nils-herrmann/clean_scirepeval huggingface dataset:
Deep Learning Convolutional Neural Networks for Radio Identification

Advances in software defined radio (SDR) technology allow unprecedented control on the entire processing chain, allowing modification of each functional block as well as sampling the changes in the input waveform. This article describes a method for uniquely identifying a specific radio among nominally similar devices using a combination of SDR sensing capability and machine learning (ML) techniques. The key benefit of this approach is that ML operates on raw I/Q samples and distinguishes devices using only the transmitter hardware-induced signal modifications that serve as a unique signature for a particular device. No higher-level decoding, feature engineering, or protocol knowledge is needed, further mitigating challenges of ID spoofing and coexistence of multiple protocols in a shared spectrum. The contributions of the article are as follows: (i) The operational blocks in a typical wireless communications processing chain are modified in a simulation study to demonstrate RF impairments, which we exploit. (ii) Using an overthe- air dataset compiled from an experimental testbed of SDRs, an optimized deep convolutional neural network architecture is proposed, and results are quantitatively compared with alternate techniques such as support vector machines and logistic regression. (iii) Research challenges for increasing the robustness of the approach, as well as the parallel processing needs for efficient training, are described. Our work demonstrates up to 90-99 percent experimental accuracy at transmitter- receiver distances varying between 2-50 ft over a noisy, multi-path wireless channel.
Scrum + Engineering Practices: Experiences of Three Microsoft Teams

The Scrum methodology is an agile software development process that works as a project management wrapper around existing engineering practices to iteratively and incrementally develop software. With Scrum, for a developer to receive credit for his or her work, he or she must demonstrate the new functionality provided by a feature at the end of each short iteration during an iteration review session. Such a short-term focus without the checks and balances of sound engineering practices may lead a team to neglect quality. In this paper we present the experiences of three teams at Microsoft using Scrum with an additional nine sound engineering practices. Our results indicate that these teams were able to improve quality, productivity, and estimation accuracy through the combination of Scrum and nine engineering practices.
Electrophysiological evidence for long-axis intrinsic diversification of the hippocampus.

The elongated structure of the hippocampus is critically involved in brain functions of profound importance. The segregation of functions along the longitudinal (septotemporal or dorsoventral) axis of the hippocampus is a slowly developed concept and currently is a widely accepted idea. The segregation of neuroanatomical connections along the hippocampal long axis can provide a basis for the interpretation of the functional segregation. However, an emerging and growing body of data strongly suggests the existence of endogenous diversification in the properties of the local neural network along the long axis of the hippocampus. In particular, recent electrophysiological research provides compelling evidence demonstrating constitutively increased network excitability in the ventral hippocampus with important implications for the endogenous initiation and propagation of physiological hippocampal oscillations yet, under favorable conditions it can also drive the local network towards hyperexcitability. In addition, important specializations in the properties of dorsal and ventral hippocampal synapses may support an optimal signal processing that contributes to the effective execution of the distinct functional roles played by the two hippocampal segments.
Symmetric Algorithm Survey: A Comparative Analysis

Information Security has become an important issue in modern world as the popularity and infiltration of internet commerce and communication technologies has emerged, making them a prospective medium to the security threats. To surmount these security threats modern data communications uses cryptography an effective, efficient and essential component for secure transmission of information by implementing security parameter counting Confidentiality, Authentication, accountability, and accuracy. To achieve data security different cryptographic algorithms (Symmetric & Asymmetric) are used that jumbles data in to scribbled format that can only be reversed by the user that have to desire key. This paper presents a comprehensive comparative analysis of different existing cryptographic algorithms (symmetric) based on their Architecture, Scalability, Flexibility, Reliability, Security and Limitation that are essential for secure communication (Wired or Wireless).
CFD Predictions of Bubbly Flow around an Energy-saving Ship with Mitsubishi Air Lubrication System

The Mitsubishi Air Lubrication System (MALS) was the first air lubrication system in the world to be applied to a newly built ship, and resulted in a substantial reduction in the ship’s resistance. Therefore, a performance estimation method using computational fluid dynamics (CFD) needs to be established as soon as possible to apply the MALS to general commercial ships. In this study, we predicted the bubble distribution around ships with the MALS using CFD, and developed a method to determine the reduction in flow resistance based on the bubble coverage around the hull. Furthermore, we also predicted the intrusion of bubbles on the area of propeller disks, which could deteriorate the performance, and confirmed that the deterioration in propeller disk performance was negligible.
Predicting Domain Generation Algorithms with Long Short-Term Memory Networks

