representation of tiny scales objects. To ameliorate, we propose a newly-designed Multi-Resolution Generative Adversarial Network (MRGAN) to simultaneously conduct multi-resolution pedestrian detection by directly generating a high-resolution pedestrian image from low-resolution image. The key idea is to explore the intrinsic relations between high-resolution pedestrians and low-resolution pedestrians to enhance the representation of low-resolution pedestrians. The classification loss will be used to conduct the training process of super-resolution generative adversarial network, so that the generated super-resolution images can benefit the tiny-scale pedestrian detection. Besides, we also define a segmentation-based perceptual loss by incorporating a pre-trained image segmentation sub-network to refine the detail information. Extensive experiments and comprehensive evaluations on public challenging benchmarks confirm that our method outperforms the state-of-the-art methods.
Data Protection as a Service in the Multi-Cloud Environment

This paper introduces a framework for Data Protection as a Service (DPaaS) to cloud computing users. Compared to the existing Data Encryption as a Service (DEaaS) such as those provided by Amazon and Google, our DPaaS framework provides more flexibility, control and visibility for protecting data in the cloud. In addition to supporting the basic data encryption capability as DEaaS does, this DPaaS framework allows data owners to define fine-grained access control policies to protect their data. Data protected by an access control policy are automatically encrypted and access is granted to user/applications according with the policy. In general, the DPaaS enables the separation of concerns between security and data management, in addition to defining a full cycle of data security automation from encryption to decryption. Our proof-of-concept prototype of the DPaaS works with hybrid multi-cloud environments including private clouds and virtual data-centers using OpenStack, CloudStack and VMWare as well as public clouds being the BT Cloud Compute platform and Amazon (AWS). Experiments on the prototype have proved the efficiency of the framework.
Regional mineral mapping of island arc terranes in southeastern Mongolia using multi-spectral remote sensing data

Over the last few decades, many porphyry Cu deposits have been discovered in southeastern (SE) Mongolia. In particular, the Gurvansayhan and Mandalovoo island arc terranes, have high potential for ore deposits. Regional mineral mapping of these terranes using data from the Landsat Enhanced Thematic Mapper Plus (ETM+) and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) have provided new geological information on the metallogenic province, revealing potential mineral deposits for future explorations. Iron oxides/hydroxides with high ETM+ band 1/3 ratio values occur extensively in Quaternary deposits located at the boundary between the Gurvansayhan and Mandalovoo island arcs terranes and basins. The high average band ratios (>1.35) registered for the porphyry Cu and Au deposits and their occurrences are associated with iron oxidation which originates from the erosion and weathering of ore zones. Regional mapping of the island arc terranes using the ASTER shortwave infrared (SWIR) logical operators showed circular or semicircular alteration patterns which usually cluster along faults. The mineral assemblages mapped by the ASTER SWIR analysis in five major porphyry Cu districts can be divided into two types. The first type includes argillic, phyllic, and propylitic mineral assemblages, as exemplified by the Shuteen, Ikh Shankhai, and Oyu Tolgoi deposits. The second type of assemblage includes phyllic and propylitic mineral assemblages, as exemplified by the Kharmagtai and Tsagaan Suvarga deposits. Within the deposits of the first type, quartz was also mapped using the ASTER thermal infrared (TIR) mineral index. The difference between the two types may be related to the presence of a lithocap. The results of a field survey including spectral reflectance measurements in the Ikh Shankhai porphyry Cu district, showed a good correlation with the image analysis results.
Novel concepts in intuitionistic fuzzy graphs with application

Theoretical concepts of graphs are highly utilized by computer science applications. Especially in research areas of computer science such as data mining, image segmentation, clustering, image capturing and networking. The intuitionistic fuzzy graphs are more flexible and compatible than fuzzy grap hs due to the fact that they have many applications in networks. The main purpose of this paper is to introduce some connectivity concepts in the intuitionistic fuzzy graphs. Analogous to fuzzy cutvertices and fuzzy bridges in fuzzy graphs, intuitionistic fuzzy cutvertices and intuitionistic fuzzy bridges are introduced and characterized. We also proposed the concept of gain and loss for paths and pairs of vertices. Finally, we give an application of intuitionistic fuzzy digraphs.
New Stereo High Dynamic Range Imaging Method Using Generative Adversarial Networks

Stereo high dynamic range (HDR) image/video can be generated by using a pair of stereo cameras with different exposure parameters. This paper proposes a new stereo HDR imaging method using generative adversarial networks (GAN) with a low dynamic range (LDR) stereo imaging system. It is assumed here that the left-view (LV) image is under-exposed and the right-view (RV) image is overexposed. First, a view exposure transfer GAN (VET-GAN) is constructed to transfer exposure information of the RV image to the LV image to generate the multi-exposure LV images, and then an HDR fusion GAN is constructed to fuse the generated multi-exposure LV images into an LV HDR image. Similarly, an RV HDR image can be generated using the same way to form a stereo HDR image pair. The experimental results show that the proposed method can obtain stereo HDR images with high visual quality and effectively avoid the ghost artifacts caused by parallax.
Flash in action

The current explosion in spatial data raises the need for efficient spatial analysis tools to extract useful information from such data. However, existing tools are neither generic nor scalable when dealing with big spatial data. This demo presents Flash ; a framework for generic and scalable spatial data analysis, with a special focus on spatial probabilistic graphical modelling (SPGM). Flash exploits Markov Logic Networks (MLN) to express SPGM as a set of declarative logical rules. In addition, it provides spatial variations of the scalable RDBMS-based learning and inference techniques of MLN to efficiently perform SPGM predictions. To show Flash effectiveness, we demonstrate three applications that use Flash in their SPGM: (1) Bird monitoring, (2) Safety analysis, and (3) Land use change tracking.
Learning Choreographic Primitives Through A Bayesian Optimized Bi-Directional LSTM Model

