Miracle-dz/UniFAR-FTUs huggingface dataset:
Multitask Learning of Temporal Connectionism in Convolutional Networks using a Joint Distribution Loss Function to Simultaneously Identify Tools and Phase in Surgical Videos

Surgical workflow analysis is of importance for understanding onset and persistence of surgical phases and individual tool usage across surgery and in each phase. It is beneficial for clinical quality control and to hospital administrators for understanding surgery planning. Video acquired during surgery typically can be leveraged for this task. Currently, a combination of convolutional neural network (CNN) and recurrent neural networks (RNN) are popularly used for video analysis in general, not only being restricted to surgical videos. In this paper, we propose a multi-task learning framework using CNN followed by a bi-directional long short term memory (Bi-LSTM) to learn to encapsulate both forward and backward temporal dependencies. Further, the joint distribution indicating set of tools associated with a phase is used as an additional loss during learning to correct for their co-occurrence in any predictions. Experimental evaluation is performed using the Cholec80 dataset. We report a mean average precision (mAP) score of 0.99 and 0.86 for tool and phase identification respectively which are higher compared to prior-art in the field.
Virtual Reality Fire Disaster Training System for Improving Disaster Awareness

The current state of disaster prevention education in schools involves people who are unaware of the necessity of education (i.e., people with no intrinsic motivation and disaster education away). To facilitate disaster prevention education, we propose a virtual reality (VR) disaster prevention training system focused on fire disasters. This system has three modes, namely, VR evacuation drills, VR firefighting training, and VR comprehensive training. First, VR evacuation drills can be learned by users by gaining experience of evacuation methods in cases of fire. Second, users can undergo VR firefighting training by gaining experience of extinguishing methods. Finally, in general training, the users can experience trial training in case of fires on the basis of the knowledge gained in the evacuation and fire drills. We conducted an experiment by dividing participants into two groups: one group was trained using existing methods employing teaching materials, whereas the other was trained using the proposed method. Subsequently, we conducted VR comprehensive training and an IMMS evaluation to investigate whether this system demonstrates an improvement over existing teaching methods. The results indicated that the proposed system achieved better results in terms of attention (+1.58), relevance (+0.7), confidence (+0.2), and user satisfaction (+2.16). Furthermore, for groups trained with the proposed system, the average evacuation risk of the player during VR comprehensive training was -6.45 p, the fire extinguishing start time was -10 s lower, and the user could act against disasters safely and rapidly.
Government eService Rating Metrics

United Nations has developed a four stage maturity model for measuring online service development consisting of Stage I: emerging information services, Stage II: enhanced information service, Stage III: transactional services and Stage IV: connected services. This paper has extended this framework further in order to obtain the maturity level score for individual eServices being provided by eGovernment. The paper attempts to score each service delivery stage usually required in order to deliver the eService successfully like beneficiary selection, spreading awareness about the eService, accessibility, ease of use, beneficiary enrolment, authentication, payment modes, enquiry, workflow tracking, delivery of benefit and user feedback. This enables us to calculate a maturity score for each service delivery aspect and thus formulate overall maturity score for the eService. The paper also tries to demonstrate the application of the proposed service delivery rating Metrics in order to determine the maturity score of an eService. The purpose of the Service Delivery Rating Metrics is to provide a tool to the Government enterprise, to rate the quality of eServices provided by its member organization to the users.
Detecting Climate Change Deniers on Twitter Using a Deep Neural Network

Climate change or global warming is a global threat to both human communities and natural systems. In recent years, there is an increasingly public debate on the existence of climate change or global warming, but data describing such discussions are difficult to access. Social media provide a new data source to survey public perceptions and attitudes toward such topics. However, enabling computers to automatically determine users' attitudes towards climate change based on social media contents is still challenging. Taking Twitter data as an example, this study analyzed public discussions about climate change and global warming in year 2016. The objectives are: (1) to develop an optimized Deep Neural Network (DNN) classifier to identify users who are climate change deniers based on tweet contents; (2) to examine the temporal patterns of climate change discussions on Twitter and its driving factors. Results demonstrate that the developed DNN model successfully identified climate change deniers based on tweet contents with an overall accuracy of 88%. There are more climate change discussions during September to December 2016, whereas the percentages of climate change deniers were lower in the same period. Public interests and attitudes on climate change were driven by extreme weather events and environmental policy changes. The developed methodology will shed lights on the utility of deep learning in natural language processing, while the results provide improved understanding of factors affecting public attitudes on climate change.
Your PIN Sounds Good! On The Feasibility of PIN Inference Through Audio Leakage

