Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamical Analysis of Rumor Spreading Model considering Node Activity in Complex Networks

The dynamic models are proposed to investigate the influence node activity has on rumor spreading process in both homogeneous and heterogeneous networks. Different from previous studies, we believe that the activity of nodes in complex networks affects the process of rumor spreading. An active node can have contact with all the nodes it directly links to, while an inactive node could only interact with its active neighbors. We explore the joint effort of activity rate, spreading rate and network topology on rumor spreading process by mean-field equations and numerical simulations, which reveals that there exists a critical curve consisting of critical activity rate and spreading rate; meanwhile, activity rate and spreading rate both have influence on the final rumor spreading scale.
Hand gestures and visual annotation in live 360 panorama-based mixed reality remote collaboration

In this paper, we investigate hand gestures and visual annotation cues overlaid in a live 360 panorama-based Mixed Reality remote collaboration. The prototype system captures 360 live panorama video of the surroundings of a local user and shares it with another person in a remote location. The two users wearing Augmented Reality or Virtual Reality head-mounted displays can collaborate using augmented visual communication cues such as virtual hand gestures, ray pointing, and drawing annotations. Our preliminary user evaluation comparing these cues found that using visual annotation cues (ray pointing and drawing annotation) helps local users perform collaborative tasks faster, easier, making less errors and with better understanding, compared to using only virtual hand gestures.
Security of Distance-Bounding

Distance-bounding protocols allow a verifier to both authenticate a prover and evaluate whether the latter is located in his vicinity. These protocols are of particular interest in contactless systems, e.g., electronic payment or access control systems, which are vulnerable to distance-based frauds. This survey analyzes and compares in a unified manner many existing distance-bounding protocols with respect to several key security and complexity features.
Datasets column: predicting the emotional impact of movies

Affective video content analysis aims at the automatic recognition of emotions elicited by videos. It has a large number of applications, including mood based personalized content recommendation, video indexing, and efficient movie visualization and browsing. Beyond the analysis of existing video material, affective computing techniques can also be used to generate new content, e.g., movie summarization, personalized soundtrack recommendation to make user-generated videos more attractive. Affective techniques can furthermore be used to enhance the user engagement with advertising content by optimizing the way ads are inserted inside videos.
A New Approach for Tooth Fusion between Cone Beam Computed Tomography Images and Crown Models

In digital orthodontic treatment, dental tooth arrangement is an essential and indispensable part in the whole process. The current arrangement technology is based on crown models and virtual gums. Thus the relationship between root and alveolar bone is lack of consideration, which could produce an unreasonable plan of dental tooth arrangement. In this work, we present a new approach for tooth fusion between Cone Beam Computed Tomography (CBCT) images and crown models, which aim to build a fusion model with complete anatomical structure. The whole process can be divided into tooth segmentation and model fusion. In first part, a super constrained tooth box is applied in initialization to reduce the interference of alveolar bone, and a modified FCM model is applied to solve the over-segmentation problem caused by image inhomogeneous. In second part, the sparse iterative closest point (SICP) model is utilized to realize the accurate registration of different coordinate systems, and the Laplacian based deformation method is adopted to keep the original shape of tooth neck. In addition, the original mesh seam method is utilized to make the transition between the crown model and the root model more natural. The results in initialization step are utilized to generate a skull model without tooth crowns. Finally, we demonstrated the created fusion model is available in dental tooth arrangement.
A survey on the tool support for the automatic evaluation of mobile accessibility

To make a mobile app or website accessible, developers must incorporate accessibility practices into the development activities and perform accessibility evaluations. Accessibility evaluation is usually a manual approach, which can be laborious and difficult to scale or reproduce. Often, the implementation and evaluation of accessibility requirements are overlooked by developers due to high demand and time to market pressure. Automating mobile accessibility evaluation can help, but few approaches and tools are currently available. In addition, few studies discuss for which accessibility guidelines automatic evaluation is supported. Therefore, the aim of this paper is to survey automated accessibility evaluation tools for mobile applications to identify which accessibility properties can be automatically evaluated given the available tools. We expect our findings will provide practitioners with an overview of which tools are available for automatic accessibility evaluation and what are their capabilities. By exploring the limitations of current approaches, we also discuss where developers have little support, thus providing guidance for future research, innovation and development in this field.
Time-Randomized Wormhole NoCs for Critical Applications

