Reviewerly/exHarmony huggingface dataset:
Design and Implementation of a Chatbot for Kurdish Language Speakers Using Chatfuel Platform

Chatbot is a software agent that is used to conduct intelligent conversations between machines and humans. Chatbots are mostly depend on Natural Language Processing (NLP). In this paper, the design and implementation of a chatbot are provided to help Kurdish speakers in using online conversations via texts to find answers instead of direct contact with human agents. The NLP-based software agent is implemented using the Chatfuel platform. Chatfuel uses artificial intelligence to communicate with humans by simulating human conversations through voice commands or texts. The proposed chatbot is tested on an electronic tourist guide that helps visitors to the religious places in the mountainous village of Barzanja that is located in Iraqi Kurdistan. The case study is conducted by using three-hundred questions and answers. One hundred volunteers participated in this study. The participant asks a question and the bot provides an answer if it recognizes the question, otherwise it provides a default answer along with a suggestion of how to use the system properly. The data of these experiment is collected, analyzed, and problems regarding Kurdish language are detected. Designing software agents for processing Kurdish texts faces many challenges. Kurdish texts have not yet been processed using natural language processing (NLP). In addition, Kurdish font disorder and the lack of standardized keyboards and writing styles makes processing Kurdish text difficult. Furthermore, Kurdish language consists of variety of different dialects with different typing styles. In this research, we specifically focus on the design of a software agent for the Central Kurdish (Sorani) dialect. We managed to solve some of the problems related to the Kurdish language and suggest solutions to others.
Interaction Design for Email Client: User Movements Reflected in Gesture-based Operation

This study looks into users’ gesture-based operations to be performed on smartphone interface, and focuses on the factor of realistic experience each operation produces. Then we devise a new interface design concept by incorporating the element of reality in the design theory of Natural User Interface (NUI). The element of reality constitutes the user experience of digital content, and NUI forms the sensory reality of operating the digital content. Proposed here is an interaction system in which the realistic experience of digital content use is emphasized using the two characteristics of gesture operations: “expressiveness” and “problem-solving ability based on spatial thinking”. This study takes as an example the emailing tasks which anyone today has had experience with, and attempts to solve the everyday issues of incorrect email transmission, based on the design concepts the authors propose. Focusing on the action of sending emails, the study is aimed at raising users’ awareness of emailing practice by creating a realistic experience of “sending something to a recipient”, using three-dimensional gesture operations on the app’s interface. On top of this, a practical email transmission/interaction system will be built by integrating the incorrect transmission prevention mechanism into the realistic experience of “sending things”, which was represented by gesture operations.
A software defect prediction method based on sampling and integration

Abstract This paper mainly analyzes the characteristics of software defect prediction from the perspective of machine learning, and proposes a semi-supervised software defect prediction method based on sampling and integration for the problem of class imbalance in software defect data and the incomplete classification of data sets. SISDP). SISDP firstly constructs a robust KNN marking model by taking a balanced sample of samples to mark a batch of unmarked data, and then iteratively adds the newly marked data to the original data set for the next marking model. , iterate until the data is marked. For the marked data set, the hybrid sampling algorithm is used to obtain the training set, and the integrated classification model composed of the multi-classification algorithm is classified and trained. SISDP not only reduces the interference of a few classes on the marking process, but also improves the generalization ability of the defect prediction model.
Mobile Pedestrian Navigation and Augmented Reality in the Virtualization of the Territory

Establishing the meanings and relationships that exist between the creation of heritage educational content of applications with elements of mobile pedestrian navigation and mobile augmented reality and learning processes through mobile devices is the objective of this research. From this portable and mobile context, a teaching process linked to territorial information on the architectural and historical heritage corresponding to the cities of Salamanca (Spain) and Santiago (Chile) has been developed, proving its educational effectiveness. Methodologically, the research focuses on two areas: the educational design of recourses for learning through augmented reality and mobile pedestrian navigation on heritage contents and the determination of the modes of understanding and educational effectiveness of the app created in relation to other traditional learning tools. In this way, the construction of a patrimonial thematic unit has been developed with the determination of the scope of this tool in the mobile learning processes considering the processes of identity and local culture.
Multi-view learning for context-aware extractive summarization

