nils-herrmann/clean_scirepeval huggingface dataset:
cMix: Mixing with Minimal Real-Time Asymmetric Cryptographic Operations

We introduce cMix, a new approach to anonymous communications. Through a precomputation, the core cMix protocol eliminates all expensive realtime public-key operations—at the senders, recipients and mixnodes—thereby decreasing real-time cryptographic latency and lowering computational costs for clients. The core real-time phase performs only a few fast modular multiplications. In these times of surveillance and extensive profiling there is a great need for an anonymous communication system that resists global attackers. One widely recognized solution to the challenge of traffic analysis is a mixnet, which anonymizes a batch of messages by sending the batch through a fixed cascade of mixnodes. Mixnets can offer excellent privacy guarantees, including unlinkability of sender and receiver, and resistance to many traffic-analysis attacks that undermine many other approaches including onion routing. Existing mixnet designs, however, suffer from high latency in part because of the need for real-time public-key operations. Precomputation greatly improves the real-time performance of cMix, while its fixed cascade of mixnodes yields the strong anonymity guarantees of mixnets. cMix is unique in not requiring any real-time public-key operations by users. Consequently, cMix is the first mixing suitable for low latency chat for lightweight devices. Our presentation includes a specification of cMix, security arguments, anonymity analysis, and a performance comparison with selected other approaches. We also give benchmarks from our prototype.
Waste Management System Based On IoT

1,2,3,4 Department of Information Technology, Matoshri Collage of Engineering & Reasearch Centre Eklahare, Nashik, India ---------------------------------------------------------------------***--------------------------------------------------------------------Abstract Waste management is one of the primary problem that the world faces irrespective of the case of developed or developing country. The key issue in the waste management is that the garbage bin at public places gets overflowed well in advance before the commencement of the next cleaning process. It in turn leads to various hazards such as bad odor & ugliness to that place which may be the root cause for spread of various diseases. To avoid all such hazardous scenario and maintain public cleanliness and health this work is mounted on a smart garbage system. The main theme of the work is to develop a smart intelligent garbage alert system for a proper garbage management .This paper proposes a smart alert system for garbage clearance by giving an alert signal to the municipal web server for instant cleaning of dustbin with proper verification based on level of garbage filling. This process is aided by the ultrasonic sensor which is interfaced with Arduino UNO to check the level of garbage filled in the dustbin and sends the alert to the municipal web server once if garbage is filled . After cleaning the dustbin, the driver confirms the task of emptying the garbage with the aid of RFID Tag. RFID is a computing technology that is used for verification process and in addition, it also enhances the smart garbage alert system by providing automatic identification of garbage filled in the dustbin and sends the status of clean-up to the server affirming that the work is done. The whole process is upheld by an embedded module integrated with RF ID and IOT Facilitation. The real time status of how waste collection is being done could be monitored and followed up by the municipality authority with the aid of this system. In addition to this the necessary remedial / alternate measures could be adapted. An Android application is developed and linked to a web server to intimate the alerts from the microcontroller to the urban office and to perform the remote monitoring of the cleaning process, done by the workers, thereby reducing the manual process of monitoring and verification. The notifications are sent to the Android application using Wi-Fi module.
Digitalisierung und Industrie 4.0 – Trend mit modischer Überhöhung?

Die Begriffe Digitalisierung und Industrie 4.0 werden gegenwärtig mit unterschiedlichen Inhalten besetzt. Das mag ein Indiz sein, dass ein v. a. im deutschsprachigen Raum sehr begrüßenswerter Trend durch Modewellen überlagert wird. Es könnte wie oft in der Informatik und verwandten Disziplinen das Verlaufsmuster Gartner Hype Cycle mit den Phasen ,,zögernder Beginn“, ,,steiler Anstieg mit übertriebenen Versprechungen“, ,,hoher Gipfel“, ,,Abfall in ein Tal“, ,,öffentlich wenig beachtete Weiterarbeit am Detail“, ,,allmählicher Wiederaufstieg“, ,,Einmündung in einen langfristigen Trend“ beobachtet werden. Dieser Verlauf impliziert Ressourcenvergeudung. Wir erörtern retardierende Momente, wie z. B. unterschiedliche Kulturen zwischen der Entwicklung von Software und Maschinen oder Probleme mit dem Lebenszyklus von individualisierten Erzeugnissen, die dazu führen könnten, dass man nach dem Gipfel in ein ,,Tal der Enttäuschungen“ stürzt.
d . tools : Integrated Prototyping for Physical Interaction Design

