OpenRaiser/Intern-Atlas huggingface dataset:
Transport or Store? Synthesizing Flow-based Microfluidic Biochips using Distributed Channel Storage
Venue: arXiv cs.ET 2017
Year: 2017

Flow-based microfluidic biochips have attracted much atten- tion in the EDA community due to their miniaturized size and execution efficiency. Previous research, however, still follows the traditional computing model with a dedicated storage unit, which actually becomes a bottleneck of the performance of bio- chips. In this paper, we propose the first architectural synthe- sis framework considering distributed storage constructed tem- porarily from transportation channels to cache fluid samples. Since distributed storage can be accessed more efficiently than a dedicated storage unit and channels can switch between the roles of transportation and storage easily, biochips with this dis- tributed computing architecture can achieve a higher execution efficiency even with fewer resources. Experimental results con- firm that the execution efficiency of a bioassay can be improved by up to 28% while the number of valves in the biochip can be reduced effectively.
A Correspondence Relaxation Approach for 3D Shape Reconstruction
Venue: arXiv cs.CV 2017
Year: 2017

This paper presents a new method for 3D shape reconstruction based on two existing methods. A 3D reconstruction from a single photograph is introduced by both papers: the first one uses a photograph and a set of existing 3D model to generate the 3D object in the photograph, while the second one uses a photograph and a selected similar model to create the 3D object in the photograph. According to their difference, we propose a relaxation based method for more accurate correspondence establishment and shape recovery. The experiment demonstrates promising results compared to the state-of-the-art work on 3D shape estimation.
Active Learning for Graph Embedding
Venue: arXiv cs.LG 2017
Year: 2017

Graph embedding provides an efficient solution for graph analysis by converting the graph into a low-dimensional space which preserves the structure information. In contrast to the graph structure data, the i.i.d. node embedding can be processed efficiently in terms of both time and space. Current semi-supervised graph embedding algorithms assume the labelled nodes are given, which may not be always true in the real world. While manually label all training data is inapplicable, how to select the subset of training data to label so as to maximize the graph analysis task performance is of great importance. This motivates our proposed active graph embedding (AGE) framework, in which we design a general active learning query strategy for any semi-supervised graph embedding algorithm. AGE selects the most informative nodes as the training labelled nodes based on the graphical information (i.e., node centrality) as well as the learnt node embedding (i.e., node classification uncertainty and node embedding representativeness). Different query criteria are combined with the time-sensitive parameters which shift the focus from graph based query criteria to embedding based criteria as the learning progresses. Experiments have been conducted on three public data sets and the results verified the effectiveness of each component of our query strategy and the power of combining them using time-sensitive parameters. Our code is available online at: https://github.com/vwz/AGE.
Design Criteria to Architect Continuous Experimentation for Self-Driving Vehicles
Venue: arXiv cs.SE 2017
Year: 2017

The software powering today's vehicles surpasses mechatronics as the dominating engineering challenge due to its fast evolving and innovative nature. In addition, the software and system architecture for upcoming vehicles with automated driving functionality is already processing ~750MB/s - corresponding to over 180 simultaneous 4K-video streams from popular video-on-demand services. Hence, self-driving cars will run so much software to resemble "small data centers on wheels" rather than just transportation vehicles. Continuous Integration, Deployment, and Experimentation have been successfully adopted for software-only products as enabling methodology for feedback-based software development. For example, a popular search engine conducts ~250 experiments each day to improve the software based on its users' behavior. This work investigates design criteria for the software architecture and the corresponding software development and deployment process for complex cyber-physical systems, with the goal of enabling Continuous Experimentation as a way to achieve continuous software evolution. Our research involved reviewing related literature on the topic to extract relevant design requirements. The study is concluded by describing the software development and deployment process and software architecture adopted by our self-driving vehicle laboratory, both based on the extracted criteria.
Beamspace SU-MIMO for Future Millimeter Wave Wireless Communications
Venue: arXiv cs.NI 2017
Year: 2017

For future networks (i.e., the fifth generation (5G) wireless networks and beyond), millimeter-wave (mmWave) communication with large available unlicensed spectrum is a promising technology that enables gigabit multimedia applications. Thanks to the short wavelength of mmWave radio, massive antenna arrays can be packed into the limited dimensions of mmWave transceivers. Therefore, with directional beamforming (BF), both mmWave transmitters (MTXs) and mmWave receivers (MRXs) are capable of supporting multiple beams in 5G networks. However, for the transmission between an MTX and an MRX, most works have only considered a single beam, which means that they do not make full potential use of mmWave. Furthermore, the connectivity of single beam transmission can easily be blocked. In this context, we propose a single-user multi-beam concurrent transmission scheme for future mmWave networks with multiple reflected paths. Based on spatial spectrum reuse, the scheme can be described as a multiple-input multiple-output (MIMO) technique in beamspace (i.e., in the beam-number domain). Moreover, this study investigates the challenges and potential solutions for implementing this scheme, including multibeam selection, cooperative beam tracking, multi-beam power allocation and synchronization. The theoretical and numerical results show that the proposed beamspace SU-MIMO can largely improve the achievable rate of the transmission between an MTX and an MRX and, meanwhile, can maintain the connectivity.
Compressed Sensing for Scalable Robotic Tactile Skins
Venue: arXiv cs.RO 2017
Year: 2017

