nils-herrmann/clean_scirepeval huggingface dataset:
Serious games in cultural heritage

Although the widespread use of gaming for leisure purposes has been well documented, the use of games to support cultural heritage purposes, such as historical teaching and learning, or for enhancing museum visits, has been less well considered. The state-of-the-art in serious game technology is identical to that of the state-of-the-art in entertainment games technology. As a result the field of serious heritage games concerns itself with recent advances in computer games, real-time computer graphics, virtual and augmented reality and artificial intelligence. On the other hand, the main strengths of serious gaming applications may be generalised as being in the areas of communication, visual expression of information, collaboration mechanisms, interactivity and entertainment. In this report, we will focus on the state-of-the-art with respect to the theories, methods and technologies used in serious heritage games. We provide an overview of existing literature of relevance to the domain, discuss the strengths and weaknesses of the described methods and point out unsolved problems and challenges. In addition, several case studies illustrating the application of methods and technologies used in cultural heritage are presented.
Aligning IT Strategy to Business Strategy in a Multi-Business Company

Aligning IT to business needs is still one of the most important challenges for many organizations. In a recent survey amongst European IT managers, 78% indicate that their IT is not aligned with business strategy. Another recent survey shows similar results. The message of Business & IT Alignment is logical and undisputed. But if this message is so clear, how can practice be so difficult? To explore the issues with and approaches to BITA in practice, a focused group discussion was organized with IT managers and CIOs of medium sized and large organizations in the Netherlands. In total 23 participants from trade, manufacturing and financial companies joined the discussions. This paper explores the practice of Business & IT Alignment in mult-business-companies. The parenting theory for the role of the corporate center is used to explain the different practical approaches that the participants in the focused groups took.
Very Low-Profile Top-Loaded UWB Coupled Sectorial Loops Antenna

In this letter, we present a new very low-profile monopole-type antenna. The proposed antenna height is 0.053λ0, where λ0 is the minimum operating wavelength and operates over 148% relative bandwidth. The antenna is based on the principle of top-loading of coupled sectorial loop antenna (CSLA). Introducing a series capacitive gap in the top-loading plate and the CSLA fins allows for achieving ultrawideband operation and very low antenna height simultaneously. The proposed structure is also relatively easy and cost-effective to fabricate using standard printed circuit board (PCB) fabrication technology. A parametric study is presented to show the effect of the different dimensions on the antenna performance. A prototype antenna is fabricated and measured. The measurements and simulation results show good agreement and indicate that a VSWR <; 2 over 0.9-6 GHz can be accomplished. It is also shown that the radiation pattern has omnidirectional characteristics over more than two octaves.
Digital watermarking for deep neural networks

Although deep neural networks have made tremendous progress in the area of multimedia representation, training neural models requires a large amount of data and time. It is well known that utilizing trained models as initial weights often achieves lower training error than neural networks that are not pre-trained. A fine-tuning step helps to both reduce the computational cost and improve the performance. Therefore, sharing trained models has been very important for the rapid progress of research and development. In addition, trained models could be important assets for the owner(s) who trained them; hence, we regard trained models as intellectual property. In this paper, we propose a digital watermarking technology for ownership authorization of deep neural networks. First, we formulate a new problem: embedding watermarks into deep neural networks. We also define requirements, embedding situations, and attack types on watermarking in deep neural networks. Second, we propose a general framework for embedding a watermark in model parameters, using a parameter regularizer. Our approach does not impair the performance of networks into which a watermark is placed because the watermark is embedded while training the host network. Finally, we perform comprehensive experiments to reveal the potential of watermarking deep neural networks as the basis of this new research effort. We show that our framework can embed a watermark during the training of a deep neural network from scratch, and during fine-tuning and distilling, without impairing its performance. The embedded watermark does not disappear even after fine-tuning or parameter pruning; the watermark remains complete even after 65% of parameters are pruned.
What's congress doing on twitter?