Wormhole-based NoCs (wNoCs) are widely accepted in high-performance domains as the most appropriate solution to interconnect an increasing number of cores in the chip. However, wNoCs suitability in the context of critical real-time applications has not been demonstrated yet. In this article, in the context of probabilistic timing analysis (PTA), we propose a PTA-compatible wNoC design that provides tight time-composable contention bounds. The proposed wNoC design builds on PTA ability to reason in probabilistic terms about hardware events impacting execution time (e.g., wNoC contention), discarding those sequences of events occurring with a negligible low probability. This allows our wNoC design to deliver improved guaranteed performance w.r. t. conventional time-deterministic setups. Our results show that performance guarantees of applications running on top of probabilistic wNoC designs improve by 40% and 93% on average for 4 × 4 and 6 × 6 wNoC setups, respectively.
An Information Theoretic Framework For Designing Information Elicitation Mechanisms That Reward Truth-telling

In the setting where information cannot be verified, we propose a simple yet powerful information theoretical framework—the Mutual Information Paradigm—for information elicitation mechanisms. Our framework pays every agent a measure of mutual information between her signal and a peer’s signal. We require that the mutual information measurement has the key property that any “data processing” on the two random variables will decrease the mutual information between them. We identify such information measures that generalize Shannon mutual information. Our Mutual Information Paradigm overcomes the two main challenges in information elicitation without verification: (1) how to incentivize high-quality reports and avoid agents colluding to report random or identical responses; (2) how to motivate agents who believe they are in the minority to report truthfully. Aided by the information measures, we found (1) we use the paradigm to design a family of novel mechanisms where truth-telling is a dominant strategy and pays better than any other strategy profile (in the multi-question, detail free, minimal setting where the number of questions is large); (2) we show the versatility of our framework by providing a unified theoretical understanding of existing mechanisms—Bayesian Truth Serum Prelec (2004) and Dasgupta and Ghosh (2013)—by mapping them into our framework such that theoretical results of those existing mechanisms can be reconstructed easily. We also give an impossibility result that illustrates, in a certain sense, the the optimality of our framework.
Recent Developments in Cartesian Genetic Programming and its Variants

Cartesian Genetic Programming (CGP) is a variant of Genetic Programming with several advantages. During the last one and a half decades, CGP has been further extended to several other forms with lots of promising advantages and applications. This article formally discusses the classical form of CGP and its six different variants proposed so far, which include Embedded CGP, Self-Modifying CGP, Recurrent CGP, Mixed-Type CGP, Balanced CGP, and Differential CGP. Also, this article makes a comparison among these variants in terms of population representations, various constraints in representation, operators and functions applied, and algorithms used. Further, future work directions and open problems in the area have been discussed.
A Survey on Self-Adaptive Security for Large-scale Open Environments

Contemporary software systems operate in heterogeneous, dynamic, and distributed environments, where security needs change at runtime. The security solutions for such systems need to be adaptive for the continuous satisfaction of the software systems’ security goals. Whilst the existing research on self-adaptive security has made notable advancement towards designing and engineering self-adaptive security solutions, there exists little work on the taxonomic analysis of the architectures of the reported research and its applicability for open and ultra-large environments. We propose an architecture-centric taxonomy for mapping and comparing the current research and identifying the future research directions in this field. The proposed taxonomy has been used to review the representative work on the architectural characteristics that self-adaptive security systems must maintain for their effective application in large-scale open environments. We reflect on the findings from the taxonomic analysis and discuss the design principles, research challenges and limitations reported in the state of the art and practice. We outline the directions for the future research on architectural level support for self-adaptive security systems for large-scale open environments.
xxTEA-VCLOUD: A Security Scheme for the Vehicular Cloud Network using a Lightweight Encryption Algorithm

The recent developments in the field of cloud computing and the increasing number of vehicles on roads have led to develop a method that exploits underutilized vehicular resources called the vehicular cloud computing (VCC). It is a paradigm that combines the vehicular ad hoc network VANET and the cloud computing to benefit from their advantages and to provide road safety to drivers. However, authentication and confidentiality are considered as the most challenging issues in VCC. To cope with those challenges, we propose a new security mechanism for the vehicular cloud based on the implementation of a lightweight encryption algorithm with several techniques to ensure authentication and confidentiality.
EnergyQARE

