Reviewerly/exHarmony huggingface dataset:
Digital Forensics and Data Mining

Digital forensic experts need to identify and collect the data stored in electronic devices. Further, this acquired data has to be analyzed to produce digital evidence. Data mining techniques have been successfully implemented in various applications across the domains. Data mining techniques help us to gain insight from a large volume of data. It helps us to predict the pattern, classify the data, and other various aspects of the data based on the users' perspective. Digital forensics is a sophisticated area of research. As the information age is revolutionizing at an inconceivable speed and the information stored in digital form is growing at a rapid rate, law enforcement agencies have a heavy reliance on digital forensic techniques that can provide timely acquisition of data, zero fault data processing, and accurate interpretation of data. This chapter gives an overview of the tasks involved in cyber forensics. It also discusses the traditional approach for digital forensics and how the integration of data mining techniques can enhance the efficiency and reliability of the existing systems used for cyber forensics.
Fast Autonomous Underwater Exploration using a Hybrid Focus Model with Semantic Representation

By combining complex exteroceptive perceptions, humans are able to seamlessly explore a new environment, build up their knowledge base, and “easily” identify novel objects or imminent hazards. This work aims at employing a similar intelligent cognition and exploration system for unmanned marine robots. The proposed system semantically discriminates the visual inputs of the robot's environment by combining task-directed and stimulation-based focus to encode artificial “attention”, to enhance exploration decision making. Additionally, the hybrid focus model can use the semantic descriptors to label a target object (e.g., a certain type of coral) with metric information (e.g., geographical locations of the coral) from a range-and-bearing sensor, allowing a metric map to be generated with semantic labels of the desired types of objects. These maps can help simplify human and robot interaction by providing a clear semantic interpretation of the robots observations. The proposed system is implemented and tested on an indoor robot and with underwater images.
Optimal Input-Dependent Edge-Cloud Partitioning for RNN Inference

Recurrent Neural Networks (RNNs) such as those based on the Long-Short Term Memory (LSTM) architecture are state-of-the-art deep learning models for sequence analysis. Given the complexity of RNN-based inference, IoT devices typically offload this task to a cloud server. However, the complexity of RNN inference strongly depends on the length of the processed input sequence. Therefore, when communication time is taken into account, it may be more convenient to process short input sequences locally and only offload long ones to the cloud. In this paper, we propose a low-overhead runtime tool that performs this decision automatically. Results based on performance profiling of real edge and cloud devices show that our method is able to reduce the total execution time of the system by up to 20% compared to solutions that execute the RNN inference fully locally or fully in the cloud.
Salient Object Detection by Pyramid Networks with Gating

In the paper, we propose a salient object detection architecture named as Gating for Double Pyramid Networks (GDPNet). It consists of two pyramid structures: Feature Pyramid Network (FPN) and Pyramid Pooling Module (PPM). FPN has the capability of capturing the inherent multi-scale and pyramid hierarchy, while PPM can exploit the global context information by different-region-based context aggregation. It is known that the irrelevant information corresponding to non-salient objects or background may deteriorate the performance of the model. We introduce two gating strategies, i.e., Cross-Gating for FPN and Single-Gating for PPM, to suppress the incurred irrelevant information in hidden features. Our proposed approach achieves state-of-the-art performance on five benchmark datasets, which demonstrates the effectiveness and robust feature extraction capability of proposed GDPNet.
Entropy fluctuations to quantify mixedness

We propose a mixedness quantifier based on entropy fluctuations. It provides information about the degree of mixedness either for finite dimensional and infinite dimensional Hilbert spaces (HS). It may be used to determine the reduction of the HS as it becomes maximum when either the state is maximally mixed, or when the HS effectively reduces its dimensions, such as in the atom field interaction where the two-level atom dictates the final dimension of the field.
Solving the Bounded-Depth Steiner Tree Problem using an Adiabatic Quantum Computer

We propose a quadratic unconstrained binary optimization (QUBO) formulation for the Bounded-Depth Steiner Tree problem suitable for adiabatic quantum computers. The numbers of qubits (dimension of QUBO matrices) required by our formulation is O(|V| <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ), where |V| represents the number of vertices. A D-Wave 2X computer consisting of 1098 active qubits is evaluated for a selection of known common graphs. Experimental results are shown and discussed. Our results are applicable to the Bounded-Depth Minimum Spanning Tree problem, as a special case.
Adaptive Clustering for Outlier Identification in High-Dimensional Data

