Reviewerly/exHarmony huggingface dataset:
Coherent Heuristic Evaluation (CoHE): Toward Increasing the Effectiveness of Heuristic Evaluation for Novice Evaluators

Heuristic evaluation (HE) is an inspection-based usability evaluation method in which a number of evaluators, typically 3–5, assess the usability of a system based on a set of usability guidelines. HE was first introduced by Nielsen and Molich and then revised by Nielsen. Since its introduction, HE has gained wide popularity among human-computer interaction (HCI) and user experience (UX) practitioners and is now one of the most common usability evaluation methods. A few years after the introduction of HE, some researchers realized that novice evaluators perform poorly on complex systems. This issue, known as the expertise effect, is one of the major challenges of HE; i.e., the more experienced the evaluator is, the better the results of the evaluation. Consequently, some researchers argue that the results of HE are the product of the evaluator’s experience rather than the method itself. To address this issue, we interviewed 15 usability experts, all of whom had at least four years of experience and who came from both academia and industry. We analyzed their responses and developed a step-by-step protocol.
Health Care Counselling Via Voicebot Using Multinomial Naive Bayes Algorithm

Voicebot bestow the user, the ability of receiving retort within minutes, no matter their geographic location, or the apparatus, they are using to transmission. Data related to the health issues are gathered from medical experts, and all of them are stored on the server side of the system. The software furnished by the system offers answers and solutions for audit and treating patients after combining descriptions of the illness and the symptoms, diagnostics, predictions and prescription. This software will be greatly useful to people to protect them from complication, in this proposed system the Voicebot is used to solve the difficulties facing by people at the time of illness, which influence the health of the person. With all these factors, the Voicebot updates its knowledge base and allows patients to benefit from using it. This designed bot is commonly used for general Health Care. Here the input is given by the user in format of text. This text will be analyzed by Multinomial Algorithm, by which the data are fetched from the Data Base and generates voice response. When examine all these bots, the oddity of the proposed system can get response by both sensory and textual methods. This graces the untaught customers to use proficiently. It bears the whole system in customer's cordial manner. The need of the proposed system is minimum time and energy and can clarify our doubts at any time.
Double-blind is good but open would be better

Peer review in software engineering is considered, same as for other disciplines, to be a key element of the research process, yet it is often perceived as not to work fully well. To understand the pains and gains in the peer review system, we ran a survey with open and closed questions with the authors and PC members of ICSE 2014/2015/2016. We received 241 responses (29% response rate). 67% of the respondents identified themselves as professors. We analyzed the responses quantitatively and qualitatively (with open coding). All questions were optional. Agreement scales had 10 points, so mild levels of agreement could be expressed but there was no undecided middle point. The resulting article appeared in Information and Software Technology in 2018 [1] and we also disclosed the anonymized data set [2].
Density Matrix Based Convolutional Neural Network for Click-Through Rate Prediction

Click-Through Rate (CTR) prediction is an important task in the online advertisement recommended filed, which aims to predict the probabilities that an ad is clicked by a user in the display of the ads. Recently, many researchers have proposed deep learning based models to learn both low-order and high-order feature interactions. However, the existing deep learning based CTR models generally learn the feature interactions by inner product, which is simple and lack of interpretability. To address this issue, we introduce a concept of quantum probability theory: density matrix. Inspired by the feature of the density matrix, we propose a novel density matrix based representation for input instances, which can contain global 2-order feature interaction information. Then we combine the advantages of both density matrix and Convolutional Neural Network and propose the Density Matrix Based Convolutional Neural Network (DMCNN), which can capture more feature interactions than other models. Experiments on Criteo and Avazu datasets demonstrate the effectiveness of our model, which achieves excellent results on multiple benchmarks.
Noise Filtering and Imbalance Class Distribution Removal for Optimizing Software Fault Prediction using Best Software Metrics Suite

Software fault detection is by far the most prevalent field of predictive analysis in the software engineering paradigm and several research centers have launched new ventures in this area. Predicting faults in software components before they are delivered to the end-users is of key importance, as it can save time, effort, and inconvenience associated with identifying and addressing these issues at later stages. This paper presents a software defect prediction technique to alleviate some basic problems of existing frameworks for predicting software defects. This study aims to combine simple noise removal, imbalanced class distribution, and software metrics selection techniques for optimizing defect prediction in software. The technique was tested on ten software fault prediction datasets. The experimental results including recall, precision, F-measure, accuracy, and ROC-AUC values show that the proposed method enhances fault prediction performance and the results obtained are better than or close to several comparative models. This proves the validity of our model.
Cloud Based Data Integrity model for Minimal Human Intervention using Smart Contracts

