Reviewerly/exHarmony huggingface dataset:
Implementation of Efficient Segmentation Method for Histopathological Images

In biomedical research domain, it is always very crucial to examine the tissue affected by disease under a microscope and determine the severity of the disease with the process called histopathology, where the labs can produce tissue slides for viewing images digitally. These histopathological images has now become fully digitized. Digitizing these slides, allows pathologist to view them on a computer rather than viewing them on the microscope. Hence cell nuclei recognition and classification plays a crucial role in early detection of cancer. This task becomes even more challenging due to its heavy noise, and small-variant sizes of cell nuclei in histopathological images. To address this issue, an optimization based super pixel clustering algorithm was employed for automatic nuclei cell segmentation. Initially, the histopathological images dataset is acquired from suitable database. And then, normalization technique is applied to remove the noise from images. After denoising, segmentation is applied by using an optimized clustering algorithm to separate the non-nuclei and nuclei cells. The main aim of the proposed method is to implement an efficient segmentation method to overcome the issues of overlapping cells by Segmenting the histopathological images of lung/breast/liver/brain cancer, the proposed work has attained good performance to further help in the early detection of cancer by extracting the relevant features for classifying the given images as benign and malignant.
KLP's Search Algorithm - A New Approach to Reduce the Average Search Time in Binary Search

There are various issues in different fields of Computers, Database Management Systems, Networks, Data Mining, and Artificial intelligence. But, the common problem in any field is searching for an element from a large amount of data. There are numerous techniques for searching element of which most commonly used technique is Binary search where the comparison is done by dividing the entire n elements into half in each iteration and matching with the key. The main objective of this paper is to reduce the expected number of comparisons during the search process. Here this paper presents a new algorithm which reduces the average number of comparisons required to search an element based on the size of the element. It is clearly shown in this paper that by considering only elements whose size is equal to the size of key reduces the number of steps required to search an element.
Encryption of lossless coded videos

Several military files and medical research reports do not tolerate any type of distortion and are at the same time secret to owners. In order to preserve source information and guarantee data security, lossless video compression with encryption seems to be the best alternative. In this work, cryptography was grafted with lossless compression to ensure a high image quality and secure the transmission of such data. Thus, the insertion of the encryption module in the lossless video compression chain appears to be better than the application of compression and encryption separately in terms of computing time. Our developed method reached about 30% gain of total bits and around 75% improvement of the compression ratio proving promising results.
Expression-Aware Face Reconstruction Via A Dual-Stream Network

Recently, 3D face reconstruction from a single image has achieved promising results by adopting the 3D Morphable Model (3DMM). However, as face images in-the-wild have various expressions, it is difficult for 3DMM to handle diverse facial expressions with a large range of variations, due to the limited expressive ability of its linear model, thereby resulting in distortion and ambiguity on facial local regions. To tackle this issue, we present a novel dual-stream network to deal with expression variations. Specifically, in the geometry stream, we propose novel Attribute Spatial Maps to record the spatial information of facial identity and expression attributes in the 2D image space separately. This avoids the interaction between the two attributes, thus keeping the identity information and further improving the ability to cope with expression changes. In the texture stream, we utilize the 3DMM albedo map to a style transfer based method for synthesizing facial appearance, which results in expression-irrelevant as well as realistic face textures. Both quantitative and qualitative evaluations on public datasets demonstrate the ability of our approach to achieve comparable results in face reconstructions under expression variations.
Compressive behaviour of aluminium rectangular hollow tube using Digital Image Correlation (DIC) method

Abstract The lightweight structural application is commonly used aluminium material, which generally applied in automotive, military, aerospace and naval applications. The rectangular cross-section hollow tube of aluminium material performs the better compressive properties compared to other cross-section shapes. In order to investigate the compressive behaviour of aluminium tube, Digital Image Correlation (DIC) method is used to study the evolution of deformation and strain value. The quasi-static compressive test was performed, and the experimental result was compared with the DIC measurement results. It was obtained that the failure behaviour of the aluminium rectangular hollow tube was buckling mode with several inwards and outwards deformation. In addition, it was concluded that the DIC method results were lower than the experimental values on the maximum compressive stress and compressive modulus, which was considered as a good agreement between this two methods with 1.7 % percentage of error. It is suggested that DIC method is a sufficient method to measure the deformation and strain values with non-contact and non-destructive features.
Are you sure? Prediction revision in automated <scp>decision‐making</scp>

