nils-herrmann/clean_scirepeval huggingface dataset:
Sequence-to-sequence prediction of personal computer software by recurrent neural network

Sequence to sequence (seq2seq) prediction is a key to many tasks of machine learning. Personal computer software sequence, as one of these tasks, was regarded as stochastic and unpredictable in the past. However, the deep neural networks (DNNs) have achieved excellent performance recently in sequence to sequence tasks, especially in the field of natural language process (NLP) such as language model, machine translation and dialogue systems. This paper examines the most popular DNNs approaches: LSTM, Encoder-Decoder network and Memory network in sequence prediction field to handle the software sequence learning and prediction task. Then three modified approaches based on these state of the art models are proposed to deal with additional information in sequence. These approaches focus on three aspects: adding information to enrich embedding input of Long-Short Term Memory, adding classifier to encoder-decoder neural network as an assistive model and processing data to be structured for memory unit in memory network. Experimental results based in real user data sets show that these proposed approaches outperform their corresponding standard DNNs and additional information can benefit the sequence neural network in different phases while constructing models. Experiments in different users shown that these modified strategies are robust and can be applied widely.
Growth charts for individuals with Smith-Lemli-Opitz syndrome.

Smith-Lemli-Opitz syndrome (SLOS) is a rare multiple congenital anomaly neurodevelopmental syndrome of impaired cholesterol synthesis. Growth restriction and developmental delay are very common clinical manifestations of SLOS. The degree, etiology, and consequences of growth restriction in SLOS remain an area of limited knowledge to the scientific community. There have been no studies describing the growth parameters and providing reference growth charts for individuals with SLOS. Our longitudinal data from 78 patients between the ages of 0.1 and 16 years with SLOS show a growth restriction of about two standard deviations below the Centers for Disease Control (CDC) norms for age. This study represents comprehensive anthropometric data from the largest cohort available, and proposes growth charts for widespread use in the management and study of individuals with SLOS.
Dynamic Routing Protocol Implementation Decision between EIGRP, OSPF and RIP Based on Technical Background Using OPNET Modeler

This paper presents th implementation decisions to be made when th choice is between protocols that involve distance vector or link state or the combination of both. Here a comparison is made between different parameters and a detailed simulation study is performed on the network with different routing protocols and it has been shown that EIGRP provides a better network convergence time, less bandwidth requirements and better CPU and memory utilization compared to OSPF and RIP. EIGRP, OSPF and RIP are the dynamic routing protocols being used in the practical networks to propagat network topology information to the neighboring routers. There have been a large number of static and dynamic routing protocols available but choice of the right protocol for routing is dependent on many parameters critical being network convergence time, scalability, memory and CPU requirements, security and bandwidth requirement etc.
Adults and children processing music: An fMRI study

The present study investigates the functional neuroanatomy of music perception with functional magnetic resonance imaging (fMRI). Three different subject groups were investigated to examine developmental aspects and effects of musical training: 10-year-old children with varying degrees of musical training, adults without formal musical training (nonmusicians), and adult musicians. Subjects made judgements on sequences that ended on chords that were music-syntactically either regular or irregular. In adults, irregular chords activated the inferior frontal gyrus, orbital frontolateral cortex, the anterior insula, ventrolateral premotor cortex, anterior and posterior areas of the superior temporal gyrus, the superior temporal sulcus, and the supramarginal gyrus. These structures presumably form different networks mediating cognitive aspects of music processing (such as processing of musical syntax and musical meaning, as well as auditory working memory), and possibly emotional aspects of music processing. In the right hemisphere, the activation pattern of children was similar to that of adults. In the left hemisphere, adults showed larger activations than children in prefrontal areas, in the supramarginal gyrus, and in temporal areas. In both adults and children, musical training was correlated with stronger activations in the frontal operculum and the anterior portion of the superior temporal gyrus.
Planar broadband annular-ring antenna with circular polarization for RFID system

Novel circular polarization (CP) design of planar annual-ring antenna with broadband operation for UHF RFID system is proposed and experimentally studied. By introducing the Wilkinson divider into the feeding network and dual L-shaped strips, the proposed CP design can easily be achieved with the impedance bandwidth (RL ≧10 dB) of about 246 MHz (25.0% @ 985 MHz) and the 3 dB axial-ratio (AR) bandwidth of about 180 MHz (19.5 % @ 925 MHz) for UHF RFID applications. The measured peak gain and radiation efficiency are about 7.7 dBic and 60% across the operating band, respectively, with unidirectional pattern in the XZ- and YZ-plane.
A novel compact printed ACS fed dual-band antenna for Bluetooth/WLAN/WiMAX applications

