Reviewerly/exHarmony huggingface dataset:
Multimodal Training as the Strategy of Human Resources Development in Conditions for Digital Transformation

The purpose of this study was to explore the opportunities and risks associated with the implementation of new educational formats, including project laboratories, hackathons, startup challenge, co-working, microlearning with regard to their social and axiological aspects into training of high-skilled professionals. The article includes the review of priority areas of the modern education meeting the trends of the digital economy and the description of its specific characteristics. The paper also dwells on the models of cluster education developed by the group of tutors, containing problem cases, draft solution to socio-humanistic tasks, prototypes of engineering projects, business cases, and innovative entrepreneurship technologies. The conducted analysis shows that the use of the opportunities provided by modern educational technologies allows designing am individual educational path, selecting principal subjects and optional courses. During studies at the project office the students develop the skills and competences making them competitive in the global market. The work allowed for the analysis of advantages and disadvantages of the innovative forms and technologies of education into the educational process, as well as for the forecast regarding the efficiency of its long-term implementation.
Use of Vedic Mathematics to Speed-up Basic Mathematical Operations in Android Based Calculator

Many times we use Vedic Sutras knowingly or unknowingly. For example, when we use i<-i-1 or i<-i+1 in our programming we use ‘EkanyunenaPurvena’ and ‘EkadhikinaPurvena’ Sutras respectively. Similarly, in computer's multiplier unit, many Vedic Sutras are being used. In our proposed application we have developed user interface for Vedic Multiplication and Vedic Addition in a calculator which is designed for Android based devices. This application reduces the complexity of mathematical calculations with the help of Vedic Mathematics. Vedic Multiplication and Vedic Addition, in our proposed calculator, can provide faster result than traditional methods. On the other hand, Vedic Multiplication and Vedic Addition consumes less power. Therefore, our proposed application not only saving power consumption but also speeds-up basic mathematical operations and reduces the computational overhead.
A Novel Methodology of Time Dependent Mean Field Based Multilayer Unsupervised Anomaly Detection Using Traffic Surveillance Videos

Real time anomaly detection has been attracting considerable interest in traffic control and management applications. As a low-cost, high-efficiency emerging technology, automated video analysis has been adopted by agencies that are convinced of the feasibility of this new technology due to the impressive developments in computer vision and image processing fields. This paper presents a two-layer learning framework for unsupervised anomaly detection based on traffic surveillance videos. The static layer uses background extraction and blurring of moving vehicles to identify static vehicles. The dynamic layer uses proposed time dependent vector field cross correlation method, processes the traffic flow into vector fields and, compares the difference between average vector fields and instant fields to determine anomaly events. This framework has been shown to have a low training cost, acceptable accuracy, and relatively high level of adaptability for real-life video sets obtained from different locations.
Bi-swarm Particle Swarm Optimizer with Novel Neighborhood Topology Strategy and its Application of Intermodal Transportation

A Bi-swarm Particle Swarm Optimizer with novel neighborhood topology strategy (BPSO-NT) is proposed in this paper. The increase of its population diversity helps to improve its global search ability. The strategy of updating neighborhood topology that plays a vital role in the particle swarm optimization algorithm (PSO) is studied by leveraging link prediction techniques. Different learning strategies are utilized to update the velocity of individuals in the two swarms of BPSO-NT. From comparison results with the state-of-the-art PSO variants on ten benchmark functions, the superiority of the proposed algorithm is demonstrated. Furthermore, BPSO-NT is applied to the intermodal transportation planning, and statistical results show that BPSO-NT outperforms other PSO variants in this practical optimization problem.
Proactive Intention Recognition for Joint Human-Robot Search and Rescue Missions Through Monte-Carlo Planning in POMDP Environments

Proactively perceiving others’ intentions is a crucial skill to effectively interact in unstructured, dynamic and novel environments. This work proposes a first step towards embedding this skill in support robots for search and rescue missions. Predicting the responders’ intentions, indeed, will enable exploration approaches which will identify and prioritise areas that are more relevant for the responder and, thus, for the task, leading to the development of safer, more robust and efficient joint exploration strategies. More specifically, this paper presents an active intention recognition paradigm to perceive, even under sensory constraints, not only the target’s position but also the first responder’s movements, which can provide information on his/her intentions (e.g. reaching the position where he/she expects the target to be). This mechanism is implemented by employing an extension of Monte-Carlo-based planning techniques for partially observable environments, where the reward function is augmented with an entropy reduction bonus. We test in simulation several configurations of reward augmentation, both information theoretic and not, as well as belief state approximations and obtain substantial improvements over the basic approach.
Non-functional Requirements Prioritization: A Systematic Literature Review