Figure 1. d.tools enables designers to rapidly prototype the circuits and code of information appliances using pictures and parts. Designers tasked with imagining future information appliances currently employ separate tools for rapidly prototyping the form (the atoms) and the interaction model (the bits) because integrated prototyping of bits and atoms requires resources and knowledge outside the reach of design generalists. Based on interviews with product designers, we created d.tools, a system enabling non-programmers to prototype the bits and the atoms of physical user interfaces in concert. d.tools lowers the threshold to prototyping functional physical interfaces through plug-and-play hardware that is closely coupled with a visual authoring environment. We evaluated the d.tools use threshold through a first-use study with thirteen participants; the study showed that the tool is accessible and encourages reflective design practice. We tested the d.tools range of design support by recreating existing research and commercial devices; this demonstrated that the visual language was sufficiently expressive for existing and emerging real-world designs.
Mayflies: A Moving Target Defense Framework for Distributed Systems

prevent attackers from gaining control of the system using well established techniques such as; perimeter-based fire walls, redundancy and replications, and encryption. However, given sufficient time and resources, all these methods can be defeated. Moving Target Defense (MTD), is a defensive strategy that aims to reduce the need to continuously fight against attacks by disrupting attackers gain-loss balance. We present Mayflies, a bio-inspired generic MTD framework for distributed systems on virtualized cloud platforms. The framework enables systems designed to defend against attacks for their entire runtime to systems that avoid attacks in time intervals. We discuss the design, algorithms and the implementation of the framework prototype. We illustrate the prototype with a quorum-based Byzantime Fault Tolerant system and report the preliminary results.
A Hierarchical Neural Network for Information Extraction of Product Attribute and Condition Sentences

This paper describes a hierarchical neural network we propose for sentence classification to extract product information from product documents. The network classifies each sentence in a document into attribute and condition classes on the basis of word sequences and sentence sequences in the document. Experimental results showed the method using the proposed network significantly outperformed baseline methods by taking semantic representation of word and sentence sequential data into account. We also evaluated the network with two different product domains (insurance and tourism domains) and found that it was effective for both the domains.
Comparison of Different Methods for Next Location Prediction

Next location prediction anticipates a person’s movement based on the history of previous sojourns. It is useful for proactive actions taken to assist the person in an ubiquitous environment. This paper evaluates next location prediction methods: dynamic Bayesian network, multi-layer perceptron, Elman net, Markov predictor, and state predictor. For the Markov and state predictor we use additionally an optimization, the confidence counter. The criterions for the comparison are the prediction accuracy, the quantity of useful predictions, the stability, the learning, the relearning, the memory and computing costs, the modelling costs, the expandability, and the ability to predict the time of entering the next location. For evaluation we use the same benchmarks containing movement sequences of real persons within an office building.
Access-Pattern-Aware On-Chip Memory Allocation for SIMD Processors

The number of cycles for each external memory access in Single Instruction Multiple Data (SIMD) processors is heavily affected by the access pattern, such as aligned, unaligned, or stride. We developed a high-performance dynamic on-chip memory-allocation method for SIMD processors by considering the memory access pattern as well as the access frequency. The access pattern and the access count for an array of a loop are determined by both code analysis and profiling, which are performed on a developed compiler framework. This framework not only conducts dynamic on-chip memory allocation but also generates optimized codes for a target processor. The proposed allocation method has been tested with several multimedia benchmarks including motion estimation, 2-D discrete cosine transform, and MPEG2 encoder programs.
EULAR recommendations for the treatment of systemic sclerosis: a report from the EULAR Scleroderma Trials and Research group (EUSTAR).

The optimal treatment of systemic sclerosis (SSc) is a challenge because the pathogenesis of SSc is unclear and it is an uncommon and clinically heterogeneous disease affecting multiple organ systems. The aim of the European League Against Rheumatism (EULAR) Scleroderma Trials and Research group (EUSTAR) was to develop evidence-based, consensus-derived recommendations for the treatment of SSc.



To obtain and maintain a high level of intrinsic quality and comparability of this approach, EULAR standard operating procedures were followed. The task force comprised 18 SSc experts from Europe, the USA and Japan, two SSc patients and three fellows for literature research. The preliminary set of research questions concerning SSc treatment was provided by 74 EUSTAR centres.



Based on discussion of the clinical research evidence from published literature, and combining this with current expert opinion and clinical experience, 14 recommendations for the treatment of SSc were formulated. The final set includes the following recommendations: three on SSc-related digital vasculopathy (Raynaud's phenomenon and ulcers); four on SSc-related pulmonary arterial hypertension; three on SSc-related gastrointestinal involvement; two on scleroderma renal crisis; one on SSc-related interstitial lung disease and one on skin involvement. Experts also formulated several questions for a future research agenda.