High-dimensional data brings new challenges and opportunities for domains such as clinical, scientific and industry data. However, the curse of dimensionality that comes with the increased dimensions causes outlier identification extremely difficult because of the scattering of data points. Furthermore, clustering in high-dimensional data is challenging due to the intervention of irrelevant dimensions where a dimension may be relevant for some clusters and irrelevant for others. To address the curse of dimensionality in outlier identification, this paper presents a novel technique that generates candidate subspaces from the high-dimensional space and refines the identification of potential outliers from each subspace using a novel iterative adaptive clustering approach. Our experimental results show that the technique is effective.
The Density-Based Descending Dimension Algorithm LLE

When computer scientists expect to obtain valuable information through data mining in massive data, they are often confronted with the excessive feature of big data and complex high-dimensional structure. Therefore, researchers have introduced the concept of manifold learning to remove the redundant features which account for a large proportion but have no value, which can help improve learning efficiency by reducing computational complexity. In the actual process, however, there will always be discordant factors interfering with our judgment on the data. Therefore, this paper proposes a new, improved LLE dimension reduction algorithm based on data density judgment, which is simply referred to as DLLE. This algorithm is aimed to avoid the interference of noise points and the influence of surface boundary blur on dimension reduction caused by irregular manifold structure, thus better preserving the true geodesic distance of the data after the dimension reduction. An experiment is carried out on several sets of 3D synthetic datasets, whose results show that the improved algorithm is superior to the traditional LLE algorithm.
Technology Integration in a Course for Prospective Mathematics Teachers

This document presents the results of a study that sought to integrate technologies into a mathematics course for prospective mathematics teachers. Two types of environments were created, one focused on technologies for interaction and communication and the other focused on technologies for the study of mathematics. The results show that the former environment acted as a classroom extension that allowed teachers to recognize students’ learning processes and needs, while the later environment allowed the creation of strategies to solve tasks.
A Quantum-Based Signcryption for Supervisory Control and Data Acquisition (SCADA) Networks

Supervisory Control and Data Acquisition (SCADA) systems are ubiquitous in industrial control processes, such as power grids, water supply systems, traffic control, oil and natural gas mining, space stations and nuclear plants. However, their security faces the threat of being compromised due to the increasing use of open-access networks. Furthermore, one of the research gaps involves the emergence of quantum computing, which has exposed a new type of risk to SCADA systems. Failure to secure SCADA systems can lead to catastrophic consequences. For example, a malicious attack can take control of the power supply to a city, shut down the water supply system, or cause malfunction of a nuclear reactor. The primary purpose of this paper is to identify the new type of attack based on quantum computing and design a novel security scheme to defend against traditional attacks as well as the quantum attack. The methodology of the proposed signcryption is built on the foundation of the classical Bennett and Brassard 1984 (BB84) cryptographic scheme and does not involve computationally expensive third-party validation. The proposed signcryption scheme provides both encryption and intrusion detection. In particular, it detects the man-in-the-middle attack that can lead to other types of attacks. We have simulated the proposed algorithm using the Quantum Information Toolkit in Python. Furthermore, we have validated and analyzed the proposed design through security verification tools, namely, Scyther and PRISM.
Comment-Mine—A Semantic Search Approach to Program Comprehension from Code Comments

Annotating programs with natural language comments is a common programming practice to increase the readability of code. While researchers have attempted specific tasks like comment quality analysis or classification, there is absence of an integrated knowledge representation based on comments to aid program comprehension. We propose Comment-Mine a semantic search architecture, which extracts knowledge related to design, implementation and evolution of a software in the form of a knowledge graph. This supports program comprehension and various comment analysis tasks. We manually annotate concepts for 5600 comments extracted from 672 C/C++ files/projects crawled from code repositories like GitHub. Comment-Mine extracts 38,992 concepts, out of which 79.8% is correct and validated using manual annotation.
Motion Detection Using Image Processing

The method proposed in this paper aims to detect motion in indoor areas for surveillance. This method requires two subsequent images captured by a single fixed camera sensor and detects motion in the interval between the two images. This is done by pre-processing and image subtraction followed by further processing to eliminate insignificant changes in the two image frames. The method presented here detects motion accurately and has good performance with low quality images.
Development of Knowledge Base for Intellectual Decision Support in the Process of Innovative Project Management

