nils-herrmann/clean_scirepeval huggingface dataset:
Weighted-Entropy-Based Quantization for Deep Neural Networks

Quantization is considered as one of the most effective methods to optimize the inference cost of neural network models for their deployment to mobile and embedded systems, which have tight resource constraints. In such approaches, it is critical to provide low-cost quantization under a tight accuracy loss constraint (e.g., 1%). In this paper, we propose a novel method for quantizing weights and activations based on the concept of weighted entropy. Unlike recent work on binary-weight neural networks, our approach is multi-bit quantization, in which weights and activations can be quantized by any number of bits depending on the target accuracy. This facilitates much more flexible exploitation of accuracy-performance trade-off provided by different levels of quantization. Moreover, our scheme provides an automated quantization flow based on conventional training algorithms, which greatly reduces the design-time effort to quantize the network. According to our extensive evaluations based on practical neural network models for image classification (AlexNet, GoogLeNet and ResNet-50/101), object detection (R-FCN with 50-layer ResNet), and language modeling (an LSTM network), our method achieves significant reductions in both the model size and the amount of computation with minimal accuracy loss. Also, compared to existing quantization schemes, ours provides higher accuracy with a similar resource constraint and requires much lower design effort.
Facial Expression Detection using Filtered Local Binary Pattern Features with ECOC Classifiers and Platt Scaling

We outline a design for a FACS-based facial expression recognition system and describe in more detail the implementation of two of its main components. Firstly we look at how features that are useful from a pattern analysis point of view can be extracted from a raw input image. We show that good results can be obtained by using the method of local binary patterns (LPB) to generate a large number of candidate features and then selecting from them using fast correlation-based filtering (FCBF). Secondly we show how Platt scaling can be used to improve the performance of an error-correcting output code (ECOC) classifier.
Semi-supervised Deep Learning with Memory

We consider the semi-supervised multi-class classification problem of learning from sparse labelled and abundant unlabelled training data. To address this problem, existing semi-supervised deep learning methods often rely on the up-to-date “network-in-training” to formulate the semi-supervised learning objective. This ignores both the discriminative feature representation and the model inference uncertainty revealed by the network in the preceding learning iterations, referred to as the memory of model learning. In this work, we propose a novel Memory-Assisted Deep Neural Network (MA-DNN) capable of exploiting the memory of model learning to enable semi-supervised learning. Specifically, we introduce a memory mechanism into the network training process as an assimilation-accommodation interaction between the network and an external memory module. Experiments demonstrate the advantages of the proposed MA-DNN model over the state-of-the-art semisupervised deep learning methods on three image classification benchmark datasets: SVHN, CIFAR10, and CIFAR100.
A KL Divergence and DNN-Based Approach to Voice Conversion without Parallel Training Sentences

We extend our recently proposed approach to cross-lingual TTS training to voice conversion, without using parallel training sentences. It employs Speaker Independent, Deep Neural Net (SIDNN) ASR to equalize the difference between source and target speakers and Kullback-Leibler Divergence (KLD) to convert spectral parameters probabilistically in the phonetic space via ASR senone posterior probabilities of the two speakers. With or without knowing the transcriptions of the target speaker’s training speech, the approach can be either supervised or unsupervised. In a supervised mode, where adequate training data of the target speaker with transcriptions is used to train a GMM-HMM TTS of the target speaker, each frame of the source speakers input data is mapped to the closest senone in thus trained TTS. The mapping is done via the posterior probabilities computed by SI-DNN ASR and the minimum KLD matching. In a unsupervised mode, all training data of the target speaker is first grouped into phonetic clusters where KLD is used as the sole distortion measure. Once the phonetic clusters are trained, each frame of the source speakers input is then mapped to the mean of the closest phonetic cluster. The final converted speech is generated with the max probability trajectory generation algorithm. Both objective and subjective evaluations show the proposed approach can achieve higher speaker similarity and better spectral distortions, when comparing with the baseline system based upon our sequential error minimization trained DNN algorithm.
Application of COReS to Compute Research Papers Similarity

Over the decades, the immense growth has been reported in research publications due to continuous developments in science. To date, various approaches have been proposed that find similarity between research papers by applying different similarity measures collectively or individually based on the content of research papers. However, the contemporary schemes are not conceptualized enough to find related research papers in a coherent manner. This paper is aimed at finding related research papers by proposing a comprehensive and conceptualized model via building ontology named COReS: Content-based Ontology for Research Paper Similarity. The ontology is built by finding the explicit relationships (i.e., super-type sub-type, disjointedness, and overlapping) between state-of-the-art similarity techniques. This paper presents the applications of the COReS model in the form of a case study followed by an experiment. The case study uses InText citation-based and vector space-based similarity measures and relationships between these measures as defined in COReS. The experiment focuses on the computation of comprehensive similarity and other content-based similarity measures and rankings of research papers according to these measures. The obtained Spearman correlation coefficient results between ranks of research papers for different similarity measures and user study-based measure, justify the application of COReS for the computation of document similarity. The COReS is in the process of evaluation for ontological errors. In the future, COReS will be enriched to provide more knowledge to improve the process of comprehensive research paper similarity computation.
Almost Optimal Exploration in Multi-Armed Bandits