Abstract With the rapid improvements in machine learning and deep learning, decisions made by automated decision support systems (DSS) will increase. Besides the accuracy of predictions, their explainability becomes more important. The algorithms can construct complex mathematical prediction models. This causes insecurity to the predictions. The insecurity rises the need for equipping the algorithms with explanations. To examine how users trust automated DSS, an experiment was conducted. Our research aim is to examine how participants supported by an DSS revise their initial prediction by four varying approaches (treatments) in a between‐subject design study. The four treatments differ in the degree of explainability to understand the predictions of the system. First we used an interpretable regression model, second a Random Forest (considered to be a black box [BB]), third the BB with a local explanation and last the BB with a global explanation. We noticed that all participants improved their predictions after receiving an advice whether it was a complete BB or an BB with an explanation. The major finding was that interpretable models were not incorporated more in the decision process than BB models or BB models with explanations.
Isotonic boosting classification rules

In many real classification problems a monotone relation between some predictors and the classes may be assumed when higher (or lower) values of those predictors are related to higher levels of the response. In this paper, we propose new boosting algorithms, based on LogitBoost, that incorporate this isotonicity information, yielding more accurate and easily interpretable rules. These algorithms are based on theoretical developments that consider isotonic regression. We show the good performance of these procedures not only on simulations, but also on real data sets coming from two very different contexts, namely cancer diagnostic and failure of induction motors.
JASSjr: The Minimalistic BM25 Search Engine for Teaching and Learning Information Retrieval

We present JASSjr, a minimalistic trec_eval compatible BM25-ranking search engine that can index small TREC data sets such as the Wall Street Journal collection. We do this for several reasons. First, to demonstrate how a term-at-a-time (TAAT) search engine works. Second, to demonstrate that a straightforward and competitive search engine with indexer can be written in under 600 lines of documented code. Third, as a way of providing a simple code-base for teaching Information Retrieval. We present two index-compatible versions (one in C/C++, the other in Java) that compile and run on MacOS, Linux, and Windows.
Survey on Privacy Preservation Technique

Data mining in privacy preservation is the process of hiding and protecting sensitive statistics of individuals. Data mining in privacy preservation foresee the risk of executing mining algorithms on secured data which should not be revealed to party running algorithm. The enormous quantity of broad data is regularly gathered and analyzed by some of the application which uses mining concept(i.e. sharing data that will be useful to end users).PPDM has become a notable issue while sharing secured data. This paper presents the PPDM techniques in a brief manner and also other privacy preservation methods in order to prevent and safeguard the data from the unauthorized parties.
A Case Study of Multi-class Classification with Diversified Precision Recall Requirements for Query Disambiguation

We introduce a new metric for measuring the performance of multi-class classifiers. This metric is a generalization of the f1 score that is defined on binary classifiers, and offers significant improvement over other generalizations such as micro- and macro-averaging. In particular, one can select coefficients that weight the per-class precision and recall, as well as the overall class importance, with a robust mathematical interpretation. When certain parameters are selected our metric yields macro-averaged statistic as a special case. We demonstrate the efficacy of this metric on an application in genealogical search.
Screencast Tutorial Video Understanding

Screencast tutorials are videos created by people to teach how to use software applications or demonstrate procedures for accomplishing tasks. It is very popular for both novice and experienced users to learn new skills, compared to other tutorial media such as text, because of the visual guidance and the ease of understanding. In this paper, we propose visual understanding of screencast tutorials as a new research problem to the computer vision community. We collect a new dataset of Adobe Photoshop video tutorials and annotate it with both low-level and high-level semantic labels. We introduce a bottom-up pipeline to understand Photoshop video tutorials. We leverage state-of-the-art object detection algorithms with domain specific visual cues to detect important events in a video tutorial and segment it into clips according to the detected events. We propose a visual cue reasoning algorithm for two high-level tasks: video retrieval and video captioning. We conduct extensive evaluations of the proposed pipeline. Experimental results show that it is effective in terms of understanding video tutorials. We believe our work will serves as a starting point for future research on this important application domain of video understanding.
FabSoften: Face Beautification via Dynamic Skin Smoothing, Guided Feathering, and Texture Restoration

Face retouching is a widespread application in modern smartphone cameras with its high business value evidenced by its broad user base. We propose a real-time face softening approach that smooths blemishes in the facial skin region, followed by a wavelet band manipulation to restore the underlying skin texture, which produces a highly appealing `beautified' face that retains its natural appearance. Softening is carried out by an attribute-aware dynamic smoothing filter that is guided by facial attributes, including the number of blemishes and coarseness of facial skin texture. The proposed solution is robust to wide variations in lighting conditions, skin nonuniformities, blemishes, the presence of facial accessories, and delicate hair-like regions. The method includes an explicit facial hair preservation module to preserve their delicate texture while smoothing blemishes. We perform a qualitative comparison of our proposed face softening approach with numerous state-of-the-art techniques and commercial products. We demonstrate the power of our method in producing beautified faces at a minimal performance cost, which enables smooth execution on low-power devices like smartphones.
THE POTENTIAL OF WEARABLE DEMONSTRATORS INTRODUCING INNOVATIVE TECHNOLOGIES