Continuous delivery and rapidly changing requirements in agile environments force the developers to put non-functional requirements (NFRs) on halt till maintenance phase. However, neglecting NFRs during prioritization phase may lead to inaccurate estimations for software projects resulting in high maintenance cost and failures. The subjective and uncertain nature of non-functional requirements makes them unfit to be prioritized using conventional prioritization methods. Although the existing literature reports on inadequate consideration given to NFRs prioritization, still no comprehensive systematic effort has been done to report the limitations and evaluation mechanisms of existing NFRs prioritization approaches. Requirements engineering society lacks a broad understanding of NFRs prioritization approaches and the challenges which need to be overcome. Therefore, we aim to investigate (i) the existing NFR prioritization techniques and their validation mechanisms, (ii) the role of Artificial Intelligence (AI) in NFRs prioritization, and (iii) the limitations of existing NFRs prioritization techniques. For this, we reviewed the literature published from 2008 till present and extracted 30 studies. The results reveal twenty-five NFRs prioritization techniques out of which only three are AI based. The major limitations we have come across are that most of the NFRs prioritization techniques are not scalable to large datasets, inter-dependencies between functional requirements (FRs) and NFRs are ignored, and the uncertainties associated with NFRs are not considered at all. However, the literature suggests that AI-based techniques and Fuzzy logic may be used to solve issues such as uncertainties i.e. ambiguities, vagueness, and subjective opinions of stakeholders. This review adds to the existing body of knowledge on NFRs and motivates the practitioners to focus on the NFR prioritization by highlighting the limitations of the existing methods.
Tactical Situation Modelling of MUM-T Helicopter Mission Scenarios using Influence Maps

The Institute of Flight Systems (IFS) at the Bundeswehr University Munich (BUM) conducts research in the area of military helicopter Manned-Unmanned Teaming (MUM-T) scenarios. In order to support the crews in suchlike missions, the IFS investigates functions that enhances automated mission management concerning the command of unmanned aerial vehicles (UAVs) during the execution of the mission. Previously conducted experiments and interviews with pilots of the German Armed Forces have shown that there is a need for the system to have a higher level of tactical situational awareness, enabling it to provide tactically relevant support. Typically, military scenarios are characterized by high complexity regarding the state space and information available. Influence Maps (IM) pose a way to represent and manage tactical information of these complex environments. In this article, we show how information of an operating area can be represented using IMs and how these IMs can then be used to conduct tactical inference or assessment. The resulting IMs are used to assess the helicopter flight route and generate tactical reconnaissance target candidates.
Ethical Hacking: Redefining Security in Information System

On defining the severe status of information security in the present world, we come across a very renowned technical term known as ‘ethical hacking’. Ethical hacking refers to the art of unmasking the vulnerabilities and the weakness in a computer or an information system. The process involves duplication of intents and actions of other malevolent hackers. Ethical hacking can be also called as ‘penetration testing’, ‘intrusion testing’, or ‘red teaming’. Talking about the term ‘hacking’, it is basically a challenging and an invigorating procedure to steal information from an unknown computer system or may be a device without the prior knowledge of the owner of that system. Now by the term ‘ethical’, we understand the process of hacking is done for an ethical purpose which will result in a boon for the society. An ethical hacker tries to recover or destroy the stolen information or data by the non-ethical hackers. The process of hacking can thus become a boon as well as a curse for the society, and it depends upon the intention of a hacker. This is no doubt that a very strong procedure and severely based on what way it is used. This paper elicits the various methodologies and concepts related to ethical hacking as well as the tools and software used in the process along with the future aspects and emerging technologies at this field.
A Virtual Sample Generation Method Based on Kernel Density Estimation and Copula Function for Imbalanced Classification

In the case of imbalanced data, classification models often achieve low accuracy. To solve this problem, this paper proposes a virtual sample generation method based on kernel density estimation and copula function. The kernel density estimation is used to estimate the probability density of each dimension of data, and the joint probability density of the samples is constructed by the copula function. The validation experiments are carried out by applying the proposed method to a numerical simulation and a yeast classification problem. Simulation results show that the proposed method can generate high-quality virtual samples and significantly improve the recognition accuracy.
Using Visualization Tools to Search for Contradictions in Planning Results