Food and education are a necessity in our lives, where a part of the people still hasn't had a square meal for the day and almost a quarter of them are illiterate. The Mid-Day Meal Scheme provided the right platform for the underprivileged children for their education as well as their daily nutrition and wellbeing. But later some unethical individuals started using this scheme for their personal gains. Findings reveal that although monitoring mechanisms are present, they are manually driven which gives space for fraudulent activities and malpractices. The proposed concept aims at an automated system that requires minimal intervention and increased scalability and security levels. These features are achieved through the means of blockchain and biometrics, which provide immutability and high levels of security. This solution is cloud-based, requiring minimal human intervention. Thus, it makes traceability and accountability easy.
Digital Competences for Language Teachers: Do Employers Seek the Skills Needed from Language Teachers Today?

Language proficiency is essential for 21st Century skills, and for the ability to obtain and utilize new competencies in the future. Digitalization is greatly affecting the language learning settings, and more digital skills are needed amongst teachers. Thus, it is interesting to see what kind of demands the new working life puts on the future language teachers in all areas of the sector. To better understand the role of national policies on digital competence and their impact on language teacher's recruitment, this study investigates existing strategies for digital competence for language teachers in 11 countries, and their representation in job announcements. The study uses qualitative content analysis as well as quantitative analysis represented by descriptive statistics. The former includes document analysis of strategies on different levels and gives an overview of existing trends and new tendencies considering digital skills for language teachers. The latter comprises a search and classification of 854 job announcements throughout Europe according to three levels of digital competences to see which skills are being asked for when hiring language teachers. The results indicate that there are discrepancies between strategies being implemented at overarching levels and the institutional practices. We also demonstrate discrepancies between the needs described for the 21st Century and the defined skills in the job announcements. Among other results, the study highlights the need for more targeted job announcements to attract teachers with the desired digital skills.
Interpreting Human Responses in Dialogue Systems using Fuzzy Semantic Similarity Measures

Dialogue systems are automated systems that interact with humans using natural language.Much work has been done on dialogue management and learning using a range of computational intelligence based approaches, however the complexity of human dialogue in different contexts still presents many challenges.The key impact of work presented in this paper is to use fuzzy semantic similarity measures embedded within a dialogue system to allow a machine to semantically comprehend human utterances in a given context and thus communicate more effectively with a human in a specific domain using natural language.To achieve this, perception based words should be understood by a machine in context of the dialogue.In this work, a simple question and answer dialogue system is implemented for a café customer satisfaction feedback survey.Both fuzzy and crisp semantic similarity measures are used within the dialogue engine to assess the accuracy and robustness of rule firing.Results from a 32 participant study, show that the fuzzy measure improves rule matching within the dialogue system by 21.88% compared with the crisp measure known as STASIS, thus providing a more natural and fluid dialogue exchange.
Handwritten Amharic characters Recognition Using CNN

Amharic has been an official language of Ethiopia since many years ago. As a consequence, there are so many handwritten documents of several types, that are written in Amharic by hand, in monasteries, libraries, museums, universities, archives and with individuals. Among them, there are documents that have been written before several years and are getting older. To preserve these documents electronically the current way of digital preservation is not sufficient. Therefore, an intelligent handwritten classifier is essential. Furthermore, we can interact with latest smart devices such as with mobile phones by writing Amharic characters by hand by integrating this Amharic character classifier with them. Because contemporary smartphones are capable of reading and recognizing inputs written by hand on their modern input devices. Amharic alphabet has about 34 families of characters and about eight members with in a family, with some exceptions. There are, a total of, 286 different characters (286 classes). For this research, I have collected 30,446 characters from about 130 different individuals. Among them 27413 are used for training and the remaining 3033 are used for testing. Convolutional Neural Network algorithm with two convolutional layers is employed to classify the aforementioned characters to 286 classes. The design is evaluated on Keras and TensorFlow frameworks. After training the system, I have got a training accuracy around 99.52% after the first epoch, which is the least of accuracies of other epochs and a testing accuracy of 99.71%, approximately. In conclusion, the result of this research can be more improved by training the system using more data and using improved Convolutional Neural Network algorithms. In addition, the system is not evaluated using many different metrices such as by changing the orientation of characters and by adding noises; even though, they are written by different individuals and different styles.
Study on application of open source intelligence from social media in the military

