Reviewerly/exHarmony huggingface dataset:
Social Media Content Analysis

Today's wealth and value are data. Data, used sensibly, are making wonders to make wise decisions for individuals, corporates, etc. The era of spending time with an individual to understand them better is gone. Individual's interests, requirements are identified easily by observing the activities an individual performs in social media. Social media, started as a tool for interaction, has grown as a platform to make and promote business. Social media content is unavoidable as the data that are going to be dealt with is huge in volume, variety, and velocity. The demand for using machine learning in analysing social media content is increasing at a faster pace in identifying influencers, demands of individuals. However, the real complexity lies in making the data from social media suitable for analysis. The type of data from social media content may be audio, video, image. The chapter attempts to give a comprehensive overview of the various pre-processing methods involved in dealing the social media content and the usage of right algorithms at the right time with suitable case examples.
A COMPARATIVE ACOUSTIC ANALYSIS OF DIPHTHONGS PRONUNCIATION BY THE STUDENTS OF FAZAIA SCHOOLS AT KARACHI PAKISTAN

In this study, diphthongs pronunciation of Fazaia Schools Students is acoustically compared with the diphthong pronunciation of Standard British English (SBE). The results of this study are important to linguists in the understanding, teaching, correction of language, and the development of pronunciation dictionaries. This is important in the context Pakistani English language speakers and particularly for the students and teachers of Fazaia schools system in Pakistan where many speakers use English as official language on regular basis. Further importance lies in the use of these acoustical models as a means of improving automatic speech recognition, realistic computer-speech generation and automatic accent recognition. The study will also aid in the study of acoustic phonetics. The experimental work was performed on a database that was the speech recording of diphthongs of Fazaia School’s students as shown in the table. The diphthongs sounds were recorded in single utterances of normal words having diphthong sound. Total 100 participants from four schools, 25 participants from each school were selected. The diphthongs pronunciation was then compared with standard British English pronunciation on the basis of Formant frequencies and time duration. This involved the calculation of the resonance peak tracks (formants) of the voiced speech, visual verification of the formant tracks (F1 & F2) and then producing a graphical representation of the locations of formant frequencies and time duration of the diphthongs.
WC2FEst-Net: Wavelet-Based Coarse-to-Fine Head Pose Estimation from a Single Image

This paper proposes a novel head pose estimation scheme that is based on image and wavelets input and conducts a coarse to fine regression. As wavelets provide low-level shape abstractions, we add them as extra channels to the input to help the neural network to make better estimation and converge. We design a coarse-to-fine regression framework that makes coarse-grained head pose classification followed by fine-grained angles estimation. This framework helps alleviate the influence of biased training sample distribution, and combines segment-wise mappings to form a better global fitting. Further, multiple streams are used in the neural network to extract a rich feature set for robust and accurate regression. Experiments show that the proposed method outperforms the state-of-the-art methods of the same type for the head pose estimation task.
Construction of the Parameter Estimates of Optical Flow Using of the Functionalization Method

The task of image motion analysis is over forty years old, but it has not lost its relevance. Optical flow competitions regarding the robust vision challenge are still commonly held. The main issues arise because images usually contain noise or differ in brightness, spectral range and orientation scale, etc. A scene image motion analysis is formulated via the task of constructing an optical flow created by a sequence of images. Optical flow is a velocity field with two components for each image point. It can be restored by matching a sequence of image frames. This paper considers approaches to solving the problem of determining parameters of optical flow based on the ideas of the quality criterion functionalization. For examining our method, we used the Middlebury optical flow benchmark.
A strategic roadmap for cybersecurity capability engineering across different environments

This is the preprint version of the work. For access to the published version, kindly contact the publisher's website: https://www.academic-bookshop.com/ourshop/prod_7115766-ICCWS-2020-Proceedings-of-the-15th-International-Conference-on-Cyber-Warfare-and-Security-PRINT-VERSION.html
Secure Count Query on Encrypted Heterogeneous Data

Cost-effective and efficient sequencing technologies have resulted in massive genomic data availability. To compute on a large-scale genomic dataset, it is often required to outsource the dataset to the cloud. To protect data confidentiality, data owners encrypt sensitive data before outsourcing. Outsourcing enhances data owners to eliminate the storage management problem. Since genome data is large in volume, secure execution of researchers query is challenging. In this paper, we propose a method to securely perform count query on datasets containing genotype, phenotype, and numeric data. Our method modifies the prefix-tree proposed by Hasan et al. [1] to incorporate numerical data. The proposed method guarantees data privacy, output privacy, and query privacy. We preserve the security through encryption and garbled circuits. For a query of 100 single-nucleotide polymorphism (SNPs) sequence, we achieve query execution time approximately 3.5 minutes in a database of 1500 records. To the best of our knowledge, this is the first proposed secure framework that addresses heterogeneous biomedical data including numeric attributes.
Visualization of Requirements to Software Architecture Component Mappings