We study the problem of exploration in stochastic Multi-Armed Bandits. Even in the simplest setting of identifying the best arm, there remains a logarithmic multiplicative gap between the known lower and upper bounds for the number of arm pulls required for the task. This extra logarithmic factor is quite meaningful in nowadays large-scale applications. We present two novel, parameterfree algorithms for identifying the best arm, in two di↵erent settings: given a target confidence and given a target budget of arm pulls, for which we prove upper bounds whose gap from the lower bound is only doublylogarithmic in the problem parameters. We corroborate our theoretical results with experiments demonstrating that our algorithm outperforms the state-of-the-art and scales better as the size of the problem increases.
Interactive procedural street modeling

This paper addresses the problem of interactively modeling large street networks. We introduce an intuitive and flexible modeling framework in which a user can create a street network from scratch or modify an existing street network. This is achieved through designing an underlying tensor field and editing the graph representing the street network. The framework is intuitive because it uses tensor fields to guide the generation of a street network. The framework is flexible because it allows the user to combine various global and local modeling operations such as brush strokes, smoothing, constraints, noise and rotation fields. Our results will show street networks and three-dimensional urban geometry of high visual quality.
Cost Estimation Model for Reuse Software

Software cost estimation is the way of predicting the effort required to develop a software system. In this process we include software reuse concept which provide the the amount of code reused in a product and estimated the cost to develop the software. In this concept we estimate cost of reuse code which is reduce the cost of software during development and maintenance. To measure the costs involved in reuse programs and to evaluate the impacts of reuse in software development.
Understanding and Improving the Latency of DRAM-Based Memory Systems

Over the past two decades, the storage capacity and access bandwidth of main memory have improved tremendously, by 128x and 20x, respectively. These improvements are mainly due to the continuous technology scaling of DRAM (dynamic random-access memory), which has been used as the physical substrate for main memory. In stark contrast with capacity and bandwidth, DRAM latency has remained almost constant, reducing by only 1.3x in the same time frame. Therefore, long DRAM latency continues to be a critical performance bottleneck in modern systems. Increasing core counts, and the emergence of increasingly more data-intensive and latency-critical applications further stress the importance of providing low-latency memory accesses. In this dissertation, we identify three main problems that contribute significantly to long latency of DRAM accesses. To address these problems, we present a series of new techniques. Our new techniques significantly improve both system performance and energy efficiency. We also examine the critical relationship between supply voltage and latency in modern DRAM chips and develop new mechanisms that exploit this voltage-latency trade-off to improve energy efficiency. First, while bulk data movement is a key operation in many applications and operating systems, contemporary systems perform this movement inefficiently, by transferring data from DRAM to the processor, and then back to DRAM, across a narrow off-chip channel. The use of this narrow channel for bulk data movement results in high latency and high energy consumption. This dissertation introduces a new DRAM design, Low-cost Inter-linked SubArrays (LISA), which provides fast and energy-efficient bulk data movement across subarrays in a DRAM chip. We show that the LISA substrate is very powerful and versatile by demonstrating that it efficiently enables several new architectural mechanisms, including low-latency data copying, reduced DRAM access latency for frequently-accessed data, and reduced preparation latency for subsequent accesses to a DRAM bank. Second, DRAM needs to be periodically refreshed to prevent data loss due to leakage. Unfortunately, while DRAM is being refreshed, a part of it becomes unavailable to serve memory requests, which degrades system performance. To address this refresh interference problem, we propose two access-refresh parallelization techniques that enable more overlapping of accesses with refreshes inside DRAM, at the cost of very modest changes to the memory controllers and DRAM chips. These two techniques together achieve performance close to an idealized system that does not require refresh. Third, we find, for the first time, that there is significant latency variation in accessing different cells of a single DRAM chip due to the irregularity in the DRAM manufacturing process. As a result, some DRAM cells are inherently faster to access, while others are in-
Measuring level of cuteness of baby images: a supervised learning scheme

