Reviewerly/exHarmony huggingface dataset:
Implementation Techniques to Parallelize Agent-Based Graph Analysis

The current trend for parallelizing scalable graph analysis is to use major big-data tools such as MapReduce, Spark/GraphX, and Storm. Each programming framework uses graph analysis strategies based on either (1) message passing among graph vertices or (2) screening of graph edges. The former keeps exchanging messages among all neighboring vertices until all of them fall into a stable state and thus no more messages are generated. The latter examines the connectivity of edges to find particular network shapes, (e.g., counting the number of triangles). Although these two approaches are well established as conventional graph solutions, the underlying algorithms may not be clear enough to keep track of how vertices, edges, and messages are processed in parallel. Contrary to these conventional approaches, agent-based graph analysis drives agents over a graph, which allows programmers to code their solutions from the drivers seat view. However, there are performance drawbacks, mainly resulting from agent management overheads, that make it impractical to use agents. The Multi-Agent Spatial Simulation (MASS) library uses reactive agents, small enough to spawn, move, and get terminated quickly. By implementing two optimization techniques in MASS: agent population control and asynchronous agent migration, we have facilitated agent-based graph analysis. This paper presents these two techniques, demonstrates agents intuitive programmability, and their competitive execution performance.
GACM based segmentation method for Lung nodule detection and classification of stages using CT images

The GACM (Gradient based Active Contour Model) is proposed for segmenting the lung region from digital CT (Computed Tomography) images. In GACM, the gradient information present in the image at lung boundaries are extracted using active contour model. Then shape and GLCM feature sets are extracted from normalized images. The PCA is used for optimal feature set evaluation based on the consistency of the feature attributes. The lung nodules detection and stage classifications are carried out using multi class Support Vector Machine which makes the decision boundary between the various classes using hyper planes. The experiments result shows that the stage classification accuracy and sensitivity achieved by the proposed method is 95.3% and 92.17% respectively. The results of proposed system are improved when compared with existing system in terms of accuracy and sensitivity.
Information Securing in Organizations

Based on a field study of information security with two large corporations, we conjecture that information security should be aligned with varying organizational structures while adapted to environmental contingencies. We thus introduce a novel concept of information-securing security-oriented actions-interactions in organizations to interpret the fit/misfit dilemma. Also, we suggest that conflicts can constantly exist between organizational security and business goals. To that end, from a dialectic perspective we elucidate how and why organizations act toward one direction first (e.g. business-oriented), then another (e.g. security-oriented), and eventually a synthesized one.
Role of Cyber Law and Mitigation Strategies in Perspective of Pakistan to Cope Cyber Threats

In this digital world, innovations and advancement have increased the number of users and the user ratio continues to double, triple and so-on. Unfortunately, the increase of crime, malware activities, has also increased. Thus, the privacy and security of systems and services is a prominent challenge for organizations, and countries. In this connection, each and every developed or developing country is practicing to design policies, strategies, cyber laws to deal with illegal activities, taken place via the internet. Pakistan is also one of those developing countries who have no any legislative policy, strategy or cyber law to deal such activities, having outdated policies and acts. Therefore, this article discusses some of the most dangerous malware attacks such as WanaCry, and Stuxnet. The article also mentions some countries with cyber policies/strategies/laws comparing them to Pakistan. The role of Pakistan is also part of the article and calls for awareness of researchers, policy/law makers. Some recommendations/suggestions have also been elaborated in this article for the re-designing of existed cyber laws.
Order-Sensitive Imputation for Clustered Missing Values (Extended Abstract)

To study the issue of missing values (MVs), we propose the Order-Sensitive Imputation for Clustered Missing values (OSICM) framework, in which missing values are imputed sequentially such that the values filled earlier in the process are also used for later imputation of other MVs. Obviously, the order of imputations is critical to the effectiveness and efficiency of OSICM framework. We formulate the searching of the optimal imputation order as an optimization problem, and show its NP-hardness. Furthermore, we devise an algorithm to find the exact optimal solution and propose two approximate/heuristic algorithms to trade off effectiveness for efficiency. Finally, we conduct extensive experiments on real and synthetic datasets to demonstrate the superiority of our OSICM framework.
Scalable Frequent Sequence Mining with Flexible Subsequence Constraints