In this research article, a compact dual band asymmetric coplanar strip-fed printed antenna is designed and presented for Bluetooth, WLAN/WiMAX and public safety applications. The dual frequency operating bands (2.45 GHz and 5.25 GHz) have been achieved by attaching two simple meander shaped radiating strips to the ACS feed line. The proposed antenna geometry is printed on a low cost FR4 substrate having thickness of 1.6mm with overall dimensions of 13 × 21.3m including uniplanar ground plane. The -10 dB impedance bandwidth of the meandered ACS-fed dual band monopole antenna is about 140MHz from 2.36-2.5 GHz, and 2500MHz from 4.5-7.0 GHz respectively, which can cover 2.4 GHz Bluetooth/WLAN, 5.2/5.8 GHz WLAN, 5.5 GHz WiMAX and 4.9 GHz US public safety bands. In addition to the simple geometry and wide impedance bandwidth features, proposed structure perform bidirectional and omnidirectional radiation pattern in both E and H-plane respectively.
A Differential Filtering Microstrip Antenna Array With Intrinsic Common-Mode Rejection

A differential third-order Chebyshev filtering microstrip antenna array with high common-mode (CM) rejection is presented. The feeding structure consisting of one line resonator and one H-shaped resonator as well as the microstrip antenna elements together provide the three stages in the bandpass filter design. Under differential-mode excitation, the H-shaped resonator can be effectively coupled with the line resonator, and thus differential signals can be radiated. However, under CM excitation, the H-shaped resonator cannot be excited so that the CM suppression can be achieved. A prototype antenna array with center frequency at 3.5 GHz has been designed and fabricated to experimentally validate the principle and design approach. Experimental results exhibit that the proposed differential filtering antenna array features high CM rejection, low cross-polarization level, and good filtering response.
The Three Layers Evaluation Model for Computer-Generated Plots

This paper describes a model for evaluating a computer-generated plot. The main motivation of this project is to provide MEXICA, our plot generator, with the capacity of evaluating its own outputs as well as assessing narratives generated by other agents that can be employed to enrich its knowledge base. We present a description of our computer model as well as an explanation of our first prototype. Then, we show the results of assessing three computergenerated narratives. The outcome suggests that we are in the right direction, although much more work
An Intermediate Guide to Estimating Multilevel Models for Categorical Data using SAS ® PROC GLIMMIX

This paper expands upon Ene et al.’s (2015) SAS Global Forum proceeding paper “Multilevel Models for Categorical Data using SAS® PROC GLIMMIX: The Basics” in which the authors presented an overview of estimating two-level models with non-normal outcomes via PROC GLIMMIX. In their paper, the authors focused on how to use GLIMMIX to estimate two-level organizational models; however, they did not include random slopes, address more complex organizational models (e.g., three-level models) or models used to estimate longitudinal data. Hence, the need for the current paper; by building from the examples in Ene et al. (2015), the current paper presents users detailed discussions and illustrations about how to use GLIMMIX to estimate models with random slopes, organizational models in situations with three levels of data, as well as two-level longitudinal data. Consistent with Ene et al.’s paper, we will present the syntax and interpretation of the estimates using a model with a dichotomous outcome as well as a model with a polytomous outcome. Concrete examples will be used to illustrate how PROC GLIMMIX can be used to estimate these models and how key pieces of the output can be used to answer corresponding research questions.
Black-box Importance Sampling

Importance sampling is widely used in machine learning and statistics, but its power is limited by the restriction of using simple proposals for which the importance weights can be tractably calculated. We address this problem by studying black-box importance sampling methods that calculate importance weights for samples generated from any unknown proposal or black-box mechanism. Our method allows us to use better and richer proposals to solve difficult problems, and (somewhat counter-intuitively) also has the additional benefit of improving the estimation accuracy beyond typical importance sampling. Both theoretical and empirical analyses are provided.
Turning a blind eye to temptation: how cognitive load can facilitate self-regulation.

