Reviewerly/exHarmony huggingface dataset:
Inspectors Academy : Pedagogical Design for Requirements Inspection Training

The core aim of requirements inspection is to ensure the high quality of already elicited requirements in the Software Requirements Specification. Teaching requirements inspection to novices is challenging, as inspecting requirements needs several skills as well as knowledge of the product and process that is hard to achieve in a classroom environment. Published studies about pedagogical design specifically for teaching requirements inspection are scarce. Our objective is to present the design and evaluation of a postgraduate course for requirements inspection training. We conducted an empirical study with 138 postgraduate students, teamed up in 34 groups to conduct requirements inspection. We performed qualitative analysis on the data collected from students' reflection reports to assess the effects of the pedagogical design in terms of benefits and challenges. We also quantitatively analyze the correlation between the students' performance in conducting inspections and their ability of writing specifications. From the analysis of students' reflections, several themes emerged such as their difficulty of working with limited information, but also revealed the benefits of learning teamwork and writing good requirements. This qualitative analysis also provides recommendations for improving the related activities. The results revealed a moderate positive correlation between the performance in writing specification and inspection.
Instance Segmentation Model Created from Three Semantic Segmentations of Mask, Boundary and Centroid Pixels Verified on GlaS Dataset

Segmentation is the key computer vision task in modern medicine applications.Instance segmentation became the prevalent way to improve segmentation performance in recent years.This work proposes a novel way to design an instance segmentation model that combines 3 semantic segmentation models dedicated for foreground, boundary and centroid predictions.It contains no detector so it is orthogonal to a standard instance segmentation design and can be used to improve the performance of a standard design.The presented custom designed model is verified on the Gland Segmentation in Colon Histology Images dataset.
Circuit Learning for Logic Regression on High Dimensional Boolean Space

Logic regression aims to find a Boolean model involving binary covariates that predicts the response of an unknown system. It has many important applications, e.g., in data analysis and system design. In the 2019 ICCAD CAD Contest, the challenge of learning a compact circuit representing a black-box input-output pattern generator in a high dimensional Boolean space is formulated as the logic regression problem. This paper presents our winning approach to the problem based on a decision-tree reasoning procedure assisted with a template based preprocessing. Our methods outperformed other contestants in the competition in both prediction accuracy and circuit size.
Construction and Automation of a Microcontrolled Solar Tracker

A solar tracker can be defined as an electromechanical system capable of following the apparent path of the Sun, in order to orient an array of solar panels and/or collectors directly to the solar rays, maximizing the collected energy. Accordingly, the present work describes the process of building and automating a micro-controlled solar tracker. Two mobile structures were built, one equipped with high-precision step motors and four luminosity sensors separated in quadrants by a cross structure, and the other equipped with DC motors and the 275 Wp solar panel, allowing the design and evaluation of the behavior of each structure separately. The control and automation system is centralized in an Arduino MEGA2560 microcontroller, which runs the tracking and positioning algorithms. The built prototype allows us to carry out studies of solar tracking strategies based on sensor and control systems applied to DC motors.
Dual-Structure Disentangling Variational Generation for Data-Limited Face Parsing

Deep learning based face parsing methods have attained state-of-the-art performance in recent years. Their superior performance heavily depends on the large-scale annotated training data. However, it is expensive and time-consuming to construct a large-scale pixel-level manually annotated dataset for face parsing. To alleviate this issue, we propose a novel Dual-Structure Disentangling Variational Generation (D2VG) network. Benefiting from the interpretable factorized latent disentanglement in VAE, D2VG can learn a joint structural distribution of facial image and its corresponding parsing map. Owing to these, it can synthesize large-scale paired face images and parsing maps from a standard Gaussian distribution. Then, we adopt both manually annotated and synthesized data to train a face parsing model in a supervised way. Since there are inaccurate pixel-level labels in synthesized parsing maps, we introduce a coarseness-tolerant learning algorithm, to effectively handle these noisy or uncertain labels. In this way, we can significantly boost the performance of face parsing. Extensive quantitative and qualitative results on HELEN, CelebAMask-HQ and LaPa demonstrate the superiority of our methods.
Using Serious Games for Learning British Sign Language Combining Video, Enhanced Interactivity, and VR Technology

