Reviewerly/exHarmony huggingface dataset:
Classification of Cervical Cytology Overlapping Cell Images with Transfer Learning Architectures

Cervical cell classification is a clinical biomarker in cervical cancer screening at early stages. An accurate and early diagnosis plays a vital role in preventing the cervical cancer. Recently, transfer learning using deep convolutional neural networks; have been deployed in many biomedical applications. The proposed work aims at applying the cutting edge pre-trained networks: AlexNet, ImageNet and Places365, to cervix images to detect the cancer. These pre-trained networks are fine-tuned and retrained for cervical cancer augmented data with benchmark CERVIX93 dataset available publically. The models were evaluated on performance measures viz; accuracy, precision, sensitivity, specificity, F-Score, MCC and kappa score. The results reflect that the AlexNet model is best for cervical cancer prediction with 99.03% accuracy and 0.98 of kappa coefficient showing a perfect agreement. Finally, the significant success rate makes the AlexNet model a useful assistive tool for radiologist and clinicians to detect the cervical cancer from pap-smear cytology images.
Creative Experiences for Engaging Communities with Cultural Heritage through Place-based Narratives

This research explores technologically advanced means to enhance audiences’ connection with cultural heritage assets through participatory creative methods that particularly reinforce young people’s sense of identity and well-being during sensitive “transitional” periods of their lives. Hence, the research investigates how communities can meaningfully connect with cultural heritage through creative experiences, while aiming at lowering the entry barriers to increasing audiences’ participation. For this, the research deploys narrative approaches to illuminate different viewpoints and interpretations of cultural heritage within communities. The contribution of the article is twofold, as it includes a novel approach for developing and re-telling communities’ narratives linked to people, objects, sites, and events in the urban landscape. At the same time, it proposes a workflow to digitise and communicate these narratives through Augmented Reality (AR) Maps by proposing methods for digitisation and producing physical printed elements for the experience. These physical elements are then augmented with digital narratives, delivered through Immersive Web technology. This concept is proposed as a means to document and disseminate the narratives in a way that enhances the public understanding and appreciation of objects and sites. The approach was tested with a class of children in a local primary school in Brighton and Hove (UK) to understand its suitability for community engagement, targeting young audiences. The significance of the research is that it demonstrates the potential of the synergy between creative and digital approaches for enabling meaningful engagement with the cultural heritage while improving the well-being of the participants as well as their sense of community and place.
UAV Swarm Real-Time Path Planning Algorithm Based on Improved Artificial Potential Field Method

In this paper, a novel real-time path planning algorithm for the unmanned aerial vehicle (UAV) swarm, based on improved artificial potential field method (APFM), is proposed to solve the issue that the traditional path planning method for a single UAV is not suitable for distributed UAV swarm. In this algorithm, UAV is not only subjected to the typical attraction of the target and repulsion of the obstacle in APFM but also to other forces. The attraction among UAV swarm is imported to keep the UAV swarm formation relatively compact, and the repulsion among UAV swarm is imported to prevent collisions between them, and the repulsion by the new obstacle is imported to solve the problem of incomplete reconnaissance obstacle outside the defense area. This method inherits APFM's advantages, such as fast calculation speed, high real-time performance, and small memory occupation. The simulation results show the local extremum problem of a single UAV subjected to the attraction equal to the repulsion in APFM is solved. UAV swarm can reach their targets in tight formation. They can well avoid obstacles and new obstacles, and avoid collision among UAV swarm. All those verify the effectiveness of the algorithm.
Competence-Aware Path Planning Via Introspective Perception

Robots deployed in the real world over extendedperiods of time need to reason about unexpected failures, learn to predict them, and to proactively take actions to avoid future failures. Existing approaches for competence-aware planning are either model-based, requiring explicit enumeration of known failure sources, or purely statistical, using state- and location-specific failure statistics to infer competence. We instead propose a structured model-free approach to competence-aware planning by reasoning about plan execution failures due to errors in perception, without requiring a priori enumeration of failure sources or requiring location-specific failure statistics. We introduce <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">competence-aware path planning via introspective perception (CPIP)</i> , a Bayesian framework to iteratively learn and exploit task-level competence in novel deployment environments. CPIP factorizes the competence-aware planning problem into two components. First, perception errors are learned in a model-free and location-agnostic setting via <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">introspective perception</i> prior to deployment in novel environments. Second, during actual deployments, the prediction of task-level failures is learned in a context-aware setting. Experiments in a simulation show that the proposed CPIP approach outperforms the frequentist baseline in multiple mobile robot tasks, and is further validated via real robot experiments in environments with perceptually challenging obstacles and terrain.
Procedures for Digital Forensics and Incident Response on Including Data Integrity Constraints on Solid-State Drives (SSD) - A Literature Review

