Reviewerly/exHarmony huggingface dataset:
A simple baseline for evaluating Expression Transfer and Anonymisation in Video Transfer

Video-to-video synthesis methods provide increasingly accessible solutions for training models on privacy-sensitive and limited-size datasets frequently encountered in domains such as affect analysis. However, there are no existing baselines that explicitly measure the extent of reliable expression transfer or privacy preservation in the generated data. In this paper, we evaluate a general-purpose video transfer method, vid2vid, on these two key tasks: expression transfer and anonymisation of identities, as well as its suitability for training affect prediction models. We provide results that form a strong baseline for future comparisons, and further motivate the need for purpose-built methods for conducting expression-preserving video transfer. Our results indicate that a significant limitation of vid2vid's expression transfer arises from conditioning on facial landmarks and optical flow, which do not carry sufficient information to preserve facial expressions. Finally, we demonstrate that vid2vid can adequately anonymise videos in some cases, though not consistently, and that the anonymisation can be improved by applying random perturbations to input landmarks, at the cost of reduced expression transfer.
Deep Convolutional Neural Networks for Scene Understanding: A Study of Semantic Segmentation Models

Semantic Image Segmentation for autonomous cars is gaining a lot of popularity in recent times with researchers trying to improvise the model as much as possible. In this paper, we have compared three models, UNet, VGG16_FCN and ResNet50_FCN, which are used for semantic image segmentation. We have trained and tested these models on the cityscape dataset where the models classify each pixel of the image into various classes. Results show that the class-wise accuracy of ResNet50_FCN is more than the other two models. We have also plotted IoU graphs for each model and we found out that ResNet50_FCN and VGG16_FCN have much better scores than the UNet model. Based on these results, we have shown that ResNet50_FCN outperforms the other two models for the case of semantic segmentation for scene understanding.
Collective Effects in Hybrid Quantum Systems

We have reached the level of technological development where it is now possible to engineer composite quantum systems with properties one would like. These hybrid quantum systems have been extensively investigated both experimentally and theoretically with an archetypical example being ensembles of atoms coupled to one form of electromagnetic field. Typically these composite systems have been operated in the low excitation regime where the overall system is effectively linear. However, it is straightforward to move to the nonlinear regime where much more complex phenomena can be observed. In this chapter we explore such phenomena in terms of a collective spin ensembles coupled to a microwave resonator. With one spin ensemble we show how the phenomena of amplitude bistability and superradiance naturally arise. Further by increasing the number of ensembles coupled to our resonator we show collective quantum effects including negative temperature relaxation. We believe our results provide a foundation for interesting future quantum technologies.
HOW the Innovation Performance in GCC HEIs is Affected by the Knowledge Management in the Era of Knowledge Economy

In the knowledge economy era, Higher Education Institutions (HEIs) plays a vital role in the nation’s sustainable development through innovation. Knowledge Management (KM) is one of most critical determinants for the overall performance of the Higher Education Institutions (HEIs) and specially to their innovation performance. The purpose of this study is to analyse the impact of different processes of KM on the Innovation Performance (IP) of the GCC HEIs. It was found that the highest implemented process of KM in GCC HEIs is Knowledge Sharing (KS) and the reward performance in GCC HEIs is the highest among all other pillars of the IP. Factors which significantly impact the IPs are: exchange of ideas and knowledge; creating new knowledge from existing knowledge; the respond to employees’ ideas; having mechanisms in place to capture knowledge from stakeholders were significant; encouragement for participation in informal discussions to share knowledge; use of latest files sharing systems to share knowledge efficiently; implementation of mechanisms that match sources of knowledge to problems and employees’ encouragement to apply their knowledge to solve problems.
Administrative Health Data Representation for Mortality and High Utilization Prediction

Administrative data, including medical claims, are frequently used to train machine learning-based models used for predicting patient outcomes. Despite many efforts in using administrative data, little systematic work has been done in understanding how the codes in such data should be represented before model construction. Traditionally, the presence/absence of codes representing diagnoses or procedures (Binary representation) over a fixed period (typically one year) is used. More recently, some studies included temporal information into data representation, such as counting, calculating time from diagnosis, and using multiple time windows. This paper investigates different methods of administrative data representation and more specifically diagnoses extracted from claims data before applying machine learning algorithms. Then the study compares two data representations (Binary and Temporal Min-Max) using two classification problems: one-year mortality prediction and high utilization of medical services prediction. The results indicated that Temporal Min-Max representation outperforms Binary representation in both predictive models. It was shown that the optimal way of representing the data is problem-dependent, thus optimization of representation parameters is required as part of the modeling.
Graph Representation based on Random Walk with Restart and Node Attributes