One in every six persons in the UK suffers a hearing loss, either as a condition they have been born with, or they developed during their life. Nine hundred thousand people in the UK are severely or profoundly deaf. Based on a study by Action on Hearing Loss UK in 2013 only 17 percent of this population, can use the British Sign Language (BSL). That leaves a massive proportion of people with a hearing impediment who do not use sign language struggling in social interaction and suffering from emotional distress. It also leaves even a larger proportion of Hearing people who cannot communicate with those of the deaf community. This paper presents a Serious Game (SG) that aims to close the communication gap between able hearing people and people with hearing impairment by providing a tool that facilitates BSL learning targeting the adult population. The paper presents the theoretical framework supporting adult learning based on which a SG game using Virtual Reality (VR) technology has been developed. The paper explains the experimental framework of the study. It presents the creation of the research instruments to facilitate the study comprising of a SG that integrates video and conventional video-based educational material. It reports and analyses the study results that demonstrate the advantage of the SG in effectively supporting users learning a set of BSL signs. It also presents qualitative outcomes that inform the further development of the game to serve learning needs. The paper closes with conclusions, directions for further development of this educational resource, and future studies.
The Development of Website on Management Information System for E-commerce and Services

Currently, the sales system is overgrowing. The concept of selling that was done manually (which is still less than optimal) becomes electronic (e-commerce). System development requires a digital platform. This platform must be able to carry out all activities that were carried out before (manually), such as collecting documents, recording transactions, and reporting. Besides, the e-commerce platform can provide support and increased performance in the sales process, both in checking stock items, transaction reports, and services. Besides, this optimization can provide services precisely and quickly to consumers. A management information system concept is needed to carry out e-commerce and services with integrated data and be stored in its development database. This prototype concept requires a method for website development. The method used is a waterfall. Website design uses the Hypertext PreProcessor (PHP) programming language and MySQL database. The design model uses two concepts: entity-relationship diagrams (ERD) and data flow diagrams (DFD). The result is a website and e-commerce services that can be accepted by users and e-commerce organizers with tests that have been carried out. System testing uses Blackbox and Whitebox testing, each of which results can be used to implement e-commerce sites and services. The website can assist officers in service and e-commerce and make it easier for officers to determine the target and service status.
Modeling and Reasoning with Preferences and Ethical Priorities in AI Systems

As AI systems make decisions that affect our lives, we must ensure that these systems operate according to the same constraints, guidelines, and ethical principles that a human would follow. Humans make many complex decisions that rely on their subjective preferences, but their decisions are usually also constrained by these ethical priorities. Hence it is essential to equip AI systems with the tools to evaluate whether or not preferences are compatible with these other priorities. In computer science, the Conditional Preference networks (CP-nets), which graphically represent conditional and qualitative preference relations, can be used to model, combine, and compare subjective preferences and ethical priorities. This chapter proposes that one can use CP-nets to measure the distance between an agent’s subjective preference and the ethical principles of the agent’s community in order to ensure that the decisions of an AI system are aligned with a given set of ethical priorities.
Telepathology Practice in Cancer Diagnosis in Saint Jean de Dieu Hospital - Tanguieta, Benin

Context.— Both the incidence of cancer and cancer-related mortality rates are high in sub-Saharan Africa, while resources for diagnosis and management are inadequate. In Benin, there is an extreme shortage of pathology services. Because of this shortage we built a histopathology laboratory equipped with an automated immunohistochemistry and a whole-slide imaging and telepathology system. Objective.— To report our experience of telepathology practice in the improvement of cancer diagnosis. Design.— The study was performed in our histopathology laboratory from January 1, 2016, to December 31, 2018. Resident laboratory technicians were trained in the preparation of microscopic and virtual slides by European pathologists. Virtual slides were stored on a Web-accessible server area for reading by 21 telepathologists in Benin and Europe. All patients with a histologic diagnosis of cancer were included in this study. Demographic data of patients, anatomic site of cancer, its histologic type, and its histologic grade were recorded. Results.— We registered 399 patients diagnosed with cancer of 1593 patients whose surgical specimens had been analyzed. There were 349 adults including 160 males and 189 females, and 50 children (both sexes) with a mean age of 53.40 years, 46.92 years, and 9.72 years, respectively. Eighty-three of 211 females (39.34%) had infiltrating breast carcinoma, and 34 of 188 males (18.09%) had prostatic carcinoma. Infiltrating carcinoma of no special type represented 51 (91.07%) of all infiltrating breast carcinomas. Prostatic carcinoma and infiltrating breast carcinoma were of high grade in 13 of 23 males (56.52%) and 34 of 56 females (60.71%), respectively. Conclusions.— Telepathology is enabling a great improvement in cancer diagnosis in our hospital.
A Video Steganalytic Approach Against Quantized Transform Coefficient-Based H.264 Steganography by Exploiting In-Loop Deblocking Filtering

