nils-herrmann/clean_scirepeval huggingface dataset:
Cyclic tempogram—A mid-level tempo representation for musicsignals

The extraction of local tempo and beat information from audio recordings constitutes a challenging task, particularly for music that reveals significant tempo variations. Furthermore, the existence of various pulse levels such as measure, tactus, and tatum often makes the determination of absolute tempo problematic. In this paper, we present a robust mid-level representation that encodes local tempo information. Similar to the well-known concept of cyclic chroma features, where pitches differing by octaves are identified, we introduce the concept of cyclic tempograms, where tempi differing by a power of two are identified. Furthermore, we describe how to derive cyclic tempograms from music signals using two different methods for periodicity analysis and finally sketch some applications to tempo-based audio segmentation.
Towards Robust and Privacy-preserving Text Representations

Written text often provides sufficient clues to identify the author, their gender, age, and other important attributes. Consequently, the authorship of training and evaluation corpora can have unforeseen impacts, including differing model performance for different user groups, as well as privacy implications. In this paper, we propose an approach to explicitly obscure important author characteristics at training time, such that representations learned are invariant to these attributes. Evaluating on two tasks, we show that this leads to increased privacy in the learned representations, as well as more robust models to varying evaluation conditions, including out-of-domain corpora.
Quantitative analysis and fracture detection of pelvic bone X-ray images

Today bone fractures are very common in our country because of road accidents or through other injuries. The X-Ray images are the most common accessibility of peoples during the accidents. But the minute fracture detection in X-Ray image is not possible due to low resolution and quality of the original X-Ray image. The complexity of bone structure and the difference in visual characteristics of fracture by their location. So it is difficult to accurately detect and locate the fractures also determine the severity of the injury. The automatic detection of fractures in X-Ray images is a significant contribution for assisting the physicians in making faster and more accurate patient diagnostic decisions and treatment planning. In this paper, an automatic hierarchical algorithm for detecting bone fracture in X-Ray image is proposed. It uses the Gray level cooccurrence matrix for detecting the fracture. The results are promising, demonstrating that the proposed method is capable of automatically detecting both major and minor fractures accurately, and shows potential for clinical application. Statistical results also indicate the superiority of the proposed methods compared to other techniques. This paper examines the development of such a system, for the detection of long-bone fractures. This project fully employed MATLAB 7.8.0 (.r2009a) as the programming tool for loading image, image processing and user interface development. Results obtained demonstrate the performance of the pelvic bone fracture detection system with some limitations.
A 0 . 4-μ m CMOS 10Gb / s 4-PAM Pre-Emphasis Serial Link Transmitter *

A serial link transmitter fabricated in a large-scale integrated 0.4m CMOS process uses multilevel signaling (4PAM) and a three-tap pre-emphasis filter to reduce intersymbol interference (ISI) caused by channel low-pass effects. Due to the process-limited on-chip frequency, the transmitter output driver is designed as a 5 : 1 multiplexer to reduce the required clock frequency to one-fifth the symbol rate, or 1 GHz. At 5 Gsym/s (10 Gb/s), a data eye opening with a height >350 mV and a width >100 ps is achieved at the source. After 10 m of a copper coaxial cable (PE142LL), the eye opening is reduced to 200 mV and 90 ps with pre-emphasis, and to zero without filtering. The chip dissipates 1 W with a 3.3-V supply and occupies 1.5 2.0 mm2 of die area.
A Simpler Approach to Matrix Completion

This paper provides the best bounds to date on the number of randomly sampled entries required to reconstruct an unknown low rank matrix. These results improve on prior work by Candès and Recht , Candès and Tao [7], and Keshavan, Montanari, and Oh [18]. The reconstruction is accomplished by minimizing the nuclear norm, or sum of the singular values, of the hidden matrix subject to agreement with the provided entries. If the underlying matrix satisfies a certain incoherence condition, then the number of entries required is equal to a quadratic logarithmic factor times the number of parameters in the singular value decomposition. The proof of this assertion is short, self contained, and uses very elementary analysis. The novel techniques herein are based on recent work in quantum information theory.
Text summarization using concept graph and BabelNet knowledge base

With rapid increasing text information, the need for a computer system to processing and analyzing this information are felt. One of the systems that exist in analyzing and processing of text is a text summarization in which large volume of text is summarized based on different algorithms. In this paper, by using BabelNet knowledge base and its concept graph, a system for summarizing text is offered. In proposed approach, concepts of words by using BabelNet knowledge base are extracted and concept graphs are produced and sentences, according to concepts and resulting graph are rated. Therefore, these rating concepts are utilized in final summarization. Also, a replication control approach is proposed in a way that selected concepts in each state are punished and this causes to produce summaries with less redundancy. To compare and evaluate the performance of the proposed method, DUC2004 is used and ROUGE used as evaluation metric. The proposed method by compared to other methods produces summaries with more quality and fewer redundancies.
MIMO channel modeling and capacity analysis for 5G millimeter-wave wireless systems

