Biblio
The heat load of the original cryomodules for the continuous electron beam accelerator facility is 50% higher than the target value of 100 W at 2.07 K for refurbished cavities operating at an accelerating gradient of 12.5 MV/m. This issue is due to the quality factor of the cavities being 50% lower in the cryomodule than when tested in a vertical cryostat, even at low RF field. Previous studies were not conclusive about the origin of the additional losses. We present the results of a systematic study of the additional losses in a five-cell cavity from a decommissioned cryomodule after attaching components, which are part of the cryomodule, such as the cold tuner, the He tank, and the cold magnetic shield, prior to cryogenic testing in a vertical cryostat. Flux-gate magnetometers and temperature sensors are used as diagnostic elements. Different cool-down procedures and tests in different residual magnetic fields were investigated during the study. Three flux-gate magnetometers attached to one of the cavities installed in the refurbished cryomodule C50-12 confirmed the hypothesis of high residual magnetic field as a major cause for the increased RF losses.
Mobile apps are widely adopted in daily life, and contain increasing security flaws. Many regulatory agencies and organizations have announced security guidelines for app development. However, most security guidelines involving technicality and compliance with this requirement is not easily feasible. Thus, we propose Mobile Apps Assessment and Analysis System (MAS), an automatic security validation system to improve guideline compliance. MAS combines static and dynamic analysis techniques, which can be used to verify whether android apps meet the security guideline requirements. We implemented MAS in practice and verified 143 real-world apps produced by the Taiwan government. Besides, we also validated 15,000 popular apps collected from Google Play Store produced in three countries. We found that most apps contain at least three security issues. Finally, we summarize the results and list the most common security flaws for consideration in further app development.
This paper presents the results of research and simulation of feature automated control of a hysteretic object and the difference between automated control and automatic control. The main feature of automatic control is in the fact that the control loop contains human being as a regulator with its limited response speed. The human reaction can be described as integrating link. The hysteretic object characteristic is switching from one state to another. This is followed by a transient process from one to another characteristic. For this reason, it is very difficult to keep the object in a desired state. Automatic operation ensures fast switching of the feedback signal that produces such a mode, which in many ways is similar to the sliding mode. In the sliding mode control signal abruptly switches from maximum to minimum and vice versa. The average value provides the necessary action to the object. Theoretical analysis and simulation show that the use of the maximum value of the control signal is not required. It is sufficient that the switching oscillation amplitude is such that the output signal varies with the movement of the object along both branches with hysteretic characteristics in the fastest cycle. The average output value in this case corresponds to the prescribed value of the control task. With automated control, the human response can be approximately modeled by integrating regulator. In this case the amplitude fluctuation could be excessively high and the frequency could be excessively low. The simulation showed that creating an artificial additional fluctuation in the control signal makes possible to provide a reduction in the amplitude and the resulting increase in the frequency of oscillation near to the prescribed value. This should be evaluated as a way to improve the quality of automated control with the helps of human being. The paper presents some practical examples of the examined method.
We propose a method for transferring an arbitrary style to only a specific object in an image. Style transfer is the process of combining the content of an image and the style of another image into a new image. Our results show that the proposed method can realize style transfer to specific object.
In a continually evolving cyber-threat landscape, the detection and prevention of cyber attacks has become a complex task. Technological developments have led organisations to digitise the majority of their operations. This practice, however, has its perils, since cybespace offers a new attack-surface. Institutions which are tasked to protect organisations from these threats utilise mainly network data and their incident response strategy remains oblivious to the needs of the organisation when it comes to protecting operational aspects. This paper presents a system able to combine threat intelligence data, attack-trend data and organisational data (along with other data sources available) in order to achieve automated network-defence actions. Our approach combines machine learning, visual analytics and information from business processes to guide through a decision-making process for a Security Operation Centre environment. We test our system on two synthetic scenarios and show that correlating network data with non-network data for automated network defences is possible and worth investigating further.
