nils-herrmann/clean_scirepeval huggingface dataset:
Anisotropic conductive films (ACFs) for ultra-fine pitch chip-on-glass (COG) applications

This paper describes the development of anisotropic conductive films (ACFs) for ultra-fine pitch chip-on-glass (COG) application. In order to have reliable COG using ACF at fine pitch, the number of conductive particles trapped between the bump and substrate pad should be enough and less conductive particle between adjacent bumps. The anisotropic conductive film is double layered structure, in which ACF and NCF layer thickness is optimized, to have as many conductive particle as possible on bump after COG bonding. In ACF layer, non-conductive particles of diameter 1/5 times smaller than the conductive particles are added to prevent an electrical short between the bumps of COG assembly. The conductive particles are naturally insulated by the nonconductive particles even though conductive particles are flowed into and agglomerated in narrow gap between bumps during COG bonding. Also, flow property of the conductive particles is restrained due to nonconductive particles, and results the number of the conductive particles constantly maintained. To ensure the insulation property at 10 /spl mu/m gap, insulating coated conductive particles were used in ACF layer composition. The double-layered ACF using low temperature curable binder system is also effective in reducing the warpage level of COG assembly due to low modulus and low bonding temperature.
Bitmap Filter: Speeding up Exact Set Similarity Joins with Bitwise Operations

The Exact Set Similarity Join problem aims to find all similar sets between two collections of sets, with respect to a threshold and a similarity function such as overlap, Jaccard, dice or cosine. The näıve approach verifies all pairs of sets and it is often considered impractical due the high number of combinations. So,  Join algorithms are usually based on the Filter-Verification Framework, that applies a series of filters to reduce the number of verified pairs. This paper presents a new filtering technique called Bitmap Filter, which is able to accelerate state-of-the-art algorithms for the exact Set  The Bitmap Filter uses hash functions to create bitmaps of fixed b bits, representing characteristics of the sets. Then, it applies bitwise operations (such as xor and population count) on the bitmaps in order to infer a similarity upper bound for each pair of sets. If the upper bound is below a given similarity threshold, the pair of sets is pruned. The Bitmap Filter benefits from the fact that bitwise operations are efficiently implemented by many modern general-purpose processors and it was easily applied to four state-of-the-art algorithms implemented in CPU: AllPairs, PPJoin, AdaptJoin and GroupJoin. Furthermore, we propose a Graphic Processor Unit (GPU) algorithm based on the näıve approach but using the Bitmap Filter to speedup the computation. The experiments considered 9 collections containing from 100 thousands up to 10 million sets and the joins were made using Jaccard thresholds from 0.50 to 0.95. The Bitmap Filter was able to improve 90% of the experiments in CPU, with speedups of up to 4.50× and 1.43× on average. Using the GPU algorithm, the experiments were able to speedup the original CPU algorithms by up to 577× using an Nvidia Geforce GTX 980 Ti.
Optimization of Resource Provisioning Cost in Cloud Computing

In cloud computing, cloud providers can offer cloud consumers two provisioning plans for computing resources, namely reservation and on-demand plans. In general, cost of utilizing computing resources provisioned by reservation plan is cheaper than that provisioned by on-demand plan, since cloud consumer has to pay to provider in advance. With the reservation plan, the consumer can reduce the total resource provisioning cost. However, the best advance reservation of resources is difficult to be achieved due to uncertainty of consumer's future demand and providers' resource prices. To address this problem, an optimal cloud resource provisioning (OCRP) algorithm is proposed by formulating a stochastic programming model. The OCRP algorithm can provision computing resources for being used in multiple provisioning stages as well as a long-term plan, e.g., four stages in a quarter plan and twelve stages in a yearly plan. The demand and price uncertainty is considered in OCRP. In this paper, different approaches to obtain the solution of the OCRP algorithm are considered including deterministic equivalent formulation, sample-average approximation, and Benders decomposition. Numerical studies are extensively performed in which the results clearly show that with the OCRP algorithm, cloud consumer can successfully minimize total cost of resource provisioning in cloud computing environments.
A novel nonlinear ensemble forecasting model incorporating GLAR and ANN for foreign exchange rates