Personal Identification Numbers (PIN) are widely used as authentication method for systems such as Automated Teller Machines (ATMs) and Point of Sale (PoS). Input devices (PIN pads) usually give the user a feedback sound when a key is pressed. In this paper, we propose an attack based on the extraction of inter-keystroke timing from the feedback sound when users type their PINs. Our attack is able to reach an accuracy of 98% with a mean error of 0.13 +/-6.66 milliseconds. We demonstrate that inter-keystroke timing significantly improves the guessing probability of certain subsets of PINs. We believe this represents a security problem that has to be taken into account for secure PIN generation. Furthermore, we identified several attack scenarios where the adversary can exploit inter-keystroke timing and additional information about the user or the PIN, such as typing behavior. Our results show that combining the inter-keystroke timing with other information drastically reduces attempts to guess a PIN, outperforming random guessing. With our attack, we are able to guess 72% of the 4-digit PINs within 3 attempts. We believe this poses a serious security problem for systems that use PIN-based authentication.
Collaborative Mobile Crowdsensing in Opportunistic D2D Networks

With the remarkable proliferation of smart mobile devices, mobile crowdsensing has emerged as a compelling paradigm to collect and share sensor data from surrounding environment. In many application scenarios, due to unavailable wireless network or expensive data transfer cost, it is desirable to offload crowdsensing data traffic on opportunistic device-to-device (D2D) networks. However, coupling between mobile crowdsensing and D2D networks, it raises new technical challenges caused by intermittent routing and indeterminate settings. Considering the operations of data sensing, relaying, aggregating, and uploading simultaneously, in this article, we study collaborative mobile crowdsensing in opportunistic D2D networks. Toward the concerns of sensing data quality, network performance and incentive budget, Minimum-Delay-Maximum-Coverage (MDMC) problem and Minimum-Overhead-Maximum-Coverage (MOMC) problem are formalized to optimally search a complete set of crowdsensing task execution schemes over user, temporal, and spatial three dimensions. By exploiting mobility traces of users, we propose an unified graph-based problem representation framework and transform MDMC and MOMC problems to a connection routing searching problem on weighted directed graphs. Greedy-based recursive optimization approaches are proposed to address the two problems with a divide-and-conquer mode. Empirical evaluation on both real-world and synthetic datasets validates the effectiveness and efficiency of our proposed approaches.
Personalized Travel Time Prediction Using a Small Number of Probe Vehicles

Predicting the travel time of a path is an important task in route planning and navigation applications. As more GPS probe data has been collected to monitor urban traffic, GPS trajectories of the probe vehicles have been frequently used to predict path travel time. However, most trajectory-based methods rely on deploying GPS devices and collect real-time data on a large taxi fleet, which can be expensive and unreliable in smaller cities. This work deals with the problem of predicting path travel time when only a small number of cars are available. We propose an algorithm that learns local congestion patterns of a compact set of frequently shared paths from historical data. Given a travel time prediction query, we identify the current congestion patterns around the query path from recent trajectories, then infer its travel time in the near future. Driver identities are also used in predicting personalized travel time. Experimental results using 10--25 taxis in urban areas of Shenzhen, China, show that personal prediction has on average 3.4mins of error on trips of duration 10--75mins. This result improves the baseline approach of using purely historical trajectories by 16.8% on average, over four regions with various degrees of path regularity. It also outperforms a state-of-the-art travel time prediction method that uses both historical trajectories and real-time trajectories.
Stochastic gradient descent for large-scale linear nonparallel SVM

In recent years, nonparallel support vector machine (NPSVM) is proposed as a nonparallel hyperplane classifier with superior performance than standard SVM and existing nonparallel classifiers such as the twin support vector machine (TWSVM). With the perfect theoretical underpinnings and great practical success, NPSVM has been used to dealing with the classification tasks on different scales. Tackling large-scale classification problem is a challenge yet significant work. Although large-scale linear NPSVM model has already been efficiently solved by the dual coordinate descent (DCD) algorithm or alternating direction method of multipliers (ADMM), we present a new strategy to solve the primal form of linear NPSVM different from existing work in this paper. Our algorithm is designed in the framework of the stochastic gradient descent (SGD), which is well suited to large-scale problem. Experiments are conducted on five large-scale data sets to confirm the effectiveness of our method.
Preparation of budesonide nanosuspensions for pulmonary delivery: Characterization, in vitro release and in vivo lung distribution studies