We contribute a scalable, open source implementation of the Pooled Time Series (PoT) algorithm from CVPR 2015. The algorithm is evaluated on approximately 6800 human trafficking (HT) videos collected from the deep and dark web, and on an open dataset: the Human Motion Database (HMDB). We describe PoT and our motivation for using it on larger data and the issues we encountered. Our new solution reimagines PoT as an Apache Hadoop-based algorithm. We demonstrate that our new Hadoop-based algorithm successfully identifies similar videos in the HT and HMDB datasets and we evaluate the algorithm qualitatively and quantitatively.
Security issues in the IoT based CPS are exacerbated with human participation in CPHS due to the vulnerabilities in both the technologies and the human involvement. A holistic framework to mitigate security threats in the IoT-based CPHS environment is presented to mitigate these issues. We have developed threat model involving human elements in the CPHS environment. Research questions, directions, and ideas with respect to securing IoT based CPHS against collaborative attacks are presented.
Approximate Computing aims at trading off computational accuracy against improvements regarding performance, resource utilization and power consumption by making use of the capability of many applications to tolerate a certain loss of quality. A key issue is the dependency of the impact of approximation on the input data as well as user preferences and environmental conditions. In this context, we therefore investigate the concept of self-adaptive image processing that is able to autonomously adapt 2D-convolution filter operators of different accuracy degrees by means of partial reconfiguration on Field-Programmable-Gate-Arrays (FPGAs). Experimental evaluation shows that the dynamic system is able to better exploit a given error tolerance than any static approximation technique due to its responsiveness to changes in input data. Additionally, it provides a user control knob to select the desired output quality via the metric threshold at runtime.
In this paper, we provide insights towards achieving more robust automatic facial expression recognition in smart environments based on our benchmark with three labeled facial expression databases. These databases are selected to test for desktop, 3D and smart environment application scenarios. This work is meant to provide a neutral comparison and guidelines for developers and researchers interested to integrate facial emotion recognition technologies in their applications, understand its limitations and adaptation as well as enhancement strategies. We also introduce and compare three different metrics for finding the primary expression in a time window of a displayed emotion. In addition, we outline facial emotion recognition limitations and enhancements for smart environments and non-frontal setups. By providing our comparison and enhancements we hope to build a bridge from affective computing research and solution providers to application developers that like to enhance new applications by including emotion based user modeling.
Affective facial expression is a key feature of non-verbal behavior and is considered as a symptom of an internal emotional state. Emotion recognition plays an important role in social communication: human-human and also for human-robot interaction. This work aims at the development of a framework able to recognise human emotions through facial expression for human-robot interaction. Simple features based on facial landmarks distances and angles are extracted to feed a dynamic probabilistic classification framework. The public online dataset Karolinska Directed Emotional Faces (KDEF) [12] is used to learn seven different emotions (e.g. angry, fearful, disgusted, happy, sad, surprised, and neutral) performed by seventy subjects. Offline and on-the-fly tests were carried out: leave-one-out cross validation tests using the dataset and on-the-fly tests during human-robot interactions. Preliminary results show that the proposed framework can correctly recognise human facial expressions with potential to be used in human-robot interaction scenarios.
The Internet of Vehicles (IoV) is a complex and dynamic mobile network system that enables information sharing between vehicles, their surrounding sensors, and clouds. While IoV opens new opportunities in various applications and services to provide safety on the road, it introduces new challenges in the field of digital forensics investigations. The existing tools and procedures of digital forensics cannot meet the highly distributed, decentralized, dynamic, and mobile infrastructures of the IoV. Forensic investigators will face challenges while identifying necessary pieces of evidence from the IoV environment, and collecting and analyzing the evidence. In this article, we propose TrustIoV - a digital forensic framework for the IoV systems that provides mechanisms to collect and store trustworthy evidence from the distributed infrastructure. Trust-IoV maintains a secure provenance of the evidence to ensure the integrity of the stored evidence and allows investigators to verify the integrity of the evidence during an investigation. Our experimental results on a simulated environment suggest that Trust-IoV can operate with minimal overhead while ensuring the trustworthiness of evidence in a strong adversarial scenario.