We consider applying stochastic process to graph representation learning. Graph is a structure composed of nodes and edges, but we can not operate graph directly, because it is meaningless to operate graph nodes directly. Graph representation learning is to map the information of graph to a low dimension vector space, so as to operate. We propose random walk with restart to sample the sequences of the graph. In the process of representation learning, we add the attributes information of nodes to enrich the embedding of the graph. Then we compare the method in the classification task and find that our proposed method is better than the previous methods.
Optimize Next State Prediction in Safe RL for 5G Ecosystem

Traditional supervised learning techniques need fairly a big number of data and computational power to train models, which is unavailable in the mobile ecosystem. However, in many situations, big data collection is completely difficult, and one may resort to Reinforcement Learning (RL) which enables the agent to learn from scratch by interacting with the environment. Since the agent learns from scratch by taking random actions, there is a possibility that an environment may transit to an unsafe state. In literature, we can find some methods which predict the transition to an unsafe state in advance and ensure that the specific action will not be executed. However, the underlying process is stochastic, and normal prediction methods will give poor accuracy. In this paper, we propose a method to predict the possible next transition state assuming that the labels are noisy. Results show that the proposed method can predict the next state with higher accuracy when compared with existing methods by considering the underlying stochasticity. The value addition of the proposed method is that it will make agents converge faster compared to traditional RL methods since we are modifying the expected trajectories in advance, and it can save a good number of computational resources. Three case studies related to 5G mobile networks were considered to explore the advantage of the proposed method.
An Analysis of Relation Extraction within Sentences from Wet Lab Protocols

Wet lab protocols (WLPs) are sets of instructions written in domain-specific natural language for step-by-step biological experimental processes. There have been efforts to annotate WLPs for shallow semantic parsing to enable reproducible procedures, text mining, and automatic conversion into a machine-readable format. However, current methods have not fully exploited the relation extraction sub-task on the protocol corpus. Neural approaches have the potential to deal with the various noise and in-domain jargon in the texts. To explore the viability of neural methods for this task, we perform a thorough analysis of both graph and nongraph neural approaches. We find that both graph neural networks with generated parameters (GP-GNNs) and Context-Aware models show advantages in relation extraction and are well suited to our goal. Specifically, the GP-GNNs and Context-Aware models demonstrate similar performance on all three WLPs datasets when the full training set is used, both outperforming the previous best results significantly. This can be explained by the observation that considering multiple relations in a sentence enhances the predictive ability. In addition, our extensive experiments demonstrate that the Context-Aware approach in particular can achieve good results even with a limited amount of training data, providing new insights for low-resource scenarios.
Developing a Semantic Search Method for Retrieving Food Related Verses and Concepts from Holy Quran Using Ontology

The Qur’an is Allah (SWT)’s greatest miracle and is the holy book of Allah (SWT). Everything about the earth and the heavens is revealed in the holy Qur’an. This holy book is the source of all knowledge and information. As a Muslim, our duty is not only to recite the Qur’an, but also it is important to gain knowledge of the Qur’an to lead our life. Ontology is one of the best ways to retrieve Quranic knowledge in a technical way. So ontology technology is used in this proposed method. The modern age is the age of the internet. With the help of internet, many search engines are found that help to find information from the Qur’an. But most of the search engines or information retrieval systems are keyword based. Keyword based search can often lead to wrong and irrelevant results. To overcome this problem, semantic search is most useful in this case. The aim of research is to develop an ontological semantic based method to retrieve the food related verses and concepts from the holy Qur’an by using natural language query. In this work, the triplet extraction algorithm has been used for generating triple, protege OWL editor 4.3 version for creating food ontology and for query of SPARQL Apache Jena fuseki server is used. Quranic data have been collected from translated English version of the consecrated Qur’an by Sahih International and Quran.gov.bd.
Excitation Signal Design for Generating Optimal Training Data for Complex Dynamic Systems