Various families of malware use domain generation algorithms (DGAs) to generate a large number of pseudo-random domain names to connect to a command and control (C2) server. In order to block DGA C2 traffic, security organizations must first discover the algorithm by reverse engineering malware samples, then generate a list of domains for a given seed. The domains are then either preregistered, sink-holed or published in a DNS blacklist. This process is not only tedious, but can be readily circumvented by malware authors. An alternative approach to stop malware from using DGAs is to intercept DNS queries on a network and predict whether domains are DGA generated. Much of the previous work in DGA detection is based on finding groupings of like domains and using their statistical properties to determine if they are DGA generated. However, these techniques are run over large time windows and cannot be used for real-time detection and prevention. In addition, many of these techniques also use contextual information such as passive DNS and aggregations of all NXDomains throughout a network. Such requirements are not only costly to integrate, they may not be possible due to real-world constraints of many systems (such as endpoint detection). An alternative to these systems is a much harder problem: detect DGA generation on a per domain basis with no information except for the domain name. Previous work to solve this harder problem exhibits poor performance and many of these systems rely heavily on manual creation of features; a time consuming process that can easily be circumvented by malware authors. This paper presents a DGA classifier that leverages long short-term memory (LSTM) networks for real-time prediction of DGAs without the need for contextual information or manually created features. In addition, the presented technique can accurately perform multiclass classification giving the ability to attribute a DGA generated domain to a specific malware family. The technique is extremely easy to implement using open source tools allowing the technique to be deployed in almost any setting. Results are significantly better than all state-of-the-art techniques, providing 0.9993 area under the receiver operating characteristic curve for binary classification and a micro-averaged F1 score of 0.9906. In other terms, the LSTM technique can provide a 90% detection rate with a 1:10000 false positive (FP) rate—a twenty times FP improvement over the next best method. Experiments in this paper are run on open datasets and code snippets are provided to reproduce the results.
Modeling Customer Lifetime Value

based, companies increasingly derive revenue from the creation and sustenance of long-term relationships with their customers. In such an environment, marketing serves the purpose of maximizing customer lifetime value (CLV) and customer equity, which is the sum of the lifetime values of the company’s customers. This article reviews a number of implementable CLV models that are useful for market segmentation and the allocation of marketing resources for acquisition, retention, and crossselling. The authors review several empirical insights that were obtained from these models and conclude with an agenda of areas that are in need of further research.
A Comprehensive Review of Smart Energy Meters in Intelligent Energy Networks

The significant increase in energy consumption and the rapid development of renewable energy, such as solar power and wind power, have brought huge challenges to energy security and the environment, which, in the meantime, stimulate the development of energy networks toward a more intelligent direction. Smart meters are the most fundamental components in the intelligent energy networks (IENs). In addition to measuring energy flows, smart energy meters can exchange the information on energy consumption and the status of energy networks between utility companies and consumers. Furthermore, smart energy meters can also be used to monitor and control home appliances and other devices according to the individual consumer's instruction. This paper systematically reviews the development and deployment of smart energy meters, including smart electricity meters, smart heat meters, and smart gas meters. By examining various functions and applications of smart energy meters, as well as associated benefits and costs, this paper provides insights and guidelines regarding the future development of smart meters.
3D Object Detection with Latent Support Surfaces

We develop a 3D object detection algorithm that uses latent support surfaces to capture contextual relationships in indoor scenes. Existing 3D representations for RGB-D images capture the local shape and appearance of object categories, but have limited power to represent objects with different visual styles. The detection of small objects is also challenging because the search space is very large in 3D scenes. However, we observe that much of the shape variation within 3D object categories can be explained by the location of a latent support surface, and smaller objects are often supported by larger objects. Therefore, we explicitly use latent support surfaces to better represent the 3D appearance of large objects, and provide contextual cues to improve the detection of small objects. We evaluate our model with 19 object categories from the SUN RGB-D database, and demonstrate state-of-the-art performance.
An architecture for Malay Tweet normalization

Research in natural language processing has increasingly focused on normalizing Twitter messages. Currently, while different well-defined approaches have been proposed for the English language, the problem remains far from being solved for other languages, such as Malay. Thus, in this paper, we propose an approach to normalize the Malay Twitter messages based on corpus-driven analysis. An architecture for Malay Tweet normalization is presented, which comprises seven main modules: (1) enhanced tokenization, (2) In-Vocabulary (IV) detection, (3) specialized dictionary query, (4) repeated letter elimination, (5) abbreviation adjusting, (6) English word translation, and (7) de-tokenization. A parallel Tweet dataset, consisting of 9000 Malay Tweets, is used in the development and testing stages. To measure the performance of the system, an evaluation is carried out. The result is promising whereby we score 0.83 in BLEU against the baseline BLEU, which scores 0.46. To compare the accuracy of the architecture with other statistical approaches, an SMT-like normalization system is implemented, trained, and evaluated with an identical parallel dataset. The experimental results demonstrate that we achieve higher accuracy by the normalization system, which is designed based on the features of Malay Tweets, compared to the SMT-like system.
A robotic and kinetic design for interactive architecture