In this study, we propose a novel nonlinear ensemble forecasting model integrating generalized linear autoregression (GLAR) with artificial neural networks (ANN) in order to obtain accurate prediction results and ameliorate forecasting performances. We compare the new model’s performance with the two individual forecasting models—GLAR and ANN—as well as with the hybrid model and the linear combination models. Empirical results obtained reveal that the prediction using the nonlinear ensemble model is generally better than those obtained using the other models presented in this study in terms of the same evaluation measurements. Our findings reveal that the nonlinear ensemble model proposed here can be used as an alternative forecasting tool for exchange rates to achieve greater forecasting accuracy and improve prediction quality further.
Light-sheet functional imaging in fictively behaving zebrafish

sists of two excitation laser beams. One beam scans the volume of the brain from the side of a horizontally positioned zebrafish but is rapidly switched off when inside an elliptical exclusion region located over the eye . Simultaneously, a second beam scans from the front, to cover the forebrain and the regions between the eyes. Together, these two beams achieve nearly complete coverage of the brain without exposing the retina to direct laser excitation, which allows unimpeded presentation of visual stimuli that are projected onto a screen below the fish. To monitor intended swimming behavior, we used existing methods for recording activity from motor neuron axons in the tail of paralyzed larval zebrafish1 (Fig. 1a and Supplementary Note). This system provides imaging speeds of up to three brain volumes per second (40 planes per brain volume); increases in camera speed will allow for faster volumetric sampling. Because light-sheet imaging may still introduce some additional sensory stimulation (excitation light scattering in the brain and reflected from the glass walls of the chamber), we assessed whether fictive behavior in 5–7 d post-fertilization (d.p.f.) fish was robust to the presence of the light sheets. We tested two visuoLight-sheet functional imaging in fictively behaving zebrafish
Early genome duplications in conifers and other seed plants

Polyploidy is a common mode of speciation and evolution in angiosperms (flowering plants). In contrast, there is little evidence to date that whole genome duplication (WGD) has played a significant role in the evolution of their putative extant sister lineage, the gymnosperms. Recent analyses of the spruce genome, the first published conifer genome, failed to detect evidence of WGDs in gene age distributions and attributed many aspects of conifer biology to a lack of WGDs. We present evidence for three ancient genome duplications during the evolution of gymnosperms, based on phylogenomic analyses of transcriptomes from 24 gymnosperms and 3 outgroups. We use a new algorithm to place these WGD events in phylogenetic context: two in the ancestry of major conifer clades (Pinaceae and cupressophyte conifers) and one in Welwitschia (Gnetales). We also confirm that a WGD hypothesized to be restricted to seed plants is indeed not shared with ferns and relatives (monilophytes), a result that was unclear in earlier studies. Contrary to previous genomic research that reported an absence of polyploidy in the ancestry of contemporary gymnosperms, our analyses indicate that polyploidy has contributed to the evolution of conifers and other gymnosperms. As in the flowering plants, the evolution of the large genome sizes of gymnosperms involved both polyploidy and repetitive element activity.
Sound source localization for real-world humanoid robots

In this paper we study the suitability and performance of conventional time delay of arrival estimation for humanoid robots. Moving away from simulated environments, we look at the influence of real-world robot's shape on the sound source localization. We present a TDOA/GCC based sound source localization method that successfully addresses this influence by utilizing a pre-measured set of TDOAs. The measuring methodology and important aspects of the implementation are thoroughly presented. Finally, an evaluation is made with the humanoid robot Nao. The experimental results are presented and discussed.  Microphone arrays, time delay of arrival, sound source localization, generalized cross correlation.
Analysis of the DVB Common Scrambling Algorithm