Mappings in software architecture from components to requirements have the potential to reveal valuable information to both architects and stakeholders. This research creates a desktop application to explore the patterns and meanings of these mappings through visualization. Observations from the visualization tool provide a foundation to potentially improve the architecture process. This could enable architects to make better decisions in order to resiliently handle feature enhancements to the architecture, in addition to scalability and maintenance.
Hierarchical approach to artificial intelligence

Abstract There are three promising directions of artificial intelligence in the 21 st century: focused solutions, i.e. solutions used only in a specific field; artificial intelligence of the general direction – a neural computer capable of performing any intellectual action typical for a man or a society and development of artificial superintelligence with scientific creativity, wisdom and communication skills. This paper is aimed at considering a hierarchical approach to the application of artificial intelligence. It is assumed that the hierarchical approach will allow more accurately choosing the required type of solution when dealing with any automation task.
Findings Annihilator(s) via Fault Injection Analysis (FIA) on Boolean Function of LILI-128

LILI-128 keystream generator was designed by Dawson et al. (2000) and it was submitted to NESSIE project. This LILI-128 algorithm is a LFSR based synchronous stream cipher come with 128 bit key length. LILI-128 was designed to implement in hardware and software based and its offer large period and linear complexity. In this algorithm, the Boolean function given with coefficients, n is equal to ten (10) and its degree, d is equal to six (6). In conducting this attack, we aim to decrease the degree of the targeted Booleanequation by find it vulnerability with constructing low degree annihilator equation(s). We adopt the Fault Injection Analysis (FIA) methodology to achieve our objectives. In this study, we found the vulnerability via annihilator(s) through FIA (inject with value of one (1)) on Boolean function of LILI-128. With these injected Boolean functions, we proceed to utilize Hao’s method to find new annihilator(s). Then we obtained new annihilator(s) on Boolean function of LILI-128 stream cipher. As a result, these newly identified annihilators successfully reduce the complexity of the published Boolean function to guess the initial secret key. It likewise gives truly necessary data on the security of these chose stream cipher concerning Fault Injection Analysis.
Evaluation of Motivating and Requiring Factors for Milestones in IT Projects

Businesses are having to carry out Information technology (IT) projects of increasing complexity due to several reasons including the advances in information technology and the implementation challenges it brings. Effective project management is key to achieving project success and, in this space, lies the opportunity to use tools for supporting the process. One of such tools are milestones; A milestone is a scheduled event of zero-time duration that marks the completion of one or more important tasks. This research studies the factors influencing the creation of milestones in IT projects and it does so through literature review, focus group, and interpretive structural modelling. Twelve factors were considered for the interpretive structural model and the results show that several factors can influence the creation of milestones in IT projects. Most important it seems are factors arising from internal and external stakeholders. Using more evidence to support inferences about the factor influences and validating the model with wide participation from practitioners are suggested as future work.
Pose Manipulation with Identity Preservation

This paper describes a new model which generates images in novel poses e.g. by altering face expression and orientation, from just a few instances of a human subject. Unlike previous approaches which require large datasets of a specific person for training, our approach may start from a scarce set of images, even from a single image. To this end, we introduce Character Adaptive Identity Normalization GAN (CainGAN) which uses spatial characteristic features extracted by an embedder and combined across source images. The identity information is propagated throughout the network by applying conditional normalization. After extensive adversarial training, CainGAN receives figures of faces from a certain individual and produces new ones while preserving the person’s identity. Experimental results show that the quality of generated images scales with the size of the input set used during inference. Furthermore, quantitative measurements indicate that CainGAN performs better compared to other methods when training data is limited.
Satellite Signal Blind Detection Method Based on Time-Frequency Diagrams Semantic Segmentation

Signal blind detection is an important signal processing function of software radio, especially in satellite signal detection. However, threshold selection, high density and low energy signals are still substantially difficult issues to resolve. This paper proposes a signal blind detection method based on time-frequency diagrams semantic segmentation. First draw the time-frequency diagram of the signal. On this basis, the generalization ability of deep learning is used to transform the radio signal recognition problem into a semantic segmentation problem in the field of image recognition, which improves the intelligence and anti-interference ability of blind signal detection. Finally, combined with spectrum analysis, the result graph of semantic segmentation is post-processed to complete the blind detection of the signal. The experimental results show that the accuracy of the validation set composed of two communication satellite samples reaches 97%, the errors of the center frequency and bandwidth are 0.005 MHZ and 0.012 MHZ, which can well satisfy production requirements.
Multitask Learning Based on Constrained Hierarchical Attention Network for Multi-aspect Sentiment Classification