As Twitter becomes a more common means for officials to communicate with their constituents, it becomes more important that we understand how officials use these communication tools. Using data from 380 members of Congress' Twitter activity during the winter of 2012, we find that officials frequently use Twitter to advertise their political positions and to provide information but rarely to request political action from their constituents or to recognize the good work of others. We highlight a number of differences in communication frequency between men and women, Senators and Representatives, Republicans and Democrats. We provide groundwork for future research examining the behavior of public officials online and testing the predictive power of officials' social media behavior.
An Empirical Study of API Stability and Adoption in the Android Ecosystem

When APIs evolve, clients make corresponding changes to their applications to utilize new or updated APIs. Despite the benefits of new or updated APIs, developers are often slow to adopt the new APIs. As a first step toward understanding the impact of API evolution on software ecosystems, we conduct an in-depth case study of the co-evolution behavior of Android API and dependent applications using the version history data found in github. Our study confirms that Android is evolving fast at a rate of 115 API updates per month on average. Client adoption, however, is not catching up with the pace of API evolution. About 28% of API references in client applications are outdated with a median lagging time of 16 months. 22% of outdated API usages eventually upgrade to use newer API versions, but the propagation time is about 14 months, much slower than the average API release interval (3 months). Fast evolving APIs are used more by clients than slow evolving APIs but the average time taken to adopt new versions is longer for fast evolving APIs. Further, API usage adaptation code is more defect prone than the one without API usage adaptation. This may indicate that developers avoid API instability.
Integrated modular avionics for next generation commercial airplanes

The goals are objectives of integrated modular avionics (IMA) are discussed. One candidate computing architecture for IMA, the Boeing 777 Airplane Information Management System (AIMS), is presented. The cabinet architecture, fault tolerance characteristics, backplane bus, and functional growth provisions are considered. The integration concepts developed for AIMS represent a first step in real-world application of the IMA technologies.<<ETX>>
An Embedded System-on-Chip Architecture for Real-time Visual Detection and Matching

Detecting and matching image features is a fundamental task in video analytics and computer vision systems. It establishes the correspondences between two images taken at different time instants or from different viewpoints. However, its large computational complexity has been a challenge to most embedded systems. This paper proposes a new FPGA-based embedded system architecture for feature detection and matching. It consists of scale-invariant feature transform (SIFT) feature detection, as well as binary robust independent elementary features (BRIEF) feature description and matching. It is able to establish accurate correspondences between consecutive frames for 720-p (1280x720) video. It optimizes the FPGA architecture for the SIFT feature detection to reduce the utilization of FPGA resources. Moreover, it implements the BRIEF feature description and matching on FPGA. Due to these contributions, the proposed system achieves feature detection and matching at 60 frame/s for 720-p video. Its processing speed can meet and even exceed the demand of most real-life real-time video analytics applications. Extensive experiments have demonstrated its efficiency and effectiveness.
Finding software license violations through binary code clone detection

Software released in binary form frequently uses third-party packages without respecting their licensing terms. For instance, many consumer devices have firmware containing the Linux kernel, without the suppliers following the requirements of the GNU General Public License. Such license violations are often accidental, e.g., when vendors receive binary code from their suppliers with no indication of its provenance. To help find such violations, we have developed the Binary Analysis Tool (BAT), a system for code clone detection in binaries. Given a binary, such as a firmware image, it attempts to detect cloning of code from repositories of packages in source and binary form. We evaluate and compare the effectiveness of three of BAT's clone detection techniques: scanning for string literals, detecting similarity through data compression, and detecting similarity by computing binary deltas.
ObfusMem: A low-overhead access obfuscation for trusted memories

Trustworthy software requires strong privacy and security guarantees from a secure trust base in hardware. While chipmakers provide hardware support for basic security and privacy primitives such as enclaves and memory encryption. these primitives do not address hiding of the memory access pattern, information about which may enable attacks on the system or reveal characteristics of sensitive user data. State-of-the-art approaches to protecting the access pattern are largely based on Oblivious RAM (ORAM). Unfortunately, current ORAM implementations suffer from very significant practicality and overhead concerns, including roughly an order of magnitude slowdown, more than 100% memory capacity overheads, and the potential for system deadlock.
 Memory technology trends are moving towards 3D and 2.5D integration, enabling significant logic capabilities and sophisticated memory interfaces. Leveraging the trends, we propose a new approach to access pattern obfuscation, called ObfusMem. ObfusMem adds the memory to the trusted computing base and incorporates cryptographic engines within the memory. ObfusMem encrypts commands and addresses on the memory bus, hence the access pattern is cryptographically obfuscated from external observers. Our evaluation shows that ObfusMem incurs an overhead of 10.9% on average, which is about an order of magnitude faster than ORAM implementations. Furthermore, ObfusMem does not incur capacity overheads and does not amplify writes. We analyze and compare the security protections provided by ObfusMem and ORAM, and highlight their differences.
Inertially stabilized platform technology Concepts and principles