The Common Scrambling Algorithm (CSA) is used to encrypt streams of video data in the Digital Video Broadcasting (DVB) system. The algorithm cascades a stream and a block cipher, apparently for a larger security margin. In this paper we set out to analyze the block cipher and the stream cipher separately and give an overview of how they interact with each other. We present a practical attack on the stream cipher. Research on the block cipher so far indicates it to be resistant against linear and algebraic cryptanalysis as well as simple slide attacks.
Unusual penetrating head injury in children: personal experience and review of the literature

A penetrating head injury belongs to the most severe traumatic brain injuries, in which communication can arise between the intracranial cavity and surrounding environment. The authors present a literature review and typical case reports of a penetrating head injury in children. The list of patients treated at the neurosurgical department in the last 5 years for penetrating TBI is briefly referred. Rapid transfer to the specialized center with subsequent urgent surgical treatment is the important point in the treatment algorithm. It is essential to clean the wound very properly with all the foreign material during the surgery and to close the dura with a water-tight suture. Wide-spectrum antibiotics are of great use. In case of large-extent brain damage, the use of anticonvulsants is recommended. The prognosis of such severe trauma could be influenced very positively by a good medical care organization; obviously, the extent of brain tissue laceration is the limiting factor.
PAC-learning in the presence of evasion adversaries

The existence of evasion attacks during the test phase of machine learning algorithms represents a significant challenge to both their deployment and understanding. These attacks can be carried out by adding imperceptible perturbations to inputs to generate adversarial examples and finding effective defenses and detectors has proven to be difficult. In this paper, we step away from the attack-defense arms race and seek to understand the limits of what can be learned in the presence of an evasion adversary. In particular, we extend the Probably Approximately Correct (PAC)-learning framework to account for the presence of an adversary. We first define corrupted hypothesis classes which arise from standard binary hypothesis classes in the presence of an evasion adversary and derive the Vapnik-Chervonenkis (VC)-dimension for these, denoted as the adversarial VC-dimension. We then show that sample complexity upper bounds from the Fundamental Theorem of Statistical learning can be extended to the case of evasion adversaries, where the sample complexity is controlled by the adversarial VC-dimension. We then explicitly derive the adversarial VC-dimension for halfspace classifiers in the presence of a sample-wise norm-constrained adversary of the type commonly studied for evasion attacks and show that it is the same as the standard VC-dimension, closing an open question. Finally, we prove that the adversarial VC-dimension can be either larger or smaller than the standard VC-dimension depending on the hypothesis class and adversary, making it an interesting object of study in its own right.
CoVeR: Learning Covariate-Specific Vector Representations with Tensor Decompositions

Word embedding is a useful approach to capture co-occurrence structures in large text corpora. However, in addition to the text data itself, we often have additional covariates associated with individual corpus documents—e.g. the demographic of the author, time and venue of publication—and we would like the embedding to naturally capture this information. We propose CoVeR, a new tensor decomposition model for vector embeddings with covariates. CoVeR jointly learns a base embedding for all the words as well as a weighted diagonal matrix to model how each covariate affects the base embedding. To obtain author or venue-specific embedding, for example, we can then simply multiply the base embedding by the associated transformation matrix. The main advantages of our approach are data efficiency and interpretability of the covariate transformation. Our experiments demonstrate that our joint model learns substantially better covariate-specific embeddings compared to the standard approach of learning a separate embedding for each covariate using only the relevant subset of data, as well as other related methods. Furthermore, CoVeR encourages the embeddings to be “topic-aligned” in that the dimensions have specific independent meanings. This allows our covariate-specific embeddings to be compared by topic, enabling downstream differential analysis. We empirically evaluate the benefits of our algorithm on datasets, and demonstrate how it can be used to address many natural questions about covariate effects. Accompanying code to this paper can be found at http://github.com/kjtian/CoVeR. Equal contribution Department of Computer Science, Stanford University Department of Management Science and Engineering, Stanford University Department of Biomedical Data Science Stanford University. Correspondence to: Kevin Tian <kjtian@stanford.edu>. Proceedings of the 35 th International Conference on Machine Learning, Stockholm, Sweden, PMLR 80, 2018.
Mechanisms of synesthesia: cognitive and physiological constraints

