Reviewerly/exHarmony huggingface dataset:
Revisiting Requirements Documentation Techniques and Challenges

Requirement documentation plays a substantial role in requirement engineering (RE) process. It is a procedure of forming the document, which is used to communicate functions, events and operations to many stakeholders in software development procedure. The documents similarly perform as a proof of all the processes and actions included in software development. The precise level of attention to clearly share requirements and the vital concepts makes software projects successful. The comprehensive and approved requirements are specified and documented to aid as the root for advanced system development activities. In spite of various trends setting research outcomes, many practitioners and researchers have observed challenges and issues with requirement documentation phase in previous literature. However, very slight devotion has been given to explore the variety of challenges of documentation phase in various documentation methods. The study unveils the need for automation and standardization of requirement documentation process and techniques. Additionally, this paper attempts to analyze the substantial challenges of requirement documentation.
Towards a Conversational Agent with “Character”

We present a simple approach to create a "persona" conversational agent. First, we take advantage of a large collection of subtitles to train a generative model based on neural networks. Second, we manually handcraft a small corpus of interactions that specify our character (from now on the "persona corpus"). Third, we enrich a retrieval based engine with this corpus. Finally, we combine both into a single agent. A preliminary evaluation shows that the generative model can hardly implement a coherent "persona, but can successfully complement the retrieval model.
Development of Presentation Robot System for Classroom Based on State of Participants

This paper described a presentation robot system that interacts with participants in a classroom. Especially, we have proposed the robot interaction to interrupt the main scenario and speak short comment based on a state of participants. For the robot's presentation, it is important to involve the participants. We are developing a presentation robot system to give a lecture for a large group at the university. Especially, we focus on the interrupt behavior of a human being based on a state of participants to involve participants. Thus, we propose the measurement of the state of participants based on face direction detection. We have developed the integrated system of a humanoid robot PALRO named Masaru, a data management server and client PCs for each participant. For detecting face direction, we apply AlexNet to divide a face direction into 4 categories corresponding with the motivation for a presentation. As an experiment, we performed a demonstration in a lecture of robotics by 55 participants. MASARU interrupts the main scenario and takes action based on the face direction result. MASARU's presentation is mostly met with acceptance shown by the evaluation button of an application and the questionnaire.
The Robosoccer as a Modern Educational Platform in the Field of Artificial Intelligence

The article outlines the robosoccer as a modern educational platform in the field of artificial intelligence.
Action Recognition in Videos Using Multi-stream Convolutional Neural Networks

Human actionDe Almeida Maia, Helena recognition aims to classify trimmed videos basedConcha, Darwin Ttito on the action being performed by one or more agents. It can be applied to a large variety of tasks, such as surveillancePedrini, Helio systems, intelligent homes, health monitoring, and human-computerTacon, Hemerson interaction. Despite the significant progress achieved through image-based deep networks, video understanding still faces challengesDe Souza Brito, André in modeling spatiotemporal relations. The inclusion of temporal information in the network may lead to significant growth in the training cost. To address this issue, we exploreDe Lima Chaves, Hugo complementary handcrafted features to feed pre-trained two-dimensional (2D) networks in a multi-stream fashion. In addition to the commonly used RGB and optical flow streams, weVieira, Marcelo Bernardes propose the use of a stream based on visual rhythm images that encode long-term information. Previous works have shown that either RGB or optical flow streams may benefit fromVillela, Saulo Moraes pre-training on ImageNet since they maintain a certain level of object shape. The visual rhythm, on the other hand, harshly deforms the silhouettes of the actors and objects. Therefore, we develop a different pre-training procedure for the latter stream using visual rhythm images extracted from a large and challenging video dataset, the Kinetics.
Authentic Quantum Nonces

Random numbers are an important ingredient in cryptographic applications, whose importance is often underestimated. For example, various protocols hinge on the requirement of using numbers only once and never again (most prominently, the one-time pad), or rest on a certain minimal entropy of a random quantity. Quantum random number generators can help fulfilling such requirements, however, they may as well be subject to attacks. Here, we consider what we coin a randomness substitution attack, in which the adversary replaces a good randomness source by another one, which produces duplicate values (over time) and perhaps numbers of low entropy. A binding between a random number and its origin is thus a certificate of quality and security, when upper level applications rest on the good properties of quantum randomness.
SISA: A secret-sharing scheme application for cloud environment