The present research shows in 4 studies that cognitive load can reduce the impact of temptations on cognition and behavior and, thus, challenges the proposition that distraction always hampers self-regulation. Participants performed different speeded categorization tasks with pictures of attractive and neutral food items (Studies 1-3) and attractive and unattractive female faces (Study 4), while we assessed their reaction times as an indicator of selective attention (Studies 1, 3, and 4) or as an indicator of hedonic thoughts about food (Study 2). Cognitive load was manipulated by a concurrent digit span task. Results show that participants displayed greater attention to tempting stimuli (Studies 1, 3, and 4) and activated hedonic thoughts in response to palatable food (Study 2), but high cognitive load completely eliminated these effects. Moreover, cognitive load during the exposure to attractive food reduced food cravings (Study 1) and increased healthy food choices (Study 3). Finally, individual differences in sensitivity to food temptations (Study 3) and interest in alternative relationship partners (Study 4) predicted selective attention to attractive stimuli, but again, only when cognitive load was low. Our findings suggest that recognizing the tempting value of attractive stimuli in our living environment requires cognitive resources. This has the important implication that, contrary to traditional views, performing a concurrent demanding task may actually diminish the captivating power of temptation and thus facilitate self-regulation.
Low kickback noise techniques for CMOS latched comparators

The latched comparator is utilized in virtually all analog-to-digital converter architectures. It uses a positive feedback mechanism to regenerate the analog input signal into a full-scale digital level. Such high voltage variations in the regeneration nodes are coupled to the input voltage - kickback noise. This paper reviews existing solutions to minimize the kickback noise and proposes two new ones. HSPICE simulations verify the effectiveness of our techniques.
A Card Game Description Language

We present initial research regarding a system capable of generating novel card games. We furthermore propose a method for computationally analysing existing games of the same genre. Ultimately, we present a formalisation of card game rules, and a context-free grammar Gcardgame capable of expressing the rules of a large variety of card games. Example derivations are given for the poker variant Texas hold ’em, Blackjack and UNO. Stochastic simulations are used both to verify the implementation of these well-known games, and to evaluate the results of new game rules derived from the grammar. In future work, this grammar will be used to evolve completely novel card games using a grammarguided genetic program.
Design and impact of an ERP and automation model in the administration sectors

Business source outlining is the most essential systems which is used across many organizations and across various functional areas like finance, organisations performance, risk, decision making, and automation. The ERP systems have changed the traditional methods of providing information in the organizations gradually. The organizations with the ERP systems installed can easily reduce the organization risk and increase the performance, production and profit. The ERP systems with functional and operational modules are found to be having more impact on the organizational risk reduction. Since The ERP system automates the decision making system with well organised information it also has greater impact on the organizations business. In this paper the design and the impact of ERP and its automation on organization finance, production, performance, risk administration, decision making are discussed.
Dynamic Subarrays for Hybrid Precoding in Wideband mmWave MIMO Systems

Hybrid analog/digital precoding architectures can address the tradeoff between achievable spectral efficiency and power consumption in large-scale MIMO systems. This makes them a promising candidate for millimeter wave systems, which deploy large antenna arrays at both the transmitter and the receiver to guarantee sufficient received signal power. Most prior work on hybrid precoding focused on narrowband channels and assumed fully connected hybrid architectures. Millimeter wave (mmWave) systems, though, are expected to be wideband with frequency selectivity. In this paper, a closed-form solution for fully connected OFDM-based hybrid analog/digital precoding is developed for frequency selective mmWave systems. This solution is then extended to partially connected but fixed architectures in which each RF chain is connected to a specific subset of the antennas. The derived solutions give insights into how the hybrid subarray structures should be designed. Based on this, a novel technique that dynamically constructs the hybrid subarrays knowing the long-term channel characteristics is developed. Simulation results show that the proposed hybrid precoding solutions achieve spectral efficiencies close to that obtained with fully digital architectures in wideband mmWave channels. Furthermore, the results indicate that the developed dynamic subarray solution outperforms the fixed hybrid subarray structures in various system and channel conditions.
Assessing the threat of web worker distributed attacks

