Reviewerly/exHarmony huggingface dataset:
Security Requirements Prioritization Techniques: A Survey and Classification Framework

Security requirements Engineering (SRE) is an activity conducted during the early stage of the SDLC. SRE involves eliciting, analyzing, and documenting security requirements. Thorough SRE can help software engineers incorporate countermeasures against malicious attacks into the software’s source code itself. Even though all security requirements are considered relevant, implementing all security mechanisms that protect against every possible threat is not feasible. Security requirements must compete not only with time and budget, but also with the constraints they inflect on a software’s availability, features, and functionalities. Thus, the process of security requirements prioritization becomes an integral task in the discipline of risk-analysis and trade-off-analysis. A sound prioritization technique provides guidance for software engineers to make educated decisions on which security requirements are of topmost importance. Even though previous research has proposed various security requirement prioritization techniques, none of the existing research efforts have provided a detailed survey and comparative analysis of existing techniques. This paper uses a literature survey approach to first define security requirements engineering. Next, we identify the state-of-the-art techniques that can be adopted to impose a well-established prioritization criterion for security requirements. Our survey identified, summarized, and compared seven (7) security requirements prioritization approaches proposed in the literature.
A General-Purpose Multi-Dimensional Convex Landscape Generator

Heuristic and evolutionary algorithms are proposed to solve challenging real-world optimization problems. In the evolutionary community, many benchmark problems for empirical evaluations of algorithms have been proposed. One of the most important classes of test problems is the class of convex functions, particularly the d-dimensional sphere function. However, the convex function type is somewhat limited. In principle, one can select a set of convex basis functions and use operations that preserve convexity to generate a family of convex functions. This method will inevitably introduce bias in favor of the basis functions. In this paper, the problem is solved by employing insights from computational geometry, which gives the first-ever general-purpose multi-dimensional convex landscape generator. The new proposed generator has the advantage of being generic, which means that it has no bias toward a specific analytical function. A set of N random d-dimensional points is generated for the construction of a d-dimensional convex hull. The upper part of the convex hull is removed by considering the normal of the polygons. The remaining part defines a convex function. It is shown that the complexity of constructing the function is O(Md3), where M is the number of polygons of the convex function. For the method to work as a benchmark function, queries of an arbitrary (d−1) dimensional input are generated, and the generator has to return the value of the convex function. The complexity of answering the query is O(Md). The convexity of the function from the generator is verified with a nonconvex ratio test. The performance of the generator is also evaluated using the Broyden–Fletcher–Goldfarb–Shanno (BFGS) gradient descent algorithm. The source code of the generator is available.
ScreenLife Capture: An open-source and user-friendly framework for collecting screenomes from Android smartphones

As our interactions with each other become increasingly digitally mediated, there is growing interest in the study of people's digital experiences. To better understand digital experiences, some researchers have proposed the use of screenomes. This involves the collection of sequential high-frequency screenshots which provide detailed objective records of individuals' interaction with screen devices over time. Despite its usefulness, there remains no readily available tool that researchers can use to run their own screenome studies. To fill this gap, we introduce ScreenLife Capture, a user-friendly and open-source software to collect screenomes from smartphones. Using this tool, researchers can set up smartphone screenome studies even with limited programming knowledge and resources. We piloted the tool in an exploratory mixed-method study of 20 college students, collecting over 740,000 screenshots over a 2-week period. We found that smartphone use is highly heterogeneous, characterized by threads of experiences. Using in-depth interviews, we also explored the impact that constant background surveillance of smartphone use had on participants. Participants generally had slight psychological discomfort which fades after a few days, would suspend screen recording for activity perceived to be extremely private, and recounted slight changes in behavior. Implications for future research is discussed.
A Parallel Quantum Circuit Implementations of LSH Hash Function for Use with Grover’s Algorithm