A honeypot provides information about the new attack and exploitation methods and allows analyzing the adversary's activities during or after exploitation. One way of an adversary to communicate with a server is via secure shell (SSH). SSH provides secure login, file transfer, X11 forwarding, and TCP/IP connections over untrusted networks. SSH is a preferred target for attacks, as it is frequently used with password-based authentication, and weak passwords are easily exploited using brute-force attacks. In this paper, we introduce a Virtual Machine Introspection based SSH honeypot. We discuss the design of the system and how to extract valuable information such as the credential used by the attacker and the entered commands. Our experiments show that the system is able to detect the adversary's activities during and after exploitation, and it has advantages compared to currently used SSH honeypot approaches.
We will focused the concept of serializability in order to ensure the correct processing of transactions. However, both serializability and relevant properties within transaction-based applications might be affected. Ensure transaction serialization in corrupt systems is one of the demands that can handle properly interrelated transactions, which prevents blocking situations that involve the inability to commit either transaction or related sub-transactions. In addition some transactions has been marked as malicious and they compromise the serialization of running system. In such context, this paper proposes an approach for the processing of transactions in a cloud of databases environment able to secure serializability in running transactions whether the system is compromised or not. We propose also an intrusion tolerant scheme to ensure the continuity of the running transactions. A case study and a simulation result are shown to illustrate the capabilities of the suggested system.
Despite significant recent progress, the best available computer vision algorithms still lag far behind human capabilities, even for recognizing individual discrete objects under various poses, illuminations, and backgrounds. Here we present a new approach to using object pose information to improve deep network learning. While existing large-scale datasets, e.g. ImageNet, do not have pose information, we leverage the newly published turntable dataset, iLab-20M, which has 22M images of 704 object instances shot under different lightings, camera viewpoints and turntable rotations, to do more controlled object recognition experiments. We introduce a new convolutional neural network architecture, what/where CNN (2W-CNN), built on a linear-chain feedforward CNN (e.g., AlexNet), augmented by hierarchical layers regularized by object poses. Pose information is only used as feedback signal during training, in addition to category information, but is not needed during test. To validate the approach, we train both 2W-CNN and AlexNet using a fraction of the dataset, and 2W-CNN achieves 6 percent performance improvement in category prediction. We show mathematically that 2W-CNN has inherent advantages over AlexNet under the stochastic gradient descent (SGD) optimization procedure. Furthermore, we fine-tune object recognition on ImageNet by using the pretrained 2W-CNN and AlexNet features on iLab-20M, results show significant improvement compared with training AlexNet from scratch. Moreover, fine-tuning 2W-CNN features performs even better than fine-tuning the pretrained AlexNet features. These results show that pretrained features on iLab-20M generalize well to natural image datasets, and 2W-CNN learns better features for object recognition than AlexNet.
Most ConvNets formulate object recognition from natural images as a single task classification problem, and attempt to learn features useful for object categories, but invariant to other factors of variation such as pose and illumination. They do not explicitly learn these other factors; instead, they usually discard them by pooling and normalization. Here, we take the opposite approach: we train ConvNets for object recognition by retaining other factors (pose in our case) and learning them jointly with object category. We design a new multi-task leaning (MTL) ConvNet, named disentangling CNN (disCNN), which explicitly enforces the disentangled representations of object identity and pose, and is trained to predict object categories and pose transformations. disCNN achieves significantly better object recognition accuracies than the baseline CNN trained solely to predict object categories on the iLab-20M dataset, a large-scale turntable dataset with detailed pose and lighting information. We further show that the pretrained features on iLab-20M generalize to both Washington RGB-D and ImageNet datasets, and the pretrained disCNN features are significantly better than the pretrained baseline CNN features for fine-tuning on ImageNet.