We study scalable algorithms for frequent sequence mining under flexible subsequence constraints. Such constraints enable applications to specify concisely which patterns are of interest and which are not. We focus on the bulk synchronous parallel model with one round of communication; this model is suitable for platforms such as MapReduce or Spark. We derive a general framework for frequent sequence mining under this model and propose the D-SEQ and D-CAND algorithms within this framework. The algorithms differ in what data are communicated and how computation is split up among workers. To the best of our knowledge, D-SEQ and D-CAND are the first scalable algorithms for frequent sequence mining with flexible constraints. We conducted an experimental study on multiple real-world datasets that suggests that our algorithms scale nearly linearly, outperform common baselines, and offer acceptable generalization overhead over existing, less general mining algorithms.
Pattern Recognition with Using Effective Algorithms and Methods of Computer Vision Library

This article discusses the possibility of using effective recognition algorithms of using the OpenCV library in computer vision area. It describes the algorithms of the Harris Corner Detection, the SURF (Speeded up robust features), SIFT (Scale-Invariant Feature Transform) and FAST (Features from Accelerated Segment Test) search algorithms, as well as their comparative analysis of the performance and recognition quality. Contour analysis for pattern recognition has also used. Applications of mathematical models and formulas for binarization of the image.
MFBN

Person Re-IDentification (Re-ID) has developed rapidly with deep learning methods, as for these methods, the base mod- els used in most of them are not customized for Re-ID task. Although some studies have carefully designed the models special for Re-ID task, these models are always not easy to be the base model and expand with new methods due to their great complexity. In this paper, we propose a novel efficient base model named as Multi-granularity Feature Boosting Network (MFBN). MFBN consists of branches with information in different granularities. MFBN combines these branches into one whole, so MFBN is easy to be ex- tended as a base model. Moreover, MFBN applies feature boosting technique to boost fine granularitiy branch features with coarse granularity branch features, and applies channel- wise attention to increase diversities between features in multiple granularities. With these improves, MFBN has surpassed popular base models and got state-of-the-art results on mainstream Re-ID datasets including Market-1501, DukeMTMC-reID and CUHK-03. MFBN achieves results of rank-1/mAP=95.2%/93.2% on Market-1501 dataset and rank-1/mAP=90.3%/88.3% on DukeMTMC-reID dataset. Code and pretrained models are available at https://github.com/hsyi/Multi-granularity-Feature-Boosting-Network
MeMa

The Memory Machine is an ambitious project that aims to develop a device to capture people's memories to create a blend of personal and factual data that builds identities, and contextualizes personal recollections. The Memory Machine has been guided by co-production and user-centred design principles to ensure users' input has a critical role in the development of the technology. Through a series of creative workshops, we facilitated participants to discuss and represent their perceptions of memory making and recollection, towards the design of the Memory Machine. This paper investigates how a creative, participatory process enabled technical topics to be explored together, as well as enabling the participants to address more challenging issues of memory; such as painful memories, memory loss, and memories at end-of-life, with a particular focus on dementia, to inform the future design of the Memory Machine.
Protecting the world of connected devices

In 1982, long before a cyber security threat to control system networks was widely recognised, a trojan attack on control system software reportedly caused a huge explosion in a Siberian gas pipeline. Even now, despite technological advancements, many systems that have been retrofitted for compatibility with the Industrial Internet of Things (IIoT) remain unprotected. In a world of connected devices, industries vulnerable to attack must upgrade their security systems. The Industrial Internet of Things (IoT) is becoming an increasingly popular route taken by malicious hackers looking to gain access to organisations' networks. Part of the problem is that network capability is often bolted onto the existing systems. And IT and operational technology (OT) teams lack co-ordination when it comes to security. Nick Boughton of Boulting Technology argues that an end-to-end cyber security approach that incorporates both IT and OT environments must be considered when integrating legacy systems into existing networks.
Video Tamper Detection Based on Convolutional Neural Network and Perceptual Hashing Learning

Perceptual hashing has been widely used in the field of multimedia security. The difficulty of the traditional perceptual hashing algorithm is to find suitable perceptual features. In this paper, we propose a perceptual hashing learning method for tamper detection based on convolutional neural network, where a hashing layer in the convolutional neural network is introduced to learn the features and hash functions. Specifically, the video is decomposed to obtain temporal representative frame (TRF) sequences containing temporal and spatial domain information. Convolutional neural network is then used to learn visual features of each TRF. We further put each feature into the hashing layer to learn independent hash functions and fuse these features to generate the video hash. Finally, the hash functions and the corresponding video hash are obtained by minimizing the classification loss and quantization error loss. Experimental results and comparisons with state-of-the-art methods show that the algorithm has better classification performance and can effectively perform tamper detection.
The One-Variable Fragment of Corsi Logic

