Reviewerly/exHarmony huggingface dataset:
Diagnosis of Simplex Herpes by Case-Based Reasoning Method

Artificial Intelligence (Artificial Intelligence or AI) is defined as the intelligence shown by an artificial entity. Such systems are generally considered computers. Intelligence is created and incorporated into a machine (computer) so that it can do work as humans can. Artificial Intelligence (Artificial Intelligence or AI) is defined as intelligence shown by an artificial entity. Such systems are generally considered computers. Intelligence is created and incorporated into a machine (computer) so that it can do work like humans can. CBR method is a method used to solve a problem by using previous experience to solve a new problem. This research will discuss about Herpes Simplex, a venereal disease. In implementing this expert system, it is used by families of patients with Herpes Simplex. The goal is that the expert system can assist the patient's family in conducting the initial diagnosis process to determine the symptoms felt by sufferers of Herpes Simplex. And with the existence of this expert system can carry out appropriate treatment by sufferers of Herpes Simplex.
Robotic Interactive Physics Parameters Estimator (RIPPE)

The ability to reason about natural laws of an environment directly contributes to successful performance in it. In this work, we present RIPPE, a framework that allows a robot to leverage existing physics simulators as its knowledge base for learning interactions with in-animate objects. To achieve this, the robot needs to initially interact with its surrounding environment and observe the effects of its behaviours. Relying on the simulator to efficiently solve the partial differential equations describing these physical interactions, the robot infers consistent physical parameters of its surroundings by repeating the same actions in simulation and evaluate how closely they match its real observations. The learning process is performed using Bayesian Optimisation techniques to sample efficiently the parameter space. We assess the utility of these inferred parameters by measuring how well they can explain physical interactions using previously unseen actions and tools.
Learning and Developing With Each Other

ICT is a powerful tool which can be used by both India and Canada to harness the growth and development in the respective regions. Researchers and practitioners can come together from Indo-Canadian communities and take the association to a more meaningful level. The current research discusses the ICT tools that can be adopted from Canada by India to improve the education quality and knowledge transmission. On the other hand, Canada government can make efforts to enhance knowledge transfer and translation through ICT by adopting tools like best practices repository, communities of practice, corporate intranet/extranet, corporate yellow pages, expertise locator, online chat/instant messaging, knowledge portals, groupware-workflow and tracking system, and document/content management that are already used in India. ICT has the potential to develop the country politically, socially, and economically.
A Practical Alphabet-Partitioning Rank/Select Data Structure

This paper proposes a practical implementation of an alphabet-partitioning compressed data structure, which represents a string within compressed space and supports the fundamental operations $$\mathsf {rank}$$ and $$\mathsf {select}$$ efficiently. We show experimental results that indicate that our implementation outperforms the current realizations of the alphabet-partitioning approach (which is one of the most efficient approaches in practice). In particular, the time for operation $$\mathsf {select}$$ can be reduced by about 80%, using only 11% more space than current alphabet-partitioning schemes. We also show the impact of our data structure on several applications, like the intersection of inverted lists (where improvements of up to 60% are achieved, using only 2% of extra space), and the distributed-computation processing of $$\mathsf {rank}$$ and $$\mathsf {select}$$ operations. As far as we know, this is the first study about the support of $$\mathsf {rank}$$ / $$\mathsf {select}$$ operations on a distributed-computing environment.
Preparation of Fe2O3-TiO2/GO Composites as a Visible Light Oxidation Photocatalyst Material

Abstract Fe 2 O 3 -TiO 2 /Graphene Oxide (Fe 2 O 3 -TiO 2 /GO) has been synthesized through a step by step synthesized. Preparation of Fe 2 O 3 -TiO 2 composites from Sukabumi iron sand with high iron content. Meanwhile, preparation of GO separately from graphite on strong acid condition H 2 SO 4 and addition oxidator KMnO 4 treatment has been carried out. First composition analyzed Sukabumi iron sand shown content of iron about 50% and content of Ti about 10%. Fe 2 O 3 -TiO 2 composites prepared with addition TiO 2 powder thus obtained a ratio of Fe 2 O 3 -TiO 2 = 1:1(w/w). Fe 2 O 3 -TiO 2 composites was roasted at 400 °C, 500 °C, and 600°C shown optimalhematite formed at 600 °C. Formation of Fe 2 O 3 -TiO 2 /GO composites were at Fe 2 O 3 -TiO 2 :GO= 1:1, 1:2, and 1:3 (w/w), respectively. Fe 2 O 3 -TiO 2 /GO composites (A, B, C) shown excellent photocatalyst activities. Fe 2 O 3 -TiO 2 :GO (1:3) composites have the optimum ability for photocatalytic activity of Rhodamine B degradation achieved about 93.96% for 40 minutes illumination.
GANemotion: Increase Vitality of Characters in Videos by Generative Adversary Networks