This article presents a method for detection of H.264 steganographic algorithms that modulate quantized transform coefficients (QTCs) for data hiding.The in-loop deblocking filter specified in the H.264 standard is used for attenuating the discontinuities at coded block boundaries.However, our investigation indicates that the deblocking filtering process would probably be affected by QTC modification.Therefore, we suggest performing steganalysis by considering the influence of QTC modification on filtering decisions and filtering operations.In the filtering of luma block edges, for each luma sample, we try to obtain the cumulative amount of modification applied to it caused by filtering operations, and determine the frequency of the modification made to its sample value.Based on this, a 768-dimensional feature set is designed for training and classification.Extensive experiments were conducted under a range of setups to evaluate the effectiveness of the proposed method.The corresponding experimental results demonstrate that, compared with the prior art, our proposed method is generally capable of providing superior performance on detecting QTC modification in H.264 video, even at low embedding strengths.
Joint Learning of Token Context and Span Feature for Span-Based Nested NER

Nested named entity recognition (NER) is a linguistic phenomenon that has received increasing attention in the NLP community. In this article, we propose a novel joint learning network of the token context, and span feature (TCSF) for nested NER. Our model is a combination of token context network (TCN) for token learning, and deep residual convolutional neural network (CNN), and span relation network (SRN) for span learning. Span features are represented at both the token level, and span relation level. The span relation representation of SRN is trained on the similarity of span, and its positional features by attention weights. The IoU metric is employed for span filtering, and analysis of the overlapping adjacent spans. Moreover, we propose a novel head-inner-tail (HIP T) operation to extend the inner features in spans for a fixed-length representation. TCSF is a fully end-to-end entity recognition model. We perform a set of comprehensive experiments, including an ablation study on the ACE, and GENIA datasets, and show state-of-the-art performance without, and with the pretrained language models.
Secure Halftone Image Steganography Based on Feature Space and Layer Embedding

Syndrome-trellis codes (STCs) are commonly used in image steganographic schemes, which aim at minimizing the embedding distortion, but most distortion models cannot capture the mutual interaction of embedding modifications (MIEMs). In this article, a secure halftone image steganographic scheme based on a feature space and layer embedding is proposed. First, a feature space is constructed by a characterization method that is designed based on the statistics of 4 ×4 pixel blocks in halftone images. Upon the feature space, a generalized steganalyzer with good classification ability is proposed, which is used to measure the embedding distortion. As a result, a distortion model based on a hybrid feature space is constructed, which outperforms some state-of-the-art models. Then, as the distortion model is established on the statistics of local regions, a layer embedding strategy is proposed to reduce MIEM. It divides the host image into multiple layers according to their relative positions in 4 ×4 blocks, and the embedding procedure is executed layer by layer. In each layer, any two pixels are located at different 4 ×4 blocks in the original image, and the distortion model makes sure that the calculation of pixel distortions is independent. Between layers, the pixel distortions of the current layer are updated according to the previous embedding modifications, thus reducing the total embedding distortion. Comparisons with prior schemes demonstrate that the proposed steganographic scheme achieves high statistical security when resisting the state-of-the-art steganalysis.
A Gradual Type System for Elixir

Elixir is a functional programming language with dynamic typing. In this paper we propose a type system that makes it possible to perform static type-checking on a significant fragment of Elixir. An important feature of the type system we introduce is that it does not require any syntactic change to the language. Type information is provided by means of function signatures which are declared in terms of Elixir typespec directives. The proposed type system is based on subtyping and is backward compatible, as it allows the presence of untyped code fragments. We have implemented a prototype of the type-checker in Elixir itself.
Approaching Adversarial Example Classification with Chaos Theory

Adversarial examples are one of the most intriguing topics in modern deep learning. Imperceptible perturbations to the input can fool robust models. In relation to this problem, attack and defense methods are being developed almost on a daily basis. In parallel, efforts are being made to simply pointing out when an input image is an adversarial example. This can help prevent potential issues, as the failure cases are easily recognizable by humans. The proposal in this work is to study how chaos theory methods can help distinguish adversarial examples from regular images. Our work is based on the assumption that deep networks behave as chaotic systems, and adversarial examples are the main manifestation of it (in the sense that a slight input variation produces a totally different output). In our experiments, we show that the Lyapunov exponents (an established measure of chaoticity), which have been recently proposed for classification of adversarial examples, are not robust to image processing transformations that alter image entropy. Furthermore, we show that entropy can complement Lyapunov exponents in such a way that the discriminating power is significantly enhanced. The proposed method achieves 65% to 100% accuracy detecting adversarials with a wide range of attacks (for example: CW, PGD, Spatial, HopSkip) for the MNIST dataset, with similar results when entropy-changing image processing methods (such as Equalization, Speckle and Gaussian noise) are applied. This is also corroborated with two other datasets, Fashion-MNIST and CIFAR 19. These results indicate that classifiers can enhance their robustness against the adversarial phenomenon, being applied in a wide variety of conditions that potentially matches real world cases and also other threatening scenarios.
NSSNet: Scale-Aware Object Counting With Non-Scale Suppression

