nils-herrmann/clean_scirepeval huggingface dataset:
Indoor Relocalization in Challenging Environments With Dual-Stream Convolutional Neural Networks

This paper presents an indoor relocalization system using a dual-stream convolutional neural network (CNN) with both color images and depth images as the network inputs. Aiming at the pose regression problem, a deep neural network architecture for RGB-D images is introduced, a training method by stages for the dual-stream CNN is presented, different depth image encoding methods are discussed, and a novel encoding method is proposed. By introducing the range information into the network through a dual-stream architecture, we not only improved the relocalization accuracy by about 20% compared with the state-of-the-art deep learning method for pose regression, but also greatly enhanced the system robustness in challenging scenes such as large-scale, dynamic, fast movement, and night-time environments. To the best of our knowledge, this is the first work to solve the indoor relocalization problems based on deep CNNs with RGB-D camera. The method is first evaluated on the Microsoft 7-Scenes data set to show its advantage in accuracy compared with other CNNs. Large-scale indoor relocalization is further presented using our method. The experimental results show that 0.3 m in position and 4° in orientation accuracy could be obtained. Finally, this method is evaluated on challenging indoor data sets collected from motion capture system. The results show that the relocalization performance is hardly affected by dynamic objects, motion blur, or night-time environments.Note to Practitioners—This paper was motivated by the limitations of the existing indoor relocalization technology that is significant for mobile robot navigation. Using this technology, robots can infer where they are in a previously visited place. Previous visual localization methods can hardly be put into wide application for the reason that they have strict requirements for the environments. When faced with challenging scenes such as large-scale environments, dynamic objects, motion blur caused by fast movement, night-time environments, or other appearance changed scenes, most existing methods tend to fail. This paper introduces deep learning into the indoor relocalization problem and uses dual-stream CNN (depth stream and color stream) to realize 6-DOF pose regression in an end-to-end manner. The localization error is about 0.3 m and 4° in a large-scale indoor environments. And what is more important, the proposed system does not lose efficiency in some challenging scenes. The proposed encoding method of depth images can also be adopted in other deep neural networks with RGB-D cameras as the sensor.
An analysis of accelerator coupling in heterogeneous architectures

Existing research on accelerators has emphasized the performance and energy e ciency improvements they can provide, devoting little attention to practical issues such as accelerator invocation and interaction with other on-chip components (e.g. cores, caches). In this paper we present a quantitative study that considers these aspects by implementing seven high-throughput accelerators following three design models: tight coupling behind a CPU, loose out-of-core coupling with Direct Memory Access (DMA) to the LLC, and loose out-of-core coupling with DMA to DRAM. A salient conclusion of our study is that working sets of non-trivial size are best served by loosely-coupled accelerators that integrate private memory blocks tailored to their needs.
A "nutrition label" for privacy

We used an iterative design process to develop a privacy label that presents to consumers the ways organizations collect, use, and share personal information. Many surveys have shown that consumers are concerned about online privacy, yet current mechanisms to present website privacy policies have not been successful. This research addresses the present gap in the communication and understanding of privacy policies, by creating an information design that improves the visual presentation and comprehensibility of privacy policies. Drawing from nutrition, warning, and energy labeling, as well as from the effort towards creating a standardized banking privacy notification, we present our process for constructing and refining a label tuned to privacy. This paper describes our design methodology; findings from two focus groups; and accuracy, timing, and likeability results from a laboratory study with 24 participants. Our study results demonstrate that compared to existing natural language privacy policies, the proposed privacy label allows participants to find information more quickly and accurately, and provides a more enjoyable information seeking experience.
Kilobot: A low cost robot with scalable operations designed for collective behaviors

In current robotics research there is a vast body of work on algorithms and control methods for groups of decentralized cooperating robots, called a swarm or collective. These algorithms are generally meant to control collectives of hundreds or even thousands of robots; however, for reasons of cost, time, or complexity, they are generally validated in simulation only, or on a group of a few tens of robots. To address this issue, this paper presents Kilobot, an open-source, low cost robot designed to make testing collective algorithms on hundreds or thousands of robots accessible to robotics researchers. To enable the possibility of large Kilobot collectives where the number of robots is an order of magnitude larger than the largest that exist today, each robot is made with only $14 worth of parts and takes 5 min to assemble. Furthermore, the robot design allows a single user to easily operate a large Kilobot collective, such as programming, powering on, and charging all robots, which would be difficult or impossible to do with many existing robotic systems. We demonstrate the capabilities of the Kilobot as a collective robot, by using a small robot test collective to implement four popular swarm behaviors: foraging, formation control, phototaxis, and synchronization.
A Balanced Filtering Branch-Line Coupler

