nils-herrmann/clean_scirepeval huggingface dataset:
Detection of plant disease using threshold, k-mean cluster and ann algorithm

Indian economy highly depends on agricultural productivity. An important role is played by the detection of disease to obtain a perfect results in agriculture, and it is natural to have disease in plants. Proper care should be taken in this area for product quality and quantity. To reduce the large amount of monitoring in field automatic detection techniques can be used. This paper discuss different processes for segmentation technique which can be applied for different lesion disease detection. Thresholding and K-means cluster algorithms are done to detect different diseases in plant leaf.
Multi-scale Temporal Modeling for Dimensional Emotion Recognition in Video

Understanding nonverbal behaviors in human machine interaction is a complex and challenge task. One of the key aspects is to recognize human emotion states accurately. This paper presents our effort to the Audio/Visual Emotion Challenge (AVEC'14), whose goal is to predict the continuous values of the emotion dimensions arousal, valence and dominance at each moment in time. The proposed method utilizes deep belief network based models to recognize emotion states from audio and visual modalities. Firstly, we employ temporal pooling functions in the deep neutral network to encode dynamic information in the features, which achieves the first time scale temporal modeling. Secondly, we combine the predicted results from different modalities and emotion temporal context information simultaneously. The proposed multimodal-temporal fusion achieves temporal modeling for the emotion states in the second time scale. Experiments results show the efficiency of each key point of the proposed method and competitive results are obtained
Brain Computer Interface Design Using Band Powers Extracted During Mental Tasks

In this paper, a brain computer interface (BCI) is designed using electroencephalogram (EEG) signals where the subjects have to think of only a single mental task. The method uses spectral power and power difference in 4 bands: delta and theta, beta, alpha and gamma. This could be used as an alternative to the existing BCI designs that require classification of several mental tasks. In addition, an attempt is made to show that different subjects require different mental task for minimising the error in BCI output. In the experimental study, EEG signals were recorded from 4 subjects while they were thinking of 4 different mental tasks. Combinations of resting (baseline) state and another mental task are studied at a time for each subject. Spectral powers in the 4 bands from 6 channels are computed using the energy of the elliptic FIR filter output. The mental tasks are detected by a neural network classifier. The results show that classification accuracy up to 97.5% is possible, provided that the most suitable mental task is used. As an application, the proposed method could be used to move a cursor on the screen. If cursor movement is used with a translation scheme like Morse code, the subjects could use the proposed BCI for constructing letters/words. This would be very useful for paralysed individuals to communicate with their external surroundings
SIMD-Oriented Fast Mersenne Twister: a 128-bit Pseudorandom Number Generator

Mersenne Twister (MT) is a widely-used fast pseudorandom number generator (PRNG) with a long period of 2 − 1, designed 10 years ago based on 32-bit operations. In this decade, CPUs for personal computers have acquired new features, such as Single Instruction Multiple Data (SIMD) operations (i.e., 128bit operations) and multi-stage pipelines. Here we propose a 128-bit based PRNG, named SIMD-oriented Fast Mersenne Twister (SFMT), which is analogous to MT but making full use of these features. Its recursion fits pipeline processing better than MT, and it is roughly twice as fast as optimised MT using SIMD operations. Moreover, the dimension of equidistribution of SFMT is better than MT. We also introduce a block-generation function, which fills an array of 32-bit integers in one call. It speeds up the generation by a factor of two. A speed comparison with other modern generators, such as multiplicative recursive generators, shows an advantage of SFMT. The implemented C-codes are downloadable from http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/SFMT/index.html.
Development of a PM-generator for a counter-rotating micro-hydro turbine

Nowadays, a great effort is done to find new alternative renewable energy sources to replace part of nuclear energy production. In this context, this paper presents a new axial counter-rotating turbine for small-hydro applications which is developed to recover the energy lost in release valves of water supply. The design of the two PM-generators, their mechanical integration in a bulb placed into the water conduit and the AC-DC Vienna converter developed for these turbines are presented. The sensorless regulation of the two generators is also briefly discussed. Finally, measurements done on the 2-kW prototype are analyzed and compared with the simulation.
On the Economics of Ransomware

While recognized as a theoretical and practical concept for over 20 years, only now ransomware has taken centerstage as one of the most prevalent cybercrimes. Various reports demonstrate the enormous burden placed on companies, which have to grapple with the ongoing attack waves. At the same time, our strategic understanding of the threat and the adversarial interaction between organizations and cybercriminals perpetrating ransomware attacks is lacking. In this paper, we develop, to the best of our knowledge, the first gametheoretic model of the ransomware ecosystem. Our model captures a multi-stage scenario involving organizations from different industry sectors facing a sophisticated ransomware attacker. We place particular emphasis on the decision of companies to invest in backup technologies as part of a contingency plan, and the economic incentives to pay a ransom if impacted by an attack. We further study to which degree comprehensive industry-wide backup investments can serve as a deterrent for ongoing attacks.
Understanding the medial ulnar collateral ligament of the elbow: Review of native ligament anatomy and function

