Reviewerly/exHarmony huggingface dataset:
Peripheral blood morphology review and diagnostic proficiency evaluation by a new Spanish EQAS during the period 2011‐2019

Abstract Introduction The Spanish Haematology and Haemotherapy Society organizes peripheral blood smear review scheme , focused on the evaluation of diagnostic proficiency of participants by blood cell morphology analysis. The objective was to evaluate the efficacy of this scheme as an educational tool to improve the diagnostic proficiency of the participants. Methods During 2011‐2019, 54 peripheral blood smears, alongside with patient details such as age, sex, blood cell counts and relevant clinical information, were sent to an average of 125 ± 13 laboratories per year. A number of 44 shipments were selected to analyse whether successive surveys of the same disease may lead to an improvement in the diagnostic success rate proposed by the laboratories. Participants were asked to select the most relevant morphological abnormalities, alongside the diagnostic orientation. Agreement of participant responses with RR was evaluated. Results Spanish laboratories showed a diagnostic proficiency greater than 80% in acute myeloid leukaemia, including acute promyelocytic leukaemia, mature B‐cell neoplasms (hairy cell leukaemia and splenic marginal zone lymphoma), chronic myeloid leukaemia, sickle cell disease, Bernard‐Soulier syndrome and infectious mononucleosis. It was important to note the significant improvement over the time in the successive shipments of the same disease, with a 31% and 13% increase in their diagnostic orientation success rate for acute myeloid leukaemia and acute promyelocytic leukaemia cases, respectively, 15% for mantle cell lymphoma and 6% for sickle cell disease. Conclusions The present study provides evidence that peripheral blood smear review scheme can be a valid educational tool to improve the clinical pathologist skills in blood morphology and haematological diagnosis.
Evaluation of Real-Time Remote 3D Rendering of Medical Images using GPUs

Remote visualization of medical data is a very attractive alternative to increased mobility, allowing volumetric data to be accessed even in devices with low processing capability. However, the amount of simultaneous accesses and the bandwidth available are natural bottlenecks for any solution in this field. This paper presents a methodology to evaluate 3D volumetric rendering client-servers systems with the goal of determining the maximum load of a specific system based on Quality of Service (QoS). With such input in mind, a system architect could project systems with better cost-benefit ratio, or even design a cloud system that predicts and rents servers based on the number of service requests. In order to check the viability of the methodology, a stress test was conducted in a client-server system developed to visualize Computed Tomography (CT) scans. Results have shown that it could handle at least 20 simultaneous remote visualizations, even in scenarios with low bandwidth, finding its upper limit when dealing with around 30 simultaneous visualizations.
Efficient Anonymous Multi-group Broadcast Encryption

Nowadays, broadcasters must supply diverse content to multiple groups without delay in platforms such as social media and streaming sites. Unfortunately, conventional broadcast encryption schemes are deemed unsuitable for such platforms since they generate an independent ciphertext for each piece of contents and hence the number of headers generated during encryption increases linearly with the size of contents. The increased number of headers will result in wasting a limited network bandwidth, which makes the application impractical. To resolve this issue, multi-channel broadcast encryption was proposed in the literature, which transmits a single header for multiple channels to several groups of viewers at a time. However, the multi-channel broadcast encryption is also impractical because it requires heavy computations, communications, and storage overheads. Moreover, it should also address additional issues, such as receiver privacy (anonymity), static user-set size, and limited encryption. In this work, we aim to tackle this problem by proposing an efficient broadcast encryption scheme, called “anonymous multi-group broadcast encryption”. This primitive achieves faster encryption and decryption, provides smaller sized public parameters, private keys, and ciphertexts. Hence, it solves the aforementioned issues of the multi-channel broadcast encryption. Specifically, the proposed scheme provides provable anonymity and confidentiality based on the External Diffie-Hellman (XDH) and $$\mathcal{P}$$ -Decisional Bilinear Diffie-Hellman (DBDH) assumptions, respectively, in the standard model.
Knowledge extraction about patients surviving breast cancer treatment through an autonomous fuzzy neural network

Cancer treatment is extremely aggressive and, in addition to causing considerable discomfort, can lead to death. Therefore, identifying aspects related to treatment assertiveness may be efficient for reducing the mortality rate of cancer patients. This paper seeks to identify the prognosis of cancer treatment survival through hybrid techniques based on the autonomous fuzzification process and artificial neural networks. The public dataset on cancer mortality is the source for conducting treatment assertiveness rating tests. The hybrid model had its results compared to other models present in the pattern classification literature with superior accuracy and identification of people likely to survive treatment (90.46%), and the fuzzy rules obtained with the execution of the model corroborate the high assertiveness of the model, even surpassing state of the art for the theme.
Td-VOS: Tracking-Driven Single-Object Video Object Segmentation