A balanced filtering branch-line coupler is proposed for the first time. The proposed design is realized by coupled microstrip lines and coupled-line-fed coupling structures. Based on this, the proposed design not only exhibits the functions of power dividing and filtering for differential-mode signals, but also can suppress the common-mode noise/signal and be easily connected with other balanced circuits. The dimension of the proposed design is similar to that of the traditional single-ended design. A prototype of the balanced filtering branch-line coupler centered at 1.87 GHz with the size of 0.33 λg × 0.42 λg is fabricated, where λg is the guided wavelength at the center frequency. The measured results exhibit the maximum insertion loss of 1.4 dB with a 3-dB fractional bandwidth of 3.5%.
Distortion correction formulas for pre-objective dual galvanometer laser scanning.

For many applications a laser beam needs to be scanned across a plane. Several methods exist to accomplish this.One method, dual galvanometer pre-objective scanning, is particularly well suited to precise laser marking applications. The laser beam impinges on a first mirror mounted on a galvanometer motor, then on a second mirror with its rota­ tion axis parallel to the laser axis, and passes then through the flat-field objective lens, which focuses the beam onto the image plane. Figure 1 shows a photograph of the SILAMATIK laser marker. This letter investigates what amount of distortion is intro­ duced and how it can be corrected electronically. We as­ sume that the lens is axial symmetric. The geometry is that of Fig. 2. Say the first mirror is rotated a mechanical angle a/2 from its rest position and the second mirror an angle 6/2. The direction vector lR of the beam that falls on the lens is
Transform a Simple Sketch to a Chinese Painting by a Multiscale Deep Neural Network

Recently, inspired by the power of deep learning, convolution neural networks can produce fantastic images at the pixel level. However, a significant limiting factor for previous approaches is that they focus on some simple datasets such as faces and bedrooms. In this paper, we propose a multiscale deep neural network to transform sketches into Chinese paintings. To synthesize more realistic imagery, we train the generative network by using both L1 loss and adversarial loss. Additionally, users can control the process of the synthesis since the generative network is feed-forward. This network can also be treated as neural style transfer by adding an edge detector. Furthermore, additional experiments on image colorization and image super-resolution demonstrate the universality of our proposed approach.
Functorial Boxes in String Diagrams

String diagrams were introduced by Roger Penrose as a handy notation to manipulate morphisms in a monoidal category. In principle, this graphical notation should encompass the various pictorial systems introduced in proof-theory (like Jean-Yves Girard’s proof-nets) and in concurrency theory (like Robin Milner’s bigraphs). This is not the case however, at least because string diagrams do not accomodate boxes — a key ingredient in these pictorial systems. In this short tutorial, based on our accidental rediscovery of an idea by Robin Cockett and Robert Seely, we explain how string diagrams may be extended with a notion of functorial box to depict a functor separating an inside world (its source category) from an outside world (its target category). We expose two elementary applications of the notation: first, we characterize graphically when a faithful balanced monoidal functor F : C −→ D transports a trace operator from the category D to the category C, and we then exploit this to construct well-behaved fixpoint operators in cartesian closed categories generated by models of linear logic; second, we explain how the categorical semantics of linear logic induces that the exponential box of proof-nets decomposes as two enshrined functorial boxes. ∗Invited paper at the conference Computer Science Logic 2006 in Szeged, Hungary. To appear in the proceedings of the conference.  This research was partially supported by the ANR Project INVAL “Invariants algébriques des systèmes informatiques”.
Deep Learning for Sensorless 3D Freehand Ultrasound Imaging

3D freehand ultrasound imaging is a very promising imaging modality but its acquisition is often neither portable nor practical because of the required external tracking hardware. Building a sensorless solution that is fully based on image analysis would thus have many potential applications. However, previously proposed approaches rely on physical models whose assumptions only hold on synthetic or phantom datasets, failing to translate to actual clinical acquisitions with sufficient accuracy. In this paper, we investigate the alternative approach of using statistical learning to circumvent this problem. To that end, we are leveraging the unique modeling capabilities of convolutional neural networks in order to build an end-to-end system where we directly predict the ultrasound probe motion from the images themselves. Based on thorough experiments using both phantom acquisitions and a set of 100 in-vivo long ultrasound sweeps for vein mapping, we show that our novel approach significantly outperforms the standard method and has direct clinical applicability, with an average drift error of merely 7% over the whole length of each ultrasound clip.
View invariant human action recognition using histograms of 3D joints