In this paper, we identify and evaluate the potential of new distributed attacks launched through web browsers using the HTML5 Web Workers API. Web worker attacks rely on the new multi-threading capabilities of Web Workers, which can allow malicious JavaScript code to run in the background of a web page without impacting foreground JavaScript performance or user experience. These background computing tasks can be used to launch application-layer DDoS attacks or offload computationally intensive attack tasks, such as password cracking, to the browsers of users visiting a compromised website. These attacks do not harm the compromised users directly but offer a potential path for attackers to gain control of large pools of computing resources, similar to botnets. We evaluate the feasibility of using online advertisement services to gain access to such computing pool and quantitatively evaluate the economics of these attacks and point out the key factors affecting the cost effectiveness of launching attacks through Web Workers in comparison with cloud computing or rented botnets.
Secure data transfer in IoT environment: Adopting both cryptography and steganography techniques

Owing to the unprecedented growth in computing power, electronics miniaturization and mobile and wireless network interconnections the internet has metamorphosed into Internet of Things (IoT) which refers to next stage of the information revolution whose context involves billions of individuals and devices interconnected to facilitate exchange of huge volume of data and information from diverse locations, demanding the consequent necessity for smart data aggregation followed by an increased obligation to index, hoard and process data with higher efficiency and effectiveness. But along with its myriad offered benefits and applications, emerges a novel complexity aspect in terms of many inherent hassles primarily security concerns during data transfer phases in IoT covering mostly data confidentiality and integrity features. Thus to enhance safe data transfer in smart IoT environment, a security scheme is proposed in this paper which addresses both the aforesaid issues, employing an integrated approach of lightweight cryptography and steganography (Simple LSB Substitution) technique during data transfer between IoT device and Home Server and adoption of combined approach of cryptography and steganography (Proposed MSB-LSB Substitution) technique during data transfer phases between Home Server and Cloud Servers.
Delay-Constrained Hybrid Computation Offloading With Cloud and Fog Computing

To satisfy the delay constraint, the computation tasks can be offloaded to some computing servers, referred to as offloading destinations. Different to most of existing works which usually consider only a single type of offloading destinations, in this paper, we study the hybrid computation offloading problem considering diverse computation and communication capabilities of two types of offloading destinations, i.e., cloud computing servers and fog computing servers. The aim is to minimize the total energy consumption for both communication and computation while completing the computation tasks within a given delay constraint. It is quite challenging because the delay cannot be easily formulated as an explicit expression but depends on the embedded communication-computation scheduling problem for the computation offloading to different destinations. To solve the computation offloading problem, we first define a new concept named computation energy efficiency and divide the problem into four subproblems according to the computation energy efficiency of different types of computation offloading and the maximum tolerable delay. For each subproblem, we give a closed-form computation offloading solution with the analysis of communication-computation scheduling under the delay constraint. The numerical results show that the proposed hybrid computation offloading solution achieves lower energy consumption than the conventional single-type computation offloading under the delay constraint.
Waveform design for QAM-FBMC systems

Due to its spectral efficiency and well-localized spectrum, a quadrature amplitude modulation based filter-bank multi-carrier (QAM-FBMC) is considered as one of the candidate for 5G mobile communications. In this paper, we propose a novel waveform design for the QAM-FBMC system considering time dispersion parameter. The utilization of the proposed filter with the real number filter coefficients enables simpler implement than the conventional multiple prototype filters with complex coefficients. Simulation results show that the proposed QAM-FBMC systems has superior spectral efficiency as compared to CP-OFDM. Moreover, a new designed filter shows the improvement in the performance compared with the conventional QAM-FBMC systems on multipath fading channel.
Image-based Localization with Spatial LSTMs

In this work we propose a new CNN+LSTM architecture for camera pose regression for indoor and outdoor scenes. CNNs allow us to learn suitable feature representations for localization that are robust against motion blur and illumination changes. We make use of LSTM units on the CNN output in spatial coordinates in order to capture contextual information. This substantially enlarges the receptive field of each pixel leading to drastic improvements in localization performance. We provide extensive quantitative comparison of CNN-based vs SIFT-based localization methods, showing the weaknesses and strengths of each. Furthermore, we present a new large-scale indoor dataset with accurate ground truth from a laser scanner. Experimental results on both indoor and outdoor public datasets show our method outperforms existing deep architectures, and can localize images in hard conditions, e.g., in the presence of mostly textureless surfaces.