Evidence-based, consensus-derived recommendations are useful for rheumatologists to help guide treatment for patients with SSc. These recommendations may also help to define directions for future clinical research in SSc.
A fast Nonlinear Model Predictive Control strategy for real-time motion control of mechanical systems

Widespread application of real-time, Nonlinear Model Predictive Control (NMPC) algorithms to systems of large scale or with fast dynamics is challenged by the high associated computational cost, in particular in presence of long prediction horizons. In this paper, a fast NMPC strategy to reduce the on-line computational cost is proposed. A Curvature-based Measure of Nonlinearity (CMoN) of the system is exploited to reduce the required number of sensitivity computations, which largely contribute to the overall computational cost. The proposed scheme is validated by a simulation study on the chain of masses motion control problem, a toy example that can be easily extended to an arbitrary dimension. Simulations have been run with long prediction horizons and large state dimensions. Results show that sensitivity computations are significantly reduced with respect to other sensitivity updating schemes, while preserving control performance.
Saliency Prediction in the Deep Learning Era: An Empirical Investigation

Visual saliency models have enjoyed a big leap in performance in recent years, thanks to advances in deep learning and large scale annotated data. Despite enormous effort and huge breakthroughs, however, models still fall short in reaching human-level accuracy. In this work, I explore the landscape of the field emphasizing on new deep saliency models, benchmarks, and datasets. A large number of image and video saliency models are reviewed and compared over two image benchmarks and two large scale video datasets. Further, I identify factors that contribute to the gap between models and humans and discuss the remaining issues that need to be addressed to build the next generation of more powerful saliency models. Some specific questions that are addressed include: in what ways current models fail, how to remedy them, what can be learned from cognitive studies of attention, how explicit saliency judgments relate to fixations, how to conduct fair model comparison, and what are the emerging applications of saliency models.
Fifty years of European history through the Lens of Computational Linguistics : the De Gasperi Project

In 2013 a collaboration was started between the Digital Humanities research unit and the Italian-German Historical institute at Fondazione Bruno Kessler, whose goal was to develop tools and strategies to give new insight into the public documents written by Alcide De Gasperi. Through the analysis of textual occurrences, semantic structures, and temporal patterns, the project, ending in 2017, has investigated the formation and evolution of De Gasperi’s political action and the rhetorical discourse that accompanied its developments. Research of this kind has contributed to better understanding De Gasperi’s thoughts, the links between his language and his political views and culture, and the instruments of his communication. On the other hand, it allows the design and development of automated research tools for the political domain. This is a distinctly interdisciplinary research project, in which historical inquiry interacts with methods of linguistic analysis and with information and communication technologies.
MicroRNA in autoimmunity and autoimmune diseases.

MicroRNAs (miRNAs) are small conserved non-coding RNA molecules that post-transcriptionally regulate gene expression by targeting the 3' untranslated region (UTR) of specific messenger RNAs (mRNAs) for degradation or translational repression. miRNA-mediated gene regulation is critical for normal cellular functions such as the cell cycle, differentiation, and apoptosis, and as much as one-third of human mRNAs may be miRNA targets. Emerging evidence has demonstrated that miRNAs play a vital role in the regulation of immunological functions and the prevention of autoimmunity. Here we review the many newly discovered roles of miRNA regulation in immune functions and in the development of autoimmunity and autoimmune disease. Specifically, we discuss the involvement of miRNA regulation in innate and adaptive immune responses, immune cell development, T regulatory cell stability and function, and differential miRNA expression in rheumatoid arthritis and systemic lupus erythematosus.
On accelerating iterative algorithms with CUDA: A case study on Conditional Random Fields training algorithm for biological sequence alignment

The accuracy of Conditional Random Fields (CRF) is achieved at the cost of huge amount of computation to train model. In this paper we designed the parallelized algorithm for the Gradient Ascent based CRF training methods for biological sequence alignment. Our contribution is mainly on two aspects: 1) We flexibly parallelized the different iterative computation patterns, and the according optimization methods are presented. 2) As for the Gibbs Sampling based training method, we designed a way to automatically predict the iteration round, so that the parallel algorithm could be run in a more efficient manner. In the experiment, these parallel algorithms achieved valuable accelerations comparing to the serial version.
Multicast routing protocols in wireless mesh networks: a survey

Wireless mesh networks (WMNs) introduce a new type of network that has been applied over the last few years. One of the most important developing issues in WMNs is multicast routing, which is a key technology that provides dissemination of data to a group of members in an efficient way. In this article, after an introduction about the structure of a WMN, multicast routing algorithms and protocols in WMNs are surveyed in a detailed and efficient manner. Moreover, effort is made to scale the study into one of the important potential capabilities of multicast routing mechanisms in WMNs, which is taking advantage of using different channels and radios association. While nodes in a single-radio mesh network operating on single-channel have restrictions for capacity, equipping mesh routers with multiple radios using multiple channels can decrease the intention of capacity problem as well as increase the aggregate bandwidth available to the network and improving the throughput. Hence, the purpose of channel assignment is to decrease the interferences while increasing the network capacity and keeping the connectivity of the network. Therefore, this article investigates the multicast protocols considering a definition of three types of WMNs, based on channel-radio association including SRSC, SRMC and MRMC. In its follow, a classification for multicast routing algorithms regarding the achieved optimal solutions will be presented. Finally, a study of MRMC and its relevant problems will be offered, considering the joint channel assignment and the multicast tree construction problem.
Dynamic Personalized Recommendation on Sparse Data