This paper presents an approach to single-object video object segmentation, only using the first-frame bounding box (without mask) to initialize. The proposed method is a tracking-driven single-object video object segmentation, which combines an effective Box2Segmentation module with a general object tracking module. Just initialize the first frame box, the Box2Segmentation module can obtain the segmentation results based on the predicted tracking bounding box. Evaluations on the single-object video object segmentation dataset DAVIS2016 show that the proposed method achieves a competitive performance with a Region Similarity score of 75.4% and a Contour Accuracy score of 73.1%, only under the settings of first-frame bounding box initialization. The proposed method outperforms SiamMask which is the most competitive method for video object segmentation under the same settings, with Region Similarity score by 5.2% and Contour Accuracy score by 7.8%. Compared with the semi-supervised VOS methods without online fine-tuning initialized by a first frame mask, the proposed method also achieves comparable results.
Generation of Information Impacts Scenarios in Management Decision Support Systems

The features of developing scenario models using cognitive maps and methods for developing of information impacts scenarios based on the analysis of the content of global computer networks are considered. This approach provides a solution to the problem of generating and ranking scenarios for impacting objects that correspond to the selected key concept analyzing the input text arrays working on a full-time basis. Improved information models and computer domain analysis tools have been developed. This makes it possible for an expert analyst to investigate these processes and generate results in a form convenient for decision-making. A method is proposed for constructing a domain model in the form of a semantic graph formed according to the monitoring of computer networks by determining the most significant concepts and the relationships between them. A method is proposed for the formation of optimal scenarios of informational impacts on target objects of a subject area based on finding many routes of influence distribution. Software and algorithmic tools for transferring data to the OWL format are developed. An example of application of the developed scenario approach is considered on the example of analysis of the movement of “Yellow vests” in France.
Research on Trusted Identification of Blockchain Uploaded Data

Due to its decentralized feature, blockchain system makes it difficult to connect blockchain with the Internet, which makes the application environment of blockchain limited, especially when the smart contract requires external data trigger. Among the existing solutions, many of them do not consider the security and credibility of the Blockchain upload data. This is contrary to the original intention of the blockchain system. To solve this problem, this paper proposes a trusted data recognition scheme based on machine learning model. This makes the data on the chain more secure and reliable.
Max-Pooled Fast Learning Gabor Capsule Network

Capsule Networks have shown good performance on traditional datasets such as MNIST. However, they are unable to replicate this excellent performance when classifying images with varied backgrounds. This paper further explores this problem by proposing a variant of the Capsule network to investigate the possibility of improving their performance on other image datasets. By substituting global Gabor filter banks followed by Max Pooling in place of Convolutional Neural Network, the initial layers are given the ability to encode texture and spatial relationships leading to improved performance. Max Pooling is used to ensure only dominant features are selected after convolution by Gabor filters resulting in the reduction of the dimensionality of feature vectors. Experiments on four image datasets show that the proposed model has superior performance over the baseline Capsule network achieving very high values for precision, recall, specificity, and accuracy. Also, the proposed model converges faster and has relatively fewer parameters compared to the baseline CapsNet.
NiCad+: Speeding the Detecting Process of NiCad

With the development of the Internet and the construction of open source software communities, there has been a surge in open source software. Code Reuse—copy-past and modify open source code, which becomes a convenient choice for developers to save time and reduce labor costs. So there are more and more similar code fragments, code clones, in code project as a popular phenomenon. The code clone may import uncertainties into the program, which is a hot spot for urgent exploration. This paper summarized code clone detection tools and techniques in four categories at present and introduced one detection tool, NiCad, with high recall and precision. However, NiCad is not perfect for large-scale code clone detection scenarios, because NiCad is slow when dealing with large-scale of codes. Therefore, we speeded the detection process of NiCad, and and named the improved tool NiCad+. We greatly improved the efficiency of NiCad without effecting its recall and precision. The time-cost of detecting code clone was remarkable shortened by reducing the matching times. When testing with BigCloneEval, it only takes 28.43% time-cost as original NiCad. When testing with varying input sizes, the speeded detection process performs better than the original one from 10 KLoC (lines of code) to 5 MLoC.
A MiniMax Agent for Playing Ntxuva Game – The Mozambican Variant of Mancala