The potential of large tactile arrays to improve robot perception for safe operation in human-dominated environments and of high-resolution tactile arrays to enable human-level dexterous manipulation is well accepted. However, the increase in the number of tactile sensing elements introduces challenges including wiring complexity, data acquisition, and data processing. To help address these challenges, we develop a tactile sensing technique based on compressed sensing. Compressed sensing simultaneously performs data sampling and compression with recovery guarantees and has been successfully applied in computer vision. We use compressed sensing techniques for tactile data acquisition to reduce hardware complexity and data transmission, while allowing fast, accurate reconstruction of the full-resolution signal. For our simulated test array of 4096 taxels, we achieve reconstruction quality equivalent to measuring all taxel signals independently (the full signal) from just 1024 measurements (the compressed signal) at a rate over 100Hz. We then apply tactile compressed sensing to the problem of object classification. Specifically, we perform object classification on the compressed tactile data based on a method called compressed learning. We obtain up to 98% classification accuracy, even with a compression ratio of 64:1.
Extending Defensive Distillation
Venue: arXiv cs.LG 2017
Year: 2017

Machine learning is vulnerable to adversarial examples: inputs carefully modified to force misclassification. Designing defenses against such inputs remains largely an open problem. In this work, we revisit defensive distillation---which is one of the mechanisms proposed to mitigate adversarial examples---to address its limitations. We view our results not only as an effective way of addressing some of the recently discovered attacks but also as reinforcing the importance of improved training techniques.
Software Vulnerability Analysis Using CPE and CVE
Venue: arXiv cs.CR 2017
Year: 2017

In this paper, we analyze the Common Platform Enumeration (CPE) dictionary and the Common Vulnerabilities and Exposures (CVE) feeds. These repositories are widely used in Vulnerability Management Systems (VMSs) to check for known vulnerabilities in software products. The analysis shows, among other issues, a lack of synchronization between both datasets that can lead to incorrect results output by VMSs relying on those datasets. To deal with these problems, we developed a method that recommends to a user a prioritized list of CPE identifiers for a given software product. The user can then assign (and, if necessary, adapt) the most suitable CPE identifier to the software so that regular (e.g., daily) checks can find known vulnerabilities for this software in the CVE feeds. Our evaluation of this method shows that this interaction is indeed necessary because a fully automated CPE assignment is prone to errors due to the CPE and CVE shortcomings. We implemented an open-source VMS that employs the proposed method and published it on GitHub.
Evaluating XMPP Communication in IEC 61499-based Distributed Energy Applications
Venue: arXiv cs.NI 2017
Year: 2017

The IEC 61499 reference model provides an international standard developed specifically for supporting the creation of distributed event-based automation systems. Functionality is abstracted into function blocks which can be coded graphically as well as via a text-based method. As one of the design goals was the ability to support distributed control applications, communication plays a central role in the IEC 61499 specification. In order to enable the deployment of functionality to distributed platforms, these platforms need to exchange data in a variety of protocols. IEC 61499 realizes the support of these protocols via "Service Interface Function Blocks" (SIFBs). In the context of smart grids and energy applications, IEC 61499 could play an important role, as these applications require coordinating several distributed control logics. Yet, the support of grid-related protocols is a pre-condition for a wide-spread utilization of IEC 61499. The eXtensible Messaging and Presence Protocol (XMPP) on the other hand is a well-established protocol for messaging, which has recently been adopted for smart grid communication. Thus, SIFBs for XMPP facilitate distributed control applications, which use XMPP for exchanging all control relevant data, being realized with the help of IEC 61499. This paper introduces the idea of integrating XMPP into SIFBs, demonstrates the prototypical implementation in an open source IEC 61499 platform and provides an evaluation of the feasibility of the result.
Attack Detection in Sensor Network Target Localization Systems with Quantized Data
Venue: arXiv cs.IT 2017
Year: 2017