Natural language processing techniques have witnessed a notable success in many applications, such as dialogue generation, machine translation, and document summarization. Among them, the study of document summarization is an active research area, which aims to generate a concise version of the original document and preserve its most informative content. However, the existing work fails to consider the interaction among raw data (in terms of contextual similarity and difference), which results in inaccurate and even conflicting outcomes. In this paper, a novel context-aware extractive summarization algorithm is proposed by utilizing the concept of multi-view learning. Multi-view data are represented using latent topics, by parameterizing multiple Gaussian distributions with trainable hyper-parameters. Then those extracted latent topics are fused and integrated to produce the final summarization output. The experiments on one of the largest online-available cartoon data resources demonstrate the superior performance of the proposed algorithm, by achieving the state-of-the-art summarization results when compared with other contemporary approaches.
SAPEM: Secure Attestation of Program Execution and Program Memory for IoT Applications

Security is one of the major challenges that devices connected to the Internet of Things (IoT) face today.Remote attestation is used to measure these devices' trustworthiness on the network by measuring the device platform's integrity.Several software-based attestation mechanisms have been proposed, but none of them can detect runtime attacks.Although some researchers have attempted to tackle these attacks, the proposed techniques require additional secured hardware parts to be integrated with the attested devices to achieve their aim.These solutions are expensive and not suitable in many cases.This paper proposes a dual attestation process, SAPEM, with two phases: static and dynamic.The static attestation phase examines the program memory of the attested device.The dynamic program ow attestation examines the execution correctness of the application code.It can detect code injection and runtime attacks that hijack the control-ow, including data attacks that affect the program control-ow.The main aim is to minimize attestation overhead while maintaining our ability to detect the speci ed attacks.We validated SAPEM by implementing it on Raspberry Pi using its TrustZone extension.We attested it against the speci ed attacks and compared its performance with the related work in the literature.The results show that SAPEM signi cantly minimizes performance overhead while reliably detecting runtime attacks at the binary level.
An End-to-End Approach for Document-level Event Factuality Identification in Chinese

The task of event factuality identification is to recognize the degree of event factuality, such as certain, possible, impossible, uncertain, or underspecified. Previous studies mostly focused on sentence-level event factuality identification and used various kinds of annotated sentence-level information. In this paper, we introduce an end-to-end approach to Chinese document-level event factuality identification. Specially, we proposed a multi-task framework to join the event detection task and the event factuality identification task, which can interact the two tasks and then provide more accurate event and factuality information. The experimental results on the Chinese DLEF corpus show that our proposed model outperforms several baselines significantly.
Workload-Driven Transactional Partitioning for Distributed Databases

Nowadays, data warehouses store massive amounts of data, and various techniques are using to access data. It also requires durable data consistency for the smooth execution of many Web applications. To support scalability, a model is used to investigate a distributed transaction management system that guarantees atomicity, consistency, isolation and durability (ACID) properties. The purpose of this paper to increase the performance and scalability of the database using the workload-driven data partitioning model. The result shows a better performance of NoSQL database using average response time on different partitioning techniques on a cloud-based database. The presented model can be helpful for application developers for building Web applications using distributed and cloud-based databases.
Shape from Caustics: Reconstruction of 3D-Printed Glass from Simulated Caustic Images

We present an efficient and effective computational frame-work for the inverse rendering problem of reconstructing the 3D shape of a piece of glass from its caustic image. Our approach is motivated by the needs of 3D glass printing, a nascent additive manufacturing technique that promises to revolutionize the production of optics elements, from lightweight mirrors to waveguides and lenses. One important problem is the reliable control of the manufacturing process by inferring the printed 3D glass shape from its caustic image. Towards this goal, we propose a novel general-purpose reconstruction algorithm based on differentiable light propagation simulation followed by a regularization scheme that takes the deposited glass volume into account. This enables incorporating arbitrary measurements of caustics into an efficient reconstruction framework. We demonstrate the effectiveness of our method and establish the influence of our hyperparameters using several sample shapes and parameter configurations.
Theoretical Estimation on the Success Rate of the Asymptotic Higher Order Optimal Distinguisher