Since the earliest development of Artificial Intelligence (AI), the AI community has been attracted by games. Because, the task of deciding which move is best to take in a given state of a game, requires the involvement of some strategies which can be seen as a form of intelligence. In games such as Chess, researchers have been able to develop an AI opponent which can defeat a human opponent by using the Tree Search algorithm. The Tree Search algorithm has been also applied in some Mancala's family games, but there is around 200 variant of the game. Even though they are governed by almost the same rules and principle, they differ in terms of configurations and complexity. To our knowledge, Ntxuva is one of the variants of Mancala that has not been explored by the AI community. This paper reports the finding of using AI methods to create intelligent agents to play Ntxuva. Different agents were created namely, a Minimax agent, Alpha-beta pruning agent, as well as a Random agent. Then, they were put into play in a round-robin tournament where the statistics of their winning rate were collected. By applying Minimax Algorithm to the computer agent, it becomes intelligent enough to play Ntxuva as a good opponent like a professional human player.
ShopSmart – Smart Shopping Application

Shopping has been a favorite past-time activity for quite some time. But what makes this activity a cause for regret is the drudgery of long waiting queues in the billing section. We aim to solve the trouble for people by using innovative measures in this regard. We automate the process of shopping by integrating smart systems to make the experience hassle free. RFID is the key technological aspect of our research. We have removed key drawbacks of previously existing solutions and have implemented a foolproof feature to make smart shopping reliable and consistent. Our proposed idea has online payment option, it removes use of paper bill, also totally prevents shoplifting.
Sentiment Analysis of English-Punjabi Code-Mixed Social Media Content to Predict Elections

On social media, the number of users are increasing exponentially. The information contents posted and tweeted by the user are also increasing exponentially. A different meaning of the sentiment is hidden inside the message. Analysing the nature of the text is still a very challenging task. The sentiment analysis is one of the emerging and challenging fields. In the proposed work, the data has been extracted from Twitter with the dataset of around 145,464 comments. In particular, the English-Punjabi dictionary has been created for opinionated word. The opinionated words are categorized into two parts as positive dictionary and negative dictionary. These are stored in gazetteer list and then a statistical technique has been applied for sentiment analysis.
Evaluating a Multi-sense Definition Generation Model for Multiple Languages

Most prior work on definition modelling has not accounted for polysemy, or has done so by considering definition modelling for a target word in a given context. In contrast, in this study, we propose a context-agnostic approach to definition modelling, based on multi-sense word embeddings, that is capable of generating multiple definitions for a target word. In further contrast to most prior work, which has primarily focused on English, we evaluate our proposed approach on fifteen different datasets covering nine languages from several language families. To evaluate our approach we consider several variations of BLEU. Our results demonstrate that our proposed multi-sense model outperforms a single-sense model on all fifteen datasets.
Self-Driving Robotic Cars

Even the behavior of a single driver can have a dramatic impact on hundreds of cars, making it more difficult to manage traffic. While the attempts to analyze and correct the traffic patterns that lead to congestion began as early in the 1930s, it wasn't until recently that scientists developed simulation techniques and advanced algorithms to create more realistic visualizations of traffic flow. In experiments conducted by Alexandre Bayen and the Liao-Cho, which included several dozen cars in a small-scale closed circuit, a single autonomous vehicle could eliminate traffic jams by moderating the speed of every car on the road. In larger simulations, the research showed that once their number rises to 5-10% of all cars in the traffic, they can manage localized traffic even in complex environments, such as merging multiple lanes of traffic into two or navigating extremely busy sections.
Explaining Data Regularities and Anomalies

In the spirit of explainable AI approaches, this paper introduces a new strategy whose aim is to linguistically describe the inner structure of a dataset. Instead of removing irregular points and focusing on the analysis of regular points, the proposed approach relies on a unified data structure, an isolation forest, to both separate regular from irregular points and to identify their inner structure using a data-driven similarity measure. In addition, clusters of regular and irregular points are then linguistically described so as to help users focus on the most characteristic properties of each cluster and to possibly understand the reason why some points are irregular.
Stochastic gradient descent possibilistic clustering

Although online versions of several well known clustering algorithms have been proposed, in order to deal effectively with the big data issue, as well as with the case where the data are available in a streaming fashion, very few of them follow the stochastic gradient descent philosophy. In this paper a novel stochastic gradient descent possibilistic clustering algorithm, called O-PCM2 is introduced. The algorithm is presented in detail and a convergence proof of it is provided, based on the general convergence results established for the family of the stochastic gradient descent algorithms. Finally, the effectiveness of the proposed algorithm is assessed through extensive experimentation on both simulated and real data sets and in comparison with other related algorithms.
Model for Cyber Security Information Sharing in Healthcare Sector