The main objective of the present article was to prepare stable and well-dispersible budesonide (BUD) nanosuspensions by microfluidizer method. The morphology, particle size, and zeta potential of formulation were investigated and in vitro release and in vivo lung distribution were evaluated. Characterizations showed that BUD nanosuspensions were spherical in shape with a smooth surface. The measured average particle size was 122.5 ± 6.3 nm, and ζ potential was − 13.6 ± 0.4 mV. In vitro release behavior of three batches BUD nanosuspensions had a good reproduction. The deposition distribution of BUD different formulations was measured using a modified multi-stage liquid collision method. The data showed that BUD nanosuspensions have the most outstanding deposition distribution with fine particle ratio 82.2%. Compared with normal particle and micronized particles, nanosuspensions were easier to be distributed in lung. After inhalation of 1 h, the drug concentration can reach 872.9 ng/g, which was extremely significantly different from normal particles (p < 0.01) and significantly different from micronized particles (p < 0.05).
Protecting mobile devices from physical memory attacks with targeted encryption

Sensitive data in a process could be scattered over the memory of a computer system for a prolonged period of time. Unfortunately, DRAM chips were proven insecure in previous studies. The problem becomes worse in the mobile environment, in which users' smartphones are easily lost or stolen. The powered-on phones may contain sensitive data in the vulnerable DRAM chips. In this paper, we propose MemVault, a mechanism to protect sensitive data in Android devices against physical memory attacks. MemVault keeps track of the propagation of well-marked sensitive data sources, and selectively encrypts tainted sensitive memory contents in the DRAM chip. When a tainted object is accessed, MemVault redirects the access to the internal RAM (iRAM), where the cipher-text object is decrypted transparently. iRAM is a system-on-chip (SoC) component which is by nature immune to physical memory exploits. We have implemented a MemVault prototype system, and have evaluated it with extensive experiments. Our results validate that MemVault effectively eliminates the occurrences of clear-text sensitive objects in DRAM chips, and imposes acceptable overheads.
Mobile health in cardiology: a review of currently available medical apps and equipment for remote monitoring

ABSTRACT Introduction: Recent developments in implantable cardioverter-defibrillators (ICDs) and smartphone technology have increased the possibilities for remote monitoring. It is the purpose of this review to give an overview of these new possibilities. Areas covered: Remote monitoring in ICD allows for early detection of lead fractures and remote follow-up of patients. Possible limitations are the lack of standardization and the possible unsafety of the data stored on the ICD. Secondly, remote monitoring of health parameters using smartphone compatible wearables and smartphone medical apps is addressed. Possible limitations include the fact that the majority of smartphone apps are unregulated by the regulatory authorities and privacy issues such as selling of app-generated data to third parties. Lastly, clinical studies with smartphone apps are discussed. Expert commentary: New technologies in ICDs and smartphones have the potential to be used for remote monitoring. However, unreliability of smartphone technology, inadequate legislation and lack of reimbursement impede implementation.
Deep Learning-Based Intrusion Detection System for Advanced Metering Infrastructure

Smart grid is an alternative solution of the conventional power grid which harnesses the power of the information technology to save the energy and meet todays' environment requirements. Due to the inherent vulnerabilities in the information technology, the smart grid is exposed to wide variety of threats that could be translated into cyber-attacks. In this paper, we develop a deep learning-based intrusion detection system to defend against cyber-attacks in the advanced metering infrastructure network. The proposed machine learning approach is trained and tested extensively on an empirical industrial dataset which is composed of several attack' categories including the scanning, buffer overflow, and denial of service attacks. Then, an experimental comparison in terms of detection accuracy is conducted to evaluate the performance of the proposed approach with Naïve Bayes, Support Vector Machine, and Random Forest. The obtained results suggest that the proposed approaches produce optimal results comparing to the other algorithms. Finally, we propose a network architecture to deploy the proposed anomaly-based intrusion detection system across the Advanced metering infrastructure network. In addition, we propose a network security architecture composed of two types of Intrusion detection system types, Host and Network based, deployed across the Advanced Metering Infrastructure network to inspect the traffic and detect the malicious one at all the levels.
UniAdapt: universal adaption of replication and indexes in distributed RDF triples stores

The Resource Description Framework (RDF), which has become the standard data model to represent web data, is facing the exploding size of the web resources leading to difficulties in terms of maintaining and querying the data. Distributed RDF triple stores and their storage layers were already under research for a decade. While multiple systems tried to employ the workload to guide partitioning and replicating the data set, they are not able to find optimal levels for both the replication and local index storage as well as the main memory cached indexes. In this paper we propose our novel unified optimization approach that enables a distributed RDF triple store to adapt its RDF storage layer in two aspects: the first aspect considers replication indexes, while the second aspect considers secondary and main memory indexes. Our system can dynamically analyze the workload, detect its queries trends, measure their effectiveness and apply them in triples' benefit functions. The system uses those functions to make fully automated decisions by either horizontally expanding each node's secondary storage by replication, or by vertically building more indexes. In the same context the system makes horizontal or vertical decisions about working nodes' main memory. The final objective of the optimization process is to decrease future query execution time.
A data modeling approach for classification problems: application to bank telemarketing prediction