This paper presents a 3-D statistical channel model of the impulse response with small-scale spatially correlated random coefficients for multi-element transmitter and receiver antenna arrays, derived using the physically-based time cluster - spatial lobe (TCSL) clustering scheme. The small-scale properties of multipath amplitudes are modeled based on 28 GHz outdoor millimeter-wave small-scale local area channel measurements. The wideband channel capacity is evaluated by considering measurement-based Rician-distributed voltage amplitudes, and the spatial autocorrelation of multipath amplitudes for each pair of transmitter and receiver antenna elements. Results indicate that Rician channels may exhibit equal or possibly greater capacity compared to Rayleigh channels, depending on the number of antennas.
Universal Multi-Factor Authentication Using Graphical Passwords

In this paper, we present a series of methods to authenticate a user with a graphical password. To that end, we employ the user¿s personal handheld device as the password decoder and the second factor of authentication. In our methods, a service provider challenges the user with an image password. To determine the appropriate click points and their order, the user needs some hint information transmitted only to her handheld device. We show that our method can overcome threats such as key-loggers, weak password, and shoulder surfing. With the increasing popularity of handheld devices such as cell phones, our approach can be leveraged by many organizations without forcing the user to memorize different passwords or carrying around different tokens.
E – Government for Developing Countries: Opportunities and Challenges

1.  The explosion of digital connectivity, the significant improvements in communication and information technologies and the enforced global competition are revolutionizing the way business is performed and the way organizations compete. A new, complex and rapidly changing economic order has emerged based on disruptive innovation, discontinuities, abrupt and seditious change. In this new landscape, knowledge constitutes the most important factor, while learning, which emerges through cooperation, together with the increased reliability and trust, is the most important process (Lundvall and Johnson, 1994). The competitive survival and ongoing sustenance of an organisation primarily depend on its ability to redefine and adopt continuously goals, purposes and its way of doing things (Malhotra, 2001). These trends suggest that private and public organizations have to reinvent themselves through 'continuous non-linear innovation' in order to sustain themselves and achieve strategic competitive advantage. The extant literature highlights the great potential of ICT tools for operational efficiency, cost reduction, quality of services, convenience, innovation and learning in private and public sectors. However, scholarly investigations have focused primarily on the effects and outcomes of ICTs (Information & Communication Technology) for the private sector. The public sector has been sidelined because it tends to lag behind in the process of technology adoption and business reinvention. Only recently has the public sector come to recognize the potential importance of ICT and e-business models as a means of improving the quality and responsiveness of the services they provide to their citizens, expanding the reach and accessibility of their services and public infrastructure and allowing citizens to experience a faster and more transparent form of access to government services. The initiatives of government agencies and departments to use ICT tools and applications, Internet and mobile devices to support good governance, strengthen existing relationships and build new partnerships within civil society, are known as eGovernment initiatives. As with e-commerce, eGovernment represents the introduction of a great wave of technological innovation as well as government reinvention. It represents a tremendous impetus to move forward in the 21 st century with higher quality, cost effective government services and a better relationship between citizens and government (Fang, 2002). Many government agencies in developed countries have taken progressive steps toward the web and ICT use, adding coherence to all local activities on the Internet, widening local access and skills, opening up interactive services for local debates, and increasing the participation of citizens on promotion and management …
Implementation of OFDM System using IFFT and FFT

Orthogonal Frequency Division Multiplexing (OFDM) is a multi-carrier system where data bits are encoded to multiple subcarriers, while being sent simultaneously. This results in the optimal usage of bandwidth. A set of orthogonal sub-carriers together forms an OFDM symbol. To avoid ISI due to multi-path, successive OFDM symbols are separated by guard band. This makes the OFDM system resistant to multipath effects. The principles of OFDM modulation have been around since 1960s. However, recently, the attention toward OFDM has grown dramatically in the field of wireless and wired communication systems. This is reflected by the adoption of this technique in applications such as digital audio/video broadcast (DAB/DVB), wireless LAN (802.11a and HiperLAN2), broadband wireless (802.16) and xDSL. In this work, a pure VHDL design, integrated with some intellectual property (IP) blocks, is employed to implement an OFDM transmitter and receiver. In this paper design of OFDM system using IFFT and FFT blocks has been introduced and simulation was done on XILINX ISE 14.2 software.  FFT, IFFT, OFDM, QAM, VHDL.
SumTime-Mousam: Configurable marine weather forecast generator