The appropriate choice of excitation signal in system identification is an important but rarely considered part of the process that determines the success of many downstream activities. This paper presents a novel methodology for excitation signal design to create high accuracy multivariable nonlinear dynamic neuro-fuzzy models. Two different approaches to experimental design are investigated. In the first, a prescribed transient manoeuvre is used. In the second, informative potential is used to deconstruct the transient into a sequence of inputs designed to cover the same input space and reduce model development time. Star discrepancy is used to evaluate the resulting designs and is shown to provide a good proxy for excitation design quality. Results are presented showing the prediction accuracy of the model in terms of an application example, achieving a minimum < 2% cumulative error over a two minute transient. It is shown that the neuro-fuzzy models identified using data from the two different approaches have similar accuracy. However, the second approach based on informative potential leads to a more generalised model and reduces the development time by a factor of four. This is a significant result that shows the importance of choosing an appropriate excitation signal.
An improved pricing algorithm for infrastructure as a service cloud

Marketing in cloud systems enables users to trade and share resources. For the sales of services, client applications and service providers negotiate to make a service level agreement. Offering prices in the negotiation of services become one that is challenging. A federal cloud is an efficient approach of recent interest to better balance risk sharing between services provider and customer. This work presents a new algorithm to increase service provider revenue and reducing user costs simultaneously. The auction of remaining time spent on resources and interactions between federal clouds increases the profits of clouds, the number of successful requests, and reduces users' costs. The simulation results confirm the expectations of the proposed approach.
Learning Dynamic Background for Moving Object Detection

Moving Object Detection (MOD) aims at extracting foreground moving objects in videos from static cameras. While low-rank based approaches have achieved impressive success in the MOD task, their performance remains limited on dynamics background scenes. The main reason is that dynamic clutters, e.g., swaying leaves and rippers, are easy to mix up with moving objects in the decomposition model which simply classify the sparse noise as foregrounds. In order to improve the generalization ability of low-rank based moving object detectors, we suggest adding an explicit dynamic clutter component in the decomposition framework with realistic dynamic background modeling. Then the dynamic clutter can be learned through object-free video data due to their self-similarity across time and space. Thus, the moving objects can be naturally separated by a tensor-based decomposition model which formulates the static background by a unidirectional low-rank tensor, learns the dynamic clutter by a two-stream neural network, and constrains moving objects with spatiotemporal continuity. To further provide a more accurate object detection result, an objectness prior is embedded into our model in an attention manner. Extensive experimental results on the challenging datasets of dynamic background clearly demonstrate the superior performance of our model over the state-of-the-art in terms of quantitative metrics and visual quality.
2021: hackers value quality over quantity

Abstract Reported intrusions are down, the amount of personal data exposed is down, so it’s all good, right? Well yes and no… Patrick O’Connor MBCS CISSP CEH explores the good and bad of events in cyber security in 2021.
Empirical analysis of the residual information needed for image classification

We study the limits of classification models applied to an abstract representation of data.Previous research has dealt with the classification of images generated from data, which are abstract pictures that greatly differ from standard real-world images. This approach might be robust to the curse of dimensionality and we wish to apply it to multi-dimensional data. Current image classification methods have been proven to be effective when applied to real-world images. We want to know how these same methods perform on abstract images, i.e. if they can work and if so, what are the necessary characteristics that the images must have for an optimized and efficient learning?We propose different empirical experiments in order to define the residual information rate required for image classification. This implies knowing the amount of information required for a classification model to perform well on abstract images.
An Improved Transfer Learning-Based Model for Malaria Detection using Blood Smear of Microscopic Cell Images

Because of insufficient medical specialists in some parts of the African and Asian continents, malaria patients' mortality rates have increased over the years. Since the people of regions generally suffer from malaria diseases, computer-aided detection (CAD) technology is required to decrease the number of casualties and reduce the waiting time for consulting by a Malaria specialist. This study shows the potential of transfer learning, a method of Deep Learning (DL) to classify the smeared blood of microscopic malaria cell images to determine whether it is parasitized or uninfected. This classification of malaria cell images will enhance the workflow of health practitioners at the frontline, especially microscopists, and provides them with a valuable alternative for malaria detection based on microscopic cell images. Although many technological advancements and evaluation techniques for identifying the infection exist, a microscopist at regions with limited resources faces challenges in improving diagnostic accuracy. We compared and evaluated a type of pre-trained CNN models, such as ResNet-50 and our appended Resnet-50+KNN. The experiment shows that our new model has the excellent capability and can perform better on malarial microscopic cell image classification with a higher accuracy rate of 98%.
Design and Implementation of Personalized Portal Web site Based on Artificial Intelligence