The attractiveness of a baby face image depends on the perception of the perceiver. However, several recent studies advocate the idea that human perceptual analysis can be approximated by statistical models. We believe that the cuteness of baby faces depends on the low level facial features extracted from different parts (e.g., mouth, eyes, nose) of the faces. In this paper, we introduce a new problem of classifying baby face images based on their cuteness level using supervised learning techniques. The proposed learning model finds the potential of a deep learning technique in measuring the level of cuteness of baby faces. Since no datasets are available to validate the proposed technique, we construct a dataset of images of baby faces, downloaded from the internet. The dataset consists of several challenges like different view-point, orientation, lighting condition, contrast and background. We annotate the data using some well-known statistical tools inherited from Reliability theory. The experiments are conducted with some well-known image features like Speeded Up Robust Feature (SURF), Histogram of Oriented Gradient (HOG), Convolutional Neural Network (CNN) on Gradient and CNN on Laplacian, and the results are presented and discussed.
The Search for Quasi-Periodicity in Islamic 5-fold Ornament

T he Penrose tilings are remarkable in that they are non-periodic (have no translational symmetry) but are clearly organised. Their structure, called quasiperiodicity, can be described in several ways, including via self-similar subdivision, tiles with matching rules, and projection of a slice of a cubic lattice in R. The tilings are also unusual for their many centres of local 5-fold and 10fold rotational symmetry, features shared by some Islamic geometric patterns. This resemblance has prompted comparison, and has led some to see precursors of the Penrose tilings and even evidence of quasi-periodicity in traditional Islamic designs. Bonner  identified three styles of self-similarity; Makovicky [20] was inspired to develop new variants of the Penrose tiles and later, with colleagues [24], overlaid Penrose-type tilings on traditional Moorish designs; more recently, Lu and Steinhardt  observed the use of subdivision in traditional Islamic design systems and overlaid Penrose kites and darts on Iranian designs. The latter article received widespread exposure in the world’s press, although some of the coverage overstated and misrepresented the actual findings. The desire to search for examples of quasi-periodicity in traditional Islamic patterns is understandable, but we must take care not to project modern motivations and abstractions into the past. An intuitive knowledge of group theory is sometimes attributed to any culture that has produced repeating patterns displaying a wide range of symmetry types, even though they had no abstract notion of a group. There are two fallacies to avoid: • abstraction: P knew about X and X is an example of Y therefore P knew Y. • deduction: P knew X and X implies Y therefore P knew Y.
Accelerated Gradient Methods for Stochastic Optimization and Online Learning

Regularized risk minimization often involves non-smooth o ptimization, either because of the loss function (e.g., hinge loss) or the regulari zer (e.g.,l1-regularizer). Gradient methods, though highly scalable and easy to implem ent, are known to converge slowly. In this paper, we develop a novel accelerat ed gradient method for stochastic optimization while still preserving their c omputational simplicity and scalability. The proposed algorithm, called SAGE (Stoc hastic Accelerated GradiEnt), exhibits fast convergence rates on stochastic c omposite optimization with convex or strongly convex objectives. Experimental re sults show that SAGE is faster than recent (sub)gradient methods including FOLO S, SMIDAS and SCD. Moreover, SAGE can also be extended for online learning, res ulting in a simple algorithm but with the best regret bounds currently known fo r these problems.
Vocal Melody Extraction Using Patch-Based CNN

A patch-based convolutional neural network (CNN) model presented in this paper for vocal melody extraction in polyphonic music is inspired from object detection in image processing. The input of the model is a novel time-frequency representation which enhances the pitch contours and suppresses the harmonic components of a signal. This succinct data representation and the patch-based CNN model enable an efficient training process with limited labeled data. Experiments on various datasets show excellent speed and competitive accuracy comparing to other deep learning approaches.
Sub-Optimal Multi-Phase Path Planning: A Method for Solving Rubik's Revenge

Rubik’s Revenge, a 4× 4× 4 variant of the Rubik’s puzzles, remains to date as an unsolved puzzle. That is to say, we do not have a method or successful categorization to optimally solve every one of its approximately 7.401×10 possible configurations. Rubik’s Cube, Rubik’s Revenge’s predecessor (3×3×3), with its approximately 4.33 × 10 possible configurations, has only recently been completely solved by Rokicki et. al, further finding that any configuration requires no more than 20 moves.  With the sheer dimension of Rubik’s Revenge and its total configuration space, a brute-force method of finding all optimal solutions would be in vain. Similar to the methods used by Rokicki et. al on Rubik’s Cube, in this paper we develop a method for solving arbitrary configurations of Rubik’s Revenge in phases, using a combination of a powerful algorithm known as IDA* and a useful definition of distance in the cube space. While time-series results were not successfully gathered, it will be shown that this method far outweighs current human-solving methods and can be used to determine loose upper bounds for the cube space. Discussion will suggest that this method can also be applied to other puzzles with the proper transformations.
Distribution of Eigenvalues for 2x2 MIMO Channel Capacity Based on Indoor Measurements