Numerical weather prediction (NWP) models produce time series data of basic weather parameters which human forecasters use as guidance while writing textual forecasts. Our studies of humans writing textual weather forecasts led us to build SUMTIME-MOUSAM, a text generator that produces textual marine weather forecasts for offshore oilrig applications. SUMTIME-MOUSAM separates control and processing. As a result of this forecasters can tailor the output text using control data derived from end user profiles. In this paper we describe the design and the implementation details of SUMTIME-MOUSAM which is currently being used by our industrial collaborator. Output from our system is post-edited by forecasters before communicating it to the end-users. We also briefly describe an evaluation of our system using the post-edit data.
Touchless Fingerprint Biometrics: A Survey on 2D and 3D Technologies

Traditional fingerprint recognition systems require that the users touch a sensor to perform biometric acquisitions. In order to increase the usability, acceptability, and accuracy of fingerprint recognition technologies, touchless systems have recently been studied. With respect to touch-based biometric techniques, these systems present important differences in most of the steps of the recognition process. Studies in the literature can be classified into technologies based on two-dimensional and three-dimensional methods. These studies also present important differences in terms of accuracy and required level of user cooperation. This paper presents a brief survey on touchless recognition technologies, proposing a classification of twodimensional and three-dimensional biometric recognition techniques.
A Data Mining Approach for Coronary Heart Disease Prediction using HRV Features and Carotid Arterial Wall Thickness

The main objective of our work has been to develop and then propose a new and unique methodology useful in developing the various features of heart rate variability (HRV) and carotid arterial wall thickness helpful in diagnosing cardiovascular disease. We also propose a suitable prediction model to enhance the reliability of medical examinations and treatments for cardiovascular disease. We analyzed HRV for three recumbent postures. The interaction effects between the recumbent postures and groups of normal people and heart patients were observed based on HRV indexes. We also measured intima-media of carotid arteries and used measurements of arterial wall thickness as other features. Patients underwent carotid artery scanning using high-resolution ultrasound devised in a previous study. In order to extract various features, we tested six classification methods. As a result, CPAR and SVM (gave about 85%-90% goodness of fit) outperforming the other classifiers.
Stiffness Control With Shape Memory Polymer in Underactuated Robotic Origamis

Underactuated systems offer compact design with easy actuation and control but at the cost of limited stable configurations and reduced dexterity compared to the directly driven and fully actuated systems. Here, we propose a compact origami-based design in which we can modulate the material stiffness of the joints and thereby control the stable configurations and the overall stiffness in an underactuated robot. The robotic origami, robogami, design uses multiple functional layers in nominally two-dimensional robots to achieve the desired functionality. To control the stiffness of the structure, we adjust the elastic modulus of a shape memory polymer using an embedded customized stretchable heater. We study the actuation of a robogami finger with three joints and determine its stable configurations and contact forces at different stiffness settings. We monitor the configuration of the finger using feedback from customized curvature sensors embedded in each joint. A scaled down version of the design is used in a two-fingered gripper and different grasp modes are achieved by activating different sets of joints.
Animated Agents for Procedural Training in Virtual Reality: Perception, Cognition, and Motor Control

This paper describes Steve, an animated agent that helps students learn to perform physical, procedural tasks. The student and Steve cohabit a three-dimensional, simulated mock-up of the student's work environment. Steve can demonstrate how to perform tasks and can also monitor students while they practice tasks, providing assistance when needed. This paper describes Steve's architecture in detail, including perception, cognition, and motor control. The perception module monitors the state of the virtual world, maintains a coherent representation of it, and provides this information to the cognition and motor control modules. The cognition module interprets its perceptual input, chooses appropriate goals, constructs and executes plans to achieve those goals, and sends out motor commands. The motor control module implements these motor commands, controlling Steve's voice, locomotion, gaze, and gestures, and allowing Steve to manipulate objects in the virtual world.
An Iterative BP-CNN Architecture for Channel Decoding

Inspired by the recent advances in deep learning, we propose a novel iterative belief propagation – convolutional neural network (BP-CNN) architecture for channel decoding under correlated noise. This architecture concatenates a trained CNN with a standard BP decoder. The standard BP decoder is used to estimate the coded bits, followed by a CNN to remove the estimation errors of the BP decoder and obtain a more accurate estimation of the channel noise. Iterating between BP and CNN will gradually improve the decoding SNR and, hence, result in better decoding performance. To train a well-behaved CNN model, we define a new loss function that involves not only the accuracy of the noise estimation but also the normality test for the estimation errors, i.e., to measure how likely the estimation errors follow a Gaussian distribution. The introduction of the normality test to the CNN training shapes the residual noise distribution and further reduces the bit error rate of the iterative decoding, compared to using the standard quadratic loss function. We carry out extensive experiments to analyze and verify the proposed framework.11Code is available at https://github.com/liangfei-info/Iterative-BP-CNN.
Semantic Gateway as a Service Architecture for IoT Interoperability