Recommendation techniques are very important in the fields of E-commerce and other web-based services. One of the main difficulties is dynamically providing high-quality recommendation on sparse data. In this paper, a novel dynamic personalized recommendation algorithm is proposed, in which information contained in both ratings and profile contents are utilized by exploring latent relations between ratings, a set of dynamic features are designed to describe user preferences in multiple phases, and finally, a recommendation is made by adaptively weighting the features. Experimental results on public data sets show that the proposed algorithm has satisfying performance.
Solid-state diffusion limitations on pulse operation of a lithium ion cell for hybrid electric vehicles

A 1D model based on physical and electrochemical processes of a lithium ion cell is used to describe constant current and hybrid pulse power haracterization (HPPC) data from a 6 Ah cell designed for hybrid electric vehicle (HEV) application. An approximate solution method for the iffusion of lithium ions within active material particles is formulated using the finite element method and implemented in the previously developed D electrochemical model as an explicit difference equation. Reaction current distribution and redistribution processes occurring during discharge nd current interrupt, respectively, are driven by gradients in equilibrium potential that arise due to solid diffusion limitations. The model is xtrapolated to predict voltage response at discharge rates up to 40 C where end of discharge is caused by negative electrode active material surface oncentrations near depletion. Simple expressions are derived from an analytical solution to describe solid-state diffusion limited current for short uration, high-rate pulses.
The effect of protein timing on muscle strength and hypertrophy: a meta-analysis

Protein timing is a popular dietary strategy designed to optimize the adaptive response to exercise. The strategy involves consuming protein in and around a training session in an effort to facilitate muscular repair and remodeling, and thereby enhance post-exercise strength- and hypertrophy-related adaptations. Despite the apparent biological plausibility of the strategy, however, the effectiveness of protein timing in chronic training studies has been decidedly mixed. The purpose of this paper therefore was to conduct a multi-level meta-regression of randomized controlled trials to determine whether protein timing is a viable strategy for enhancing post-exercise muscular adaptations. The strength analysis comprised 478 subjects and 96 ESs, nested within 41 treatment or control groups and 20 studies. The hypertrophy analysis comprised 525 subjects and 132 ESs, nested with 47 treatment or control groups and 23 studies. A simple pooled analysis of protein timing without controlling for covariates showed a small to moderate effect on muscle hypertrophy with no significant effect found on muscle strength. In the full meta-regression model controlling for all covariates, however, no significant differences were found between treatment and control for strength or hypertrophy. The reduced model was not significantly different from the full model for either strength or hypertrophy. With respect to hypertrophy, total protein intake was the strongest predictor of ES magnitude. These results refute the commonly held belief that the timing of protein intake in and around a training session is critical to muscular adaptations and indicate that consuming adequate protein in combination with resistance exercise is the key factor for maximizing muscle protein accretion.
Reduction of Speckle Noise and Image Enhancement of Images Using Filtering Technique

Reducing noise from the medical images, a satellite image etc. is a challenge for the researchers in digital image processing. Several approaches are there for noise reduction. Generally speckle noise is commonly found in synthetic aperture radar images, satellite images and medical images. This paper proposes filtering techniques for the removal of speckle noise from the digital images. Quantitative measures are done by using signal to noise ration and noise level is measured by the standard deviation.
Time Warp Edit Distance with Stiffness Adjustment for Time Series Matching

In a way similar to the string-to-string correction problem, we address discrete time series similarity in light of a time-series-to-time-series-correction problem for which the similarity between two time series is measured as the minimum cost sequence of edit operations needed to transform one time series into another. To define the edit operations, we use the paradigm of a graphical editing process and end up with a dynamic programming algorithm that we call time warp edit distance (TWED). TWED is slightly different in form from dynamic time warping (DTW), longest common subsequence (LCSS), or edit distance with real penalty (ERP) algorithms. In particular, it highlights a parameter that controls a kind of stiffness of the elastic measure along the time axis. We show that the similarity provided by TWED is a potentially useful metric in time series retrieval applications since it could benefit from the triangular inequality property to speed up the retrieval process while tuning the parameters of the elastic measure. In that context, a lower bound is derived to link the matching of time series into down sampled representation spaces to the matching into the original space. The empiric quality of the TWED distance is evaluated on a simple classification task. Compared to edit distance, DTW, LCSS, and ERP, TWED has proved to be quite effective on the considered experimental task.