Synesthesia is a conscious experience of systematically induced sensory attributes that are not experienced by most people under comparable conditions. Recent findings from cognitive psychology, functional brain imaging and electrophysiology have shed considerable light on the nature of synesthesia and its neurocognitive underpinnings. These cognitive and physiological findings are discussed with respect to a neuroanatomical framework comprising hierarchically organized cortical sensory pathways. We advance a neurobiological theory of synesthesia that fits within this neuroanatomical framework.
ReRAM Crossbar based Recurrent Neural Network for human activity detection

We present a programmable high-efficient Recurrent Neural Network (RNN) with Synapses design using Resistive Random Access Memory (ReRAM). The presented ReRAM-RNN employs crossbar ReRAM arrays as synapses. A fast synapses programming methodology is realized by CMOS-based neuron with in-built programming circuitry. The simulations are performed using experimentally verified physical resistive switching model, instead of only functional models, providing better estimate of system speed and power efficiency. Simulation results show that ReRAM-RNN can provide higher computation efficiency and/or more compact design than software realization of RNN, and dedicated CMOS based digital-and analog-RNN. We show that the efficiency improvement of ReRAM-based neural network design is more significant in feedback networks than in feedforward networks.
Beautiful Math, Part 5: Colorful Archimedean Tilings from Dynamical Systems

The art of tiling originated very early in the history of civilization. Almost every known human society has made use of tilings in some form or another. In particular, tilings using only regular polygons have great visual appeal. Decorated regular tilings with continuous and symmetrical patterns were widely used in decoration field, such as mosaics, pavements, and brick walls. In science, these tilings provide inspiration for synthetic organic chemistry. Building on previous CG&A “Beautiful Math” articles, the authors propose an invariant mapping method to create colorful patterns on Archimedean tilings (1-uniform tilings). The resulting patterns simultaneously have global crystallographic symmetry and local cyclic or dihedral symmetry.
Three-dimensional kinematics of competitive and recreational cyclists across different workloads during cycling.

Although the link between sagittal plane motion and exercise intensity has been highlighted, no study assessed if different workloads lead to changes in three-dimensional cycling kinematics. This study compared three-dimensional joint and segment kinematics between competitive and recreational road cyclists across different workloads. Twenty-four road male cyclists (12 competitive and 12 recreational) underwent an incremental workload test to determine aerobic peak power output. In a following session, cyclists performed four trials at sub-maximal workloads (65, 75, 85 and 95% of their aerobic peak power output) at 90 rpm of pedalling cadence. Mean hip adduction, thigh rotation, shank rotation, pelvis inclination (latero-lateral and anterior-posterior), spine inclination and rotation were computed at the power section of the crank cycle (12 o'clock to 6 o'clock crank positions) using three-dimensional kinematics. Greater lateral spine inclination (p < .01, 5-16%, effect sizes = 0.09-0.25) and larger spine rotation (p < .01, 16-29%, effect sizes = 0.31-0.70) were observed for recreational cyclists than competitive cyclists across workload trials. No differences in segment and joint angles were observed from changes in workload with significant individual effects on spine inclination (p < .01). No workload effects were found in segment angles but differences, although small, existed when comparing competitive road to recreational cyclists. When conducting assessment of joint and segment motions, workload between 65 and 95% of individual cyclists' peak power output could be used.
Averaging of motion capture recordings for movements’ templates generation

