nils-herrmann/clean_scirepeval huggingface dataset:
A review on methods and classifiers in lip reading

The idea of lip reading as a visual technique which people may use to translate lip movement into phrases without relying on speech itself is fascinating. There are numerous application areas in which lip reading could provide full assistance. Although there may be a downside to using the lip reading system, whether it may range from problems such as time constraint to minor word recognition mistakes, further development of the system should be an active cycle. Ongoing research into improvement of the lip reading system performance by way of suitable choices of feature extraction and classifiers is essential to track the developing trend in both fields of technology as well as in pattern recognition. This paper discusses a short review on the existing methods and classifiers which have been used in previous work in the field of lip reading.
Learning Dexterous In-Hand Manipulation

We use reinforcement learning (RL) to learn dexterous in-hand manipulation policies which can perform vision-based object reorientation on a physical Shadow Dexterous Hand. The training is performed in a simulated environment in which we randomize many of the physical properties of the system like friction coefficients and an object’s appearance. Our policies transfer to the physical robot despite being trained entirely in simulation. Our method does not rely on any human demonstrations, but many behaviors found in human manipulation emerge naturally, including finger gaiting, multi-finger coordination, and the controlled use of gravity. Our results were obtained using the same distributed RL system that was used to train OpenAI Five [43]. We also include a video of our results: https://youtu. be/jwSbzNHGflM.
Design and performance analysis of Cockroft-Walton Voltage Multiplier (CWVM) energy harvesting for low power applications

In this paper, the performance analysis of Cockroft-Walton Voltage Multiplier (CWVM) and effect of employing the cascaded CWG stages has been analyzed for energy harvesting applications. In this proposed model, square wave has been employed as an input to Cockroft-Walton Voltage Multiplier for analyzing the performance of CWG. In this proposed system, the Cockroft-Walton Voltage Multiplier is fed with both low and high frequencies in the range of Hz to MHz keeping Vin as 1V and appertaining 5 stage Cockroft-Walton Voltage Multiplier network and yielding different Vout. In this demonstrative work, square wave of different frequencies — 10 Hz, 100 Hz, 1kHz, 10 kHz, 100 kHz, 1MHz, 2 MHz, 3 MHz, 4 MHz, 5 MHz and 10 MHz respectively, are applied as input to Cockroft-Walton voltage multiplier and corresponding output voltages are recorded and analyzed. The maximum output voltage obtained voltage for 10 kHz is 16.45 V at fourth stage of CWVM for all stages that make it more appealing for devices like humidity sensor devices, dimming fluorescent ballasts, dimming LED drivers. It has been observed that for frequencies below 10 kHz, the output falls on employing more than 5 CWGA stages whereas when the Cockroft-Walton Voltage Multiplier operates at frequencies more than 10 kHz, the output voltage increase with increase in number of stages of multiplier. The Schottky diode has been employed over crystal diodes to enhance the output efficiency of multiplier circuit because it has less dropout voltage drop than crystal diodes. A single stage CWG has been tested by feeding different input waveforms — square, triangular and sine wave and it has been concluded that the output voltage of CWG is maximum for square input and minimum for sine wave because square wave resting at peak values for maximum time provided sufficient time for capacitors in CWG to charge to maximum values. It has been concluded that the output voltage of a Cockroft-Walton Voltage Multiplier is a complete function of input signal frequency and input waveform shape. A hardware prototype of the proposed Cockroft-Walton Voltage Multiplier has been developed to validate the analytical results.
Topography of projections from amygdala to bed nuclei of the stria terminalis

A collection of 125 PHAL experiments in the rat has been analyzed to characterize the organization of projections from each amygdalar cell group (except the nucleus of the lateral olfactory tract) to the bed nuclei of the stria terminalis, which surround the crossing of the anterior commissure. The results suggest three organizing principles of these connections. First, the central nucleus, and certain other amygdalar cell groups associated with the main olfactory system, innervate preferentially various parts of the lateral and medial halves of the bed nuclear anterior division, and these projections travel via both the stria terminalis and ansa peduncularis (ventral pathway). Second, in contrast, the medial nucleus, and the rest of the amygdalar cell groups associated with the accessory and main olfactory systems innervate preferentially the posterior division, and the medial half of the anterior division, of the bed nuclei. And third, the lateral and anterior basolateral nuclei of the amygdala (associated with the frontotemporal association cortical system) do not project significantly to the bed nuclei. For comparison, inputs to the bed nuclei from the ventral subiculum, infralimbic area, and endopiriform nucleus are also described. The functional significance of these projections is discussed with reference to what is known about the output of the bed nuclei.
Steady-State Power Flow Model of Energy Router Embedded AC Network and Its Application in Optimizing Power System Operation