Aspect-level sentiment classification (ALSC) aims to distinguish the sentiment polarity of each given aspect in text. A user-generated review usually contains several aspects with different sentiment for each aspect, but most existing approaches only identify one aspect-specific sentiment polarity. Moreover, the prior works using attention mechanisms will introduce inherent noise and reduce the performance of the work. Therefore, we propose a model called Multitask Learning based on Constrained HiErarchical ATtention network (ML-CHEAT), a simple but effective method, which uses the regularization unit to limit the attention weight of each aspect. In addition, the ML-CHEAT uses the hierarchical attention network to learn the potential relationship between aspect features and sentiment features. Furthermore, we extend our approach to multitask learning to optimize the parameters update in the backpropagation and improve the performance of the model. Experimental results on SemEval competition datasets demonstrate the effectiveness and reliability of our approach.
CFCSS : Based on CF Network Convolutional Seq2Seq Model for Abstractive Summarization

Aiming to provide automatic document summarization model with channel dependency and global information to capture deeply text architecture and semantic content, this paper puts forward a method based on CF Network convolutional Seq2Seq model for abstractive summarization(CFCSS). The input of this method is combined with the position vector so that the model architecture can obtain the position information. At the same time, the residual network block and multi-step attention mechanism are used to model key information. In addition, this paper uses gated linear units(GLU) for nonlinear calculation and Copy mechanism to solve rare words problems. Finally, the validity of the model is verified on CNN/Daily Mail corpus, and the results show that the proposed model outperforms than the state-of-the-art methods with consistent substitution and significant statistical significance.
A Secure Neural Network Prediction Model with Multiple Data Providers

With the rapid development of neural network theory, the issue of privacy has attracted much attention, especially for the prediction or classification of some sensitive information, a neural network model that can protect privacy is needed. On the basis of training the model with plaintext data, this article aims to replace the existing model through multiple key homomorphic encryption algorithm combined with bootstrapping and relinearization technology. With the help of real number coding, decoding of the resulting polynomial variant and the approximation of the activation function homomorphic filtering method we implement the prediction of neural network in encryption mode. In our mode, single input or multiple input data can be aggregated and used to complete the data prediction under the premise of the Sematic Secure. Experimental results show that the prediction accuracy of the neural network after encryption comes close to that before encryption, and during the whole process, the server cannot get any real information about the data providers.
Rwanda as a Knowledge Society

Generating and developing knowledge societies is a key element for sustainable development as defined in the 2030 Agenda for Sustainable Development Goals adopted by the United Nations in 2015. Based on a limited natural resource base, Rwanda chose to take an approach to development that differs from that of its neighbours by making ICTs the cornerstone of its development. With this focus, government of Rwanda (GoR) took a Pro-ICT led public policy that has led to several public reforms such as but not limited to liberalization of the telecom sector, enactment of laws to govern electronic messages, signatures, transactions, data protection, cyber-security and ICT usage, development of relevant infrastructure and establishment of key institutions such as the Rwanda Utilities and Regulatory Agency (RURA) and Rwanda Information Society Authority (RISA). These reforms have in turn led to a fast-growing ICT sector in Rwanda compared to that of the neighbours. To-date, Rwanda is one of the fastest growing African countries in ICT. In 2015, Rwanda emerged as the third best ICT country in Sub-Saharan Africa behind South Africa and Seychelles. In 2016, it moved one position up and emerged 2nd behind Seychelles. The fast-growing ICT sector has stimulated entrepreneurial creativity and growth across the economy. This chapter examines the best practices that Rwanda has applied in her journey to a knowledge society that could possibly help other countries in the region pursuing the same objective. The chapter also briefly reviews challenges and gaps in Rwanda's journey to a knowledge society and suggests recommendations for further improvement.
Deploying authentication in the wild: towards greater ecological validity in security usability studies