Abstract With the gradual popularization of information technology and network technology, various types of open source information, especially social media information, have shown an explosive growth trend. Social media intelligence not only possesses ideological and value-level values, but also directly serves military defense. It will help stimulate open source intelligence to play a huge role in combat operations that combining communications and psychological knowledge to explore and exploit frontier technologies in artificial intelligence, such as machine learning and data mining, and conducting research on social media big data collection, analysis, mining, and prediction technologies. This paper focuses on the preliminary analysis of the application of open source information such as social media in the field of military intelligence.
An Embedding-based Deep Learning Approach for Movie Recommendation

Recommender systems are employed either as tools or algorithms, whose key task is to efficiently predict the ratings for items and recommend items, using the data generated by users. It assists users in finding items that they will like. Hence, recommendation systems are becoming an essential part of some applications, e-commerce websites, and online streaming services, etc. This paper emphasizes on recommendation system for movies whose main objective is to propose a movie recommendation system through a deep learning technique. The size and complication of websites have increased due to the rapid growth of the internet. On these websites, it has become time-consuming and extremely difficult for the users to find the information that they are searching for. Therefore, a collaborative filtering-based movie recommendation system using deep learning and embedding is proposed. The proposed system is evaluated by calculating the RMSE and MAE values. The proposed method is compared with other machine learning algorithms and some state-of-the-art methods. Our model gave an MAE score of 0.7305 and an RMSE score of 0.9311. It performs better than some previous approaches to collaborative-filtering based movie recommendation.
Assessment of Encryption and Decryption Schemes for Secure Data Transmission in Healthcare Systems

In the biomedical research community, transmitting a patient medical record via wireless means to an administrative centre or other medical centres is increasingly common. However, due to the open nature of wireless media, the security of such a system is a major concern, so, it is desirable to have a reliable security scheme. Amidst the numerous methods used to secure medical data, encryption schemes are becoming more popular due to their performance and relative simplicity. In this study, the performance of some data encryption and decryption schemes used to secure medical data is evaluated. These schemes are Blowfish, DES, AES, RC4, RSA, ECC, CBE, MTLM and CEC. The performance of these schemes was assessed through their execution time, throughput, average data rate and information entropy. For this performance assessment, some medical data were used for this task. The results showed that the performance of CBE, MTLM and CEC was better. CBE and MTLM offer a secure way to encrypt data with a significant reduction in the execution time. Moreover, if some of these schemes were combined to form a hybrid system, an enhancement in the security of medical data over wireless communication networks is guaranteed.
Secure DNA Motif-Finding Method Based on Sampling Candidate Pruning

With the continuous exploration of genetic research, gradually exposed privacy issues become the bottleneck that limits its development. DNA motif finding is an important study to understand the regulation of gene expression; however, the existing methods generally ignore the potential sensitive information that may be exposed in the process. In this work, we utilize the <?TeX $\varepsilon $?> -differential privacy model to provide provable privacy guarantees which is independent of attackers’ background knowledge. Our method makes use of sample databases to prune the generated candidate motifs to lower the magnitude of added noise. Furthermore, to improve the utility of mining results, a strategy of threshold modification is designed to reduce the propagation and random sampling errors in the mining process. Extensive experiments on actual DNA databases confirm that our approach can privately find DNA motifs with high utility and efficiency.
Determinative Brain Storm Optimization

Brain Storm Optimization (BSO) is a swarm intelligence optimization algorithm, based on the human brainstorming process. The ideas of a brainstorming process comprise the solutions of the algorithm, which iteratively applies solution grouping, generation and selection operators. Several modifications of BSO have been proposed to enhance its performance. In this paper, we propose a novel modification enabling faster convergence of BSO to optimal solutions, without requiring setting an upper bound of algorithm iterations. It considers a brainstorming scenario where participating groups with similar ideas recognize that their ideas are similar, and together, collaborate for the determination of a better solution. The proposed modification, called Determinative BSO (DBSO), implements this scenario by applying a cluster merging strategy for merging groups of similar solutions, while following elitist selection. Experimental results using eleven benchmark functions show that the proposed modified BSO performs better than both the original and a state-of-the-art algorithm.
Maximum likelihood-based robust state estimation over a horizon length during measurement outliers