Power market operators have recently introduced smart grid demand response (DR), in which electricity consumers regulate their power usage following market requirements. DR helps stabilize the grid and enables integrating a larger amount of intermittent renewable power generation. Data centers provide unique opportunities for DR participation due to their flexibility in both workload servicing and power consumption. While prior studies have focused on data center participation in legacy DR programs such as dynamic energy pricing and peak shaving, this article studies data centers in emerging DR programs, i.e., demand side capacity reserves. Among different types of capacity reserves, regulation service reserves (RSRs) are especially attractive due to their relatively higher value. This article proposes EnergyQARE, the Energy and Quality-of-Service (QoS) Aware RSR Enabler, an approach that enables data center RSR provision in real-life scenarios. EnergyQARE not only provides a bidding strategy in RSR provision, but also contains a runtime policy that adaptively modulates data center power through server power management and server provisioning based on workload QoS feedback. To reflect real-life scenarios, this runtime policy handles a heterogeneous set of jobs and considers transition time delay of servers. Simulated numerical results demonstrate that in a general data center scenario, EnergyQARE provides close to 50% of data center average power consumption as reserves to the market and saves up to 44% in data center electricity cost, while still meeting workload QoS constraints. Case studies in this article show that the percentages of savings are not sensitive to a specific type of non-interactive workload, or the size of the data center, although they depend strongly on data center utilization and parameters of server power states.
Introducing IoT Competencies to First-Year University Students With The Tiles Toolkit

Advances in the field of Internet of Things (IoT) are introducing innovations in multiple domains including smart cities, healthcare and transportation. An increasing number of jobs today require IoT competences that university courses need to be prepared to deliver. Yet, teaching IoT topics is a challenging task due to complexity and unstructured nature of the IoT. It requires to deliver skills in multiple domains including design, hardware and software engineering and it is often hard to find an entry point to the field. In this paper we explore using the Tiles ideation toolkit as a way to teach bachelor students in IT topics IoT fundamentals. Tiles is composed by a set of 150 cards and a workshop procedure for collaborative ideation. We performed a user study with 60 computer science students to investigate how Tiles can be used as an experiential learning tool to develop basic knowledge in IoT and to train design thinking skills. Results show the tool was accepted as useful and fun to play with. Nearly all students managed to develop a simple IoT idea during the three-hours workshop. Learning outcomes were observed in about half of participants, although time constraints and high stress levels impacted the participants' experience.
Designing for citizen-centric smart cities: supporting people's needs of a community garden

There are a variety of smart city agendas but in recent times a citizen-centric focus has emerged. Citizen-centricity requires a different focus in terms of purpose and outcomes such as providing benefits that cross the digital divide and supporting people's general wellbeing and happiness. It is submitted that the design of the technology that enables smartness and citizen-centricity is another consideration to achieving these aims. Through a process to design technology for a community garden with the express aims of meeting the needs of people, three design implications for design have been identified: supporting a conceptual understanding of that technology and its place in a large system; supporting people's ability to perceive technology and its functionality; and, the technology must be designed to support people's experience of the space in which it is located. To some extent these implications contradict existing design intentions for technology of this nature, such as invisibility.
Comparison of eye-tracking data with physiological signals for estimating level of understanding

We propose an e-learning content recommendation system that estimates a learner's level of understanding of a second language sentence. The system analyzes the eye-tracking data of a learner reading a text, and automatically selects the next text based on the estimation. This paper describes the system design and experimentally compares the estimation accuracies of two estimation methods (multiple regression and a neural network) and two kinds of learner-response data (eye-tracking data alone and both eye-tracking data and physiological signals). The neural network achieved higher accuracy than multiple regression, and eye-tracking data alone yielded the same or higher accuracy than the combined eye-tracking and physiological data. The average accuracy rate of the neural network using eye-tracking data was 67.86%. 1
Learning with Wearable Devices reveals Learners' Best Time to Learn

When is the best learning timing for a learner, and can we develop a system to tell each learner of their individual learning timing? In this paper, we describe the application of a consumer grade wearable sensor (activity tracker) to e-Learning. In considering a technique to estimate the relationship between a learner's learning timing and their performance, we focused on a previous study [1], which showed the relationship between sleeping timing, memorization timing, and recall performance. However, with this method, it is difficult to perform experiments for a large number of learners over a long period of time. Therefore, we propose a learning record / analysis method using an activity tracker and an e-Learning application. The effectiveness of this method was evaluated experimentally. We show that the application's learning record and the activity tracker's sleep record are useful for estimating the relationship between the learner's learning timing and performance. By realizing the learning record / analysis method for a large number of learners over a long period of time using the activity tracker, we can expect the discovery of characteristics of learners and effective feedback methods that have hitherto been unknown.
Synthesis of Facial Expressions in Photographs