The medial ulnar collateral ligament complex of the elbow, which is comprised of the anterior bundle [AB, more formally referred to as the medial ulnar collateral ligament (MUCL)], posterior (PB), and transverse ligament, is commonly injured in overhead throwing athletes. Attenuation or rupture of the ligament results in valgus instability with variable clinical presentations. The AB or MUCL is the strongest component of the ligamentous complex and the primary restraint to valgus stress. It is also composed of two separate bands (anterior and posterior) that provide reciprocal function with the anterior band tight in extension, and the posterior band tight in flexion. In individuals who fail comprehensive non-operative treatment, surgical repair or reconstruction of the MUCL is commonly required to restore elbow function and stability. A comprehensive understanding of the anatomy and biomechanical properties of the MUCL is imperative to optimize reconstructive efforts, and to enhance clinical and radiographic outcomes. Our understanding of the native anatomy and biomechanics of the MUCL has evolved over time. The precise locations of the origin and insertion footprint centers guide surgeons in proper graft placement with relation to bony anatomic landmarks. In recent studies, the ulnar insertion of the MUCL is described as larger than previously thought, with the center of the footprint at varying distances relative to the ulnar ridge, joint line, or sublime tubercle. The purpose of this review is to consolidate and summarize the existing literature regarding the native anatomy, biomechanical, and clinical significance of the entire medial ulnar collateral ligament complex, including the MUCL (AB), PB, and transverse ligament.
Wegener's granulomatosis: clinical manifestations, differential diagnosis, and management of ocular and systemic disease.

Wegener's granulomatosis (WG) is a systemic inflammatory disease whose histopathologic features often include necrosis, granuloma formation, and vasculitis of small-to-medium-sized vessels. WG involves many interrelated pathogenic pathways that are genetic, cell-mediated, neutrophil-mediated, humoral, and environmental. WG most commonly involves the upper respiratory tract, lungs, and kidneys, but has been reported to affect almost any organ. Ophthalmologic involvement is an important cause of morbidity in WG patients, occurring in approximately one-half of patients. The presence of unexplained orbital inflammatory disease, scleritis, peripheral ulcerative keratitis, cicatricial conjunctivitis, nasolacrimal duct stenosis, retinal vascular occlusion, or infrequently uveitis should raise the question of possible WG. A thorough clinical examination, laboratory testing, radiologic imaging, and histologic examination are essential to diagnosing WG and excluding potential mimics. Previously a uniformly fatal disease, treatment with cytotoxic and immunosuppressive agents has greatly improved survival. Treatment-related morbidity is a serious limitation of conventional therapies, leading to numerous ongoing studies of alternative agents.
Preparation and Application of Standardized Typical Volatile Components Fraction from Turmeric (Curcuma longa L.) by Supercritical Fluid Extraction and Step Molecular Distillation

A green and reliable method using supercritical fluid extraction (SFE) and molecular distillation (MD) was optimized for the separation and purification of standardized typical volatile components fraction (STVCF) from turmeric to solve the shortage of reference compounds in quality control (QC) of volatile components. A high quality essential oil with 76.0% typical components of turmeric was extracted by SFE. A sequential distillation strategy was performed by MD. The total recovery and purity of prepared STVCF were 97.3% and 90.3%, respectively. Additionally, a strategy, i.e., STVCF-based qualification and quantitative evaluation of major bioactive analytes by multiple calibrated components, was proposed to easily and effectively control the quality of turmeric. Compared with the individual calibration curve method, the STVCF-based quantification method was demonstrated to be credible and was effectively adapted for solving the shortage of reference volatile compounds and improving the QC of typical volatile components in turmeric, especially its functional products.
A Quick Startup Technique for High- $Q$ Oscillators Using Precisely Timed Energy Injection