We introduced some of the basic principles, techniques, and key design issues common to ISPs used in many diverse scientific, military, and commercial applications, and we touched on some of the less intuitive effects that must be dealt with. Many of these effects can influence the initial configuration of the system as well as design details later in the design process. The successful design of an ISP usually requires a multidisciplinary design team. The design of an ISP must often be closely coordinated with that of other major subsystems such as the primary sensor and the optics. The role of the systems engineer in the design process is perhaps the most critical because other members of the team may not be aware of the consequences of many of the effects discussed above. Inertially stabilized platforms (ISPs) are used to stabilize and point a broad array of sensors, cameras, telescopes, and weapon systems.
Real-Time Mitigation of Short-Range Leakage in Automotive FMCW Radar Transceivers

Frequency-modulated continuous wave radar systems suffer from permanent leakage of the transmit signal into the receive path. Besides leakage within the radar device itself, an unwanted object placed in front of the antennas causes so-called short-range (SR) leakage. In an automotive application, for instance, it originates from signal reflections of the car’s own bumper. Particularly the residual phase noise of the downconverted SR leakage signal causes a severe degradation of the achievable sensitivity. In an earlier work, we proposed an SR leakage cancellation concept that is feasible for integration in a monolithic microwave integrated circuit. In this brief, we present a hardware prototype that holistically proves our concept with discrete components. The fundamental theory and properties of the concept are proven with measurements. Further, we propose a digital design for real-time operation of the cancellation algorithm on a field programmable gate array. Ultimately, by employing measurements with a bumper mounted in front of the antennas, we show that the leakage canceller significantly improves the sensitivity of the radar.
A 0.19-V minimum input low energy level shifter for extremely low-voltage VLSIs

In this paper, we propose a low-power level shifter (LS) capable of converting extremely low-input voltage into high-output voltage. The proposed LS consists of a pre-amplifier with a logic error correction circuit and an output latch stage. The pre-amplifier generates complementary amplified signals, and the latch stage converts them into full-swing output signals. Simulated results demonstrated that the proposed LS in a 0.18-μm CMOS process can convert a 0.19-V input into 1.8-V output correctly. The energy and the delay time of the proposed LS were 0.24 pJ and 21.4 ns when the low supply voltage, high supply voltage, and the input pulse frequency, were 0.4, 1.8 V, and 100 kHz, respectively.
Alopex: A Correlation-Based Learning Algorithm for Feedforward and Recurrent Neural Networks

We present a learning algorithm for neural networks, called Alopex. Instead of error gradient, Alopex uses local correlations between changes in individual weights and changes in the global error measure. The algorithm does not make any assumptions about transfer functions of individual neurons, and does not explicitly depend on the functional form of the error measure. Hence, it can be used in networks with arbitrary transfer functions and for minimizing a large class of error measures. The learning algorithm is the same for feedforward and recurrent networks. All the weights in a network are updated simultaneously, using only local computations. This allows complete parallelization of the algorithm. The algorithm is stochastic and it uses a temperature parameter in a manner similar to that in simulated annealing. A heuristic annealing schedule is presented that is effective in finding global minima of error surfaces. In this paper, we report extensive simulation studies illustrating these advantages and show that learning times are comparable to those for standard gradient descent methods. Feedforward networks trained with Alopex are used to solve the MONK's problems and symmetry problems. Recurrent networks trained with the same algorithm are used for solving temporal XOR problems. Scaling properties of the algorithm are demonstrated using encoder problems of different sizes and advantages of appropriate error measures are illustrated using a variety of problems.
Analyzing Writing Styles with Coh-Metrix

Computer scientists, linguists, stylometricians, and cognitive scientists have successfully divided corpora into modes, domains, genres, registers, and authors. The limitations for these successes, however, often result from insufficient indices with which their corpora are analyzed. In this paper, we use Coh-Metrix, a computational tool that analyzes text on over 200 indices of cohesion and difficulty. We demonstrate how, with the benefit of statistical analysis, texts can be analyzed for subtle, yet meaningful differences. In this paper, we report evidence that authors within the same register can be computationally distinguished despite evidence that stylistic markers can also shift significantly over time.
Active Learning for Reward Estimation in Inverse Reinforcement Learning

Inverse reinforcement learning addresses the general problem of recovering a reward function from samples of a policy provided by an expert/demonstrator. In this paper, we introduce active learning for inverse reinforcement learning. We propose an algorithm that allows the agent to query the demonstrator for samples at specific states, instead of relying only on samples provided at “arbitrary” states. The purpose of our algorithm is to estimate the reward function with similar accuracy as other methods from the literature while reducing the amount of policy samples required from the expert. We also discuss the use of our algorithm in higher dimensional problems, using both Monte Carlo and gradient methods. We present illustrative results of our algorithm in several simulated examples of different complexities.
Adaptive neuron apoptosis for accelerating deep learning on large scale systems