Background/Purpose: To get evidence from suspect computers running on Windows Operating System, law enforcement agencies and corporations follow many standard procedures relevant to Digital Forensics and Incident Response processes. The primary contrast between forensics and incident response is that forensics is evidence-driven and is often more closely connected with criminal activity, while incident response is more focused on discovering, containing, and recovering from breach of security incidents. A guideline is often intended to simplify certain procedures in accordance with a predefined routine or good practice. As data storage technology progresses from hard disc drives (HDDs) to solid-state drives (SSDs), it has become more difficult for Digital Forensics Analysts to perform evidence acquisition tasks from suspicious systems due to file integrity issues. Existing forensic principles and methods were created mostly on the basis of hard disc drive technology. This literature survey analyses several guidelines to identify gaps in SSD Forensic challenges and makes recommendations for improvement. Objective: To survey leading Digital Forensics and Incident Response guidelines on how SSD forensic acquisition procedures are outlined and to find the gaps and suggest enhancements that might be made. Design/Methodology/Approach: Data from academic papers, web articles, and other sources is analysed and presented using ABCD analysis. Findings/Results: Cyber Security Framework is a vital aspect of an organisations strategy to safeguard its IT assets from cyber assaults and other form of damages. Most organisation use NIST framework since it is being generally acknowledged. However, owing to quick improvement in new technologies CSF’s need to be kept up to date in order to confront emerging cyber security threats. After verifying the policy framework of NIST 800-61, it was determined that the SSD forensic gathering approach which raises problems about data integrity has not been addressed. Originality/Value: A study comparing and contrasting different CSFs in the field of Digital Forensics and Incident Response with the most recent emerging technologies will draw more attention to this area from a wider range of stakeholders, allowing the policy framework to keep pace with the most recent emerging technologies in the same time frame. Paper Type: Literature Review Paper.
McEliece cryptosystem: simulation and security vulnerabilities

While symmetric key cryptosystems utilise single private key at both ends for encryption and decryption purposes and this mentioned scenario gives advantages of better speed-up as compare to public key cryptosystems. Still, security is a challenge while performing compatibly efficient key distribution and secure private data transfer among entities in an untrusted environment. McEliece cryptosystem, designed in 1978, is a public key based cryptosystem whose security is based on some unknown natured error-correcting codes. This paper gives state-of-the-art detailed overview on this cryptosystem, its component-wise algorithmic description and implementation. Various attacks on McEliece cryptosystem are discussed separately. The experimental results employing Goppa codes are also presented in the paper where the simulations are performed on various extension degrees. Based on the simulations performed, we concluded the results along with the various issues faced while implementation.
Software Complex of Biometric Identification Based on Neural Network Face Recognition

The article describes the development of a security facial recognition system for the university. Analyzed the latest solutions in the field of recognition and identification of persons. The software and hardware architecture of the developed system, the methods incorporated in the recognition program are described. Examples of the program implementation faces 2D-Recognition with Viola-Johns method and Neural Networks deep learning are given.
HASN: A hierarchical attack surface network for system security analysis

Attack surfaces, as one of the security models, can help people to analyse the security of systems in cyberspace, such as risk assessment by utilizing various security metrics or providing a cost-effective network hardening solution. Numerous attack surface models have been proposed in the past decade, but they are not appropriate for describing complex systems with heterogeneous components. To address this limitation, we propose to use a two-layer Hierarchical Attack Surface Network (HASN) that models the data interactions and resource distribution of the system in a component-oriented view. First, we formally define the HASN by extending the entry point and exit point framework. Second, in order to assess data input risk and output risk on the HASN, we propose two behaviour models and two simulation-based risk metrics. Last, we conduct experiments for three network systems. Our experimental results show that the proposed approach is applicable and effective.
Chaotic artificial bee colony with elite opposition-based learning