Grover’s search algorithm accelerates the key search on the symmetric key cipher and the pre-image attack on the hash function. To conduct Grover’s search algorithm, the target cipher algorithm should be efficiently implemented in a quantum circuit. Currently, small quantum computers are difficult to operate with large quantum circuits due to limited performance. Therefore, if a large quantum computer that can operate Grover’s algorithm appears, it is expected that a cipher attack will be possible. In this paper, we propose a parallel structure quantum circuit for the Korean hash function standard (i.e., LSH). The proposed quantum circuit designed a parallel operation structure for the message expansion (i.e., MsgExp) function and the mix function, which are the internal structures of the LSH hash function. This approach shows an efficient result for quantum circuit implementation in terms of quantum resources by reducing the depth of the quantum circuit by about 96% through the trade-off of appropriate quantum resources compared to previous work. This result can be a reference for the implementation of a parallel quantum circuit in the future and is expected to advance the attack timing of the search algorithm for Grover’s LSH hash function.
FastCNN: Towards Fast and Accurate Spatiotemporal Network for HEVC Compressed Video Enhancement

Deep neural networks have achieved remarkable success in HEVC compressed video quality enhancement. However, most existing multiframe-based methods either deliver unsatisfactory results or consume a significant amount of resources to leverage temporal information of neighboring frames. For the sake of practicality, a thorough investigation of the architecture design of the video quality enhancement network regarding enhancement performance, model parameters, and running speed is essential. In this article, we first propose an efficient alignment module that can quickly and accurately aggregate the spatiotemporal information of neighboring frames. The proposed module estimates deformable offsets progressively in lower-resolution space motivated by the observation of offset correlations between adjacent pixels. Then, the quantization parameter (QP) that represents compression level prior knowledge is utilized to guide aligned feature enhancement. By combining alignment feature distillation with residual feature correction, we obtain an efficient QP attention block. To save the storage space of the network, we design a hash buffer to store QP embedding features. These efficient components allow our network to effectively exploit temporal redundancies and obtain favorable enhancement capability while maintaining a lightweight structure and fast running speed. Extensive experiments demonstrate that the proposed approach outperforms state-of-the-art methods over different QPs by up to 0.09 to 0.11 dB, whereas the inference time can be reduced by up to 69%.
A Differentiable Parallel Sampler for Efficient Video Classification

It is crucial to sample a small portion of relevant frames for efficient video classification. The existing methods mainly develop hand-designed sampling strategies or learn sequential selection policies. However, there are two challenges to be solved. First, hand-designed sampling strategies are intrinsically non-adaptive to different video backbones. Second, sequential frame selection policies ignore temporal relations among all video frames. The sequential selection process also hinders the application of these video samplers in speed-critical systems. In this article, we propose a differentiable parallel video sampling network (PSN) to tackle the aforementioned challenges, First, we optimize the video sampler with a differentiable surrogate loss, allowing to dynamically learn the sampler with the cooperation from the video classification model. Our sampler considers the feedback from all frames jointly, eliminating the learning difficulties of sequential decision making. The learning process is fully gradient-based, making the sampler be learned efficiently. Our video sampler can assess a set of frames swiftly and determine the importance of each frame in parallel. Second, we propose to model the inter-relation among contextual frames, which encourages the sampler to select frames based on a comprehensive inspection of the entire video. We observe that a simple context relation mining instantiation would significantly improve the classification performance. The experimental results on three standard video recognition benchmarks demonstrate the efficacy and efficiency of our framework.
Real single-channel color image encryption method based on computational ghost imaging

Abstract A real single-channel color image encryption method based on computational ghost imaging is proposed. Compared with the currently proposed color single-channel encryption scheme, the method does not decompose the color image into three gray-scale images or encode it into a grayscale image for encryption. Instead, the color speckle patterns are used as the key to directly encrypt. The ciphertext is a grayscale image and thus has camouflage properties to a certain extent. Furthermore, the optical encryption setup is easy to be implemented and the key is generated by a chaotic system, which not only guarantees security but also reduces the volume of key transmission. The feasibility and high security of this method are supported by both computer simulations and optical experiments.
The medium is not the message

Abstract Linguistic expressions in social media vary along many axes, including author style, the specific medium and its affordances, and others. In this paper, we argue that different registers must be distinguished within social media and that register should be included as an important factor independent of (social) medium in analyses of variable linguistic phenomena. We introduce a new German cross-media corpus, consisting of blog posts and tweets from the same 44 authors. We define the registers as ‘Informative’, ‘Narrative’, and ‘Persuasive’, based on situational characteristics of the texts. We then correlate the registers with two variable linguistic phenomena: German modal and intensifying particles. In each case, we document considerable inter- and intraindividual variation in the expressions used and their frequency across texts. The statistical analysis shows that the register grouping corresponds more closely to linguistic similarities between texts than the grouping by medium does.
Popularity of Optimization Techniques in Sentiment Analysis