When designing most types of activities, as well as when needed to audit the results achieved, a problem arises to analyze simultaneously data included in the descriptions of individual elements of this activity.The purpose of such analysis may be to identify contradictions among disparate information descriptions of processes included in the general activity, as well as to search for possible ways of improving the results achieved.The paper presents the results of a study of visualization potential in the analysis of large volumes of heterogeneous data.The results obtained have been used in the development of visual research tools which help to improve the design of complex processes and to solve the applied problem of planning the educational process.
Multi-task Learning Based on Question-Answering Style Reviews for Aspect Category Classifcation and Aspect Term Extraction

The following topics are dealt with: learning (artificial intelligence); cloud computing; optimisation; feature extraction; Internet; convolutional neural nets; resource allocation; scheduling; data analysis; mobile computing.
Performance of Convolutional Neural Network in Detecting Plasmodium Parasites

Automated Plasmodium detection still remains a challenging problem in biomedical engineering field. The variation of data becomes one of the problems that should be carefully handled. Whilst, deep learning approach is growing rapidly. Convolutional neural network (CNN) is one of the deep learning methods that is able to learn the characteristic of object effectively and had been widely used in several fields including biomedical engineering. Hence, this research work proposes a scheme to detect Plasmodium on thin blood smear images using CNN model. In this work, two CNN architectures are compared. The two architectures are simple CNN with two convolutional layers and transfer learning model with pretrained ResNet-50 as the feature extractor. The transfer learning model successfully achieves positive predictive value and sensitivity of 98.46% and 97.00%, respectively. This result indicates that the proposed scheme is suitable for detection of Plasmodium parasites.
Impact of using augmented reality applications in the educational environment

Abstract This study discusses a literature review that focuses on the use of Augmented Reality in the educational environment. The author reviews articles published in the last 10 years that discuss the use of Augmented Reality in learning media at all levels of education. A total of 30 articles were analyzed thoroughly. The result shows various types of research methods were found and the levels of education varied from early childhood education, elementary school, junior high school, senior high school, vocational school, and tertiary education, even the technology was also used for students with special needs (disability). The research designs found also varied such as Experiment, Analysis, Action Research, Surveys, Mixed Method, and Research-Based and Development. The most prominent role of Augemented Reality in educational environment is to increase students’ thinking ability, understanding, motivation, response, and learning outcomes towards the learning process. Most of the researches were conducted in secondary level while little attention was given in vocational level. Thus, this article would recommend to future studies to conduct more researches in vocational setting in order to display more evindence about the phenomenon of Augmented Reality in educational setting.
Employing Blockchain Technology in Instructional Design and Learning Content Creation

Blockchain technology, a secure ledger of transactions distributed among a network of computers is the technology that enables value transaction on the Internet, which has the potential to create new foundations for our economic and social systems. Blockchain technology has widely been used in the finance and e-commerce industries for processing payments and smart contracts. Consensus algorithms, which are used to achieve agreement on data among distributed systems, are mostly determine by the type of blockchain, i.e. public, private, or federated, and are used for the verification and validation of transactions in such blockchain platforms. Current application of blockchain technology in the educational sector has mostly been for keeping student records, storing and validating diploma and degree certificate for academic credentials. Most of the available consensus algorithms for blockchain provide some form of reward to miners e.g. in the form of cryptocurrency. For blockchain to be adapted and used for other educational applications such as the creation of learning contents, the right consensus algorithm is needed. Since a Learning Content Creation will have little or no reward for participants and also not need “mining”, the consensus model selection will need to treat participants fairly so to not push them away. In this introductory paper, we briefly review the available consensus algorithms and provide our insight how the Blockchain framework can be adapted for instructional design and learning content creation. A proposed framework which will lead to further studies and the development of such system is discussed here.
Tunica Media Localization in Intravascular Image with Shadow Artifact Constraint using Circular-like Estimating Techniques