Artificial bee colony (ABC) algorithm is a promising evolutionary algorithm inspired by the foraging behaviour of honey bee swarm, which has obtained satisfactory solutions in diverse applications. However, the basic ABC often demonstrates insufficient exploitation capability in some cases. To address this concerning issue, a chaotic artificial bee colony with elite opposition-based learning strategy (CEOABC) is proposed in this paper. During the search process, CEOABC employs the chaotic local search to promote the exploitation ability. Moreover, the elite opposition-based learning strategy is utilised to exploit the potential information of the exhausted solution. Experimental results compared with several ABC variants show that CEOABC is a competitive approach for global optimisation.
Transformational leadership as an antecedent for organisational commitment and job performance in the banking sector of Jordan

Guided by social-exchange-theory, present study examined transformational leadership (TL) as an antecedent for organisational commitment (OC) and its subsequent influence on job performance (JP). Through using a structured self-administered questionnaire, data were collected surveying (439) non-executive employees operating in (15) banks listed in Amman-Stock-Exchange through non-probability purposive sampling. Structural-equation-modelling using SMART-PLS was utilised and the result reported that TL components achieved 44%, 42.5% and 35.4% of the variance in affective, continuous and normative commitment respectively. Idealised influence did not have any significant effect on any level of OC, whereas intellectual stimulation influenced the three levels of OC. Individualised consideration influenced only affective and continuous commitment, and inspirational motivation influenced only normative and continuous commitment. Moreover, only affective commitment was significant in influencing JP and accounted for 82% of the variance. Following the results, discussion and implication were provided. Also, directions and suggestions for future researches were presented.
Splicing Localization Based on Noise Level Inconsistencies in Residuals of Color Channel Differences

With the rapid usage of networking sites there is an enormous increase in image sharing over the internet. At the same time altering or tempering images has become much easier with the availability of photo editing software. Splicing is one of the tempering method, where an object from one image is copied and pasted into another image, is often used with the aim of either getting attention for fun or misleading the general masses. Thus, authenticity of images shared on internet is debatable. Active research is going on in the field of image forensics in order to examine the trustworthiness of the images. Amongst several techniques available for dealing with image splicing, the statistical based methods are gaining attention in research community as it uses image’s local statistics. We propose a simple and effective method based on noise inconsistencies in residuals of Color channel difference for forensic analysis to localize the splicing image forgery. First the image is decomposed in to super pixels and extracted in regular shapes. From each super pixel, three color channel differences are extracted and noise level is estimated on the residual. Finally, the super pixels are clustered into two groups using Farthest Distributed Centroids Clustering (FDCC) method for classifying superpixel as tampered or original. The experimental results show the simplicity and effectiveness of the proposed method over the state of the art.
Research on the cultivation of students' autonomous learning ability based on MOOC-based network interactive teaching

The traditional network interactive teaching system does not consider the real-time interaction of teaching information resources, and there is a problem that students' teaching feedback is not in place. To this end, an interactive teaching system based on MOOC is proposed. The teaching system adopts the B/S mode, and divides the system into a presentation layer, a business layer and a data layer to improve the processing speed. And an interactive communication module is designed, which realises the real-time interaction of the video classroom in the network era, and achieves the purpose of cultivating students' independent learning ability. The experimental results show that the system improves the cognitive ability of students by about 20%, and improves the ability of students to develop learning plans, indicating that the system has a significant impact on the cultivation of students' self-learning ability.
Agile research

In this paper we ask: “how might we take the ideas, the methods and the underlying philosophy behind agile software development and explore applying them in the context of doing research — even research that does not involve software development?” We look at some examples of agile research methods and think about how they might inspire the design of even better methods. We also try to address some potential criticisms of an approach that aims to minimize a need for Big Design Up Front by developing tighter iteration cycles, coupled with reflection and learning as part of a process for doing research.
TikTok dangers

The TikTok video-sharing platform is coming under intense scrutiny after the US Government declared it a security risk and researchers unveiled a number of vulnerabilities.
A cryptographic data hiding algorithm with high cover text capacity