The Internet of Things (IoT) is set to occupy a substantial component of future Internet. The IoT connects sensors and devices that record physical observations to applications and services of the Internet[1]. As a successor to technologies such as RFID and Wireless Sensor Networks (WSN), the IoT has stumbled into vertical silos of proprietary systems, providing little or no interoperability with similar systems. As the IoT represents future state of the Internet, an intelligent and scalable architecture is required to provide connectivity between these silos, enabling discovery of physical sensors and interpretation of messages between the things. This paper proposes a gateway and Semantic Web enabled IoT architecture to provide interoperability between systems, which utilizes established communication and data standards. The Semantic Gateway as Service (SGS) allows translation between messaging protocols such as XMPP, CoAP and MQTT via a multi-protocol proxy architecture. Utilization of broadly accepted specifications such as W3Cs Semantic Sensor Network (SSN) ontology for semantic annotations of sensor data provide semantic interoperability between messages and support semantic reasoning to obtain higher-level actionable knowledge from low-level sensor data.
Conversational Flow in Oxford-style Debates

Public debates are a common platform for presenting and juxtaposing diverging views on important issues. In this work we propose a methodology for tracking how ideas flow between participants throughout a debate. We use this approach in a case study of Oxfordstyle debates—a competitive format where the winner is determined by audience votes—and show how the outcome of a debate depends on aspects of conversational flow. In particular, we find that winners tend to make better use of a debate’s interactive component than losers, by actively pursuing their opponents’ points rather than promoting their own ideas over the course of the conversation.
Horizon-split ambient occlusion

Ambient occlusion is a technique that computes the amount of light reaching a point on a diffuse surface based on its directly visible occluders. It gives perceptual clues of depth, curvature, and spatial proximity and thus is important for realistic rendering. Traditionally, ambient occlusion is calculated by integrating the visibility function over the normal-oriented hemisphere around any given surface point. In this paper we show this hemisphere can be partitioned into two regions by a horizon line defined by the surface in a local neighborhood of such point. We introduce an image-space algorithm for finding an approximation of this horizon and, furthermore, we provide an analytical closed form solution for the occlusion below the horizon, while the rest of the occlusion is computed by sampling based on a distribution to improve the convergence. The proposed ambient occlusion algorithm operates on the depth buffer of the scene being rendered and the associated per-pixel normal buffer. It can be implemented on graphics hardware in a pixel shader, independently of the scene geometry. We introduce heuristics to reduce artifacts due to the incompleteness of the input data and we include parameters to make the algorithm easy to customize for quality or performance purposes. We show that our technique can render high-quality ambient occlusion at interactive frame rates on current GPUs. CR Categories: I.3.3 [Computer Graphics]: Picture/Image Generation—; I.3.7 [Computer Graphics]: Three-Dimensional Graphics and Realism—;
Building user profiles from topic models for personalised search

Personalisation is an important area in the field of IR that attempts to adapt ranking algorithms so that the results returned are tuned towards the searcher's interests. In this work we use query logs to build personalised ranking models in which user profiles are constructed based on the representation of clicked documents over a topic space. Instead of employing a human-generated ontology, we use novel latent topic models to determine these topics. Our experiments show that by subtly introducing user profiles as part of the ranking algorithm, rather than by re-ranking an existing list, we can provide personalised ranked lists of documents which improve significantly over a non-personalised baseline. Further examination shows that the performance of the personalised system is particularly good in cases where prior knowledge of the search query is limited.
Inferring Networks of Substitutable and Complementary Products

To design a useful recommender system, it is important to understand how products relate to each other. For example, while a user is browsing mobile phones, it might make sense to recommend other phones, but once they buy a phone, we might instead want to recommend batteries, cases, or chargers. In economics, these two types of recommendations are referred to as substitutes and complements: substitutes are products that can be purchased instead of each other, while complements are products that can be purchased in addition to each other. Such relationships are essential as they help us to identify items that are relevant to a user's search.
 Our goal in this paper is to learn the semantics of substitutes and complements from the text of online reviews. We treat this as a supervised learning problem, trained using networks of products derived from browsing and co-purchasing logs. Methodologically, we build topic models that are trained to automatically discover topics from product reviews that are successful at predicting and explaining such relationships. Experimentally, we evaluate our system on the Amazon product catalog, a large dataset consisting of 9 million products, 237 million links, and 144 million reviews.