The energy router is an emerging device concept that is based on an advanced power electronic technique. It is able to realize flexible and dynamic electric power distribution in power systems analogous to the function of information routers in the Internet. It is of great interest to investigate how the energy router can be used to optimize power system operation. This paper formulates the steady-state power flow model of the energy router embedded system network and the related optimal power flow formulation. The role of the energy router in providing extra flexibility to optimize the system operation is studied. Case studies are carried out on a modified IEEE RTS-79 system and a modified IEEE 118 bus system with the energy router. The results show that the energy router is able to optimize the operation of the power system through controlling the power injections and voltage of ports of the energy router. Operating objective such as adjusting branch power flow, improving bus voltage, and reducing active power losses of the grid can be reached under different objective functions.
A Unified Model for Extractive and Abstractive Summarization using Inconsistency Loss

We propose a unified model combining the strength of extractive and abstractive summarization. On the one hand, a simple extractive model can obtain sentence-level attention with high ROUGE scores but less readable. On the other hand, a more complicated abstractive model can obtain word-level dynamic attention to generate a more readable paragraph. In our model, sentence-level attention is used to modulate the word-level attention such that words in less attended sentences are less likely to be generated. Moreover, a novel inconsistency loss function is introduced to penalize the inconsistency between two levels of attentions. By end-to-end training our model with the inconsistency loss and original losses of extractive and abstractive models, we achieve state-of-theart ROUGE scores while being the most informative and readable summarization on the CNN/Daily Mail dataset in a solid human evaluation.
Emotion Recognition from Multi-Channel EEG through Parallel Convolutional Recurrent Neural Network

As a challenging pattern recognition task, automatic real-time emotion recognition based on multi-channel EEG signals is becoming an important computer-aided method for emotion disorder diagnose in neurology and psychiatry. Traditional machine learning approaches require to design and extract various features from single or multiple channels based on comprehensive domain knowledge. Consequently, these approaches may be an obstacle for non-domain experts. On the contrast, deep learning approaches have been used successfully in many recent literatures to learn features and classify different types of data. In this paper, baseline signals are considered and a simple but effective pre-processing method has been proposed to improve the recognition accuracy. Meanwhile, a hybrid neural network which combines `Convolutional Neural Network (CNN)’ and `Recurrent Neural Network (RNN)’ has been applied to classify human emotion states by effectively learning compositional spatial-temporal representation of raw EEG streams. The CNN module is used to mine the inter-channel correlation among physically adjacent EEG signals by converting the chain-like EEG sequence into 2D-like frame sequence. The LSTM module is adopted to mine contextual information. Experiments are carried out in a segment-level emotion identification task, on the DEAP benchmarking dataset. Our experimental results indicate that the proposed pre-processing method can increase emotion recognition accuracy by 32% approximately and the model achieves a high performance with a mean accuracy of 90.80% and 91.03% on valence and arousal classification task respectively.
Modeling By Shortest Data Description *

It is known that the usual criteria resulting from such, should we dare to say, ad hoc principles as the least squares, the minimum prediction error, and the maximum likelihood principles, cannot be used to estimate the order nor other structure parameters. This very fact seems to suggest that these time-honored principles are successful only insofar as they happen to coincide with one or another facet of some more powerful and comprehensive principle. The shortest description length of individual recursively definable objects was studied by Kolmogorov[1] and others, and it gives rise to the algorithmic notion of entropy. In statistical estimation, however, the data always includes a random element, and the original notion of entropy of random variables turns out to be the appropriate one to use. In a peripheral way we also invoke a simple combinatorial entropy notion which, however, need not cause any con-
A 4.86 mW 15-bit 22.5 MS/s pipelined ADC with 74 dB SNDR in 90 nm CMOS using averaging correlated level shifting technique

This paper proposes an averaging correlated level shifting (Averaging-CLS) technique as a switched-capacitor amplification technique for reducing finite opamp gain error. The Averaging-CLS inverts the finite opamp gain error in the second amplifying phase and then averages it with that in the first amplifying phase. With Averaging-CLS, a high-resolution pipelined ADC can employ medium-accuracy high-efficiency ring amplifiers, which lowers the power consumption and cost of the ADC. Compared with conventional CLS, Averaging-CLS halves the sampling capacitance under the same SNR requirement. A 15-bit pipelined ADC, which operates from a 1.2 V power supply and utilizes an input range of 2.2 V peak-to-peak differential, was realized in a 90 nm CMOS technology. The ADC achieves a 74 dB peak SNDR at 22.5 MS/s conversion rate and consumes 4.86 mW, resulting in the Walden and Schreier Figure-of-Merits of 52.7fJ/conv.-step and 167.6dB, respectively.
A 1.6 mW 5.4 GHz transformer-feedback gm-boosted current-reuse LNA in 0.18/μm CMOS