Cryptography and steganography are foremost techniques used to ensure security and confidentiality of secret information. In steganography, data hiding capacity with security is a great challenge for the researchers. In present scheme, a novel approach with combination of steganography and cryptography is proposed to achieve high data hiding capacity with greater security. To accomplish this goal, three level encryption is applied to hide secret message by using bit complement and bit right rotation. Different Unicode characters such as zero width non-joiner (ZWNJ), zero width joiner (ZWJ) and zero width character (ZWC), are used to conceal secret information into English cover text. To embed secret data into cover text, firstly, algorithm implements three level encryption on confidential data and then resulting binary is embedded into cover text by using Unicode characters. The results revealed that recently designed algorithm has higher data security due to light weight and efficient encryption mechanism along with 2-bit/char cover text capacity. Moreover, there is no overhead of secret key generation and exchange from source to destination. Projected technique is easy to implement, hard to break and reduces intruder's attention.
Cyber autonomy

In 2016, the Defense Advanced Research Projects Agency hosted the Cyber Grand Challenge, a competition which invited participating finalist teams to develop automated cyber defense systems that can self-discover, prove, and correct software vulnerabilities at real time – without human intervention. Many vendors are touting network automation programs with existing security information and event management tools as cyber autonomy. The dilemma between the need to patch system vulnerabilities and the need to maintain business or operational continuity also places pressure on software migration processes and time. Software migration (or modernization) is the current practice of modernizing software to a newer version. In contrast to the rate of new apps developed, the rate of discovery of vulnerabilities are lagging behind significantly. This builds a strong case for automation, especially because the software vulnerability identification and remediation processes can be done at rate faster than production.
A fuzzy approach to prioritise DEA ranked association rules

Association rule mining discovers interesting information from large databases. Frequency, reliability and domain knowledge form the multiple criteria for evaluation these association rules. Data envelopment analysis (DEA) is a popular technique used to rank association rules based on the previously mentioned multiple criteria. A decision maker might be interested to have a priority list of these ranked rules based on business and situational requirements. We present an approach to prioritise DEA ranked association rules based on the preference and desirability of the decision maker for different criteria. A modified generalised fuzzy evaluation method (MGFEM) obtains vector-valued fuzzy scores of a group of decision makers and aggregate them to form a preference. A fuzzy logic-based decision support mechanism prioritises these rules based on the decision maker's desirability using the membership function and preference obtained from MGFEM. An example of DEA ranked association rules is presented to explain this innovative approach for prioritisation.
Soft multimedia assisted new energy productive landscape design based on environmental analysis and edge-driven artificial intelligence

New energy landscape architecture is a new energy building integrated with aesthetics and art design, which can meet people's dual needs of low energy consumption and architectural aesthetics. In recent years, the development speed of new energy landscape architecture is increasing, and the design and research of new energy landscape architecture has become a hot spot. The new values, aesthetics, technology and design concepts will always stimulate and promote the urban landscape design to constantly enrich itself. Therefore, it is a new exploration direction to shape new urban landscape design through multimedia technology. Multimedia technology uses computer to process text, graphics, images, sound, animation, video and other information to establish logical relationship and human–computer interaction. The virtual reality technology in multimedia integrates the latest development achievements of computer graphics, multimedia, artificial intelligence, multi-sensor, network, parallel processing and other technologies. Multimedia technology makes the colour of the landscape form the most visual impact factors, it is easy to leave a deep impression on people, to achieve the visual effect of the landscape. Therefore, this paper studies soft multimedia assisted new energy productive landscape design based on the environmental analysis and edge-driven artificial intelligence. The designed framework is efficient through the comparison simulations.
Reversible selective embedding for DICOM image security and integrity using visual cryptography

Information security has been one of the biggest challenges in the era of telemedicine applications. To protect the patient's private records in medical images, several traditional methods are used. One of them is hiding the sensitive part of the image by embedding a different message into it. The data embedding can be done using methods like reversible data hiding (RDH) or reversible watermarking in the region of interest (ROI) of the original medical images. For ensuring the authenticity and integrity of the received medical image, an innovative data embedding technique is proposed in this paper. The proposed technique chooses selective blocks from the ROI based on the smoothness function and the region of non-interest (RONI) is embedded into ROI using some reversible operations. The proposed technique is an application of visual cryptography where the embedded region cannot be visually detected by an attacker. The experimental results prove the reversibility and the efficiency of the proposed approach.