In this paper we propose, describe and evaluate the novel motion capture (MoCap) data averaging framework. It incorporates hierarchical kinematic model, angle coordinates’ preprocessing methods, that recalculate the original MoCap recording making it applicable for further averaging algorithms, and finally signals averaging processing. We have tested two signal averaging methods namely Kalman Filter (KF) and Dynamic Time Warping barycenter averaging (DBA). The propose methods have been tested on MoCap recordings of elite Karate athlete, multiple champion of Oyama karate knockdown kumite who performed 28 different karate techniques repeated 10 times each. The proposed methods proved to have not only high effectiveness measured with root-mean-square deviation (4.04 ± 5.03 degrees for KF and 5.57 ± 6.27 for DBA) and normalized Dynamic Time Warping distance (0.90 ± 1.58 degrees for KF and 0.93 ± 1.23 for DBA), but also the reconstruction and visualization of those recordings persists all crucial aspects of those complicated actions. The proposed methodology has many important applications in classification, clustering, kinematic analysis and coaching. Our approach generates an averaged full body motion template that can be practically used for example for human actions recognition. In order to prove it we have evaluated templates generated by our method in human action classification tasks using DTW classifier. We have made two experiments. In first leave - one - out cross - validation we have obtained 100% correct recognitions. In second experiment when we classified recordings of one person using templates of another recognition rate 94.2% was obtained.
Scenario and countermeasure for replay attack using join request messages in LoRaWAN

LPWAN (Low Power Wide Area Networks) technologies have been attracting attention continuously in IoT (Internet of Things). LoRaWAN is present on the market as a LPWAN technology and it has features such as low power consumption, low transceiver chip cost and wide coverage area. In the LoRaWAN, end devices must perform a join procedure for participating in the network. Attackers could exploit the join procedure because it has vulnerability in terms of security. Replay attack is a method of exploiting the vulnerability in the join procedure. In this paper, we propose a attack scenario and a countermeasure against replay attack that may occur in the join request transfer process.
Requirements for Energy Efficient Edge Computing: A Survey

Internet of Things is evolving heavily in these times. One of the major obstacle is energy consumption in the IoT devices (sensor nodes and wireless gateways). The IoT devices are often battery powered wireless devices and thus reducing the energy consumption in these devices is essential to lengthen the lifetime of the device without battery change. It is possible to lengthen battery lifetime by efficient but lightweight sensor data analysis in close proximity of the sensor. Performing part of the sensor data analysis in the end device can reduce the amount of data needed to transmit wirelessly. Transmitting data wirelessly is very energy consuming task. At the same time, the privacy and security should not be compromised. It requires effective but computationally lightweight encryption schemes. This survey goes thru many aspects to consider in edge and fog devices to minimize energy consumption and thus lengthen the device and the network lifetime.
Speech Recognition using Artificial Neural Networks and Hidden Markov Models

In this paper, we compare two different methods for automatic Arabic speech recognition for isolated words and sentences. Isolated word/sentence recognition was performed using cepstral feature extraction by linear predictive coding, as well as Hidden Markov Models (HMM) for pattern training and classification. We implemented a new pattern classification method, where we used Neural Networks trained using the Al-Alaoui Algorithm. This new method gave comparable results to the already implemented HMM method for the recognition of words, and it has overcome HMM in the recognition of sentences. The speech recognition system implemented is part of the Teaching and Learning Using Information Technology (TLIT) project which would implement a set of reading lessons to assist adult illiterates in developing better reading capabilities.
A physics-based compact gallium nitride power semiconductor device model for advanced power electronics design

A physics-based compact gallium nitride power semiconductor device model is presented in this work, which is the first of its kind. The model derivation is based on the classical drift-diffusion model of carrier transport, which expresses the channel current as a function of device threshold voltage and externally applied electric fields. The model is implemented in the Saber® circuit simulator using the MAST hardware description language. The model allows the user to extract the parameters from the dc I-V and C-V characteristics that are also available in the device datasheets. A commercial 80 V EPC GaN HEMT is used to demonstrate the dynamic validation of the model against the transient device characteristics in a double-pulse test and a boost converter circuit configuration. The simulated versus measured device characteristics show good agreement and validate the model for power electronics design and applications using the next generation of GaN HEMT devices.