The advancement of medical technologies allows diagnosis processes more convenient and time saving. Ultrasound technique is being used in a number of medical diagnosis processes. Vascular diseases are one of the group of diseases that used ultrasound techniques to diagnose. These types of diseases may cause serious illnesses such as heart failure, coronary artery disease, and cardiomyopathy. Ultrasound technique often provides a low-quality image due to the safety precaution. Thus, Intravascular images produced by ultrasound techniques often have low contrast, high noises, and few shadows. These features of the low-quality image made it difficult to identify each layer of the Intra Vascular which is needed to segment the actual area of the plaque. In this paper, Circular-like estimating techniques is proposed to locate the Tunica Media on low-quality Intravascular image. The proposed method provides an interesting result with the JI of 87.45% for images with shadow constraint and 92.06% for nonshadow constraint images in dataset A. In addition, the proposed method achieved JI of 63.90% for shadow constraint images with various patterns in dataset B. These results implied that the proposed method can reasonably apply with both shadow and non-shadow artifacts while those earlier works have to attempt them with different methods.
Stochastic Partitioning for Extended Object Probability Hypothesis Density Filters

This paper presents a new likelihood-based partitioning method of the measurement set for the extended object probability hypothesis density (PHD) filter framework. Recent work has mostly relied on heuristic partitioning methods that cluster the measurement data based on a distance measure between the single measurements. This can lead to poor filter performance if the tracked extended objects are closely spaced. The proposed method called Stochastic Partitioning (StP) is based on sampling methods and was inspired by a former work of Granström et. al. In this work, the StP method is applied to a Gaussian inverse Wishart (GIW) PHD filter and compared to a second filter implementation that uses the heuristic Distance Partitioning (DP) method. The performance is evaluated in Monte Carlo simulations in a scenario where two objects approach each other. It is shown that the sampling based StP method leads to an improved filter performance compared to DP.
Deep Learning Based Analysis in Oncological Studies: Colorectal Cancer Staging

The introduction of deep learning in the medical sector for the diagnosis and classification of tissue images has proven to have the best accuracy level over the years. Hence in this study, the utilization of deep learning in colorectal cancer staging as well as the use of AlexNet Architecture in the training of datasets were used in the prediction and detection of colorectal cancer. Moreover, the novelty of the technique used in this study involved a direct analysis of the dataset. Colorectal cancer was analyzed using deep learning technique and AlexNet architecture (CNN) from a dataset of 7180 from 50 different patients having colorectal cancer, which eventually produced 900 images. The results revealed that deep learning was analyzed on several classes: adipose tissue, back tissue, debris tissue, lymphocytes tissues, mucus tissue, smooth muscle, normal tissue, cancer associated stroma and adenocarcinoma epithelium, which had an accuracy of 97.8% for all the classes, (1, 1, 0.952, 0.952, 1, 1, 0.952, and 0.952) respectively, other classes had a precision of 1 except adenocarcinoma epithelium with 0.95, (1, 1, 0.975, 0.975, 1, 1, 0.975, and 0.95) respectively for the F1-score. Hence the result revealed that deep-learning technique for colorectal cancer is very effect.
A Document Visualization Strategy Based on Semantic Multimedia Big Data

The integration of semantic web and big data is a key factor in the definition of efficient model to represent knowledge and implement real world applications. In this paper we present a multimedia knowledge base implemented as a semantic multimedia big data storing semantic and linguistic relations between concepts and their multimedia representations. Moreover, we propose a document visualization strategy based on statistical and semantic analysis of textual and visual contents. The proposed approach has been implemented in a tool, called Semantic Tag Cloud, whose task is to show in a concise way the main topic of a document using both textual features and images. We also propose a case study of our approach and an evaluation from a user perception point of view.
Automating Multi-level Performance Elastic Components for IBM Streams

Streaming applications exhibit abundant opportunities for pipeline parallelism, data parallelism and task parallelism. Prior work in IBM Streams introduced an elastic threading model that sought the best performance by automatically tuning the number of threads. In this paper, we introduce the ability to automatically discover where that threading model is profitable. However this introduces a new challenge: we have separate performance elastic mechanisms that are designed with different objectives, leading to potential negative interactions and unintended performance degradation. We present our experiences in overcoming these challenges by showing how to coordinate separate but interfering elasticity mechanisms to maxmize performance gains with stable and fast parallelism exploitation. We first describe an elastic performance mechanism that automatically adapts different threading models to different regions of an application. We then show a coherent ecosystem for coordinating this threading model elasticty with thread count elasticity. This system is an online, stable multi-level elastic coordination scheme that adapts different regions of a streaming application to different threading models and number of threads. We implemented this multi-level coordination scheme in IBM Streams and demonstrated that it (a) scales to over a hundred threads; (b) can improve performance by an order of magnitude on two different processor architectures when an application can benefit from multiple threading models; and (c) achieves performance comparable to hand-optimized applications but with much fewer threads.