We consider a sensor network focused on target localization, where sensors measure the signal strength emitted from the target. Each measurement is quantized to one bit and sent to the fusion center. A general attack is considered at some sensors that attempts to cause the fusion center to produce an inaccurate estimation of the target location with a large mean-square-error. The attack is a combination of man-in-the-middle, hacking, and spoofing attacks that can effectively change both signals going into and coming out of the sensor nodes in a realistic manner. We show that the essential effect of attacks is to alter the estimated distance between the target and each attacked sensor to a different extent, giving rise to a geometric inconsistency among the attacked and unattacked sensors. Hence, with the help of two secure sensors, a class of detectors are proposed to detect the attacked sensors by scrutinizing the existence of the geometric inconsistency. We show that the false alarm and miss probabilities of the proposed detectors decrease exponentially as the number of measurement samples increases, which implies that for sufficiently large number of samples, the proposed detectors can identify the attacked and unattacked sensors with any required accuracy.
A Deep Learning Based 6 Degree-of-Freedom Localization Method for Endoscopic Capsule Robots
Venue: arXiv cs.CV 2017
Year: 2017

We present a robust deep learning based 6 degrees-of-freedom (DoF) localization system for endoscopic capsule robots. Our system mainly focuses on localization of endoscopic capsule robots inside the GI tract using only visual information captured by a mono camera integrated to the robot. The proposed system is a 23-layer deep convolutional neural network (CNN) that is capable to estimate the pose of the robot in real time using a standard CPU. The dataset for the evaluation of the system was recorded inside a surgical human stomach model with realistic surface texture, softness, and surface liquid properties so that the pre-trained CNN architecture can be transferred confidently into a real endoscopic scenario. An average error of 7:1% and 3:4% for translation and rotation has been obtained, respectively. The results accomplished from the experiments demonstrate that a CNN pre-trained with raw 2D endoscopic images performs accurately inside the GI tract and is robust to various challenges posed by reflection distortions, lens imperfections, vignetting, noise, motion blur, low resolution, and lack of unique landmarks to track.
Beyond Agile: Studying The Participatory Process in Software Development
Venue: arXiv cs.SE 2017
Year: 2017

We see that the collaborative and participatory nature of software development continues to evolve, shape and be shaped by communication channels that are used by developer communities of practice--both by traditional communication channels (e.g., telephone, in-person interactions), as well as social features that may be standalone or integrated with other development tools (e.g., email, chat, and forums). Within a community of practice, software is a combination of the externalized knowledge (e.g., code, documentation, history of activities) as well as the tacit knowledge that resides in the community members' heads (e.g., experience of when to use an API, or design constraints that are not written down). Communication channels and development tools support developers in forming and sharing both externalized and tacit knowledge in a highly collaborative manner. However, not much is known about the impact this participatory culture is having on software development practices nor on the knowledge-building processes. The research community has been studying the tools and communication channels used by developers in an effort to broaden our understanding. But, studying only the tools and channels can only provide a narrow perspective. We believe that software development has evolved beyond the agile process, into a Participatory Process--A knowledge building process which is characterized by the (1) knowledge activities and actions, (2) stakeholder roles, and (3) is enabled by socially enhanced tools and communication channels. Thus, it is important to gain an understanding of each one of the components, and the way these components interact and shape each other.
WordFence: Text Detection in Natural Images with Border Awareness
Venue: arXiv cs.CV 2017
Year: 2017

In recent years, text recognition has achieved remarkable success in recognizing scanned document text. However, word recognition in natural images is still an open problem, which generally requires time consuming post-processing steps. We present a novel architecture for individual word detection in scene images based on semantic segmentation. Our contributions are twofold: the concept of WordFence, which detects border areas surrounding each individual word and a novel pixelwise weighted softmax loss function which penalizes background and emphasizes small text regions. WordFence ensures that each word is detected individually, and the new loss function provides a strong training signal to both text and word border localization. The proposed technique avoids intensive post-processing, producing an end-to-end word detection system. We achieve superior localization recall on common benchmark datasets - 92% recall on ICDAR11 and ICDAR13 and 63% recall on SVT. Furthermore, our end-to-end word recognition system achieves state-of-the-art 86% F-Score on ICDAR13.
Concolic Execution as a General Method of Determining Local Malware Signatures
Venue: arXiv cs.CR 2017
Year: 2017

A commonly shared component of antivirus suites is a local database of malware signatures that is used during the static analysis process. Despite possible encryption, heuristic obfuscation, or attempts to hide this database from malicious end-users (or competitors), a currently avoidable eventuality for offline static analysis is a need to use the contents of the database in local computation to detect malicious files. This work serves as a preliminary exploration of the use of concolic execution as a general-case technique for reverse-engineering malware signature database contents: indeed, the existence of a practical technique to such an end would certainly require the use of true (in the sense of provable security) obfuscation in order for malware databases to remain private against capable attackers--a major obstacle given the scarcity of truly practical secure obfuscation constructions. Our work, however, only shows that existing tools (at the time of this report) for concolic execution have severe limitations which prevent the realization of this strategy.
A Method for Determining Weights of Criterias and Alternative of Fuzzy Group Decision Making Problem
Venue: arXiv cs.AI 2017
Year: 2017