This paper establishes a probability model of multiple paths scheme of quantum key distribution with public nodes among a set of paths which are used to transmit the key between the source node and the destination node. Then in order to be used in universal net topologies, combining with the key routing in the QKD network, the algorithm of the multiple paths scheme of key distribution we propose includes two major aspects: one is an approach which can confirm the number and the distance of the selection of paths, and the other is the strategy of stochastic paths with labels that can decrease the number of public nodes and avoid the phenomenon that the old scheme may produce loops and often get the nodes apart from the destination node father than current nodes. Finally, the paper demonstrates the rationality of the probability model and strategies about the algorithm.
Security and privacy of big data becomes challenging as data grows and more accessible by more and more clients. Large-scale data storage is becoming a necessity for healthcare, business segments, government departments, scientific endeavors and individuals. Our research will focus on the privacy, security and how we can make sure that big data is secured. Managing security policy is a challenge that our framework will handle for big data. Privacy policy needs to be integrated, flexible, context-aware and customizable. We will build a framework to receive data from customer and then analyze data received, extract privacy policy and then identify the sensitive data. In this paper we will present the techniques for privacy policy which will be created to be used in our framework.
Mobile Ad-hoc Network (MANET) is a prominent technology in the wireless networking field in which the movables nodes operates in distributed manner and collaborates with each other in order to provide the multi-hop communication between the source and destination nodes. Generally, the main assumption considered in the MANET is that each node is trusted node. However, in the real scenario, there are some unreliable nodes which perform black hole attack in which the misbehaving nodes attract all the traffic towards itself by giving false information of having the minimum path towards the destination with a very high destination sequence number and drops all the data packets. In the paper, we have presented different categories for black hole attack mitigation techniques and also presented the summary of various techniques along with its drawbacks that need to be considered while designing an efficient protocol.
Cloud services are widely used to virtualize the management and actuation of the real-world the Internet of Things (IoT). Due to the increasing privacy concerns regarding querying untrusted cloud servers, query anonymity has become a critical issue to all the stakeholders which are related to assessment of the dependability and security of the IoT system. The paper presents our study on the problem of query receiver-anonymity in the cloud-based IoT system, where the trade-off between the offered query-anonymity and the incurred communication is considered. The paper will investigate whether the accepted worst-case communication cost is sufficient to achieve a specific query anonymity or not. By way of extensive theoretical analysis, it shows that the bounds of worst-case communication cost is quadratically increased as the offered level of anonymity is increased, and they are quadratic in the network diameter for the opposite range. Extensive simulation is conducted to verify the analytical assertions.
This research paper identifies security issues; especially energy based security attacks and enhances security of the system. It is very essential to consider Security of the system to be developed in the initial Phases of the software Cycle of Software Development (SDLC) as many billions of bucks are drained owing to security flaws in software caused due to improper or no security process. Security breaches that occur on software system are in umpteen numbers. Scientific Literature propose many solutions to overcome security issues, all security mechanisms are reactive in nature. In this paper new security solution is proposed that is proactive in nature especially for energy based denial of service attacks which is frequent in the recent past. Proposed solution is based on energy consumption by system known as energy points.
Barrier coverage has been widely adopted to prevent unauthorized invasion of important areas in sensor networks. As sensors are typically placed outdoors, they are susceptible to getting faulty. Previous works assumed that faulty sensors are easy to recognize, e.g., they may stop functioning or output apparently deviant sensory data. In practice, it is, however, extremely difficult to recognize faulty sensors as well as their invalid output. We, in this paper, propose a novel fault-tolerant intrusion detection algorithm (TrusDet) based on trust management to address this challenging issue. TrusDet comprises of three steps: i) sensor-level detection, ii) sink-level decision by collective voting, and iii) trust management and fault determination. In the Step i) and ii), TrusDet divides the surveillance area into a set of fine- grained subareas and exploits temporal and spatial correlation of sensory output among sensors in different subareas to yield a more accurate and robust performance of barrier coverage. In the Step iii), TrusDet builds a trust management based framework to determine the confidence level of sensors being faulty. We implement TrusDet on HC- SR501 infrared sensors and demonstrate that TrusDet has a desired performance.