In this paper, we present a novel approach for human action recognition with histograms of 3D joint locations (HOJ3D) as a compact representation of postures. We extract the 3D skeletal joint locations from Kinect depth maps using Shotton et al.'s method . The HOJ3D computed from the action depth sequences are reprojected using LDA and then clustered into k posture visual words, which represent the prototypical poses of actions. The temporal evolutions of those visual words are modeled by discrete hidden Markov models (HMMs). In addition, due to the design of our spherical coordinate system and the robust 3D skeleton estimation from Kinect, our method demonstrates significant view invariance on our 3D action dataset. Our dataset is composed of 200 3D sequences of 10 indoor activities performed by 10 individuals in varied views. Our method is real-time and achieves superior results on the challenging 3D action dataset. We also tested our algorithm on the MSR Action 3D dataset and our algorithm outperforms Li et al.  on most of the cases.
Experiences with rapid mobile game development using unity engine

In this work, we describe our experiences with FunCopter, a casual game we have designed and implemented using Unity engine, suitable for portable devices. We emphasize some general principles, particularly with respect to rapid game development and constrained graphics. In addition to that, we present the main activities realized in each software engineering phase and the lessons we learned during its development. These descriptions include details from the initial concept to the fully realized, playable game. This work will share general but important experiences with Unity, particularly interesting for game developers in beginner's level.
Detecting Code-Switching between Turkish-English Language Pair

Code-switching (usage of different languages within a single conversation context in an alternative manner) is a highly increasing phenomenon in social media and colloquial usage which poses different challenges for natural language processing. This paper introduces the first study for the detection of TurkishEnglish code-switching and also a small test data collected from social media in order to smooth the way for further studies. The proposed system using character level n-grams and conditional random fields (CRFs) obtains 95.6% micro-averaged F1-score on the introduced test data set.
Multiple SVM-RFE for gene selection in cancer classification with expression data

This paper proposes a new feature selection method that uses a backward elimination procedure similar to that implemented in support vector machine recursive feature elimination (SVM-RFE). Unlike the SVM-RFE method, at each step, the proposed approach computes the feature ranking score from a statistical analysis of weight vectors of multiple linear SVMs trained on subsamples of the original training data. We tested the proposed method on four gene expression datasets for cancer classification. The results show that the proposed feature selection method selects better gene subsets than the original SVM-RFE and improves the classification accuracy. A Gene Ontology-based similarity assessment indicates that the selected subsets are functionally diverse, further validating our gene selection method. This investigation also suggests that, for gene expression-based cancer classification, average test error from multiple partitions of training and test sets can be recommended as a reference of performance quality.
Ka-Band Phased Array Antenna for High-Data-Rate SATCOM

The general issue of this letter deals with the design of a phased array antenna for high-data-rate SATCOM. A final demonstrator antenna could be installed on an unmanned aerial vehicle (UAV) to communicate with a satellite in Ka-band. First, a compact reflection-type phase shifter is designed and realized. Second, the conception of a phased array antenna prototype is detailed. Third, a new calibration method is involved that can provide the bias voltage to be applied to each phase shifter in order to scan the beam in the desired direction.
Flexible Access Management System for Campus VLAN Based on OpenFlow

Using a lot of VLANs on campus networks has become popular for deploying many logical networks over minimal fibers/cables. A campus-wide Wi-Fi system, for example, requires a lot of VLANs for separating the access networks from other campus networks and for realizing a sophisticated access control such as guest-/home-users separation and security filtering. The requirement is high especially when a network roaming system, such as eduroam, is introduced. The conventional VLAN based on IEEE802.1Q has some limitations, and the system configuration work is laborious. In this paper, we propose a flexible campus VLAN system based on Open Flow to solve the problems. In addition, we introduce a prototype system and show evaluation results. As the results, we confirm that the proposed system can realize flexible network configurations and sophisticated access control with a much simplified network management work.
Scene Classification via a Gradient Boosting Random Convolutional Network Framework