Security defies one of the galactic barrier when pondering the use of cloud services. Data outsourcing in the cloud (DOC) is conventionally based on data encryption, which imparts impregnable security, but deteriorating the efficiency and rimming the functionalities of cloud. In this proposal we aim to possess them by proposing a secret sharing scheme that depends on partitioning the data and distributed storage over multi-clouds. The scheme used in the proposal uses a combination of symmetric (AES) and asymmetric (RSA) encryption techniques to share the data among the peers in the cloud environment. The proficiency of the suggested scheme has been demonstrated by the empirical results.
Enhancing Security of AES Using Key Dependent Dynamic Sbox

Nowadays most of the data transmission is done through digital communication and hence security and efficient data transmission is always a desirable property for any data communication. At present, Advance encryption standard (AES) is most widely used cryptographic technique. This AES uses static s box for encryption and decryption which remains constant irrespective of the key. Numerous analysts have experimented and revealed that there are some weak points [1] in the static s-box as it is static and known to all. Therefore, in our proposed algorithm we modify AES such that it uses dynamic sbox which is key dependent and compare the avalanche effect of basic AES with our proposed algorithm.
An Improved ORB Algorithm of Extracting Features Based on Local Region Adaptive Threshold

For the features extraction of image, an improved Oriented FAST and Rotated BRIEF (ORB) algorithm of extracting features based on local region adaptive threshold is proposed, which not only can it solve the problem that the traditional ORB feature extraction algorithm cannot adapt to the local brightness change of the image, but also solve the phenomenon that the extracted feature points exist clusters. Firstly, extracting local region adaptive threshold features on each pyramid image based on constructed an image pyramid. Then, the feature points are divided by the quad-tree algorithm and the direction and the descriptor of the feature points are calculated. After that, the Fast Library for Approximate Nearest Neighbors (FLANN) is used to the match feature points, the mismatch points are eliminated by Lowe's algorithm and rotation consistency. Finally, using the Random sample consensus (RANSAC) to get the fine matching image. The method proposed in this paper is carried out on the Oxford images. Experiments show that the proposed method can extract more stable feature points under different fuzzy, illumination and compression conditions, which improve the matching accuracy of feature points.
Film Genre Prediction Based on Film Content and Screenplay Structure

In this study, we propose a method to classify genre-based films using film screenplays. The proposed method vectorizes films into two aspects, i.e., film content (i.e., what films tell viewers) and screenplay structure (i.e., how the films narrate stories), and classifies film genres using the support vector machine method. We applied the Doc2Vec algorithm to screenplay structure and to handle film content. In film production, for vectorizing films, we used the statistics of the four screenplay elements: scene, action, dialogue, and transition. Compared with baseline methods, the evaluation showed that the proposed method is better for classifying films of specific genres.
Investigation of Human-Service Robot Division and Interaction for Catering Services

The purposes of this study are to explore the principles and rules of human-service robot division and interaction when introducing service robots into the catering service industry. Focus group interview and in-depth expert interview were conducted. The results indicated that the participants had high acceptance of importing service robot into catering service. They feel the service robots are somewhat novel and attractively. From the perspective of task orientation, the participants think that the service robots could be introduced to all of the catering service process in the future. However, they also hoped that the human and robots can cooperate and work together in the processes. They hope to have more interaction with robots. They are delight to see the service robot can bring customers different feelings and experience. They don't want to face a standard and ice-cold robot. The participants hope that the service robots have a wide variety of external appearance to meet the style of restaurants. They also anticipate the robot can act various body movements and facial expression. Immediate assistance to customers and service workers are expected. Incorrect message transmission can be tolerant by the customers, however, exact communication with service personnel is considered to be very important. The service workers have willing to work together with service robots and hope the high-risk and heavy work can be done by service robots to reduce accidents and improve the reliability and safety. The feeling of safety and more affect feeling is needed for the customers. Besides, the participants are more concerned about the value that service robots can generate. Cost down of human resources and reduced workload was expected especially. Application of service robots does not necessarily imply that manual and cognitive tasks will be replaced by machines. It is essential to serve and observe the emotional needs of customers and service workers. High-skilled workers may become more productive as they become co-workers of machines and both complement each other even at the same level of value creation. Therefore, thinking about how to cooperate and work together for the workers and service robot while introducing service robot into catering service is an important topic. The customers and service workers are looking forward to get along with an interactive, emotional, and reliable service robot.
A Simple Yet Effective Approach to Robust Optimization Over Time