Increasing the vitality of facial expression for characters in videos can greatly improve the user experience on entertainments, like in films, animations, news broadcasting, or even a static painting. Recent advances have been made on facial expression migration, especially by GANs for better performance, while most of them cannot be automatically applied in consecutive frames. In this paper, we propose GANemotion, an automatic facial expression migration architecture for real-time videos. Three main modules are designed for this purpose: audio emotion classification module, facial expression classification module, and facial expression migration module on GANs. The outputs of the first two modules will be the inputs of the third module, where the emotion type acquired from text analysis will be the target generation label and image from facial classification is the object for processing. Expression attention algorithm and standard AU library are specially designed for avoiding distortion and processing on non-real world characters. Experiments have been applied to each module and the whole architecture respectively. They show the feasibility of GANemotion with higher classification accuracy, and vivid generation, breaking the limits of non-real world characters and low illumination conditions.
Legal Issues of Deception Systems in the Industrial Control Systems

Deception systems, and within them deception industrial control systems, present a newly emerging type of defence in cybersecurity, providing for the detection, analysis and defence against cyber-attacks. The deception technology focuses on the attackers, their point of view and methodology used to exploit and navigate networks to identify and exfiltrate data. The chapter discusses the nature of the deception Industrial Control Systems and the legal issues encompassed with their use. It provides the legal framework of the fundamental right to privacy and the fundamental right to personal data protection, as well as the legal framework of the liability, predominantly in the area of tort law, applicable to the use of the deception Industrial Control Systems, the provider of these systems must be aware of.
Development of a Simulation Experiment to Investigate In-Flight Startle using Fuzzy Cognitive Maps and Pupillometry

Loss of control in-flight (LOC-I), following loss of situational awareness and startle has been identified as a leading cause of aviation-based fatalities in recent decades. This has led to significant effort toward improving safety records, particularly in the fields of flight crew training and in-flight support technologies that aid better decision making and management of reactions to a startling occurrence. One way to achieve quality decision making in the cockpit is by providing adequate cueing and response activating mechanisms carefully designed to aid human information processing. These response performances, especially in the context of reactionary management of startle in flight, could be honed through simulator-based training. This paper discusses the setup background development of a startle causality dynamics using fuzzy cognitive mapping. This mapping provides an objective, strategy framework; for determining the required training for a management of the startle reflex in extenuating circumstances made significantly worse by human factors, such as erroneous knee jerk reactions.
Meningkatkan Kecerdasan Emosional Siswa Melalui Kebiasaan Membaca Al-Quran

The purpose of this research is to find out the level and the correlation between the habit of reading the Qur’an to the emotional quotient of student class VIII MTs Al-Hamid Banjarmasin. This research used a quantitative approach. The data collection device uses the Likert scale, scale aspect of the habit of reading Qur’an 38 items, and emotional quotient scale 37 items that have been tested for validity and reliability. The sampling technique used was nonprobability sampling, with a total of 89 peoples — the data analysis using SPSS software for Windows 21.0. Quantitative data shows the habit of reading Qur’an in the medium category (61,79 percent) and emotional quotient in the medium type (66,29 percent). A correlation analysis results use a Pearson product moment technique in the can calculated r value is 0,460 with p-value 0,000 while the r table is at the 5 percent significance level with N = 89 of 0,213 show off that the habit of reading Qur’an affected the level of emotional quotient. As for the magnitude of the contribution of the practice of reading the Qur’an variable to the emotional quotient of MTs Al-Hamid Banjarmasin in the amount of (rxy2 x 100) or 21,16 percent.
Joint Face Detection and Alignment Using Focal Loss-Based Multi-task Convolutional Neural Networks

In order to learn more hard negative example features of face, MTCNN increases the proportion of negative to positive examples. However, the training data of MTCNN suffer from the extreme foreground-background class imbalance, which results in a large number of well-classified negative examples overwhelming the detector during training. To solve this problem, we introduce Focal Loss to MTCNN to improve face classification performance. Besides, the Batch Normalization is adopted to speed up training and prevent network performance degradation caused by overfitting. The experimental results demonstrate that the proposed method has a better performance than MTCNN on face classification, and has a good comprehensive performance on facial landmark localization.
3D Morphable Models (Dagstuhl Seminar 19102)