In this paper, we constructed a model to determine weights of criterias and presented a solution for determining the optimal alternative by using the constructed model and relationship analysis between criterias in fuzzy group decision-making problem with different forms of preference information of decision makers on criterias.
New Reinforcement Learning Using a Chaotic Neural Network for Emergence of "Thinking" - "Exploration" Grows into "Thinking" through Learning -
Venue: arXiv cs.AI 2017
Year: 2017

Expectation for the emergence of higher functions is getting larger in the framework of end-to-end reinforcement learning using a recurrent neural network. However, the emergence of "thinking" that is a typical higher function is difficult to realize because "thinking" needs non fixed-point, flow-type attractors with both convergence and transition dynamics. Furthermore, in order to introduce "inspiration" or "discovery" in "thinking", not completely random but unexpected transition should be also required. By analogy to "chaotic itinerancy", we have hypothesized that "exploration" grows into "thinking" through learning by forming flow-type attractors on chaotic random-like dynamics. It is expected that if rational dynamics are learned in a chaotic neural network (ChNN), coexistence of rational state transition, inspiration-like state transition and also random-like exploration for unknown situation can be realized. Based on the above idea, we have proposed new reinforcement learning using a ChNN as an actor. The positioning of exploration is completely different from the conventional one. The chaotic dynamics inside the ChNN produces exploration factors by itself. Since external random numbers for stochastic action selection are not used, exploration factors cannot be isolated from the output. Therefore, the learning method is also completely different from the conventional one. At each non-feedback connection, one variable named causality trace takes in and maintains the input through the connection according to the change in its output. Using the trace and TD error, the weight is updated. In this paper, as the result of a recent simple task to see whether the new learning works or not, it is shown that a robot with two wheels and two visual sensors reaches a target while avoiding an obstacle after learning though there are still many rooms for improvement.
Toward QoE-Driven Dynamic Control Scheme Switching for Time-Delayed Teleoperation Systems: A Dedicated Case Study
Venue: arXiv cs.HC 2017
Year: 2017

Networked teleoperation with haptic feedback is a prime example for the emerging Tactile Internet, which requires a careful orchestration of haptic communication and control. One major challenge in this context is how to maximize the user's quality-of-experience (QoE) while ensuring at the same time the stability of the global control loop in the presence of communication delay. In this paper, we propose a dynamic control scheme switching strategy for teleoperation systems, which maximizes the QoE for time-varying communication delay. In order to validate the feasibility of the proposed approach, we perform a dedicated case study for a virtual teleoperation environment consisting of a one-dimensional spring-damper system, and conduct extensive subjective tests under various delay conditions for two control schemes: (1) teleoperation with the time-domain passivity approach (TDPA), which is highly delay-sensitive but supports highly dynamic interaction between the operator and a potentially quickly changing remote environment; (2) model-mediated teleoperation (MMT), which is tolerable to relatively larger communication delays, but unsuitable for quickly changing, highly dynamic remote environments. For both schemes, we use recently proposed extensions, which incorporate perceptual data reduction to reduce the required packet rate between the operator and the teleoperator. One key contribution of this paper lies in the exploration of the intrinsic relationship among QoE, communication delay and the control schemes which provides a fundamental guidance, not only to this research, but also to the future joint optimization of communication and control for time-delayed teleoperation systems.
Simulation of Urban Expansion and Farmland Loss in China by Integrating Cellular Automata and Random Forest
Venue: arXiv cs.CY 2017
Year: 2017

China has encountered serious land loss problems along with urban expansion due to rapid urbanization. Without considering complicated spatiotemporal heterogeneity, previous studies could not extract urban transition rules at large scale well. This study proposed a random forest algorithm (RFA) based cellular automata (CA) model to simulate China's urban expansion and farmland loss in a fine scale from 2000 to 2030. The objectives of this study are to 1) mine urban conversion rules in different homogeneous economic development regions, and 2) simulate China's urban expansion process and farmland loss at high spatial resolution (30 meters). Firstly, we clustered several homogeneous economic development regions among China according to official statistical data. Secondly, we constructed a RFA-based CA model to mine complex urban conversion rules and carried out simulation of urban expansion and farmland loss at each homo-region. The proposed model was implemented on Tianhe-1 supercomputer located in Guangzhou, China. The accuracy evaluation demonstrates that the simulation result of proposed RFA-based CA model is more in agreement with actual land use change. This study proves that the primary factor of farmland loss in China is rapid urbanization from 2000, and the farmland loss rate is expected to slow down gradually and will stabilize from 2010 to 2030. It shows that China is able to preserve the 1.20 million km farmland without crossing the "red line" within the next 20 years, but the situation remains severe.