Abstract Since its first publication at ASIACRYPT 2014, higher order optimal distinguisher (HOOD) has been the most efficient style of higher order side channel attacks that can be used to evaluate the physical security of a masking device. In practice, the efficiency of HOOD can be empirically evaluated with the success rate (SR) metric. In the empirical evaluation, a large number of power traces are needed, and HOOD should be repeated thousands of times under the values of different parameters, which can make the evaluation process cumbersome and the evaluation price high. In light of this, the exact relationship between the SR of the asymptotic HOOD and the values of different parameters is theoretically built, and the soundness of the theoretical analysis is empirically verified in both the simulated scenario and the real scenario. Then, by setting the values of different parameters, the SR of the asymptotic HOOD can be theoretically estimated. Here, as the signal-to-noise ratio of a masking device approaches to zero, the SR of the asymptotic HOOD approaches to the SR of HOOD. Overall, this contribution may help evaluators to efficiently evaluate the physical security of a masking device with HOOD.
Bacterial Foraging Optimization Based on Multi-colony Cooperation Strategy

We introduce two effective strategies into the conventional bacterial foraging optimization (BFO) to improve information utilization and promote information exchange, and propose a powerful multi-colony cooperation BFO (McCBFO). The first strategy randomly divides the whole population into multiple non-overlapping colonies, and the number of colonies in each generation is determined randomly rather than fixed. In other words, the same individual has the opportunity to be assigned to different colonies in different generations to maximize the diversity within the colony and avoid concentrated distribution. The second strategy aims to accelerate the search efficiency by improving information sharing within the same colony and strengthening the information exchange between different colonies. Specifically, the information of the best individual in each colony is shared with other individuals from the same colony and guides them to evolve towards the potential area. Besides, the poorer half of each colony exchange genetic information with randomly selected individuals from other colonies to generate diverse offspring individuals. To analyze the performance of two proposed strategies, we run BFO, (BFO + the second strategy), and McCBFO on three different dimensions (i.e., 2-D, 10-D, and 30-D) of 28 benchmark function from the CEC2013 test suite, and each function is independently run 30 times. The experimental results confirmed that our proposed McCBFO can accelerate the convergence speed and improve the convergence accuracy significantly, and we recommend using the second strategy only for simple problems while their combination for complex problems.
The Effect of Kahoot on Developing EFL Saudi Students' Vocabulary Acquisition, Reading Comprehension, and their Attitudes towards such a Strategy

The study investigated the effect of kahoot on developing EFL Saudi students’ vocabulary acquisition, reading comprehension, and their attitudes towards it. To achieve this aim, the researcher adopted the experimental approach and employed a sample consisting of seventy seven male EFL Saudi students at Imam Mohammed Ibn Saud Islamic University at Deanship of Preparatory Programs. The researcher purposefully chose two scientific stream classes assigned one class consisting of (38) students as an experimental group and the other consisting of (39) students as a control group. The traditional method was used in teaching the control group, while kahoot was used with the experimental one in the second term of the academic year (2018-2019).In the current study, three instruments were used: a vocabulary test, a reading comprehension test, and a questionnaire. The findings of the study showed that there are statistically significant differences at (a= 0.05) between the means of both groups on the students' post vocabulary acquisition and reading comprehension tests, in favor of the experimental group. This could be attributed to using kahoot. This indicated that using kahoot as an instructional strategy had a positive effect on developing student’s vocabulary acquisition and reading comprehension.
Virtual Machine Replication in the Cloud Computing System Using Fuzzy Inference System

Cloud computing provides various services and scalable computing resources through the internet. Due to the features that allow sharing and multiplexing computing resources across numerous tenants, cloud reliability has gained an extensive foothold in recent times. However, in a cloud computing-based environment, it is critical to enhance the reliability of cloud services such as the virtual machine (VM)-based services. To ensure the reliability of VMs in the Infrastructure as a Service (IaaS) cloud computing model, this paper proposes two fuzzy inference systems (FIS), namely Physical Machine FIS1 (PMFIS1) and PMFIS2 for fault tolerance of VMs in cloud computing. The proposed inference systems use replication as a fault tolerance mechanism for VMs. These systems aid in the selection of optimal physical machines (PMs) to place the replicas of virtual machines (VMs). Implementation of proposed FIS is performed in MATLAB to compare the FISs with each other in terms of complexity, flexibility, and better selection of PMs. However, from the simulation result, it is observed that the PMFIS1 is less complex than PMFIS2, but the PMFIS2 is more flexible and makes a better selection of PM than PMFIS1.
Influence of Distance Learning Format on the Motivation of Undergraduate Students