3D Morphable Models is a statistical object model separating shape from appearance variation. Typically, they are used as a statistical prior in computer graphics and vision. This report summarizes the Dagstuhl seminar on 3D Morphable Models, March 3-8, 2019. It was a first specific meeting of a broader group of people working with 3D Morphable Models of faces and bodies. This meeting of 26 researchers was held 20 years after the seminal work was published at Siggraph. We summarize the discussions, presentations and results of this workshop.
Design and Implementation of Unreliable CFDP Protocol over Elliptic Curve Cryptography

This paper aims to use the Elliptic Curve Cryptography (ECC) algorithm over the CFDP protocol for communication. ECC is a method of public key cryptography (PKC) built upon elliptic curves on finite fields. ECC yields corresponding cryptographic strength as RSA-based system with smaller keys. The Consultative Committee for Space Data Systems (CCSDS) file delivery protocol (CFDP) renders QoS, as per objective requirements and communication capability, spanning from an unacknowledged to a fully acknowledged privilege with error recovery by retransmission. In the proposed design, ECC is used for security for transmission of data over CFDP.
Metaflow: A Better Traffic Abstraction for Distributed Applications

Distributed applications usually feature a set of correlated flows between two consecutive computation stages. The scheduling of these flows has a crucial influence on job completion time. Coflow improves performance by optimizing the finish time of the entire set of flows. However, the flows and computing tasks in one application have more complex relationships that exceed the coflow's harrier assumption. In this context, scheduling via coflow abstraction may hurt application performance. Accordingly, we propose metaflow, a traffic abstraction derived from the computation graph of the application. Metaflow reveals the detailed flow requirements of the application and makes it easier to reduce the job completion time. Based on metaflow, we devise a scheduling heuristic called MSA. MSA is able to find shorter jobs more accurately for inter-job scheduling, and maximize the overlap between communication and computation for intra-job scheduling. To demonstrate the effectiveness of our work, we have conducted extensive simulations with both synthetic single jobs and production traces containing multiple jobs. The simulation results verify that MSA adapts well to different jobs and can achieve a significant increase in average speed of over 1.98x on a real-life workload.
Image Captioning with Partially Rewarded Imitation Learning

Current state-of-the-art image captioning algorithms have achieved great progress via reinforcement learning or generative adversarial nets, with hand-craft metrics such as CIDEr as the reward for the former and signals from adversarial discriminative networks for the latter. Despite the high scores on metrics or improvement in diversity gained from the application of these methods, they suffer from distinction with human-written sentences and drop of ratings on metrics respectively.In this paper, we propose a novel training objective for image captioning that consists of two parts representing explicit and implicit knowledge respectively. Optimizing the new reward partially with imitation learning, we devise an algorithm in which the caption generator is trained to maximize the combination of CIDEr and predictions from adversarial discriminator. Experiments on MSCOCO dataset demonstrate that the proposed method can integrate the strengths of state-of-the-arts, producing more human-like captions while maintaining comparable performance on traditional metrics.
Cognitive modelling of Chinese herbal medicine’s effect on breast cancer

Traditional Chinese medicine (TCM) has recently attracted increasing interests in cancer treatment. It was found that TCM-based treatment, combined with other therapies, can help improve patients' life quality. However, the existing research in TCM lacks a systematic modelling for the causal relationship of the factors related to the diagnosis and decision making.In this paper, we proposed the use of fuzzy cognitive map (FCM) to represent the cognition of TCMs usage in cancer treatment.Through a case analysis, we analyse and summarise the effects of Chinese herbal medicine in breast cancer management.FCMs can visually represent the cognitive knowledge, particularly the causal relationship among key factors of TCM effects and the related breast cancer status.
MML Inference of Hierarchical Probabilistic Finite State Machine

A Finite State Machine (FSM) is a mathematical model of computation which can effectively model a sequence of words or tokens. A grammar representing a collection of tokens in a finite alphabet might contain regularities that are not fully captured by a deterministic formal grammar. Therefore, the simple FSM model is extended to include some probabilistic structure in the grammar which is now termed as Probabilistic Finite State Machine (PFSM). We extend earlier work on inferring PFSMs using the Bayesian informationtheoretic Minimum Message Length (MML) principle to the case of inferring hierarchical PFSMs (HPFSMs). HPFSMs consist of an outer PFSM whose states can internally contain a PFSM (or, recursively, an HPFSM). The alphabet of each such internally contained PFSM can be smaller than the complete HPFSM. HPFSMs can often represent the behaviour of a PFSM more concisely, and MML's ability to deal with both discrete structures and continuous probabilities renders MML well suited to this more general inference. We empirically compare on pseudo-random data-sets.
Partitioning Large-Scale Property Graph for Efficient Distributed Query Processing