High-quality (<inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula>) oscillators are notorious for being extremely slow during startup. Their long startup time increases the average power consumption in duty-cycled systems. This paper presents a novel precisely timed energy injection technique to speed up the startup behavior of high-<inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> oscillators. The proposed solution is also insensitive to the frequency variations of the injection signal over a wide enough range that makes it possible to employ an integrated oscillator to provide the injection signal. A theoretical analysis is carried out to calculate the optimal injection duration. As a proof-of-concept, the proposed technique is incorporated in the design of crystal oscillators and is realized in a TSMC 65-nm CMOS technology. To verify the robustness of our technique across resonator parameters and frequency variations, six crystal resonators from different manufacturers with different packagings and <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> factors were tested. The implemented IC includes multiple crystal oscillators at 1.84, 10, and 50 MHz frequencies, with measured startup times of 58, 10, and 2 <inline-formula> <tex-math notation="LaTeX">$\mu \text{s}$ </tex-math></inline-formula>, while consuming 6.7, 45.5, and 195 <inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula> at steady state, respectively. To the authors’ best knowledge, this is the fastest, reported startup time in the literature, with >15<inline-formula> <tex-math notation="LaTeX">$\times$ </tex-math></inline-formula> improvement over prior art, while requiring the smallest startup energy (~12 nJ).
[Auricular malformations].

Reconstruction of complex auricular malformations is one of the longest surgical technique to master, because it requires an extremely detailed analysis of the anomaly and of the skin potential, as well as a to learn how to carve a complex 3D structure in costal cartilage. Small anomalies can be taken care of by any plastic surgeon, providing that he/she is aware of all the refinements of ear surgery. In this chapter, we analyze retrospectively 30 years of auricular reconstruction, ranging from small anomalies to microtia (2500 cases), excluding aesthetics variants such as prominent ears.
Enhancing CNN Incremental Learning Capability with an Expanded Network

One fundamental problem of the convolutional neural network (CNN) is catastrophic forgetting, which occurs when new object classes and data are added while the original dataset is not available any more. Training the network only using the new dataset deteriorates the performance with respect to the old dataset. To overcome this problem, we propose an expanded network architecture, called the ExpandNet, to enhance the CNN incremental learning capability. Our solution keeps filters of the original networks on one hand, yet adds additional filters to the convolutional layers as well as the fully connected layers on the other hand. The proposed new architecture does not need any information of the original dataset, and it is trained using the new dataset only. Extensive evaluations based on the CIFAR −10 and the CIFAR −100 datasets show that the proposed method has a slower forgetting rate as compared to several existing incremental learning networks.
TailCutter: Wisely cutting tail latency in cloud CDN under cost constraints

Cloud computing platforms enable applications to offer low latency access to user data by offering storage services in several geographically distributed data centers. In this paper, we identify the high tail latency problem in cloud CDN via analyzing a large-scale dataset collected from 783,944 users in a major cloud CDN. We find that the data downloading latency in cloud CDN is highly variable, which may significantly degrade the user experience of applications. To address the problem, we present TailCutter, a workload scheduling mechanism that aims at optimizing the tail latency while meeting the cost constraint given by application providers. We further design the Maximum Tail Minimization Algorithm (MTMA) working in TailCutter mechanism to optimally solve the Tail Latency Minimization (TLM) problem in polynomial time. We implement TailCutter across data centers of Amazon S3 and Microsoft Azure. Our extensive evaluation using large-scale real world data traces shows that TailCutter can reduce up to 68% 99th percentile user-perceived latency in comparison with alternative solutions under cost constraints.
Fast Algebraic Rewriting Based on And-Inverter Graphs

Constructing algebraic polynomials using computer algebra techniques is believed to be state-of-the-art in analyzing gate-level arithmetic circuits. However, the existing approach applies algebraic rewriting directly to the gate-level netlist, which has potential memory explosion problem. This paper introduces an algebraic rewriting technique based on the and-inverter graph (AIG) representation of gate-level designs. Using AIG-based cut-enumeration and truth table computation, an efficient order of algebraic rewriting is identified, resulting in dramatic simplifications of the polynomial under construction. An automatic approach, which further reduces the complexity of algebraic rewriting by handling redundant polynomials, is also proposed.
Psychophysical elements of wearability

Wearable technology presents a wealth of new HCI issues. In particular, this paper addresses the impact of the physical interaction between the user's body and the device's physical form on the user's mental representation of self and cognitive abilities, a blend of HCI and ergonomics that is unique to wearable computing. We explore the human sensory mechanisms that facilitate perception of worn objects and the elements of sensation that influence the comfort of worn objects, and discuss the psychological elements that may cause worn objects to be forgotten or detected, wearable or not. We discuss the implications of un-wearability on attention and cognitive capability.
Action Branching Architectures for Deep Reinforcement Learning

