Miracle-dz/UniFAR-FTUs huggingface dataset:
What makes driving difficult? : Perceived effort and eye measures follow visible semantic complexity factors

A majority (95%) of crashes can be attributed to humans, with the highest cause category (41%) involving errors of recognition (i.e., inattention, distraction, inadequate surveillance) [1]. Driving safety research often claims that as much as 90% of the information that drivers use is visual. However, these claims have been hampered by a lack of numerical measurement systems [2]. Presently, we develop an ordinal visual driving scene complexity measurement based on human judgments and eye behavior. Mimicking the rebuilding of situation awareness in take-over conditions we presented 60 randomly ordered video clips (3 s duration), varying complexity factors of traffic density, road curvature, and miscellaneous visual features. Eyes of 15 participants were recorded while viewing the clips, and participants rated “how much effort for you to take control and drive within that segment? ” on a 100 point scale. Effort ratings showed a monotonic increase with the number of complexity factors present. A statistically significant increase was also found for saccade amplitude, whereas a statistically significant decrease was found for fixation duration. Pupil size also showed a significant increase but only between 2 complexity levels and at a relatively less convincing strength. In conclusion, the present complexity factor coding scheme apparently corresponds to subjective effort. Further consideration should be given to relating eye tracking measures to visual driving scene components and task demands. In real-time driving systems, both human occupant(s) and computerized processes may observe the same scene at the same time, and matching the machine quantification of the situation to intuitive human judgments is expected to aid in the adherence to advisories and acceptance of automated aids.
Predictability and Prediction of Human Mobility Based on Application-Collected Location Data

In the modern information society, analysis of human mobility becomes increasingly essential in various areas such as city planning and resource management. With users’ historical trajectories, the inherent patterns of their movements can be extracted and utilized to accurately predict the future movements. Plenty of previous work adopted traditional Markov model, which suffers when the trajectory becomes sparse or it shows distinct mobility patterns in different time of day. In this paper, based on an app-collected dataset of 100,000 individuals’ actively uploaded location information, we comprehensively analyze the mobility and predictability of each user. To approach the theoretical predictability and overcome the shortcomings of traditional Markov model, we propose a time-variant Markov model based on Gibbs sampling for mobility prediction. Specifically, we model human mobility as several interconnected Markov chains, each chain corresponds to a movement pattern of a period of time. Then, we adopt Gibbs sampling method to simultaneously recover the missing part of trajectories and train the Markov chains, in order to solve the unevenly distribution and the high missing rate. Results show that our prediction algorithm can achieve 11.2 percent higher prediction accuracy than the benchmark method, especially on sparse trajectories. In addition, we discover a high correlation between prediction accuracy and predictability, with correlation coefficient reaching 0.81. Finally, we investigate various factors including spatial and temporal resolution, orders of Markov models, and radius of gyration, in order to further explore the predictability under different circumstances.
Novel Sustainable and Heterogeneous Offloading Management Techniques in Proactive Cloudlets

Cloudlet-based mobile cloud offloading is an emerging technology designed to augment mobile elements by migrating resource-hungry components to adjacent local resource pooling. However, the Cloudlet resources are usually limited in terms of the computing utility, storage and network bandwidth. In this scenario, the remote Cloud infrastructure can provide additional computing and storage utility during run-time; the heterogeneous offloading methods for different mobile applications and diverse offloading resources complicate the Cloudlet-based offloading and resource allocation process. As a result, a considerable amount of delay is caused by setting up the execution environment, communication overhead and waiting in the queue, which significantly downgrade the QoS and the usability of such systems. In this paper, we propose a novel hybrid offloading model to solve the heterogeneous resource-constraint offloading issues in the Cloudlet, concerning the offloading energy and execution efficiency. A queue-based offloading framework is developed to formulate and analyze the mixed migration-based and partition-based offloading behaviors in the Cloudlet. The execution and energy-aware heterogeneous offloading resource allocation problem is formalized, and a Particle Swarm Optimization heuristic solution is presented. A SARIMA-based load prediction model is designed in the Cloudlet to achieve fine-grain proactive resource allocation. Experimental results reveal that the proposed framework can effectively reduce the offloading energy cost and execution time, compared to currently existing solutions.
Determining the best-fit programmers using Bayes' theorem and artificial neural network