The paper deals with a problem of organizing the intellectual decision making support in innovative projects management based on the knowledge base. The urgency of the task directed on managing the economic systems innovative development in current competitive market conditions is substantiated. Existing approaches and tools to modeling and decision support in strategic management of the developed territorial systems are considered. The paper also introduces an approach to the knowledge base development for intellectual decision support in innovative project management, based on the ontological analysis of knowledge acquisition from various sources. The effective tools to forming the strategic planning innovative system at the regional level, which could ensure the integration of potential innovative projects and programs in the existing model of the reproduction process are presented. One of the main properties of knowledge management systems is emergence, due to which the interaction between specialists based on formalization of knowledge representation and processing in intellectual components development (ontology, knowledge base) contributes to the creation of new knowledge. An advantage of the ontology-based approach to innovation process management is the semantic integration of data and knowledge for the decision support system. It is proposed on the basis of ontological engineering to form a comprehensive tool to development of an integrated model for representing and processing of knowledge at various stages of the innovative projects management process. Further study logic involves development of an integrated ontology that combines a set of ontologies formed to describe the various fields of specialists knowledge in the field of innovative project management, which will provide a clear understanding of the subject concepts, as well as the rules and cases of decision-making based on them. The proposed approach to organizing the intellectual decision support system is focused on the use of public administration in determining the parameters of medium-term development of the Russian Federation, as well as in development of measures and mechanisms for social, economic and budgetary policy. Increasing the degree of scientific validity and accuracy of the forecast estimates will reduce the risks in adoption and implementation of management decisions. In theoretical aspect it is proposed to develop at the regional level the territorial systems management models and to form the scientific foundation for further development and comprehensive utilization technology of adaptive management and imitation based on modern intelligent information technologies applied for solving the problem of strategies and programs justification in innovative development in Russian regions.
ESSENCE: GPU-based and dynamic key-dependent efficient stream cipher for multimedia contents

Data Confidentiality (DC) is considered one of the most important security services. Currently, a set of existing cipher algorithms is being used to ensure DC. However, researchers constantly investigate the design and implementation of more efficient cipher schemes. To this end, different versions of AES have been implemented efficiently on GPUs to increase the efficiency over big data. However, AES implementation on GPU exhibits limitations in terms of latency and hence, it might not be a suitable solution for high data rates in modern systems and applications. This often leads to a trade-off between system performance and security level. To address these challenges, we propose “ESSENCE”, a lightweight stream cipher scheme, which combines two different Pseudo-Random Number Generators (PRNG), and based on a dynamic key approach. The scheme achieves a high level of security with minimal latency and required resources when compared to existing cipher standards such as AES. Moreover, the implementation of the proposed dynamic key-dependent cipher scheme on GPU is more efficient compared to all existing AES implementations on GPUs. Experimental results indicate that the proposed cipher is highly efficient with a throughput more than 115 GB/s on a Titan X GPU, and more than 372 GB/s on a Titan V100 GPU. Thus, ESSENCE can be considered as a promising stream cipher candidate with high randomness degree (BigCrush of TestU01), periodicity, and key sensitivity.
ORB-PC feature-based image registration

In remote sensing, intensity variation and rotational distortion are unavoidable issues for multimodal image registration. To address the intensity variation issue, Radiation-Invariant Feature Transform (RIFT) feature-based image registration method was developed. The RIFT produced good results for horizontal and vertical transformation in multimodal images. However, the extracted features in RIFT produce some feature matching errors due to the rotational distortion and hence reduces the registration accuracy. Oriented FAST and Rotated BRIEF (ORB) can produce better rotation invariant features since it was basically designed to deal with the mentioned issue. However, it is not suitable for solving the intensity variation problem. Therefore, in this research work, we integrated the Phase Congruence (PC) to ORB as PC is robust to illumination and intensity variations while the ORB is robust to rotational distortion. Hence the combination of both methods has been utilized to overcome all the mentioned issues simultaneously in order to enhance the accuracy of the image registration. Experiments were conducted in order to validate the superiority of the proposed method over recently introduced RIFT method in terms of accuracy.
A qualitative–quantitative study of science mapping by different algorithms: The Polish journals landscape

By applying different clustering algorithms, the author strived to construct the best visual representation of scientific domains and disciplines in Poland. Journals and their disciplinary categories constituted a data set. A comparative analysis of maps was based on both qualitative and quantitative approaches. Complex patterns of eight maps were evaluated taking into account both the local proximity of disciplines and the whole structure of presented domains. Final clustering quality value was introduced and calculated in reference to the knowledge domains. The authors underlined the role of quantitative and qualitative methods in combination in the mapping evaluation. The best results were obtained with the T-distributed stochastic neighbour embedding (t-SNE) algorithm. This youngest technique may have the biggest potential for semantic information studies and in the scope of broadly understood semantic solutions.
Environment Dependent Neural Network Model For Occupancy Detection