Recently, many communities have published their domain knowledge in the form of graphs. A significant trend is that the relations between pairs of nodes in the graph have become so complex that these data can be treated as property graphs. Complex properties imply semantic constraints which can distinguish elements of property graph into different semantic-related aspects. If the property graph can be partitioned according to these aspects, cross-partition communication would be reduced when evaluating SPARQL queries, for the reason that a query can often be evaluated in a single aspect. Conventional graph partition methods, however, pay little attention to semantic constraints in property graphs. To solve this problem, we propose a new property graph partition method for query optimization. First, we propose the property semantic reachable path as basic partition element, with which to capture the semantic-related aspects in a property graph. Second, to combine paths into partitions, we develop a space-efficient algorithm which merges vertices rather than entire paths. Extensive experiments over representative property graph data confirm the effectiveness of our semantic awareness approach. The performance of our approach is comparable with leading competitors, in terms of load balance, data redundancy, and network overhead.
Anti-Phishing System Using Intelligent Techniques

Phishing is one of the web threats; a phishing attack is considered successful if the user unknowing submits personal information to the attacker. Developing countries such as Iraq may have been facing internet threats like phishing. The phishing attack has plenty of approaches and techniques to conduct a well-designed approach. In this paper, an anti-phishing system has been presented to determine if a website is legitimate or not. The proposed system depends on retrieving information from various influential sources and analyzing the information by taking a final decision about the status of links based on proposed Fuzzy-PNN scheme. This paper tries to reduce the financial loss for both web subscribers and business owners based on proposed techniques to mitigate phishing.
Tracking Big5 Traits Based on Mobile User Log Data

In this paper, anonymized mobile phone log data have been used to predict users' personality in the context of Big5 model in a privacy-preserving manner. First, the Big5 concepts are presented. Afterwards, we present how to calculate Big5 indicators from the available mobile data sets. Hereafter, Big5 traits can be predicted based on those just-specified indicators. As a proof of our concepts, implementation results will be presented in the context of TB5 (Tracking Big5) tool, which has been designed and developed to predict Big5 personalities in a representative manner.
Modification of Parallelization of Modified Merge Sort Algorithm

An important issue in sorting large data sets in the NoSQL databases is the ability to sort process parallelism in order to accelerate the application. The work presents the use of the parallelized method for merging strings in a modified merge sort algorithm. The static tests of the proposed sort algorithm verify the stability and the theoretical time complexity of the method.
Horcruxes for Everyone - A Framework for Key-Loss Recovery by Splitting Trust

Many modern applications require users to manage keys on their own devices, which, in case of device loss or failure, may lead to serious consequences, e.g., losing access to their Bitcoin wallet. These applications need a secure and user-friendly strategy that protects users from losing keys while preserving the keys' confidentiality. Fortunately, password-protected secret sharing (PPSS) can be used to design such a key-loss recovery strategy: It enables users to split their keys into shares, to distribute these shares across third parties and, if necessary, to recover keys via password authentication. However, deploying PPSS in a key-loss recovery strategy leaves the following practical questions unanswered: Which third parties should a user pick to diversify the trust? How can these third parties be recruited? And: How can other applications benefit from such a strategy? In this paper, we develop a framework for key-loss recovery, which allows users to distribute shares in a hierarchy that is aligned with relevant trust factors. As part of the framework, we propose a management app that supports users in building and managing hierarchical trust policies, and that offers its service to other applications. To convince organizations to operate servers, we implement our framework with a focus on server-side cost-efficiency. We extend a PPSS scheme with hierarchical trust policies, add efficient prevention of online guessing, and measure the performance of the overall system at-scale on AWS. The cost projection shows that deploying our framework is inexpensive: 40 organizations, each operating server resources for less than $20, support 50 million users when splitting and recovering their keys.
Single-Shot 3D Mesh Estimation via Adversarial Domain Adaptation

We present an adversarial deep domain adaptation (ADA) approach for training deep neural networks that estimate 3D pose and shape of a human from a single image. Existing datasets of in-the-wild images of humans have limited availability of 3D ground truth. We propose a novel deep architecture for 3D pose estimation and leverage the variations in pose, body shape and background in the synthetic datasets to train our network. Using ADA we adapt our network to real human images by designing a pipeline for joint 3D pose and shape estimation. Thus, we propose an ADA-based single-shot, straightforward, (no reprojection, no iterative refinement), end-to-end training approach via joint optimization on real and synthetic images. Through joint training on real and synthetic data, our network extracts features that are robust to domain shift. These features are then used to estimate the 3D mesh parameters in a single shot with no supervision on real samples. We compute the regression loss on synthetic samples with ground-truth mesh parameters. Knowledge is transferred from synthetic to real data through ADA without direct key point-based supervision.