Abstract With the introduction of ‘5G’ data transfer gets faster and further reaching than ever before. This new communication technology paves the way for an exchange of skills and competencies between humans and machines. This raises the question of how future users can profit and understand the potential brought about by these technologies. This paper elaborates the use of demonstrators in a pilot study as research tools and assesses their potential. It gives first insights why demonstrators are suitable to set a basis for public recognition for body-worn CPS and how to promote innovative visions.
CONCEPTUAL MODEL FOR (SEMI-) AUTOMATED DERIVATION OF EVALUATION CRITERIA IN REQUIREMENTS MODELLING

Abstract Requirements act as a limitation of the solution space, which represents the stakeholders’ needs and guides the whole product development process. Therefore, forgotten requirements can lead to wrong decisions when using them as a basis for decision-making. This contribution introduces a novel approach to link the requirement and evaluation criteria models to address this problem. For setting up those criteria consistently, the requirements are classified using natural language processing and derived by a ruleset based on a developed mapping between requirement classes and criteria types.
Data Poisoning Attacks against Differentially Private Recommender Systems

Recommender systems based on collaborative filtering are highly vulnerable to data poisoning attacks, where a determined attacker injects fake users with false user-item feedback, with an objective to either corrupt the recommender system or promote/demote a target set of items. Recently, differential privacy was explored as a defense technique against data poisoning attacks in the typical machine learning setting. In this paper, we study the effectiveness of differential privacy against such attacks on matrix factorization based collaborative filtering systems. Concretely, we conduct extensive experiments for evaluating robustness to injection of malicious user profiles by simulating common types of shilling attacks on real-world data and comparing the predictions of typical matrix factorization with differentially private matrix factorization.
Mobile Learning Application Impact Towards Student Performance in Programming Subject

Abstract Writing a program is significant basic proficiency for computer science students. However, majority of the students found it is difficult to learn and score a high mark. In an era of rapidly changing technologies, mobile learning has received increasing emphasis and widely used among educational practitioners and researchers. Thus, the objective of this study is to investigate the factors that influence students learning performance and evaluate the effectiveness of mobile learning using Learn C application in Programming subject. Prior to the factors that influence students learning outcome, a survey was conducted with 88 first year undergraduate students from Faculty of Computer Systems &amp; Software Engineering. Next, a pre-experimental one group pretest-posttest research design was employed with 17 students as respondents to examine the impact of mobile learning application. The collected data was then analysed using thematic analysis, descriptive and inferential statistics. The results showed that there are several factors that affect students learning performance in programming that is, misunderstanding, lack of practices, poor logical thinking and problem solving, no prior knowledge, psychological disorder, less interest, ineffective teaching method and bad attitude. The paired sample t-test of student’s pre-test and post-test scores demonstrate that there were no significant differences before and after the treatment through mobile learning application given. The study has gone some way in enhancing our understanding of factors that affect student in programming learning which could be used to facilitate the development of the students programming abilities.
Advance Watermarking Algorithm Using SURF with DWT and DCT for CT Images

Aiming at the requirements of watermarking technology for medical images, a medical CT image watermarking algorithm based on compressed sensing and multiple transform domains is proposed to make the watermarking highly invisible. Compressing and preprocessing the watermark image can not only play the role of watermark encryption, but also reduce the amount of embedded watermark information. Perform discrete wavelet transform, discrete cosine transform on the perceptual watermark image and the original image, and then use singular value decomposition to perform watermark embedding. Finally, use SURF, Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) for watermark extraction. Simulation results show that the algorithm has high concealment for medical CT images, and it is robust to common image attack methods.
Physical Security Detectors for Critical Infrastructures Against New-Age Threat of Drones and Human Intrusion

Modern critical infrastructures are increasingly turning into distributed, complex Cyber-Physical systems that need proactive protection and fast restoration to mitigate physical or cyber incidents or attacks. Addressing the need for early stage threat detection against physical intrusion, the paper presents two physical security sensors developed within the DEFENDER project for detecting the intrusion of drones and humans using video analytics. The continuous stream of media data obtained from the region of vulnerability and proximity is processed using Region based Fully Connected Neural Network deep-learning model. The novelty of the pro-posed system relies in the processing of multi-threaded media input streams for achieving real-time threat identification. The video analytics solution has been validated using NVIDIA GeForce GTX 1080 for drone detection and NVIDIA GeForce RTX 2070 Max-Q Design for detecting human intruders. The experimental test bed for the validation of the proposed system has been constructed to include environments and situations that are commonly faced by critical infrastructure operators such as the area of protection, tradeoff between angle of coverage against distance of coverage.
Semi-Blind Source Scanner Identification