Robust optimization over time (ROOT) refers to an optimization problem where its performance is evaluated over a period of future time. Most of the existing algorithms use particle swarm optimization combined with another method which predicts future solutions to the optimization problem. We argue that this approach may perform subpar and suggest instead a method based on a random sampling of the search space. We prove its theoretical guarantees and show that it significantly outperforms the state-of-the-art methods for ROOT.
Comparing and Analysis of Different Optimization Techniques on Sparse Multi-Class Data

It is a matter of fact that there are certain optimization techniques for sparse data and multi-class data. Optimization techniques generally maximizes or minimizes an error function which depends on the internal parameter of our training model. The effect of different major optimizers on sparse multi-class data can be observed, analyzed and compared based on their loss and accuracy curve. For the analysis, CIFAR-10 data are chosen and trained on a convolutional neural network for different optimizers and then validate on testing data. The objective of this study was to find out the effects of different major optimization techniques in various applications of image processing and state of the art Deep learning. The Result of the study reveals that Nadam will be the best suiting optimization techniques for sparse multi-class datasets.
Game Genre Classification from Icon and Screenshot Images Using Convolutional Neural Networks

In this paper, we study game genre classification using typical game images provided in game stores such as an icon or screenshots. The proposed method is based on a convolutional neural network and a soft voting ensemble technique. Network depth and dropout strategy are examined to obtain the best network architecture for performing the task. The ensemble technique is applied to boost the classification accuracy. Experiments are conducted on Android game dataset that consists of 25,001 icon images and 180,553 screenshots from 17 game genres. Our proposed method can achieve 40.3% and 46.7% classification accuracies for single icon and screenshot classification tasks, respectively. It also outperforms the human performance in both tasks. In addition, it can provide accuracy of 55.3% when multiple screenshots and icon of the game are used together for classifying their genre.
Towards a Conceptual Framework for Security Requirements Work in Agile Software Development

Security requirement work plays a key role in achieving cost-effective and adequate security in a software development project. Knowledge about software companies' experiences of security requirement work is important in order to bridge the observed gap between software security practices and security risks in many projects today. Particularly, such knowledge can help researchers improve on available practices and recommendations. This article uses the results of published empirical studies on security requirement work to create a conceptual framework that shows key concepts related to work context, this work itself and the effects of this work. The resulting framework points to the following research challenges: 1) Identifying and understanding factors important for the effect of security requirements work; 2) Understanding what is the importance of the chosen requirements approach itself, and; 3) Properly taking into account contextual factors, especially factors related to individuals and interactions, in planning and analysis of empirical studies on security requirements work.
An Automatic Text Classification Method Based on Hierarchical Taxonomies, Neural Networks and Document Embedding: The NETHIC Tool

This work describes an automatic text classification method implemented in a software tool called NETHIC, which takes advantage of the inner capabilities of highly-scalable neural networks combined with the expressiveness of hierarchical taxonomies. As such, NETHIC succeeds in bringing about a mechanism for text classification that proves to be significantly effective as well as efficient. The tool had undergone an experimentation process against both a generic and a domain-specific corpus, outputting promising results. On the basis of this experimentation, NETHIC has been now further refined and extended by adding a document embedding mechanism, which has shown improvements in terms of performance on the individual networks and on the whole hierarchical model.
Multimodal Retrieval of Similar Soccer Videos Based on Optimal Combination of Multiple Distance Measures

This paper presents a new multimodal method for retrieval of similar soccer videos based on optimal combination of multiple distance measures. Our method first extracts three types of Convolutional Neural Network-based features focusing the players' actions, the audience's cheers and prompt reports. Then, by applying the optimal distance measure to each feature, we calculate the similarities between a query video and videos in a database. Finally, we realize accurate retrieval of similar soccer videos by integrating these similarities. Experiments on actual soccer videos demonstrate encouraging results.
Prediction of Tuberculosis Using Supervised Learning Techniques Under Pakistani Patients