A data mining-based technique is proposed for the selection and employment of the best-fit programmers to meet the needs of software companies. The proposed technique incorporates Bayes' theorem and artificial neural network (ANN). The datasets used were from two software companies (Company 1 and Company 2) in India, covering the years 2010–2015. Bayes' theorem is used for identifying the prognostic attributes of the best-fit programmers, while the ANN classifier is used for predicting the best-fit programmers. Using a confusion matrix, the ANN classifier performance is 97.2 and 87.3%, 95.8 and 54.5%, and 100 and 75% with regard to accuracy, precision, and recall on the two test datasets of Company 1 and Company 2, respectively. The results show that the technique is effective for predicting the best-fit programmers. Software companies can use this technique in their recruitment and selection process to determine the best-fit employees for the programmer posts. The proposed technique can also be adapted for application in other disciplines such as sports, education, etc., to identify the most suitable person to fill a relevant position.
Dimensions of perceived risk of mobile banking apps: An empirical investigation of South African retail banking customers

Banking transactions carried out in the uncertain conditions of mobile commerce involve high levels of perceived risk and require substantial levels of trust. Therefore, gaining customer trust and reducing the influence of risk is imperative to developing and nurturing long-lasting and strong relationship between customers and retail banks. However, limited research is currently investigating the effects of overall perceived risk and trust on retail banking customers use of mobile commerce, particularly from the perspective of emerging African economies. This study investigates the effects of perceived risk and institution-based trust on the use of mobile banking apps among South African retail bank customers. The model was tested using responses obtained from 352 users of the mobile banking services of the five major retail banks (ABSA, Capitec, FNB, Nedbank, and Standard Bank) in South Africa. The findings of this study suggest that institution-based trust has a significant positive influence on use behaviour of mobile banking apps. Furthermore, perceived risk has a significant negative influence on use behaviour; and, lastly, institution-based trust is found to have a significant negative influence on perceived risk. Key words/ phrases: South Africa, Perceived risk, Mobile banking apps, Institution-based trust, Kalpa Publications in Computing Volume 12, 2019, Pages 342–356 Proceedings of 4th International Conference on the Internet, Cyber Security and Information Systems 2019 K. Njenga (ed.), ICICIS 2019 (Kalpa Publications in Computing, vol. 12), pp. 342–356
Game-Theoretic Safety Assurance for Human-Centered Robotic Systems

In order for autonomous systems like robots, drones, and self-driving cars to be reliably introduced into our society, they must have the ability to actively account for safety during their operation. While safety analysis has traditionally been conducted offline for controlled environments like cages on factory floors, the much higher complexity of open, human-populated spaces like our homes, cities, and roads makes it unviable to rely on common design-time assumptions, since these may be violated once the system is deployed. Instead, the next generation of robotic technologies will need to reason about safety online, constructing high-confidence assurances informed by ongoing observations of the environment and other agents, in spite of models of them being necessarily fallible.This dissertation aims to lay down the necessary foundations to enable autonomous systems to ensure their own safety in complex, changing, and uncertain environments, by explicitly reasoning about the gap between their models and the real world. It first introduces a suite of novel robust optimal control formulations and algorithmic tools that permit tractable safety analysis in time-varying, multi-agent systems, as well as safe real-time robotic navigation in partially unknown environments; these approaches are demonstrated on large-scale unmanned air traffic simulation and physical quadrotor platforms. After this, it draws on Bayesian machine learning methods to translate model-based guarantees into high-confidence assurances, monitoring the reliability of predictive models in light of changing evidence about the physical system and surrounding agents. This principle is first applied to a general safety framework allowing the use of learning-based control (e.g. reinforcement learning) for safety-critical robotic systems such as drones, and then combined with insights from cognitive science and dynamic game theory to enable safe human-centered navigation and interaction; these techniques are showcased on physical quadrotors—flying in unmodeled wind and among human pedestrians—and simulated highway driving. The dissertation ends with a discussion of challenges and opportunities ahead, including the bridging of safety analysis and reinforcement learning and the need to ``close the loop'' around learning and adaptation in order to deploy increasingly advanced autonomous systems with confidence.
Joint Modeling of TDD and Decoupled Uplink/Downlink Access in 5G HetNets With Multiple Small Cells Deployment

Due to highly variant traffic in downlink (DL) and uplink (UL) in heterogeneous networks (HetNets), dynamic time-division duplexing (TDD) is proposed to dynamically allocate UL and DL resources. Under the same circumstances, downlink and uplink decoupled access (DUDA) is introduced to balance between UL and DL transmissions and to further improve the system performance. Rather than belonging to a specific cell, a mobile user can receive the downlink traffic from one base station (BS) and send uplink traffic through another BS. In this article, we analytically investigate a joint TDD and DUDA statistical model with multiple small cells deployment. This model is based on a geometric probability approach. Taking all possible TDD subframes combinations between the macro and small cells, coupled and decoupled cell associations strategies are investigated in details. We derive analytical expressions for the capacity and the interference, considering a network of one macro cell and multiple small cells. We build on the derived capacity expressions to measure the decoupling gain and thus, identify the location of the interferer small cell where the decoupled mode maintains a higher gain in both DL and UL. Monte-Carlo simulations results are presented to validate the accuracy of the statistical model.
Emerging Applications of Generative Adversarial Networks