Due to the recent advances in satellite sensors, a large amount of high-resolution remote sensing images is now being obtained each day. How to automatically recognize and analyze scenes from these satellite images effectively and efficiently has become a big challenge in the remote sensing field. Recently, a lot of work in scene classification has been proposed, focusing on deep neural networks, which learn hierarchical internal feature representations from image data sets and produce state-of-the-art performance. However, most methods, including the traditional shallow methods and deep neural networks, only concentrate on training a single model. Meanwhile, neural network ensembles have proved to be a powerful and practical tool for a number of different predictive tasks. Can we find a way to combine different deep neural networks effectively and efficiently for scene classification? In this paper, we propose a gradient boosting random convolutional network (GBRCN) framework for scene classification, which can effectively combine many deep neural networks. As far as we know, this is the first time that a deep ensemble framework has been proposed for scene classification. Moreover, in the experiments, the proposed method was applied to two challenging high-resolution data sets: 1) the UC Merced data set containing 21 different aerial scene categories with a submeter resolution and 2) a Sydney data set containing eight land-use categories with a 1.0-m spatial resolution. The proposed GBRCN framework outperformed the state-of-the-art methods with the UC Merced data set, including the traditional single convolutional network approach. For the Sydney data set, the proposed method again obtained the best accuracy, demonstrating that the proposed framework can provide more accurate classification results than the state-of-the-art methods.
Designing mobile interfaces for novice and low-literacy users

While mobile phones have found broad application in bringing health, financial, and other services to the developing world, usability remains a major hurdle for novice and low-literacy populations. In this article, we take two steps to evaluate and improve the usability of mobile interfaces for such users. First, we offer an ethnographic study of the usability barriers facing 90 low-literacy subjects in India, Kenya, the Philippines, and South Africa. Then, via two studies involving over 70 subjects in India, we quantitatively compare the usability of different points in the mobile design space. In addition to text interfaces such as electronic forms, SMS, and USSD, we consider three text-free interfaces: a spoken dialog system, a graphical interface, and a live operator.
 Our results confirm that textual interfaces are unusable by first-time low-literacy users, and error prone for literate but novice users. In the context of healthcare, we find that a live operator is up to ten times more accurate than text-based interfaces, and can also be cost effective in countries such as India. In the context of mobile banking, we find that task completion is highest with a graphical interface, but those who understand the spoken dialog system can use it more quickly due to their comfort and familiarity with speech. We synthesize our findings into a set of design recommendations.
A Survey on the Security of Stateful SDN Data Planes

Software-defined networking (SDN) emerged as an attempt to introduce network innovations faster, and to radically simplify and automate the management of large networks. SDN traditionally leverages OpenFlow as device-level abstraction. Since OpenFlow permits the programmer to “just” abstract a static flow-table, any stateful control and processing intelligence is necessarily delegated to the network controller. Motivated by the latency and signaling overhead that comes along with such a two-tiered SDN programming model, in the last couple of years several works have proposed innovative switch-level (data plane) programming abstractions capable to deploy some smartness directly inside the network switches, e.g., in the form of localized stateful flow processing. Furthermore, the possible inclusion of states and state maintenance primitives inside the switches is currently being debated in the OpenFlow standardization community itself. In this paper, after having provided the reader with a background on such emerging stateful SDN data plane proposals, we focus our attention on the security implications that data plane programmability brings about. Also via the identification of potential attack scenarios, we specifically highlight possible vulnerabilities specific to stateful in-switch processing (including denial of service and saturation attacks), which we believe should be carefully taken into consideration in the ongoing design of current and future proposals for stateful SDN data planes.
2D Color Barcodes for Mobile Phones

We propose a new high capacity color barcode, named HCC2D (High Capacity Colored 2-Dimensional), which use colors to increase the barcode data density. The introduction and recognition of colored modules poses some new and non-trivial computer vision challenges, such as handling the color distortions introduced by the hardware equipment that realizes the Print&Scan process. We developed a prototype for generating and reading the HCC2D code format, both on desktops (Linux and Windows platforms) and on mobile phones (Android platforms). We tested this prototype in many experiments considering different operating scenarios and data densities, and compared it to known 2-dimensional barcodes.
The EDA challenges in the dark silicon era

Technology scaling has resulted in smaller and faster transistors in successive technology generations. However, transistor power consumption no longer scales commensurately with integration density and, consequently, it is projected that in future technology nodes it will only be possible to simultaneously power on a fraction of cores on a multi-core chip in order to stay within the power budget. The part of the chip that is powered off is referred to as dark silicon and brings new challenges as well as opportunities for the design community, particularly in the context of the interaction of dark silicon with thermal, reliability and variability concerns. In this perspectives paper we describe these new challenges and opportunities, and provide preliminary experimental evidence in their support.