In the modern society almost all services are based on data-networks and networked systems. Especially through the growing digitalization an increasing number of services is connected to data-networks. One example of a highly digitalized domain is the healthcare sector, where a cyber-attack could cause extreme circumstances. Decision making requires knowledge about the current situation. Particularly, in the complex domain of cyber security where the boundaries of physical world do not exist, situational awareness has a determinative role. One source for required cyber security information is obtained by sharing information: different organizations share the information between each other. Typically, Cyber security information has classification levels, which affects challenges for information sharing between different organizations. In this study, the model for information sharing between different organizations with different classification levels is developed and tested in the simulation. Because of its importance, the developed model is tested in the simulation of a healthcare domain; however, it can be utilized in all business sectors. In this paper, not only the developed model with simulation results is presented, but also tested in real life scenarios within an existing project.
The minimal polynomials of modified de Bruijn sequences revisited

Let q be a prime power and Fq the finite field with q elements. In this paper, it is shown that the minimal polynomial of a modified de Bruijn sequence of order n over Fq cannot be the product of an irreducible polynomial of degree n over Fq and any polynomial of degree k over Fq if n≥4k, which in fact improves several previous results ([5], [13]). Based on this result, a non-trivial lower bound 5n/4 is given for linear complexities of modified de Bruijn sequences of order n distinct from m-sequences, which is the first non-trivial lower bound with no restriction on n.
Developing an Open-Source Lightweight Game Engine with DNN Support

With the growth of artificial intelligence and deep learning technology, we have many active research works to apply the related techniques in various fields. To test and apply the latest machine learning techniques in gaming, it will be very useful to have a light-weight game engine for quick prototyping. Our game engine is implemented in a cost-effective way, in comparison to well-known commercial proprietary game engines, by utilizing open source products. Due to its simple internal architecture, our game engine is especially beneficial for modifying and reviewing the new functions through quick and repetitive tests. In addition, the game engine has a DNN (deep neural network) module, with which the proposed game engine can apply deep learning techniques to the game features, through applying deep learning algorithms in real-time. Our DNN module uses a simple C++ function interface, rather than additional programming languages and/or scripts. This simplicity enables us to apply machine learning techniques more efficiently and casually to the game applications. We also found some technical issues during our development with open sources. These issues mostly occurred while integrating various open source products into a single game engine. We present details of these technical issues and our solutions.
Data Annotation and Multi-Emotion Classification for Social Media Text

In recent years, sentiment or emotion analysis has become a key research area due to its vast potential applications in getting insights from social media comments, marketing, political science, psychology, human-computer interaction, and artificial intelligence. Emotion analysis deals with identifying the emotions in any given data such as text, speech, or image. The current work proposes to identify and associate social media text to multiple emotions with varying degrees. The data collection and annotation process employed in the proposed work is a combination of manual and semi-supervised annotation method where each tweet is mapped to a six dimensional emotion vector. Totally six human emotions such as happy, sad, anger, disgust, surprise, and fear are considered for emotion-tagging. Word mover`s distance (WMD) based on twitter word embeddings (word2vec) is proposed to develop a labelled dataset in the current work. A set of classifiers is developed on the labelled dataset to identify emotions at the tweet-level in any given text data. In the current work, KNN, tree-based, and neural network classifiers are developed.
Effect of Cultural Misunderstanding Warning in MT-Mediated Communication

Thanks to today’s technologies, the world’s borders have been fading away and intercultural collaboration has become easier and easier. Language and cultural differences are common problems in inter-cultural collaboration. Machine translation (MT) is now available to overcome the language barrier, so people can easily express and understand messages in different languages. However, misunderstandings often plague users from different cultures, especially in MT-mediated communication. To communicate productively, it is important to avoid such misunderstandings. One existing work proposed the idea of using automated cultural difference detection to warn the users of misunderstanding. However, no study has examined how such warnings affect the communication. To eliminate this gap, we conduct a controlled experiment on how users react to the warnings and what are the results in terms of communication. The results show that, with the data from cultural difference detection, warning the user of cultural misunderstanding can help reduce misunderstandings and increase awareness of cultural differences. The results of this experiment confirm the effectiveness of cultural misunderstanding alerts and suggest new directions in multilingual chat design.