With the idea of generative adversarial network (GAN) being brought up since 2014, a serial of research emerged in this field. While bring many exciting opportunities, these applications also raise the awareness of the risk of fake images which may cause huge damage. In this paper, we revisit the history of GAN’s development and summarize some important models and applications. Additionally, we discuss the worries which GAN would bring to people and possible solutions.
Designing a Mobile-Based Self-Care Application for Patients with Cutaneous Leishmaniasis: An Effective Step in Patients’ Self-Care and Participation

Introduction: Today, the introduction of smartphones into the healthcare field has led to rapid growth in application development; provided many important tasks such as maintaining health and access to information, communications and counseling, referral and data collection, management and supervision of patients; and facilitated patient self-care and participation. Material and Methods: In order to design a mobile-based self-care application, the designing purpose was first defined and then related literature was reviewed and different kinds of software designed in this field were identified. Using the specified information elements, UML diagrams (Use-Case, Sequence, and Activity) were plotted by Visual Paradigm Enterprise 8 software. In fact, a feasibility assessment was carried out, based on which the application prototype was developed and provided to 10 patients to achieve their opinions. In the next step, the final application components were determined based on user feedback and experts approval. In the final step, Quiz Satisfaction Survey Questionnaire v.7. 0 was used to assess users' satisfaction. Results: Based on user questions and answers, and answers available in the database, the application offers appropriate therapies that emphasize the presence of patients in health centers. Conclusion: The presented application helps with providing rapid information and awareness about different aspects of the disease.
Opossum - Introducing and Evaluating a Model-based Optimization Tool for Grasshopper

This paper presents Opossum, a new optimization plug-in for Grasshopper, a visual data-flow modelling software popular among architects. Opossum is the first publicly available, model-based optimization tool aimed at architectural design optimization and especially applicable to problems that involve time-intensive simulations of for example day-lighting and building energy. The paper details Opossum's design and implementation and compares its performance to four single-objective and one multi-objective solver. The test problem is time-intensive and simulation-based: optimizing a screened facade for daylight and glare. Opossum outperforms the other single-objective solvers and finds the most accurate approximation of the Pareto front.
Estimation of Design Parameters for Cellular WCDMA Network

The estimation of design parameters for WCDMA network which support different data rate (Multimedia) services is an essential step in network planning. The estimated parameters includes: Cell coverage range using path loss propagation model, average and maximum path loss, throughput, capacity / km, number of users per cells blocking capacity, tracking efficiency. The parameters are computed for different data rates.
Missing Air Pollution Data Recovery Based on Long-Short Term Context Encoder

Air pollution has become a global challenge, and obtaining real-time air quality information is urgently needed. Although the governments have been trying their best in delivering accurate air quality reports, missing air pollution data remains a key challenge. Based on the temporal-spatial correlation of the data, we propose a novel long-short term context encoder (LSCE) structure for recovering missing air pollution data. The original context encoder approach based on image completion focuses on reconstructing rectangular missing regions. Differing from traditional methods, our fully convolutional neural network architecture enjoys the following novelties. First, LSCE can recover irregular missing data patterns. Second, we devise two data pre-processing strategies to produce two types of context encoders, namely, the long-short term cutting context encoder (LSCCE) and the long-short term sliding context encoder (LSSCE). Compared with LSCCE, LSSCE increases the number of training data matrixes. Finally, we investigate the significance of adaptive training in addressing different types of missing data. Our simulation results have demonstrated that our approach, especially, LSSCE, can outperform existing missing data recovery methods. Besides, our techniques can be widely applicable for recovering other temporally and spatially correlated missing data, such as vehicular traffic or meteorology data.
Hierarchical Visual-aware Minimax Ranking Based on Co-purchase Data for Personalized Recommendation