Based on the non line-of-sight (NLOS) indoor measurements performed in the corridors on the third floor of the Electronics and Telecommunications Research Institute (ETRI) building in Daejeon city, Republic of Korea, we investigated the distribution of the eigenvalues of HH*, where H denotes a 2×2 multiple-input multiple-output (MIMO) channel matrix. Using the observation that the distribution of the measured eigenvalues matches well with the Gamma distribution, we propose a model of eigenvalues as Gamma distributed random variables that prominently feature both transmitting and receiving correlations. Using the model with positive integer k_i, i=1, 2, which is the shape parameter of a Gamma distribution, we derive the closed-form ergodic capacity of the 2×2 MIMO channel. Validation results show that the proposed model enables the evaluation of the outage and ergodic capacities of the correlated 2×2 MIMO channel in the NLOS indoor corridor environment.
Design of Circular/Dual-Frequency Linear Polarization Antennas Based on the Anisotropic Complementary Split Ring Resonator

A novel kind of circular polarization and dual-frequency linear polarization microstrip antennas is proposed. The designs are based on the anisotropic property of the complementary split ring resonator. When the complementary split ring resonator is etched on the patch of the probe-fed microstrip antenna, the gap orientation asymmetric or symmetric to the current propagating direction will render the antenna to radiate circular polarization waves or dual-frequency linear polarization waves. Details of the experimental results compared with the simulated results are presented and discussed.
Scene Graph Generation by Belief RNNs

Understanding and describing the scene beyond an image has great value. It is a 1 step forward from recognizing individual objects and their relationships in isolation. 2 In addition, Scene graph is a crucial step towards a deeper understanding of a visual 3 scene. We present a method in an end-to-end model that given an image generates 4 a scene graph including nodes that corresponds to the objects in the image and 5 an edges corresponds to relationship between objects. Our main contribution is 6 introducing a novel deep structure prediction module Belief RNNs that performs 7 learning on a large graphs in a very efficient and generic way. 8
Neonatal Pain Expression Recognition Using Transfer Learning

Transfer learning using pre-trained Convolutional Neural Networks (CNNs) has been successfully applied to images for different classification tasks. In this paper, we propose a new pipeline for pain expression recognition in neonates using transfer learning. Specifically, we propose to exploit a pre-trained CNN that was originally trained on a relatively similar dataset for face recognition (VGG Face) as well as CNNs that were pre-trained on a relatively different dataset for image classification (iVGG F,M, and S) to extract deep features from neonates' faces. In the final stage, several supervised machine learning classifiers are trained to classify neonates' facial expression into pain or no pain expression. The proposed pipeline achieved, on a testing dataset, 0.841 AUC and 90.34 accuracy, which is approx. 7 higher than the accuracy of handcrafted traditional features. We also propose to combine deep features with traditional features and hypothesize that the mixed features would improve pain classification performance. Combining deep features with traditional features achieved 92.71 accuracy and 0.948 AUC. These results show that transfer learning, which is a faster and more practical option than training CNN from the scratch, can be used to extract useful features for pain expression recognition in neonates. It also shows that combining deep features with traditional handcrafted features is a good practice to improve the performance of pain expression recognition and possibly the performance of similar applications.
Nanostructured Materials for Room-Temperature Gas Sensors.

Sensor technology has an important effect on many aspects in our society, and has gained much progress, propelled by the development of nanoscience and nanotechnology. Current research efforts are directed toward developing high-performance gas sensors with low operating temperature at low fabrication costs. A gas sensor working at room temperature is very appealing as it provides very low power consumption and does not require a heater for high-temperature operation, and hence simplifies the fabrication of sensor devices and reduces the operating cost. Nanostructured materials are at the core of the development of any room-temperature sensing platform. The most important advances with regard to fundamental research, sensing mechanisms, and application of nanostructured materials for room-temperature conductometric sensor devices are reviewed here. Particular emphasis is given to the relation between the nanostructure and sensor properties in an attempt to address structure-property correlations. Finally, some future research perspectives and new challenges that the field of room-temperature sensors will have to address are also discussed.
Smart RFID based Interactive Kiosk cart using wireless sensor node

Now days it is common to see people getting enthusiast in online shopping through e-commerce websites but still the shopping centers are popular. We come across many types of carts used for shopping in malls and shopping centers. We are proposing an Interactive Kiosk based cart which uses the RFID technology to identify the products details which is already available in the database. We propose to have facility to browse the available products list onscreen in the display connected to beagle bone black cart. The cart is interacting with the Main Server and it will have the facility to generate the bill for all the products added into the cart. The proposed system will be helpful for avoiding queues in shopping malls for billing. With the proposed design customers will have an ease to shop by avoiding conventional queue system for billing generation and hence the shopping becomes easy and enjoyable.