The synthesis of facial expressions has applicationsin areas such as interactive games, biometrics systems, and training of people with disorders, among others. Although this is an area relatively well explored in the literature, there are no recent studies proposing to systematize an overview of research in the area. This systematic review analyzes the approaches to the synthesis of facial expressions in photographs, as well as important aspects of the synthesis process, such as preprocessing techniques, databases, and evaluation metrics. Forty-eight studies from three different scientific databases were analyzed. From these studies, we established an overview of the process, including all the stages used to synthesize expressions in facial images. We also analyze important aspects involved in these stages such as methods and techniques of each stage, databases, and evaluation metrics. We observed that machine learning approaches are the most widely used to synthesize expressions. Landmark identification, deformation, mapping, fusion, and training are common tasks considered in the approaches. We also found that few studies used metrics to evaluate the results, and most studies used public databases. Although the studies analyzed generated consistent and realistic results while preserving the identity of the subject, there are still research themes to be exploited.
BARAN: Bimodal Adaptive Reconfigurable-Allocator Network-on-Chip

Virtual channels are employed to improve the throughput under high traffic loads in Networks-on-Chips (NoCs). However, they can impose non-negligible overheads on performance by prolonging clock cycle time, especially under low traffic loads where the impact of virtual channels on performance is trivial. In this article, we propose a novel architecture, called BARAN, that can either improve on-chip network performance or reduce its power consumption (depending on the specific implementation chosen), not both at the same time, when virtual channels are underutilized; that is, the average number of virtual channel allocation requests per cycle is lower than the number of total virtual channels. We also introduce a reconfigurable arbitration logic within the BARAN architecture that can be configured to have multiple latencies and, hence, multiple slack times. The increased slack times are then used to reduce the supply voltage of the routers or increase their clock frequency in order to reduce power consumption or improve the performance of the whole NoC system. The power-centric design of BARAN reduces NoC power consumption by 43.4% and 40.6% under CMP and GPU workloads, on average, respectively, compared to a baseline architecture while imposing negligible area and performance overheads. The performance-centric design of BARAN reduces the average packet latency by 45.4% and 42.1%, on average, under CMP and GPU workloads, respectively, compared to the baseline architecture while increasing power consumption by 39.7% and 43.7%, on average. Moreover, the performance-centric BARAN postpones the network saturation rate by 11.5% under uniform random traffic compared to the baseline architecture.
Use of voice activated interfaces by people with intellectual disability

People with intellectual disability are keen users of information technology, but the need for spelling and typing skills often presents a barrier to information and media search and access. The paper presents a study to understand how people with intellectual disabilities can use Voice Activated Interfaces (VAIs) to access information and assist in daily activities. The study involves observations and video analysis of 18 adults with intellectual disability using VAIs and performing 4 tasks: calibrating the VAIs, using voice assistant (Siri or Google) to search images, using voice to query Youtube, and using the voice assistant to perform a daily task (managing calendar, finding directions, etc.). 72% of participants stated that this was their preferred form of input. 50% could perform all four tasks they attempted with successful outcomes, and 55% three of the tasks. We identify the main barriers and opportunities for existing VAIs and suggest future improvements mainly around audio feedback given to participants. Notably, we found that participants' mental model of the VAIs was that of a person, implications for which include the user having to speak in long polite sentences and expecting voice responses and feedback about the state of the device. We suggest ways that VAIs can be adjusted so that they are more inclusive.
KBQA: constructing structured query graph from keyword query for semantic search

It is often very difficult to locate information on the Web because of its large and rapidly increasing amount of data. One key reason for this is traditional keyword-based search engines focus only on the resources whose title or content exactly matches the query keywords. People usually want to find the best matching resource itself to their query, not the documents which contain the resource. Recently, one promising way to meet this kind of requirement must be ontology-based approach for semantic search. However, it is also obvious there is still non-negligible gap between average users and ontological approach. To overcome this limitation of ontological approach such as Semantic Web, it is essential to provide an efficient method to fill the gap while taking full advantage of semantic technologies. To this end, we devise a method to generate alternative SPARQL queries from the typical natural language based query to the conventional search engines and evaluate the most matched SPARQL query among the alternatives by considering the characteristics of the target knowledge bases. We then implement a prototype system to evaluate the proposed method and validate its empirical performance and accuracy.