Discrete-action algorithms have been central to numerous recent successes of deep reinforcement learning. However, applying these algorithms to high-dimensional action tasks requires tackling the combinatorial increase of the number of possible actions with the number of action dimensions. This problem is further exacerbated for continuous-action tasks that require fine control of actions via discretization. In this paper, we propose a novel neural architecture featuring a shared decision module followed by several network branches, one for each action dimension. This approach achieves a linear increase of the number of network outputs with the number of degrees of freedom by allowing a level of independence for each individual action dimension. To illustrate the approach, we present a novel agent, called Branching Dueling Q-Network (BDQ), as a branching variant of the Dueling Double Deep Q-Network (Dueling DDQN). We evaluate the performance of our agent on a set of challenging continuous control tasks. The empirical results show that the proposed agent scales gracefully to environments with increasing action dimensionality and indicate the significance of the shared decision module in coordination of the distributed action branches. Furthermore, we show that the proposed agent performs competitively against a state-of-the-art continuous control algorithm, Deep Deterministic Policy Gradient (DDPG).
Tunable metamaterials for optimization of wireless power transfer systems

Metamaterials (MMs) have been proposed to improve the performance of wireless power transfer (WPT) systems. The performance of identical unit cells having the same transmitter and receiver self-resonance is presented in the literature. This paper presents the optimization of tunable MM for performance improvement in WPT systems. Furthermore, a figure of merit (FOM) is proposed for the optimization of WPT systems with MMs. It is found that both transferred power and power transfer efficiency can be improved significantly by using the proposed FOM and tunable MM, particularly under misaligned conditions.
Recommender Systems - Introduction to the Special Section

Recommender systems assist and augment this natural social process. In a typical recommender system people provide recommendations as inputs, which the system then aggregates and directs to appropriate recipients. In some cases the primary transformation is in the aggregation; in others the system’s value lies in its ability to make good matches between the recommenders and those seeking recommendations. The developers of the first recommender system, Tapestry , coined the phrase “collaborative filtering” and several others have adopted it. We prefer the more general term “recommender system” for two reasons. First, recommenders may not explictly collaborate with recipients, who may be unknown to each other. Second, recommendations may suggest particularly interesting items, in addition to indicating those that should be filtered out. This special section includes descriptions of five recommender systems. A sixth article analyzes incentives for provision of recommendations. Figure 1 places the systems in a technical design space defined by five dimensions. First, the contents of an evaluation can be anything from a single bit (recommended or not) to unstructured textual annotations. Second, recommendations may be entered explicitly, but several systems gather implicit evaluations: GroupLens monitors users’ reading times; PHOAKS mines Usenet articles for mentions of URLs; and Siteseer mines personal bookmark lists. Third, recommendations may be anonymous, tagged with the source’s identity, or tagged with a pseudonym. The fourth dimension, and one of the richest areas for exploration, is how to aggregate evaluations. GroupLens, PHOAKS, and Siteseer employ variants on weighted voting. Fab takes that one step further to combine evaluations with content analysis. ReferralWeb combines suggested links between people to form longer referral chains. Finally, the (perhaps aggregated) evaluations may be used in several ways: negative recommendations may be filtered out, the items may be sorted according to numeric evaluations, or evaluations may accompany items in a display. Figures 2 and 3 identify dimensions of the domain space: The kinds of items being recommended and the people among whom evaluations are shared. Consider, first, the domain of items. The sheer volume is an important variable: Detailed textual reviews of restaurants or movies may be practical, but applying the same approach to thousands of daily Netnews messages would not. Ephemeral media such as netnews (most news servers throw away articles after one or two weeks) place a premium on gathering and distributing evaluations quickly, while evaluations for 19th century books can be gathered at a more leisurely pace. The last dimension describes the cost structure of choices people make about the items. Is it very costly to miss IT IS OFTEN NECESSARY TO MAKE CHOICES WITHOUT SUFFICIENT personal experience of the alternatives. In everyday life, we rely on
Adaptation to progressive lenses by presbyopes

All humans will become presbyopic as part of the aging process where the eye losses the ability to focus at different depths. Progressive additive lenses (PALs) allow a person to focus on objects located at near versus far by combing lenses of different strengths within the same spectacle. However, it is unknown why some patients easily adapt to wearing these lenses while others struggle and complain of vertigo, swim, and nausea as well as experience difficulties with balance. Sixteen presbyopes (nine who adapted to PALs and seven who had tried but could not adapt) participated in this study. This research investigated vergence dynamics and its adaptation using a short-term motor learning experiment to asses the ability to adapt. Vergence dynamics were on average faster and the ability to change vergence dynamics was also greater for presbyopes who adapted to progressive lenses compared to those who could not. Data suggest that vergence dynamics and its adaptation may be used to predict which patients will easily adapt to progressive lenses and discern those who will have difficulty.