The one-variable fragment of the first-order logic of linear intuitionistic Kripke models, referred to here as Corsi logic, is shown to have as its modal counterpart the many-valued modal logic $$\mathsf {S5(\mathbf {G})}$$. It is also shown that $$\mathsf {S5(\mathbf {G})}$$ can be interpreted in the crisp many-valued modal logic $$\mathsf {S5(\mathbf {G})^C}$$, the modal counterpart of the one-variable fragment of first-order Gödel logic. Finally, an algebraic finite model property is proved for $$\mathsf {S5(\mathbf {G})^C}$$ and used to establish co-NP-completeness for validity in the aforementioned modal logics and one-variable fragments.
A Faster Algorithm for Computing the Kernel of Maximum Agreement Subtrees

The maximum agreement subtree method determines the consensus of a collection of phylogenetic trees by identifying maximum cardinality subsets of leaves for which all input trees agree. The trees induced by these maximum cardinality subsets are maximum agreement subtrees (MASTs). A single MAST may be misleading, since there can exist two MASTs which share almost no leaves; nevertheless, it may be impossible to inspect all MASTs, since the number of MASTs can be exponential in the number of leaves. To overcome this drawback, Swenson et al. suggested to further summarize the information common to all MASTs by their intersection, which is called the kernel agreement subtree (KAST). The construction of the KAST is the focus of this paper. Swenson et al. had an O(kn <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> + n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> + n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d + 1</sup> )O(kn3+n4+nd+1) time algorithm for computing the KAST of kk trees on nn leaves, in which at least one tree has maximum degree dd. In this paper, an O(kn <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> + n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sup> )O(kn3+nd)-time algorithm is presented. We demonstrate the efficiency of our algorithm on simulated trees as well as on ribosomal RNA alignments, where trees with 13,000 taxa took only hours to process, whereas the previous algorithm did not terminate after a week of computation.
Towards Self-managing Systems Through Decentralised Constraint Optimisation

With increases in cyber threats and network complexity the need for self-managing systems that can heal, optimise and coordinate themselves is becoming paramount. Self-managing systems encompass numerous properties, often referred to as self- $$*$$ properties. This dissertation proposal hypothesises that using Distributed Constraint Optimisation (DisCO) to model each self- $$*$$ properties yields a decentralised system whereby attacks on individual management nodes can be isolated. Consequently, the proposed dissertation seeks to address the challenges associated with realising self- $$*$$ properties as DisCO problems.
Collaborative Correlation Filter Tracking with Online Re-detection

Visual object tracking is a challenging task in computer vision. Recently, due to high efficiency and performance, correlation filter-based trackers have attracted much attention. In this paper, we propose a collaborative correlation filter tracking framework. First, we learn multiple kernelized correlation filters for different features independently, and fuse their response maps to obtain a more reliable response map for translation estimation. Then, we learn a scale correlation filter to handle the scale variation. Moreover, in order to further improve the tracking accuracy, we build an online detector to re-detect objects in local neighbor region, when the tracking result is unreliable. Extensive evaluations on the recent benchmark datasets show that the proposed algorithm performs favorably against several state-of-the-art methods.
Kernel Analysis based on SVDD for Face Recognition from Image Set

This paper addresses the problem of Face Recognition based on Image Set (FRIS) by kernel learning and proposed an extended kernel discriminant analysis framework for FRIS. By support vector machine learning, an image set from the original input space is mapped into the model space and described wit h Support Vector Domain Description (SVDD) to handle the underlying non-linearity in data space. In model space, a hyper-sphere encloses most of the mapped data, and the outliers lie outside the hyper-sphere. By exploring an efficient metric for the data domains in model space, we derive a kernel function maps the data from the model space to a high-dimensional feature space, to which many Euclidean algorithms can be generalized. The proposed method is evaluated on face recognition tasks. Comparisons with several state-of-the-art FRIS methods are performed on ChokePoint and CMU MoBo video database. The proposed methods have demonstrated promising performance.
Understanding User Interface Preferences for XR Environments When Exploring Physics and Engineering Principles