Buildings with interactive systems using less energy, offering more occupant comfort, and featuring better space efficiency, flexibility and smartness has become a compelling challenge in our changing world. This paper introduces a robotic and kinetic design for interactive architecture relating to the design of smart and sustainable living environments. The kernel of interactive architecture is the integration of mechanics, electronics, and computers in a robotic building module that can sense and transform itself in response to change. A prototype of interactive architecture called Eco-Spirit is presented. Key functionalities of the Eco-Spirit system are identified with respect to five components: a mechanical vehicle, a motor system, an environmental awareness system, a photovoltaic solar module, and a control circuit system. Methods and techniques for developing such capabilities are described. Applications for using the Eco-Spirit system in support of interactive architecture are reviewed. The paper closes with a discussion of research issues, challenges, and future research directions.
CaMKII is essential for the cellular clock and coupling between morning and evening behavioral rhythms.

Daily behavioral rhythms in mammals are governed by the central circadian clock, located in the suprachiasmatic nucleus (SCN). The behavioral rhythms persist even in constant darkness, with a stable activity time due to coupling between two oscillators that determine the morning and evening activities. Accumulating evidence supports a prerequisite role for Ca(2+) in the robust oscillation of the SCN, yet the underlying molecular mechanism remains elusive. Here, we show that Ca(2+)/calmodulin-dependent protein kinase II (CaMKII) activity is essential for not only the cellular oscillation but also synchronization among oscillators in the SCN. A kinase-dead mutation in mouse CaMKIIα weakened the behavioral rhythmicity and elicited decoupling between the morning and evening activity rhythms, sometimes causing arrhythmicity. In the mutant SCN, the right and left nuclei showed uncoupled oscillations. Cellular and biochemical analyses revealed that Ca(2+)-calmodulin-CaMKII signaling contributes to activation of E-box-dependent gene expression through promoting dimerization of circadian locomotor output cycles kaput (CLOCK) and brain and muscle Arnt-like protein 1 (BMAL1). These results demonstrate a dual role of CaMKII as a component of cell-autonomous clockwork and as a synchronizer integrating circadian behavioral activities.
Blockchain for social good: a quantitative analysis

The rise of blockchain technologies has given a boost to social good projects, which are trying to exploit various characteristic features of blockchains: the quick and inexpensive transfer of cryptocurrency, the transparency of transactions, the ability to tokenize any kind of assets, and the increase in trustworthiness due to decentralization. However, the swift pace of innovation in blockchain technologies, and the hype that has surrounded their "disruptive potential", make it difficult to understand whether these technologies are applied correctly, and what one should expect when trying to apply them to social good projects. This paper addresses these issues, by systematically analysing a collection of 120 blockchain-enabled social good projects. Focussing on measurable and objective aspects, we try to answer various relevant questions: which features of blockchains are most commonly used? Do projects have success in fund raising? Are they making appropriate choices on the blockchain architecture? How many projects are released to the public, and how many are eventually abandoned?
Predicting Taxi–Passenger Demand Using Streaming Data

Informed driving is increasingly becoming a key feature for increasing the sustainability of taxi companies. The sensors that are installed in each vehicle are providing new opportunities for automatically discovering knowledge, which, in return, delivers information for real-time decision making. Intelligent transportation systems for taxi dispatching and for finding time-saving routes are already exploring these sensing data. This paper introduces a novel methodology for predicting the spatial distribution of taxi-passengers for a short-term time horizon using streaming data. First, the information was aggregated into a histogram time series. Then, three time-series forecasting techniques were combined to originate a prediction. Experimental tests were conducted using the online data that are transmitted by 441 vehicles of a fleet running in the city of Porto, Portugal. The results demonstrated that the proposed framework can provide effective insight into the spatiotemporal distribution of taxi-passenger demand for a 30-min horizon.
Implementation of a CNN accelerator on an Embedded SoC Platform using SDSoC