In this paper, a novel approach towards horizon-based maximum likelihood (ML) state estimator is proposed that makes the state estimation process more robust against unmodeled and unstructured noise and disturbances in the state-space models. State space models provide a powerful way to perform state and parameter estimation for dynamical systems. However, if the measurements are contaminated by outliers and disturbances with no known models, the estimation process is highly biased. A ML-based approach is used to find a batch solution for the filtering problem. Based on ML solution, a robust algorithm is proposed that seamlessly estimates dynamic state in the presence of zero or non-zero mean measurement outliers. The proposed algorithm that dictates this switching uses an explicit outlier detection mechanism that enables its seamless working. Simulations have been carried out for a moving target tracking application as an example to demonstrate the resilience of the proposed method against zero-mean and non-zero mean measurement outliers in comparison to state-of-the-art methods.
Improving the Usability of Voice User Interfaces: A New Set of Ergonomic Criteria

Technological progress has made possible to create voice user interfaces that feel more natural, as smart speakers notably show. As those develop, issues of usability have also been spotted that underline the fact that the way we are used to designing interfaces do not apply directly to voice interactions. Since heuristic and ergonomic criteria are the first way designers can improve their interfaces, we propose a new set of criteria for VUIs, based for the first time on an extensive review of academic and professional guidelines, and plugged into existing general heuristics. These criteria underline the need for VUIs to make up for the lack of visual cues, to take into account the social nature of conversations, as well as the need to own the fact that talking objects will be interpreted as full characters with a personality, and therefore need to be designed as such.
Reconstruction of Undersampled Analitic Signals under First Order Aliasing

One of the techniques for processing signals in a wide frequency band is sub-Nyquist sampling, which reduces the performance requirements of the equipment. In this case, the effect of aliasing takes place. The paper proposes an approach to eliminate the distortion caused by aliasing. For these purposes, multi-channel multi-rate processing is used. It lies in the fact that the processing of the same signal is carried out in several channels. In different channels, sampling is performed at different sampling frequencies, each of which does not satisfy the Nyquist–Shannon sampling theorem. The paper deals with the processing of analytic signals. Since the article is limited to first-order aliasing, the number of channels is two.
Computational intelligence for modeling of pavement surface characteristics

Pavement surface texture condition is an important parameter for pavement monitoring in project and network management. One of the well-known methods for pavement texture evaluation and asphalt modeling is image processing. The image-based methods are widely used in the image enhancement and analysis in texture assessment field. This chapter presents a study on computational intelligence methods for image processing and enhancement. In this chapter different transform methods used for asphalt surface image enhancement are discussed and an assessment is made on computational intelligence methods by a number of assessing parameters. The comparison among the obtained results shows the efficiency of these methods in providing enhanced images with higher quality.
Confronting Information Security’s Elephant, the Unintentional Insider Threat

It is well recognized that individuals within organizations represent a significant threat to information security as they are both common targets of external attackers and can be sources of malicious behavior themselves. Notwithstanding these facts, one additional aspect of human influence in the security domain is largely overlooked: the role of unintentional human error. Such lack of emphasis is surprising given relatively recent reports that highlight error's central role in being the root cause for numerous security breaches. Unfortunately, efforts that recognize human error's influence suffer from not employing a commonly accepted error framework and lexicon. We thus take this opportunity to review what the data show regarding error-based breaches across various types of organizations and create a nomenclature and taxonomy rooted in the rich history of safety research that can be applied to the information security domain. Our efforts represent a significant step in an effort to classify, monitor, and compare the myriad aspects of human error in information security in the hopes that more effective security education, training, and awareness (SETA) programs can be devised. Further, we believe our efforts underscore the importance of revisiting the daily demands placed on organizational insiders in the workplace.
Channel-Based Network for Fast Object Detection of 3D LiDAR

Currently, there are various methods of LiDAR-based object detection networks. In this paper, we propose a channel-based object detection network using LiDAR channel information. The proposed method is a 2D convolution network with data alignment processing stages including a single-step detection stage. The network consists of a channel internal convolution network, channel external convolution network and detection network. First, the convolutional network within the channel divides the LiDAR data for each channel to find features within the channel. Second, the convolutional network outside the channel combines the LiDAR data divided for each channel to find features between the channels. Finally, the detection network finds objects with the features obtained. We evaluate our proposed network using our 16-channel lidar and popular KITTI dataset. We can confirm that the proposed method detects objects quickly while maintaining performance when compared with the existing network.