In this investigation we seek to understand user technology interface preferences and simulation features for exploring physics-based concepts found in engineering educational programs. A 3D magnetic field emanating from a bar magnet was used as the representative physics concept. The investigation developed three different virtual environments using three different technologies and exploiting the capabilities available through the use of those technologies. This provides a suite of platforms to compare and identify the types of interactions and control capabilities a user prefers when exploring this type of phenomena. These environments will now enable the behaviors and preferences to be identified through observation of the user as they navigate the event and manipulate various options available to them depending upon the technology used.
Developing an Integrated Approach for the Scenario-based Management of Simulation Games

The paper introduces the approach for the scenario-based management of simulation games. Nowadays one of the main requirements for simulation games is the diversity of modeled situations and their correspondence to a real life conditions as it supports a training of students and professionals with appropriate competencies. Modern computer technologies allow creating complex simulation games with multiple scenarios to be played. The objective of the research is to introduce an integrated approach for the game scenario generation, modeling and management that support simulation games' governance in line with the study course content. This requires elaborating of a formal approach, which is described in the paper.
Cash Note With High Performance Security

Rapid creation of fake notes affects the financial status, the value of the currency, unemployment rate, etc. In this paper, a cash note is generated with high level security features – it has specific serial number which will be encoded in that cash note with a procedure. Even if a person encodes the serial number in a fake cash note, the decoder will identify it as a fake one. If serial number and cash denomination at the decoding match with those of encoded cash note, then that note can be treated as genuine one. Otherwise it can be considered as a fake one. First, each digit of the serial number on the cash note is converted into binary number with a procedure and can be located beside the count indicator. Every cash note has some denomination and its decimal value is converted into binary number which can be kept beside the mode indicator. The count indicator and mode indicator are generated in the course of operation. The obtained encoded binary number is divided into codewords of 8 bits each. Each bit of code word is arranged at random locations in the cash note.
Cognitive Personal Security Systems

In this paper will be described possible applications of cognitive systems for security purposes and cryptographic protocols. In particular will be presented the ways of using advanced cognitive approaches and personal, cognitive features for creation of secure protocols, oriented on user authentication or secure data management in Cloud and Big data resources. The new computing area called cognitive cryptography will be also introduced.
HCP: A Matlab package to create beautiful heatmaps with richly annotated covariates

A heatmap is a graphical technique that maps 2-dimensional matrices of numerical values to colors to provide an immediate and intuitive visualization of the underlying patterns (Eisen, Spellman, Brown, & Botstein, 1998).Heatmaps are often used in conjunction with cluster analysis to re-order observations and/or features by similarity and thus, rendering common and distinct patterns more apparent.When generating these visualizations, it is often of interest to interpret the underlying patterns in the context of other data sources.In the field of bioinformatics, heatmaps are frequently used to visualize high-throughput and high-dimensional datasets, such as those derived from profiling biological samples with -omic technologies (whole genome sequencing, transcriptomics and proteomics).Often, biological samples (for example, patient tumour samples) are characterized at multiple -omic level and it is of interest to contrast and compare patterns captured at the different molecular layers along with their associations with other observable features (covariates).The concurrent display of continuous or categorical covariates enriches the visualization with additional information such as group membership.
Insights in Machine Learning for Cyber-Security Assessment

Information transactions and sharing is a common activity in daily life, at work, private and leisure activities. Currently, companies perform most of information delivery, storing and sharing electronically through internal and external connections. External connections are source of cyber-risk as unauthorized connections could provoke enormous damages. In this framework, cyber-risk assessment is a must for companies as their incomings are in risk, but its implementation is cumbersome. Financial issues produced by breaches in information infrastructures and procedures security are covered by the so called cyber-insurance. Cyber-security management requires a lot of resources both economical and human, involving all the departments and people of the company. Among other activities, alerting of potential risks and damages before any attack is produced, or providing the appropriate means, after a cyber-attack, to prevent a new one and restore, when possible, the damages, are responsibilities of decision-makers. In this scenario, insurability assessment is a must for insurance companies, and risk management engineers are the staff responsible to analyse and report about cyber insurance acceptance test. In order to optimize and automatize the procedure of insurability assessment, artificial intelligence and machine learning techniques constitutes an appropriate tool. In this paper the authors focus the attention on the analysis of the collected data used to take decisions in order to determine dependencies, correlations and take further steps to predict insurability, which in next investigations will be optimized through machine learning techniques.