Tuberculosis (TB) is a long-lasting malady, transmitted by peoples, cows, birds etc., it impacts practically whole parts of the human body yet the vast majority of the frequency are found in lungs issue. Tuberculosis (TB) begun by a bacterium to be specific mycobacterium. For the distinguishing proof of tuberculosis persistent, mycobacterium must be found in mucus. For this reason, a special culture is arranged where the mycobacterium tuberculosis (MTB) microbes are be duplicated and this entire procedure required a period of somewhere around one and half month. Additionally, the accomplishment of the treatment of tuberculosis is critical for the peoples because of its lengthy treatment period. It is anticipated that the malady is leveled but in under-developing nations, it is as yet a major issue. There is a requirement for the mechanized timely forecasting of the tuberculosis (TB) which hence needs extra datasets of the disease and more comparative investigation on these datasets. In our investigation, we propose another dataset in low-socioeconomic status country like Pakistan and show how this dataset can be utilized for the timely forecasting of tuberculosis (TB).
Hunting multiple bumps in graphs

Bump hunting is an important approach to the extraction of insights from Euclidean datasets. Recently, it has been explored for graph datasets for the first time, and a single bump is hunted in an unweighted graph in this exploration. Here, we extend this exploration by hunting multiple bumps in a weighted graph. Given a weighted graph and a set of query nodes exhibiting a property of interest, our objective is to find k non-overlapping and connected subgraphs, i.e., bumps, in which the discrepancy between the numbers of query and non-query nodes is maximized and the sum of edge costs is minimized simultaneously. We prove that our extended bump hunting problem can be transformed to a recently formulated Prize-Collecting Steiner Forest Problem (PCSFP). We further prove that PCSFP is NP-hard even in trees. Then, we propose a fast approximation algorithm for solving PCSFP in trees. Based on this algorithm, we improve the state-of-the-art approximation algorithm for solving PCSFP in graphs, and prove that the solutions of our improvement are always better than or equal to those of the state-of-the-art algorithm. Moreover, we adapt the existing bump hunting algorithms for solving our extended bump hunting problem. We evaluate our methodology via real datasets, and show that 1) our improvement scales well to large graphs, while producing solutions that dominate those of the state-of-the-art algorithm; and 2) our adaptation of an existing bump hunting algorithm can also produce solutions that are better than those of the state-of-the-art algorithm in some cases.
Constructing secure‐channel free identity‐based encryption with equality test for vehicle‐data sharing in cloud computing

Abstract With the rapid rise of connected vehicles on the road, there is an exponential growth in vehicle‐related data (vehicle‐data) generated and accumulated by connected vehicles. Taking advantage of cloud computing, enabling a vehicle‐data sharing system can make various services based on vehicle‐data available. However, the untrusted nature of the cloud server raises a challenge on how to balance privacy and functionality over the vehicle‐data outsourced to the vehicle‐data sharing system. Identity‐based encryption with equality test (IBEET) is a viable solution to this challenge as it not only ensures data privacy but also enables searchable functionality over the encrypted data while dealing with the certificate management issue that exists in public key encryption with equality test (PKEET). However, the existing IBEET schemes are unable to be applicable into the vehicle‐data sharing in cloud computing due to the fact that they did not consider the consequences of unexpected exposure of trapdoors given to cloud server and different levels of computing capabilities of the system entities. Therefore, this article introduces the notion of secure‐channel‐free IBEET (SCF‐IBEET) scheme and presents a generic construction of the SCF‐IBEET scheme by addressing the above‐mentioned issues simultaneously. The proposed SCF‐IBEET construction achieves one‐wayness under chosen‐identity and chosen‐ciphertext attacks, indistinguishability under chosen‐identity and chosen‐ciphertext attacks, and trapdoor anonymity securities with respect to five different types of adversaries. Furthermore, the security comparison and efficiency analysis indicate that the proposed construction is reasonable with regard to security and viable in terms of efficiency to be applied to practical scenarios as vehicle‐data sharing in cloud computing.
Headway in Quantum Domain for Machine Learning Towards Improved Artificial Intelligence

A vital, transformative and impactful field on our society is levied on us by the incoming quantum computation and information technologies. The undergoing research on the interaction of fields of quantum computation with the machine learning and artificial intelligence is witnessing significant investigations. Evidently there has been a major demonstration of quantum enhancements in interactive learning tasks and the optimization of quantum experiments using ML sowing the seeds of interdisciplinary activities to improve their influence in our hugely populated data intensive digital world. The quantum mechanical world is evidently taking shapes beyond theoretical analysis with the aid of AI previously uncharted. This composite review, to the best of our knowledge, makes an attempt to explore the recent headway for actuating AI and ML in the quantum domain.