Occupancy detection in buildings crucially involves with the improvement of energy efficiency, space utility, heating ventilation and air condition (HVAC) control and hence optimization of user comfort. The human occupancy information is extracted through different environmental sensors like CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> sensor, light sensors, humidity sensors etc. This paper presents an environment dependent artificial neural network (ANN) approach to Figure out the problem of binary classification of human occupancy inside a building space. This ANN model has been modified to a hyper parameter tuned neural network, dependent of run-time environmental factors based on some empirical formulation. This study has been performed to enhance the classification accuracy with an ample quantity of training samples and hence extend an intelligent occupancy detection model. Employing the statistical concept of occupancy, this proposed ANN model is developed and evaluated with University of California, Irvine (UCI) repository occupancy data. Prospective results demonstrate a faithful performance of the model with guaranteed classification accuracy.
Discovering Robustly Connected Subgraphs with Simple Descriptions

We study the problem of discovering robustly connected subgraphs that have simple descriptions. Our aim is, hence, to discover vertex sets which not only a) induce a subgraph that is difficult to fragment into disconnected components, but also b) can be selected from the entire graph using just a simple conjunctive query on their vertex attributes. Since many subgraphs do not have such a simple logical description, first mining robust subgraphs and post-hoc discovering their description leads to sub-optimal results. Instead, we propose to optimise over describable subgraphs only. To do so efficiently we propose a non-redundant iterative deepening approach, which we equip with a linear-time tight optimistic estimator that allows pruning large parts of the search space. Extensive empirical evaluation shows that our method can handle large real-world graphs, and discovers easily interpretable and meaningful subgraphs.
Application of genetic programming algorithm for designing decision trees and their ensembles

Abstract Decision tree is a machine learning algorithm that is very effective in classification problems. Decision tree is a topical algorithm because of the good interpretability of the results of their work. However, decision tree has the drawback that standard algorithms don’t allow obtaining the optimal structure of the decision tree. To solve this drawback, it’s proposed to use the genetic programming algorithm. This algorithm is one of the branches of evolutionary algorithms and has proven itself for the design of intelligent information technologies. Genetic programming, in one of their implementations, searches for solutions in tree space, which is well suited for designing decision trees. Ensembles that are based on decision trees have high efficiency. In this paper, random forest and gradient boosting are considered. In ensembles, it’s proposed to combine decision trees that are designed by the genetic programming algorithm. Algorithms was tested on classification problems.
Bidirectional Heuristic Search for Motion Planning with an Extend Operator

Sampling-based approaches are often favored in robotics for high-dimensional motion planning for their fast exploration of the search space. However, at best they offer asymptotic guarantees on solution quality due to their inherent stochasticity. While planning, the majority of effort is often spent near the start and goal configurations with a large amount of free space in between. Bidirectional approaches such as RRT-Connect exploit this fact by greedily extending and connecting search frontiers that simultaneously propagate from the start and goal configurations of a planning problem. In this work, we use such an extend operator for bidirectional heuristic search-based planners, which typically struggle with high-dimensionality. In doing so, we address the difficulty that these bidirectional planners face with connecting frontiers of both search efforts while providing suboptimality bounds on solution quality. We validate our simple approach on high-dimensional manipulation tasks, demonstrating significantly reduced search effort when compared against other popular bidirectional algorithms, both search-based and sampling. Our algorithm maintains theoretical guarantees on suboptimality and completeness for a given resolution. In addition, the solutions found by our planner are of higher quality compared to those found by the other baseline algorithms.
Train Scheduling: Hardness and Algorithms

We introduce the Train Scheduling Problem which can be described as follows: Given m trains via their tracks, i.e., curves in the plane, and the trains’ lengths, we want to compute a schedule that moves collision-free and with limited speed the trains along their tracks such that the maximal travel time is minimized. We prove that there is no FPTAS for the Train Scheduling Problem unless P = NP. Furthermore, we provide near-optimal runtime algorithms extending existing schedules.
Security of Big Data Based on the Technology of Cloud Computing

To solve the problem of big data security and privacy protection, and expound the concept of cloud computing, big data and the relationship between them, the existing security and privacy protection method characteristic and problems were studied. A reference model is proposed which is based on cloud platform. In this model the physical level, data layer, interface layer and application layer step by step in to implement the system security risk early warning and threat perception, this provides an effective solution for the research of big data security. At the same time, a future research direction that uses the blockchain to solve cloud security and privacy protection is also pointed out.