Performing arts is an essential aspect of Intangible Cultural Heritage (ICH), requiring tools for its modelling. In this paper, we introduce a Bayesian Optimized Bi-directional LSTM model, called BOBi-LSTM, that automatically estimates dancers' poses through 3D skeleton data processing. Bi-directionality models non-causal relationships occurred in a dance performance, in the sense that future dancer's steps depend on previous/current steps. Additionally, long-range dependence correlates choreographic primitives on a long time (memory) window. To model the aforementioned principles, we modify the conventional LSTM networks under a Bayesian Optimized framework in order to define the best network structure. Experimental results and comparisons for different types of dances are given to showcase how the proposed BOBi-LSTM out-performs traditional classifiers.
Person Re-Identification With Joint Verification and Identification of Identity-Attribute Labels

One of the major challenges in person Re-Identification (ReID) is the inconsistent visual appearance of a person.Current works on visual feature and distance metric learning have achieved significant achievements, but still suffer from the limited robustness to pose variations, viewpoint changes, etc.This makes person ReID among multiple cameras still challenging.This work is motivated to learn mid-level human attributes which are robust to visual appearance variations and could be used as efficient features for person matching.We propose a supervised multi-task learning framework which considers attribute label information with joint identification-verification network to simultaneously learn an attribute-semantic and identity-discriminative feature representation.Specifically, this framework adopts the part-based deep neural network and learn three different tasks simultaneously: person identification, person verifications and attribute identification, so as to discover and capture concurrently complementary discriminative information about a person image from global and local image features and mid-level attribute features in one deep neural network.With the multi-task learning architecture, we obtain a discriminative model that reaches a synergy in distinguishing different person images, as manifested with the competitive accuracy on three person ReID datasets: Market1501, DukeMTMC-reID and VIPeR.
A Framework for Adaptive User Interface Generation based on User Behavioural Patterns

The concept of adaptivity is crucial in enterprise software systems with a large user base. Adaptive user interfaces (AUI) is an emerging research area that enables customized user experience based on user activities. Most of the existing studies that are in the conceptual level do not provide production level adaptivity for mainstream user interaction. This paper presents a generic software platform for automatic AUI generation by analyzing user behaviour patterns and customizing web user interfaces using machine learning. AdaBoost classifier showed 100% accuracy for large UI components and user scenarios (n=800). The AUI generator supports the configuration and automation of capturing user behaviour, data storage, processing, querying analysis results and dynamic rendering of the user interface. AUI platform had SUS value of 80.75. The SUS scores for the UIs without AUI was 57.3 and with AUI scored 64.35 on average. The proposed AUI platform provides production level UI design means to meet dynamic adaptability on user traits.
On the Homogenization of Heterogeneous Inertial-Based Databases for Human Activity Recognition

In the last years supervised machine learning techniques are largely employed for automatic Human Activity Recognition (HAR) using inertial sensors, such as accelerometer and gyroscope. HAR has many applications in several domains such as, for example, healthcare, sport, and entertainment. Machine learning scientists made available to the community a plenty of labeled databases for benchmarking that, unfortunately, are not consistent, both syntactically (e.g., different sampling frequency) and semantically (e.g., labels with different meanings). Commonly, due to this inconsistency, scientists evaluate their progress on individual databases separately, which corresponds to training and testing using the same database. Coherent merging of existing databases would enable: 1) evaluation of generalization capabilities of methods across databases; 2) use of deep learning techniques that, unlike traditional ones, require much more labeled data for the training process. Moreover, the growth in the daily use of wearable devices will produce a big amount of inertial data which, if not correctly labeled, cannot be efficiently exploited for the study of automatic HAR. In this paper we propose a semi-automatic procedure to coherently merge existing databases based on signal and word similarity. Preliminary experiments demonstrates the effectiveness of the proposed procedure.
Training Accurate Binary Neural Networks from Scratch

Binary neural networks are a promising approach to execute convolutional neural networks on devices with low computational power. Previous work on this subject often quantizes pretrained full-precision models and uses complex training strategies. In our work, we focus on increasing the performance of binary neural networks by training from scratch with a simple training strategy. In our experiments we show that we are able to achieve state-of-the-art results on standard benchmark datasets. Further, we analyze how full-precision network structures can be adapted for efficient binary networks and adopt a network architecture based on a DenseNet for binary networks, which lets us improve the state-of-the-art even further. Our source code can be found online: https://github.com/hpi-xnor/BMXNet-v2.
Detection of Alphabets for Machine Translation of Sign Language Using Deep Neural Net

Recognition of sign language by hand gestures is one of the classical problems in computer vision. Conventional tools used for sign language translation involves application of linear classifiers such as kNN and Support Vector Machine (SVM)to perform the classification of hand gestures images. However, these methods require sophisticated features for classification. To automate the feature extraction and feature selection procedure, a Deep Neural Network (DNN)based machine translation is proposed in this work. Here, the images of English Sign Language (ESL)are identified using Deep Learning (DL)approach. A custom DNN with three convolution layers and three Max-Pooling layers are designed for this purpose. A top validation accuracy of 82% was obtained for the DNN structure proposed in this paper.