In today’s scenarios, online marketing and social networking require sentiment for opinion mining to understand its customers and users. The sentiment analysis involves extracting information from the text and symbols shared by the individuals over the website reflecting their opinions. It describes various emotions of the customers based on any product. Sentiment analysis is applicable to monitor social media that recognized the mood of customers against the brand or any other product. It has been observed that a variety of techniques were used to optimize the features extracted during sentiment analysis. In the present paper, the author has presented a detailed literature survey to outline the popularity of optimization techniques used in the field of sentiment analysis. The literature review conducted over the authenticated research published in the last decade had illustrated that most of the researchers had implemented Ant Colony Optimization (ACO) and Particle Swarm Intelligence (PSO) as optimization techniques. In addition to this hybrid, optimization had also been emerging in recent years. The work outcomes are supported by the graphical illustrations to show the rising popularity of optimization techniques in the field of sentiment analysis.
Expert System for Question Answering on Anomalous Events and Mitigation Strategies Using Bidirectional Transformers and Knowledge Graphs

Abstract Daily drilling reports provide vital information for well planning as they capture anomalous events and mitigation measures during drilling operations. Previous works predominantly focus on search frameworks for information retrieval from these reports. However, the context between searches is lost, preventing users from narrowing down to the exact answer. Here, we present a transformer-based closed domain conversational agent for longer dialogues to guide users to contextual information for anomalous drilling events through natural language. Automated text extraction, cleaning and validation tasks are initially performed to resolve data quality issues prior to language modeling on a validated data set. Subsequently, a knowledge-based graph is created by node embedding using entity extractions and by learning the semantic-level relationships between entity nodes such as well names and events. Further, conversational agents are trained on the knowledge graphs for natural dialogue generation using neural machine translation models. Here, users’ questions are translated into a query in a structured language that is evaluated directly over the knowledge graph in order to generate the desired answers. The workflow was tested on an asset with multiple wells experiencing several anomalous events during drilling such as stuck pipe, circulation losses and kicks. The end-to-end workflow was tested on its ability to retrieve anomalous events and present mitigation measures in the aforementioned data set based on the descriptions input by survey participants. Performance on the anomaly extraction, attribute mapping and mitigation performance were evaluated through F1 scores. A significantly high F1 score was recorded for anomaly extraction. This is predominantly driven by high precision due to explicit modeling of the reports as a knowledge graph. In addition to testing the workflow end to end, we tested the knowledge graph representation in isolation. For this, ranking metrics and triple classification with negative samples were used for the evaluation. The adjusted mean rank index was close to one, indicating high performance. Structured querying on the knowledge graphs also showed high accuracy for classifying anomalous events in the drilling report. The work described in this paper automates the end-to-end workflow for building an expert system for answering questions about anomalous events and mitigation strategies using daily drilling reports. Our novel approach using a knowledge graph with a transformer-based conversational agent enables users to perform detailed interactive investigation of anomalous events observed in daily drilling reports and create mitigation strategies. The workflow also allows for incorporating prior domain knowledge from drilling experts.
A Framework for Learning Event Sequences and Explaining Detected Anomalies in a Smart Home Environment

This paper presents a framework for learning event sequences for anomaly detection in a smart home environment. It addresses environment conditions, device grouping, system performance and explainability of anomalies. Our method models user behavior as sequences of events, triggered by interaction of the home residents with the Internet of Things (IoT) devices. Based on a given set of recorded event sequences, the system can learn the habitual behavior of the residents. An anomaly is described as deviation from that normal behavior, previously learned by the system. One key feature of our framework is the explainability of detected anomalies, which is implemented through a simple rule analysis.
Re-Live History: An immersive virtual reality learning experience of prehistoric intangible cultural heritage