The article examines the features of the influence of the distance learning format on the motivation of undergraduate students to study in higher education programs. The analysis of scientific discourse on the topic of student motivation is carried out. The studies of distance education have been analyzed. An empirical study aimed at identifying changes in the motivation of students and the characteristic features of these changes has been carried out. The negative influence of distance education on the motivation of students to study has been revealed. A forecast of the further development of distance education and its role in the development of the institution of higher education in general has been made.
Short-Term Trajectory Planning in TORCS using Deep Reinforcement Learning

The applications of deep reinforcement learning to racing games so far struggled to reach a performance competitive with the state of the art in this field. Previous work, mainly focused on a low-level input design, show that artificial agents are able to learn to stay on track starting from no driving knowledge; however, the final performance is still far from those of competitive driving. The scope of this work is to investigate in which measure rising the abstraction level can help the learning process. Using The Open Racing Car Simulator (TORCS) environment and the Deep Deterministic Policy Gradients (DDPG) algorithm, we develop artificial agents, considering both numerical and visual inputs, based on deep neural networks. These agents learn to compute either a target point on track or, additionally, a correction to the target maximum speed at the current position, which are then provided as input to a low-level control logic. Our results show that our approach is able to achieve a fair performance, though extremely sensitive to the low-level logic. Further work is necessary in order to understand how to fully exploit a high-level control design.
Robust and Accurate Object Velocity Detection by Stereo Camera for Autonomous Driving

Although the number of camera-based sensors mounted on vehicles has recently increased dramatically, robust and accurate object velocity detection is difficult. Additionally, it is still common to use radar as a fusion system. We have developed a method to accurately detect the velocity of object using a camera, based on a large-scale dataset collected over 20 years by the automotive manufacturer, SUBARU. The proposed method consists of three methods: a High Dynamic Range (HDR) detection method that fuses multiple stereo disparity images, a fusion method that combines the results of monocular and stereo recognitions, and a new velocity calculation method. The evaluation was carried out using measurement devices and a test course that can quantitatively reproduce severe environment by mounting the developed stereo camera on an actual vehicle.
Face Recognition with Inception-Based CNN Models

In this work, three deep learning models are developed and tested for accuracy on the ORL dataset. A new model has been proposed by computing the mean of encodings to improve the accuracy of models. Several sub-models are developed to derive the optimum number of images to be considered in computing the mean of encodings. Two sub-sets of datasets are extracted from ORL dataset for testing purposes. The performance of deep learning models is compared with the classical face recognition methods like PCA and LBP and with statistical methods like singular value based hidden Markov models. It has been determined that proposed inception model by computing mean of encodings outperforms all other models for ORL dataset and the optimum number of images to compute the mean is three.
SMT Versus NMT: An Experiment with Punjabi–English

In this paper, a comparison of the Statistical machine translation (SMT) and neural machine translation (NMT) for Punjabi to English in the fixed domain of health and tourism is provided. We have tried to answer does NMT perform equivalent well or better with respect to the SMT system? We have developed the three base models viz., SMT-based model using the Moses toolkit, followed by long short-term memory (LSTM) model and bidirectional LSTM model using the OpenNMT toolkit. All three models used the Punjabi–English parallel corpus of TDIL health and tourism. Finally, the quality of translation is validated using the automatic parameter like the BLEU score and the TER score. We observed that bidirectional LSTM performs better than simple LSTM an SMT system.
Multi-label classification with classifier chains of ANN models

Multi-label classification is a generalization of a multi-class classification problem where one entity can belong to more than one class from the class set. Recent works have proposed multiple methods of solving this problem that involves both statistical and deep learning methods. While methods exist for using deep learning models for this problem, most of them require the model to have a high dimension output vector and the property of inter-dependency of classes has not been explored. An ensemble of statistical models called the chain classifiers can be used to address these issues. This study explores methods of using neural network classifiers in the classifier chain model and tries to address some problems with such architecture while compare their performance on different types of data using different metrics with each other and with other well performing multi-label classification methods.