A fully-integrated LNA in 0.18/xm CMOS simultaneously achieves high gain, low noise figure (NF), good third-order input intercept linearity (IIP3), and low DC bias current consumption: 19 dB, 2.4 dB, −14.2 dBm and 1.3 mA, respectively, from a 1.2 V supply. The single-ended LNA uses a common-gate common-source (CG-CS) topology and operates at 5.4 GHz for WLAN applications. Using gm — boosting, current-reuse and transformer-feedback techniques, the LNA mitigates several design issues seen in the widely used common-source common-source current-reuse (CS-CS) LNAs and improves the IIP3 of CG-CS schemes by 6 dB, without increasing power and area consumption.
Affective Computing and Sentiment Analysis

Understanding emotions is an important aspect of personal development and growth, and as such it is a key tile for the emulation of human intelligence. Besides being important for the advancement of AI, emotion processing is also important for the closely related task of polarity detection. The opportunity to automatically capture the general public's sentiments about social events, political movements, marketing campaigns, and product preferences has raised interest in both the scientific community, for the exciting open challenges, and the business world, for the remarkable fallouts in marketing and financial market prediction. This has led to the emerging fields of affective computing and sentiment analysis, which leverage human-computer interaction, information retrieval, and multimodal signal processing for distilling people's sentiments from the ever-growing amount of online social data.
OPAQUE: An Asymmetric PAKE Protocol Secure Against Pre-Computation Attacks

Exchange (PAKE) protocols allow two parties that only share a password to establish a shared key in a way that is immune to offline attacks. Asymmetric PAKE (aPAKE) strengthens this notion for the more common client-server setting where the server stores a mapping of the password and security is required even upon server compromise, that is, the only allowed attack in this case is an (inevitable) offline exhaustive dictionary attack against individual user passwords. Unfortunately, current aPAKE protocols (that dispense with the use of servers’ public keys) allow for pre-computation attacks that lead to the instantaneous compromise of user passwords upon server compromise, thus forgoing much of the intended aPAKE security. Indeed, these protocols use – in essential ways – deterministic password mappings or use random “salt” transmitted in the clear from servers to users, and thus are vulnerable to pre-computation attacks. We initiate the study of Strong aPAKE protocols that are secure as aPAKE’s but are also secure against pre-computation attacks. We formalize this notion in the Universally Composable (UC) settings and present two modular constructions using an Oblivious PRF as a main tool. The first builds a Strong aPAKE from any aPAKE (which in turn can be constructed from any PAKE ) while the second builds a Strong aPAKE from any authenticated key-exchange protocol secure against reverse impersonation (a.k.a. KCI). Using the latter transformation, we show a practical instantiation of a UC-secure Strong aPAKE in the Random Oracle model. The protocol (“OPAQUE”) consists of 2 messages (3 with mutual authentication), requires 3 and 4 exponentiations for server and client, respectively (2 to 4 of which can be fixed-base depending on optimizations), provides forward secrecy, is PKI-free, supports user-side hash iterations, has a built-in facility for password-based storage and retrieval of secrets and credentials, and accommodates a user-transparent server-side threshold implementation.
MultiPoseNet: Fast Multi-Person Pose Estimation Using Pose Residual Network

In this paper, we present MultiPoseNet, a novel bottom-up multi-person pose estimation architecture that combines a multi-task model with a novel assignment method. MultiPoseNet can jointly handle person detection, person segmentation and pose estimation problems. The novel assignment method is implemented by the Pose Residual Network (PRN) which receives keypoint and person detections, and produces accurate poses by assigning keypoints to person instances. On the COCO keypoints dataset, our pose estimation method outperforms all previous bottom-up methods both in accuracy (+4-point mAP over previous best result) and speed; it also performs on par with the best top-down methods while being at least 4x faster. Our method is the fastest real time system with ∼23 frames/sec.
Concept and Dimensions of Web 4 . 0

Web 4.0 is a new evolution of the Web paradigm based on multiple models, technologies and social relationships. The concept of Web 4.0 is not totally clear and unanimous in literature, because it is composed by several dimensions. In this sense, this study uses a systematic review approach to clarify the concept of Web 4.0 and explore its various dimensions, analyzing if they have elements in common. The findings indicate that the number of studies published from 2009 to 2017 on this field significantly increased, having reached a peak in 2014. Furthermore, we identified five dimensions associated with the Web 4.0 paradigm, in which the terms “pervasive computing” and “ubiquitous computing” are the most widely used in the literature. On the other side, terms such as “Web 4.0”, “symbiotic Web” and “Web social computing” are not often used. Indexing Terms/
Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture

Recent studies have demonstrated the usefulness of optical indices from hyperspectral remote sensing in the assessment of vegetation biophysical variables both in forestry and agriculture. Those indices are, however, the combined response to variations of several vegetation and environmental properties, such as Leaf Area Index (LAI), leaf chlorophyll content, canopy shadows, and background soil reflectance. Of particular significance to precision agriculture is chlorophyll content, an indicator of photosynthesis activity, which is related to the nitrogen concentration in green vegetation and serves as a measure of the crop response to nitrogen application. This paper presents a combined modeling and indices-based approach to predicting the crop chlorophyll content from remote sensing data while minimizing LAI (vegetation parameter) influence and underlying soil (background) effects. This combined method has been developed first using simulated data and followed by evaluation in terms of quantitative predictive capability using real hyperspectral airborne data. Simulations consisted of leaf and canopy reflectance modeling with PROSPECT and SAILH radiative transfer models. In this modeling study, we developed an index that integrates advantages of indices minimizing soil background effects and indices that are sensitive to chlorophyll concentration. Simulated data have shown that the proposed index Transformed Chlorophyll Absorption in Reflectance Index/Optimized Soil-Adjusted Vegetation Index (TCARI/OSAVI) is both very sensitive to chlorophyll content variations and very resistant to the variations of LAI and solar zenith angle. It was therefore possible to generate a predictive equation to estimate leaf chlorophyll content from the combined optical index derived from above-canopy reflectance. This relationship was evaluated by application to hyperspectral CASI imagery collected over corn crops in three experimental farms from Ontario and Quebec, Canada. The results presented here are from the L’Acadie, Quebec, Agriculture and AgriFood Canada research site. Images of predicted leaf chlorophyll content were generated. Evaluation showed chlorophyll variability over crop plots with various levels of nitrogen, and revealed an excellent agreement with ground truth, with a correlation of r = .81 between estimated and field measured chlorophyll content data. D 2002 Elsevier Science Inc. All rights reserved.
Learning to SMILE(S)

This paper shows how one can directly apply natural language processing (NLP) methods to classification problems in cheminformatics. Connection between these seemingly separate fields is shown by considering standard textual representation of compound, SMILES. The problem of activity prediction against a target protein is considered, which is a crucial part of computer aided drug design process. Conducted experiments show that this way one can not only outrank state of the art results of hand crafted representations but also gets direct structural insights into the way decisions are made.
Chukwa: A large-scale monitoring system

We describe the design and initial implementation of Chukwa, a data collection system for monitoring and analyzing large distributed systems. Chukwa is built on top of Hadoop, an open source distributed filesystem and MapReduce implementation, and inherits Hadoop’s scalability and robustness. Chukwa also includes a flexible and powerful toolkit for displaying monitoring and analysis results, in order to make the best use of this collected data.
The MIT Encyclopedia of Cognitive Sciences: Sign Languages

Sign languages (alternatively, signed languages) are human languages whose forms consist of sequences of movements and configurations of the hands and arms, face, and upper torso. Typically, sign languages are perceived through the visual mode. Sign languages thus contrast, of course, with spoken languages, whose forms consist of sounds produced by sequences of movements and configurations of the mouth and vocal tract. More informally, then, sign languages are visual-gestural languages, whereas spoken languages are auditory-vocal languages.
Anonymity and Online Commenting: The Broken Windows Effect and the End of Drive-by Commenting

In this study we ask how regulations about commenter identity affect the quantity and quality of discussion on commenting fora. In December 2013, the Huffington Post changed the rules for its comment forums to require participants to authenticate their accounts through Facebook. This enabled a large-scale 'before and after' analysis. We collected over 42m comments on 55,000 HuffPo articles published in the period January 2013 to June 2014 and analysed them to determine how changes in identity disclosure impacted on discussions in the publication's comment pages. We first report our main results on the quantity of online commenting, where we find both a reduction and a shift in its distribution from politicised to blander topics. We then discuss the quality of discussion. Here we focus on the subset of 18.9m commenters who were active both before and after the change, in order to disentangle the effects of the worst offenders withdrawing and the remaining commenters modifying their tone. We find a 'broken windows' effect, whereby comment quality improves even when we exclude interaction with trolls and spammers.
The strategic planning of e-commerce business to deployment with TOWS matrix by using K-mean and linear regression

The expansion and growth of e-commerce business has increased steadily every year. Both original and new operators who are interested in this business because the returns are worth it. But it's not always true that the success of this business. Due to the strategic planning and implementation of business strategy mistakes. Therefore, strategic planning and business strategy, it is necessary and very important. In this paper present the method of selection business strategies planning for the business's e-commerce by using SWOT analysis to help plan and implement strategies in alignment with K-mean method, selection using linear regression for selecting the most appropriate strategy, modeling by using ensemble classifier with Random forest method. Performance measurement this model accuracy is rated over 80 percent through experiments.