Today, Convolution Neural Networks (CNN) is adopted by various application areas such as computer vision, speech recognition, and natural language processing. Due to a massive amount of computing for CNN, CNN running on an embedded platform may not meet the performance requirement. In this paper, we propose a system-on-chip (SoC) CNN architecture synthesized by high level synthesis (HLS). HLS is an effective hardware (HW) synthesis method in terms of both development effort and performance. However, the implementation should be optimized carefully in order to achieve a satisfactory performance. Thus, we apply several optimization techniques to the proposed CNN architecture to satisfy the performance requirement. The proposed CNN architecture implemented on a Xilinx's Zynq platform has achieved 23% faster and 9.05 times better throughput per energy consumption than an implementation on an Intel i7 Core processor.
Mastering 3D Printing

Mastering 3D Printing shows you how to get the most out of your printer, including how to design models, choose materials, work with different printers, and integrate 3D printing with traditional prototyping to make techniques like sand casting more efficient. you’ve printed key chains. you’ve printed simple toys. now you’re ready to innovate with your 3D printer to start a business or teach and inspire others. Joan horvath has been an educator, engineer, author, and startup 3D printing company team member. She shows you all of the technical details you need to know to go beyond simple model printing to make your 3D printer work for you as a prototyping device, a teaching tool, or a business machine.
Enterprise Social Media: Definition, History, and Prospects for the Study of Social Technologies in Organizations

Social media are increasingly implemented in work organizations as tools for communication among employees. It is important that we develop an understanding of how they enable and constrain the communicative activities through which work is accomplished because it is these very dynamics that constitute and perpetuate organizations. We begin by offering a definition of enterprise social media and providing a rough historical account of the various avenues through which these technologies have entered and continue to enter the workplace. We also review areas of research covered by papers in this special issue and papers on enterprise social media published elsewhere to take stock of the current state of out knowledge and to propose directions for future research.
Sentiment analysis on facebook group using lexicon based approach

Internet is one of the primary sources of Big Data. Rise of the social networking platforms are creating enormous amount of data in every second where human emotions are constantly expressed in real-time. The sentiment behind each post, comments, likes can be found using opinion mining. It is possible to determine business values from these objects and events if sentiment analysis is done on the huge amount of data. Here, we have chosen FOODBANK which is a very popular Facebook group in Bangladesh; to analyze sentiment of the data to find out their market values.
Citation function, polarity and influence classification

Current methods for assessing the impact of authors and scientific media employ tools such as H-Index, Co-Citation and PageRank. These tools are primarily based on citation counting, which considers all citations to be equal. This type of methods can produce perverse incentives to publish controversial or incomplete papers, as mixed or negative reviews often generate larger citation counts and better indexes, regardless of whether the citations were critical or exerted minimal influence on the citing document. Passing citations that are employed to establish background, which do not have a real impact on the citing paper, are common in scientific literature. However, these citations have equal weight in impact evaluations. Notable researchers have emphasized the need to correct this situation by developing estimation methods that consider the different roles of quotations in citing papers. To accomplish this type of evaluation, a context citation analysis should be applied to determine the nature of the citations. We propose that citations should be categorized using four dimensions – FUNCTION, POLARITY, ASPECTS and INFLUENCE – as these dimensions provide adequate information that can be employed toward the generation of a qualitative method to measure the impact of a given publication in a citing paper. In this paper, we used interchangeably the words influence and impact. We present a method for obtaining this information using our proposed classification scheme and manually annotated corpus, which is marked with meaningful keywords and labels to help identify the characteristics or properties that constitute what we call ASPECTS. We develop a classification scheme which considers purpose definition shared by previous works. Our contribution is to abstract purpose classes from several other schemes and divide a complex structure in more manageable parts, to attain a simple system that combines low granularity dimensions but nevertheless produces a fine-grained classification. For annotators, the classification process is simple because in a first step, the coders distinguish only four primary classes, and in a second pass, they add the information contained in ASPECTS keyword and labels to obtain the more specific functions. This way, we gain a high granularity labeling that gives enough information about the citations to characterize and classify them, and we achieve this detailed coding with a straightforward process where the level of human error could be minimized. https:/www.cambridge.org/core/terms.  Downloaded from https:/www.cambridge.org/core. Kainan University, on 11 Apr 2017 at 11:36:26, subject to the Cambridge Core terms of use, available at 2 M. Hernández-Alvarez et al.
A Global Analysis of Emoji Usage

Emojis are a quickly spreading and rather unknown communication phenomenon which occasionally receives attention in the mainstream press, but lacks the scientific exploration it deserves. This paper is a first attempt at investigating the global distribution of emojis. We perform our analysis of the spatial distribution of emojis on a dataset of ∼17 million (and growing) geo-encoded tweets containing emojis by running a cluster analysis over countries represented as emoji distributions and performing correlation analysis of emoji distributions and World Development Indicators. We show that emoji usage tends to draw quite a realistic picture of the living conditions in various parts of our world.