We present novel techniques to accelerate the convergence of Deep Learning algorithms by conducting low overhead removal of redundant neurons — apoptosis of neurons — which do not contribute to model learning, during the training phase itself. We provide in-depth theoretical underpinnings of our heuristics (bounding accuracy loss and handling apoptosis of several neuron types), and present the methods to conduct adaptive neuron apoptosis. Specifically, we are able to improve the training time for several datasets by 2–3x, while reducing the number of parameters by up to 30× (4–5× on average) on datasets such as ImageNet classification. For the Higgs Boson dataset, our implementation improves the accuracy (measured by Area Under Curve (AUC)) for classification from 0.88/1 to 0.94/1, while reducing the number of parameters by 3x in comparison to existing literature. The proposed methods achieve a 2.44x speedup in comparison to the default (no apoptosis) algorithm.
Mobile Edge Computing via a UAV-Mounted Cloudlet: Optimal Bit Allocation and Path Planning

Unmanned Aerial Vehicles (UAVs) have been recently considered as means to provide enhanced coverage or relaying services to mobile users (MUs) in wireless systems with limited or no infrastructure. In this paper, a UAV-based mobile cloud computing system is studied in which a moving UAV is endowed with computing capabilities to offer computation offloading opportunities to MUs with limited local processing capabilities. The system aims at minimizing the total mobile energy consumption while satisfying quality of service requirements of the offloaded mobile application. Offloading is enabled by uplink and downlink communications between the mobile devices and the UAV that take place by means of frequency division duplex (FDD) via orthogonal or non-orthogonal multiple access (NOMA) schemes. The problem of jointly optimizing the bit allocation for uplink and downlink communication as well as for computing at the UAV, along with the cloudlet’s trajectory under latency and UAV’s energy budget constraints is formulated and addressed by leveraging successive convex approximation (SCA) strategies. Numerical results demonstrate the significant energy savings that can be accrued by means of the proposed joint optimization of bit allocation and cloudlet’s trajectory as compared to local mobile execution as well as to partial optimization approaches that design only the bit allocation or the cloudlet’s trajectory.
Codebook-Based Audio Feature Representation for Music Information Retrieval

Digital music has become prolific in the web in recent decades. Automated recommendation systems are essential for users to discover music they love and for artists to reach appropriate audience. When manual annotations and user preference data is lacking (e.g. for new artists) these systems must rely on content based methods. Besides powerful machine learning tools for classification and retrieval, a key component for successful recommendation is the audio content representation. Good representations should capture informative musical patterns in the audio signal of songs. These representations should be concise, to enable efficient (low storage, easy indexing, fast search) management of huge music repositories, and should also be easy and fast to compute, to enable real-time interaction with a user supplying new songs to the system. Before designing new audio features, we explore the usage of traditional local features, while adding a stage of encoding with a pre-computed codebook and a stage of pooling to get compact vectorial representations. We experiment with different encoding methods, namely the LASSO, vector quantization (VQ) and cosine similarity (CS). We evaluate the representations' quality in two music information retrieval applications: query-by-tag and query-by-example. Our results show that concise representations can be used for successful performance in both applications. We recommend using top-τ VQ encoding, which consistently performs well in both applications, and requires much less computation time than the LASSO.
Multivehicle Cooperative Driving Using Cooperative Perception: Design and Experimental Validation

In this paper, we present a multivehicle cooperative driving system architecture using cooperative perception along with experimental validation. For this goal, we first propose a multimodal cooperative perception system that provides see-through, lifted-seat, satellite and all-around views to drivers. Using the extended range information from the system, we then realize cooperative driving by a see-through forward collision warning, overtaking/lane-changing assistance, and automated hidden obstacle avoidance. We demonstrate the capabilities and features of our system through real-world experiments using four vehicles on the road.
Wavelet networks

A wavelet network concept, which is based on wavelet transform theory, is proposed as an alternative to feedforward neural networks for approximating arbitrary nonlinear functions. The basic idea is to replace the neurons by ;wavelons', i.e., computing units obtained by cascading an affine transform and a multidimensional wavelet. Then these affine transforms and the synaptic weights must be identified from possibly noise corrupted input/output data. An algorithm of backpropagation type is proposed for wavelet network training, and experimental results are reported.