Abstract Pico is a token-based login method that claims to be simultaneously more usable and more secure than passwords. It does not ask users to remember any secrets, nor to type one-time passwords. We evaluate Pico’s claim with two deployments and user studies, one on a web-based service and another within an organization. Our main aim is to collect actionable intelligence on how to improve the usability and deployability of Pico. In our first study we team up with an established website, Gyazo, to offer this alternative login mechanism to users intent on performing a real task of image sharing. From the lessons of this first study, we retarget Pico’s focus from replacing web passwords to replacing desktop login passwords; and thus in our second study we engage with a government organization, Innovate UK, to offer employees the ability to lock and unlock their computer automatically based on proximity. We focus particularly on the ecological validity of the trials and we thereby gain valuable insights into the viability of Pico, not only through the actual responses from the participants but also through the many practical challenges we had to face and overcome. Reflecting on the bigger picture, from our experience we believe the security usability community would greatly benefit from pushing towards greater ecological validity in published work, despite the considerable difficulties and costs involved.
Transfer learning for wearable computers

Wearable sensors are taking a bold stance in becoming the principal component of monitoring systems with wide applications in health monitoring, assisted living, sport studies, entertainment, and diet monitoring. Various wearable sensors with different sensing capabilities including smartwatches, smartphones, wrist-band sensors, sports shoes, and sensors embedded in clothing have been used for the aforementioned applications. However, in the presence of the user's diverse preference and requirement of various environments, changes in the configuration and type of sensors are highly possible. For example, a user who has been using a smartphone for a while may acquire a new smartwatch. Besides changes in the sensor modalities and configurations, the changes in the user characteristics are highly possible. A system trained on the data of specific users should be able to work for a new user with different characteristics such as different body mass index, skin tone, physiological attributes, and even sensor placement preferences. As a result of such changes in the environment, the machine learning and signal processing components should be updated or retrained. Otherwise, the performance of the models designed on the old training data will be significantly degraded when those models are used with the data collected under a new configuration. The main challenge with retraining the underlying signal processing and machine learning models is that it requires collecting a sufficiently large amount of labeled training data. The process of collecting such data is very challenging and overwhelming if the users are asked to provide them. Therefore, it is of paramount interest to transfer the knowledge from an old domain to a new domain, with minimum effort required by the users. In dynamic environments, where the configuration of the wearable systems can constantly change over time, the notion of transfer learning for these devices becomes tremendously important.
An Information Model of Image Segmentation Algorithm Based on Redundancy Minimization

This paper is devoted to the study of the information-theoretical approach to the problem of image segmentation quality. We consider a system that includes a segmentation algorithm with a parameter on which the number of segments depends, and a procedure for selecting parameter value that provides the segmentation quality measure with minimum. As a quality measure, we use an information redundancy index. To study the properties of the system, a new simplified mathematical model is proposed. It is shown that for the proposed model, the redundancy measure has a minimum. The validity of the model is confirmed by a computational experiment. An experiment conducted on images from the Berkeley Segmentation Dataset showed that a segmented image corresponding to a minimum of the redundancy measure gives the highest information similarity to the ground truth images available in the BSDS500 database.
Multicloud API Binding Generation from Documentation

We present industry experience from implementing retargetable cloud API binding generator. The analysis is implemented in Haskell, using type classes, types a la carte, and code generation monad. It also targets Haskell, and allows us to bind cloud APIs on short notice, and unprecedented scale.
Self‐supervised learning for outlier detection

The identification of outliers is mainly based on unannotated data and therefore constitutes an unsupervised problem. The lack of a label leads to numerous challenges that do not occur or only occur to a lesser extent when using annotated data and supervised methods. In this paper, we focus on two of these challenges: the selection of hyperparameters and the selection of informative features. To this end, we propose a method to transform the unsupervised problem of outlier detection into a supervised problem. Benchmarking our approach against common outlier detection methods shows clear advantages of our method when many irrelevant features are present. Furthermore, the proposed approach also scores very well in the selection of hyperparameters, i.e., compared to methods with randomly selected hyperparameters.
Querying the Semantic Web via Rules

The problem of querying RDF data is a central issue for the development of the Semantic Web. The query language SPARQL has become the standard language for querying RDF since its W3C standardization in 2008. However, the 2008 version of this language missed some important functionalities: reasoning capabilities to deal with RDFS and OWL vocabularies, navigational capabilities to exploit the graph structure of RDF data, and a general form of recursion much needed to express some natural queries. To overcome those limitations, a new version of SPARQL, called SPARQL 1.1, was released in 2013, which includes entailment regimes for RDFS and OWL vocabularies, and a mechanism to express navigation patterns through regular expressions. Nevertheless, there are useful navigation patterns that cannot be expressed in SPARQL 1.1, and the language lacks a general mechanism to express recursive queries. This chapter is a gentle introduction to a tractable rule-based query language, in fact, an extension of Datalog with value invention, stratified negation, and falsum, that is powerful enough to define SPARQL queries enhanced with the desired functionalities focussing on a core fragment of the OWL 2 QL profile of OWL 2.