In this paper, we focus on the identification of a scanner at the origin of a given scanned document provided by a public or private organization. Such documents have generally a particular form and content that can be used to simplify the identification process. The unavailability of a precise model of the noise produced by a scanner make it hard to identify it with high accuracy. Previously, source scanner identification schemes proposed to use different well-known filters which are not perfectly adapted to solve this problem. The proposed approach is designed to suppress the maximum of the image's content taking advantage of the original document form. By doing so, the extraction of the scanner noise or signature from acquired images is achieved with better performance than with the filters used in literature. Once the signature extracted, we employ the Euclidean distance to identify the source scanner of a scanned document under test. Experimental results achieved on documents corresponding to registration forms, filled by hand and then scanned with 5 common use scanners illustrate the superior classification performance when compared with the most recent methods. We also demonstrate the robustness of the proposed solution against lossy compression.
Iterative Dynamic Generic Learning For Single Sample Face Recognition With A Contaminated Gallery

This paper studies a new challenging problem in face recognition (FR) with single sample per person (SSPP), i.e., SSPP FR with a contaminated gallery (SSPP-CG FR), where the gallery is contaminated by variations. In SSPP-CG FR, the popular generic learning methods will suffer serious performance degradation because the applied prototype plus variation (P+V) model is not suitable in such scenarios. The reasons are twofold: 1) The contaminated gallery samples yield bad prototypes to represent the persons; 2) The generated variation dictionary is simply based on the subtraction of average face from generic samples of the same person and cannot well depict the intra-personal variations. To tackle SSPPCG FR, we propose a novel Iterative Dynamic Generic Learning (IDGL) method, where the labeled gallery and unlabeled query sets are fed into a dynamic label feedback network for learning. Specifically, IDGL first recovers the prototypes via a semi-supervised low-rank representation (SSLRR) framework and learns a representative variation dictionary by extracting the “sample-specific” corruptions from an auxiliary generic set. Then, it puts them into the P+V model to estimate labels for query samples. Subsequently, the estimated labels are used as the feedbacks to modify the SSLRR, thus updating new prototypes for the next round of P+V based label estimation. With the dynamic learning network, the accuracy of the estimated labels is improved iteratively owing to the steadily enhanced prototypes. Experiments on various benchmark databases have verified the superiority of IDGL.
Applying Sensor Uncertainty Mitigation Schemes to Detect-and-Avoid Systems

Impact of sensor noise on the performance of Detect-And-Avoid (DAA) systems can be reduced by implementing various mitigation schemes. This paper evaluates the Sensor Uncertainty Mitigation (SUM) method, implemented in the Detect and Avoid Alerting Logic for Unmanned Systems (DAIDALUS) algorithm, a reference implementation in the DAA minimum operational performance standards. DAIDALUS SUM performance is evaluated using a few safety and operational suitability metrics and compared with more traditional approaches using static safety buffers. A large number of encounters representative of low-speed unmanned aircraft against non-cooperative manned aircraft are simulated and evaluated. An air-to-air radar model produces representative sensor noise for the DAA system. Results show that increasing the tunable parameters for horizontal and vertical uncertainty in DAIDALUS SUM improves the safety metric at the cost of increasing the number of system alerts leading to increased workload. A range of SUM parameters is recommended as suitable values for the type of operations considered for this work. General trends and optimal SUM configurations were found to be nearly the same for two large and very different encounter data sets.
Reuse of Similarly Behaving Software Through Polymorphism-Inspired Variability Mechanisms

In many cases, software artifacts share similarity across projects and development teams. However, often this similarity is only partially reflected on the level of design and implementation, and therefore the possibilities for its detection are limited in current variability analysis, clone detection, and application search approaches. In this paper, we propose a method for identification and comparison of similarly behaving software. The method, supported by a prototype tool, analyzes the behavioral similarity of object-oriented code artifacts based on shallow (behavior interface) and deep (behavior transformation) descriptions of the exhibited operations. It further recommends on suitable mechanisms inspired by the notion of polymorphism in order to guide and support current and future reuse. The approach was evaluated on two data-sets, obtained following two different scenarios: clone-and-own and independent development by different teams.