In object counting, objects often exhibit different sizes at different scales, even if they have similar physical sizes in reality. This is particularly true when targeting crowd counting and vehicle counting in intelligent transportation. Failing to model such variations leads to the mismatch between the object size and image scale. To address this problem, existing methods often extract multi-scale features, but they either still generate the single-scale prediction or lack an explicit suppression mechanism to eliminate predictions engendered by inappropriate scales. Our scale analysis manifests that, the single-scale estimation only works well for objects of certain sizes, and a suppression operator is required to isolate the estimation of a specific scale. In this work, we propose a scale-aware counting network termed NSSNet. NSSNet has two key features: it not only i) generates multi-scale predictions but also ii) applies a novel non-scale suppression (NSS) operator to suppress scale-mismatched estimations. NSS is inspired by the widely-used non-maximum suppression (NMS). In contrast to NMS that only reserves the maximum response, NSS filters out those clearly wrong predictions (the remaining predictions may still be from multiple scales). We evaluate NSSNet on four standard crowd and vehicle counting benchmarks and report state-of-the-art performance. We also show the scale adaptability of NSSNet through a controlled multi-scale experiment. Code and pretrained models are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://git.io/nssnet</uri> .
White Blood Cells Detection using YOLOv3 with CNN Feature Extraction Models

There are several types of blood cancer.One of them is Leukaemia.This is due to leukocyte or white blood cell (WBCs) production problem in the bone marrow.Detection at earlier stage is important so that the patient is able to get a proper treatment.The conventional detection and blood count method is less efficient and it is done manually by pathologist.Thus, there will be a long line to wait for the results and also delay the treatment.A faster detection procedure and technique will have high impact on the real time diagnostic.Fortunately, these problems are able to overcome by making the blood test procedures automatic.One of the effort is the development of deep learning for WBCs detection and classification.In computer aided WBCs detection, the You Only Look Once (YOLO) based platform present a promising outcome.However, the investigation of optimal YOLO structure remains vague.This paper investigate the effect of the deep learning based WBCs detection using You Only Look Once version 3 (YOLOv3) with different pretrained Convolutional Neural Network (CNN) model.The models that been tested are the Alexnet, Visual Geometry Group 16 (VGG16), Darknet19 and the existing YOLOv3 feature extraction model, the Darknet53.The architecture consist of the bounding box for class prediction, feature extraction, and additional convolutional layers.It was trained with 242 WBCs images from Local Initiatives Support Corporation (LISC) dataset.The final outcome shows that the YOLOv3 architecture with Alexnet as its feature extractor produced the highest mean average precision of 98% and have better performance than the other models.
Sympérasmology: A Proposal for the Theory of Synthetic System Knowledge

Owing to the results of research in system science, artificial intelligence, and cognitive engineering, engineered systems are becoming more and more powered by knowledge. Complementing common-sense and scientific knowledge, system knowledge is maturing into a crucial productive asset. However, an overall theory of the knowledge of intellectualized systems does not exist. Some researchers suggest that time has come to establish a philosophically underpinned theoretical framework. This motion is seconded by the on-going intelligence revolution, in which artificial intelligence becomes a productive power, enabler of smart systems, and a strong transformer of the social life. The goal of this paper is to propose a skeleton of the needed generic theory of system knowledge (and a possible new branch of philosophical studies). The major assumption is that a significant part of the synthetic system knowledge (SSK) is “sympérasma”, that is, knowledge conjectured, inferred, constructed, or otherwise derived during the operation of systems. This part will become even more dominant in the future. Starting out from the above term, the paper suggests calling this theory “sympérasmology”. Some specific domains of “sympérasmological” studies, such as (i) manifestations of SSK, (ii) mechanisms of generating SSK, (iii) dependability of SSK, (iv) operational power of SSK, (v) composability of SSK, and (vi) advancement of SSK, are identified. It is understood that the essence and status of SSK cannot be investigated without considering the related cognitive processes and technological enablers. The paper presents a number of open questions relevant for follow-up discussions.