In this paper, we present a new data modeling approach for five common classification algorithms to optimize the prediction of telemarketing target calls for selling bank long-term deposits. A Portuguese retail bank addressed, from 2008 until 2013, data on its clients, products and social-economic attributes including the effects of the financial crisis. An original set of 150 features has been explored and 21 features are retained for the proposed approach including label. This paper introduces a new modeling approach that preprocessed separately each type of features and normalize them to optimize prediction performance. To evaluate the proposed approach, this paper compares the results obtained with five most known machine learning techniques: Naïve Bayes (NB), Logistic Regression (LR), Decision Trees (DT), Artificial Neural Network (ANN) and Support Vector Machines (SVM) and it yielded better improved performances for all these algorithms in terms of accuracy and f-measure.
The child is father to the man: Developmental roles for proteins of importance for neurodegenerative disease

Although Alzheimer's and Parkinson's diseases predominately affect elderly adults, the proteins that play a role in the pathogenesis of these diseases are expressed throughout life. In fact, many of the proteins hypothesized to be important in the progression of neurodegeneration play direct or indirect roles in the development of the central nervous system. The systems affected by these proteins include neural stem cell fate decisions, neuronal differentiation, cellular migration, protection from oxidative stress, and programmed cell death. Insights into the developmental roles of these proteins may ultimately impact the understanding of neurodegenerative diseases and lead to the discovery of novel treatments. ANN NEUROL 2010;67:151–158
SCADA security using SSH honeypot

Industrial Control System (ICS) is a term that refers to control systems in production, transmission and distribution architecture in Smart Grid. These systems can be SCADA (Supervisory Control and Data Acquisition System) and DCS (Distributed Control Systems). ICS have moved from proprietary system to open and standard technologies interconnected with others networks such as Internet. This move to interconnecting ICS with others networks have exposed this system to different attacks and have revealed serious weaknesses. So, these systems must deployed protection measures like IDS, Firewalls, IPS and others. However, detection based on these measures is often based on prior knowledge of the attacks themselves and are not able to study the behavior and techniques used by attackers, which means that new attacks are not detectable by them. So, in order to detect new attacks, understand malicious activities targeting ICS, and analyses attackers' behaviors and techniques used by them, in this article, we use a SSH honeypot tool called Kippo in order to log brute force attacks and shell interaction performed by attackers in order to take attention away in the production server.
Thermal covert channel in bluetooth low energy networks

This paper investigates thermal covert channels in the context of the Internet-of-Things. More, precisely we demonstrate such a channel on BLE-enabled devices. We explain how the core design principles of BLE link-layer protocol, combined with well-timed intensive CPU calculations can be leveraged to mount a thermal covert channel between two devices. We implement the attack on three different hardware architectures, a Raspberry Pi 3B, a Motorola X 2014 and an iPhone 5s, and analyze the performance of the channel. We show that we have a similar throughput as previous comparable work, but we greatly improve the range of the channel.
Home Edge Computing Architecture for Smart and Sustainable Agriculture and Breeding

Challenges of today and tomorrow in developing countries to ensure sustainable food security for their populations require smart agriculture and breeding. This necessarily depends on water control, soil erosion, livestock management, and so on. At the same time, Internet of Things (IoT) represents the latest evolution of the Internet and can significantly improve the ability to collect, analyze and retrieve data that we can then transform into information, knowledge and finally knowing. In the context of ensuring smart and sustainable agriculture, the importance of IoT seems obvious. Note that, IoT has implications for bandwidth, latency and processing speeds, given the huge amount of data to collect. Edge computing and Systems are one of the emerging solutions to reduce latency and improve bandwidth utilization for real-time applications and services. Thus, to achieve these aims, we propose, in this paper, a new three-tier architecture (3-TIER) for smart agriculture. It is based on that of Home Edge Computing allowing us to achieve ultra-low latency. This architecture will also allow us to effectively solve the problems related to agriculture and livestock breeding, but also to be able to resolve considerably the conflicts between farmers and herders. This proposal will be followed by an experimental validation of HEC architecture using the EdgeCloudSim simulator.