Personalized recommendation aims at ranking a set of items according to the learnt preferences of the user. Existing methods optimize the ranking function by considering an item that the user has not bought yet as a negative item and assuming that the user prefers the positive item that he has bought to the negative item. The strategy is to exclude irrelevant items from the dataset to narrow down the set of potential positive items to improve ranking accuracy. It conflicts with the goal of recommendation from the seller’s point of view, which aims to enlarge that set for each user. In this paper, we diminish this limitation by proposing a novel learning method called Hierarchical Visual-aware Minimax Ranking (H-VMMR), in which a new concept of predictive sampling is proposed to sample items in a close relationship with the positive items (e.g., substitutes, compliments). We set up the problem by maximizing the preference discrepancy between positive and negative items, as well as minimizing the gap between positive and predictive items based on visual features. We also build a hierarchical learning model based on co-purchase data to solve the data sparsity problem. Our method is able to enlarge the set of potential positive items as well as true negative items during ranking. The experimental results show that our H-VMMR outperforms the state-of-the-art learning methods.
Technological boundary-spanning search, crowdfunding interaction and crowdfunding innovation performance: a mediated moderation model of knowledge sharing

ABSTRACT The purpose of this study is to examine the effect of technological boundary-spanning search and crowdfunding interaction on crowdfunding innovation performance. The study investigated technology-oriented innovative crowdfunding projects on three major Chinese crowdfunding platforms. A hierarchical regression analysis is applied to explore the model’s hypotheses. The results show that crowdfunding interaction breadth and interaction depth both partially mediate the relationship between technological boundary-spanning search and crowdfunding innovation performance. The findings also demonstrate that knowledge sharing moderates the path from technological boundary-spanning search to crowdfunding interaction depth and the path from crowdfunding interaction depth to crowdfunding innovation performance.
Anomaly detection in real-world networks

The overwhelming amount of data that is nowadays available, leads to an increased demand for techniques that automatically identify abnormal or anomalous behavior. In this paper, we consider this problem in the context of networks and investigate how network-based anomaly detection techniques can be used for the purpose of law enforcement and criminal investigations in the field of electronic discovery. This problem is firstly investigated on static networks. Examples of anomalies in static networks are network intruders in physical networks or spammers spreading unwanted advertisements in online social networks. While existing methods typically identify anomalies from a local perspective, we propose a novel method that identifies nodes from a global perspective. The proposed community-aware CADA algorithm outperforms previous methods on both synthetic and real-world data and scales remarkably well to larger networks. Second, we approach the problem in networks that change over time. We compare two existing methods that indicate to what extent two graphs are similar to each other. Results on real-world data show that the techniques successfully indicate points in time when something significant happened in the network. In general, these network-driven techniques can uncover critical information about events in the data that was otherwise not discovered.
A Survey on Multimodal Biometrics Authentication and Template Protection

Biometric systems occupies a vast space in the field of security systems. Most of the applications make use of biometric systems such as attendance system, locker systems in banks, hospitals, industries, etc. Besides authentication provided by these biometric systems there is also need to protect the templates that are stored in them. This work involves a review of multiple biometrics such as fingerprint, face, hand vein, iris, signature, etc and the techniques used for authentication, fusion and template protection. Different traits and techniques are compared to obtain the most unique and suitable methods for biometric authentication and template protection. This paper also includes the comparison between the unimodal and multimodal biometric systems. Parameters such as Genuine Acceptance Rate(GAR), Equal Error Rate(EER) ,False Acceptance Rate(FAR) and False Reject Rate(FRR) are used to evaluate the various unimodal and multimodal biometric systems.
A Parking Slot Allocation Framework Based on Virtual Voting and Adaptive Pricing Algorithm

Parking lot allocation problem has received much attention in recent years. There have been various works in the literature that target the parking slot allocation problem. However, most of these works use algorithms that run on centralized servers and are based on some predictions on historical data. Due to the dynamic nature of vehicular networks, the accuracy of such prediction models is not high which ends up in a chaotic situation for the parking lot owners as well as the vehicle owners. Therefore, a distributed Parking slot Allocation Framework based on Adaptive Pricing Algorithm and Virtual Voting is proposed in this paper. The proposed model is based on virtual voting and hashgraph consensus algorithm. Using the model, all users and parking lot owners can easily come to consensus finality about the allocation of a parking slot with the use of minimal bandwidth. The proposed model provides a fair, fast and cost-optimal parking slot allocation method. The perfect ordering of allocation requests is also maintained based on consensus timestamp. Further, an adaptive pricing model is proposed to enhance the overall revenue of the parking lot owners and comfort of the users. The proposed model is deterministic and can reduce the average parking cost and time. Performance evaluations reveal that the proposed model outperforms its counterparts in terms of accurate parking slot allocation, reduced cost and parking lot resource utilization.