The great changes brought about by the third industrial revolution have always affected us. The development of information technology has greatly promoted people’s lives and production. Therefore, the scientific and technological revolution has always received people’s attention and attention. Artificial intelligence has begun to enter people’s production and life, and gradually occupy a place in the technological revolution. With the increase in system requirements and content, not only is it difficult to ensure the stability of the portal system, but also the home page is difficult to meet the needs of different users. The design idea mainly comes from the personalized portal system. In order to meet the collaborative development factors of multiple development teams, the user's personalized needs improve the efficiency of software development, and the purpose of this article is to design and implement a personalized portal architecture design based on artificial intelligence. This article uses the OSGI development platform for design research. The practical application research results of this paper show that the architecture design has reached the project’s expected goals and can meet the portal construction requirements of most enterprises and institutions.
ACM SIGEVO best dissertation award 2021

The SIGEVO Best Dissertation Award was created in 2019 to recognize excellent thesis research by doctoral candidates in the field of evolutionary computing. Doctoral dissertation awards are also given by other Special Interest Groups of ACM, such as SIGCOMM, SIGKDD, SIGARCH and others. The SIGEVO Best Dissertation Award is given annually to a maximum of 1 winner and a maximum of 2 honorable mentions. The award presentation takes place at the awards ceremony of GECCO. The award carries a monetary value of $1,000 contributed by SIGEVO to be awarded to the winner. The award winner and honorable mentions each receive a plaque.
Enterprise Financial Data Sharing Based on Information Fusion Cloud Computing Environment

At present, many companies have many problems such as high financial costs, low financial management capabilities, and redundant frameworks; at the same time, the SASAC requires that the enterprise’s financial strategy transfer from “profit-driven” to “value-driven”, finance separate from accounting to improve the operational efficiency of the company. Under this background, more and more enterprise respond to the call of the SASAC; in order to achieve the goals of corporate financial cost savings and financial management efficiency improved, we began to provide services through financial sharing. The research of information fusion theory involves many basic theories, which can be roughly divided into two large categories from the algorithmic point of view: probabilistic statistical method and artificial intelligence method. The main task of artificial intelligence is to realize the computer for some learning, thinking process, and wisdom formation of simulation, and an important goal of information integration is the human brain comprehensive processing ability simulation, so artificial intelligence method will have broad application prospects in the field of information fusion; the common methods have D-S evidence reasoning, fuzzy theory, neural network, genetic algorithm, rough set, and other information fusion methods. The purpose of this paper is to proceed from the internal financial situation of the enterprise, analyze data security issues in the operation of financial shared services, and find a breakthrough in solving problems. But, with constantly expanding of enterprise group financial sharing service scale, the urgent problem to be solved is how to ensure the financial sharing services provided by enterprises in the cloud computing environment. This paper combines financial sharing service theory and information security theory and provides reference for building financial sharing information security for similar enterprises. For some enterprise that have not established a financial shared service center yet, they can learn from the establishment of the financial sharing information security system in this paper and provide a reference for enterprise to avoid the same types of risks and problems. For enterprise that has established and has begun to practice a financial shared information security system, appropriate risk aversion measures combined with actual situation of the enterprise with four dimensions related to information security system optimization was formulated and described in this paper. In summary, in the background of cloud computing, financial sharing services have highly simplified operational applications, and data storage capabilities and computational analysis capabilities have been improved greatly. Not only can it improve the quality of accounting information but also provide technical support for the financial sharing service center of the enterprise group, perform financial functions better, and enhance decision support and strategic driving force, with dual practical significance and theoretical significance.
3D Modelling using Sequential and Convolutional Generative Adversarial Networks

We propose a novel solution for solving a specific problem of generating realistic and varied 3D models for target objects. Existing processes for 3D modelling involve human inspection of CAD models and borrowing parts from them. There have been inspiring advances made by 3D GANs that generate highly varied object shapes but do not adequately attend to objects that are symmetrical or have limited CAD models available as a training data-set. The benefits of the novel model developed by us are three fold: first, it generates realistic shapes by understanding underlying geometry of objects using a limited training data-set; second, it outperforms the 3D-GAN when generating symmetrical 3D object shapes; third, it bridges a research gap by delivering a solution that requires minimal training time and computational resources.