The use of immersive virtual reality for learning is a growing opportunity that has so far suffered from limited application in the classroom, particularly with students in the 11 to 12 year bracket. Due to more concern being shown toward usability rather educational goals, mixed feelings exist about the technology’s ability to teach. Meanwhile, historical games usually have fun as the main or sole objective, which may cause problems by diminishing the value of the depicted cultural heritage and supersede the intended learning outcomes of the experience. This research aims to contribute toward this gap by working closely with teachers in developing an immersive virtual reality learning experience to teach prehistoric intangible cultural heritage to history students aged 11 to 12 years. The research question of this study is how to go about designing an immersive learning experience for secondary school teachers to teach 11 to 12 year old students about prehistoric cultural heritage on which very little documented evidence is available. To this end, the Re-Live History project was built upon a virtual reality navigation experience of a Maltese Neolithic hypogeum, adding a representation of intangible cultural heritage in the form of human behavior. A content requirement study from heritage experts’ perspective was carried out, followed by a similar study from the history teachers’ perspective. These provided which learning outcomes can be potentially addressed by the immersive learning experience, what form of intangible cultural heritage can be represented, and what success criteria were to be used for its evaluation. A prototype of the experience was then developed and reviewed by the heritage experts and subsequently developed into the experience evaluated by teachers and heads of department. Evaluation was carried out in terms of authenticity relative to the historic site, ease of navigation, impact in terms of achievable learning outcomes, and utility in the classroom. This ensured that educational objectives were given priority and should help teachers embrace and adopt the technology in the classroom. Future work should pilot the use of the IVR in the classroom and provide further empirical evidence to its ability to help such students achieve the learning outcomes expected by the syllabus.
Network Aware Resource Optimization Using Nature Inspired Optimization Algorithm for Task Scheduling in Cloud Infrastructure

Cloud is a pay-per use infra-structed which has invited huge clients to cloud, in order to get reliable services without extra maintenance or infrastructure cost. Growing cloud services and migration of small business to cloud have led to high load on cloud service providers, which leads to the need of better optimization algorithm in order to manage the machine better performance and meet better quality of services to the client. Cloud broker or agent plays an important role to achieve this using intelligent task scheduling algorithm to manage the task in such a way to optimize the performance of the cloud services and data center. Currently various optimization algorithms are proposed but most of them take execution time into consideration but not the network delay between the client and the data center. Hence, to overcome this, an optimization algorithm is proposed in this work using execution time and network delay as the optimization parameters. The nature inspired grasshopper optimization is proposed which is compared with the exiting PSO and ACO models to study the performance. The results show that the proposed algorithm out performs the existing models with execution time, total time and network delay as performance metrics. It demonstrates how the suggested, naturally inspired GOA algorithm beats the existing ACO and PSO algorithms for task scheduling in the cloud with scaling loads requiring 5 virtual machines and 2 data centers. More objective functions, such as power and cost-effective algorithms, can be added to the work to further expand it. This study compares the efficacy of several algorithms based on the predetermined criteria while also examining related algorithms. To determine the best algorithm possible, it is intended to offer each approach individually, analyze the results, and plot the resulting graphs.
GazeCues: Exploring the Effects of Gaze-based Visual Cues in Virtual Reality Exploration Games

This paper examines different gaze-based cue visualization techniques in a Virtual Reality (VR) exploration game. It is argued that gaze could be a valuable design tool for providing cues to players. However, little is known about how these elements could be visually presented and integrated to inform players and fit into the game world. An exploratory study was carried out to investigate four different design approaches (subtle, overlaid-virtual, integrated, emphasized) and their effects on aspects such as clarity, usefulness, and curiosity. In general, the visualizations led to different player impressions on all scales. While players perceived the subtle and emphasized variants as more aesthetically appealing, integrated and virtual-overlaid solutions received more positive ratings regarding tool-related aspects such as ease of extraction and accurateness. We anticipate future work could employ gaze-based cues in various application contexts based on their utility as eye input in VR games continues developing.
Outpace Reality: A Novel Augmented-Walking Technique for Virtual Reality Games

The size of most virtual environments exceeds the tracking space available for physical walking. One solution to this disparity is to extend the available walking range by augmenting users' actual movements. However, the resulting increase in visual flow can easily cause cybersickness. Therefore, we present a novel augmented-walking approach for virtual reality games. Our core concept is a virtual tunnel that spans the entire travel distance when viewed from the outside. However, its interior is only a fraction as long, allowing users to cover the distance by real walking. Whereas the tunnel hides the visual flow from the applied movement acceleration, windows on the tunnel's walls still reveal the actual expedited motion. Our evaluation reveals that our approach avoids cybersickness while enhancing physical activity and preserving presence. We finish our paper with a discussion of the design considerations and limitations of our proposed locomotion technique.