nils-herrmann/clean_scirepeval huggingface dataset:
Diagnosis of retinal health in digital fundus images using continuous wavelet transform (CWT) and entropies

Vision is paramount to humans to lead an active personal and professional life. The prevalence of ocular diseases is rising, and diseases such as glaucoma, Diabetic Retinopathy (DR) and Age-related Macular Degeneration (AMD) are the leading causes of blindness in developed countries. Identifying these diseases in mass screening programmes is time-consuming, labor-intensive and the diagnosis can be subjective. The use of an automated computer aided diagnosis system will reduce the time taken for analysis and will also reduce the inter-observer subjective variabilities in image interpretation. In this work, we propose one such system for the automatic classification of normal from abnormal (DR, AMD, glaucoma) images. We had a total of 404 normal and 1082 abnormal fundus images in our database. As the first step, 2D-Continuous Wavelet Transform (CWT) decomposition on the fundus images of two classes was performed. Subsequently, energy features and various entropies namely Yager, Renyi, Kapoor, Shannon, and Fuzzy were extracted from the decomposed images. Then, adaptive synthetic sampling approach was applied to balance the normal and abnormal datasets. Next, the extracted features were ranked according to the significances using Particle Swarm Optimization (PSO). Thereupon, the ranked and selected features were used to train the random forest classifier using stratified 10-fold cross validation. Overall, the proposed system presented a performance rate of 92.48%, and a sensitivity and specificity of 89.37% and 95.58% respectively using 15 features. This novel system shows promise in detecting abnormal fundus images, and hence, could be a valuable adjunct eye health screening tool that could be employed in polyclinics, and thereby reduce the workload of specialists at hospitals.
Avoiding monotony: improving the diversity of recommendation lists

The primary premise upon which top-N recommender systems operate is that similar users are likely to have similar tastes with regard to their product choices. For this reason, recommender algorithms depend deeply on similarity metrics to build the recommendation lists for end-users.
 However, it has been noted that the products offered on recommendation lists are often too similar to each other and attention has been paid towards the goal of improving diversity to avoid monotonous recommendations.
 Noting that the retrieval of a set of items matching a user query is a common problem across many applications of information retrieval, we model the competing goals of maximizing the diversity of the retrieved list while maintaining adequate similarity to the user query as a binary optimization problem. We explore a solution strategy to this optimization problem by relaxing it to a trust-region problem.This leads to a parameterized eigenvalue problem whose solution is finally quantized to the required binary solution. We apply this approach to the top-N prediction problem, evaluate the system performance on the Movielens dataset and compare it with a standard item-based top-N algorithm. A new evaluation metric ItemNovelty is proposed in this work. Improvements on both diversity and accuracy are obtained compared to the benchmark algorithm.
Estimating and Interpreting The Instantaneous Frequency of a Signal-Part 1 : Fundamentals

The frequency of a sinusoidal signal is a well defined quantity. However, often in practice, signals are not truly sinusoidal, or even aggregates of sinusoidal components. Nonstationary signals in particular do not lend themselves well to decomposition into sinusoidal components. For such signals, the notion of frequency loses its effectiveness, and one needs to use a parameter which accounts for the time-varying nature of the process. This need has given rise to the idea of instantaneous frequency. The instantaneous frequency (IF) of a signal is a parameter which is often of significant practical importance. In many situations such as seismic, radar, sonar, communications, and biomedical applications, the IF is a good descriptor of some physical phenomenon. This paper discusses the concept of instantaneous frequency, its definitions, and the correspondence between the various mathematical models formulated for representation of IF. The paper also considers the extent to which the IF corresponds to our intuitive expectation of reality. A historical review of the successive attempts to define the IF is presented. Then the relationships between the IF and the group-delay, analytic signal, and bandwidth-time (BT) product are explored, as well as the relationship with time-frequency distributions. Finally, the notions of monocomponent and multicomponent signals, and instantaneous bandwidth are discussed. It is shown that all these notions are well described in the context of the theory presented.
Deep Prior

The recent literature on deep learning offers new tools to learn a rich probability distribution over high dimensional data such as images or sounds. In this work we investigate the possibility of learning the prior distribution over neural network parameters using such tools. Our resulting variational Bayes algorithm generalizes well to new tasks, even when very few training examples are provided. Furthermore, this learned prior allows the model to extrapolate correctly far from a given task’s training data on a meta-dataset of periodic signals. 1 Learning a Rich Prior Bayesian Neural Networks [1, 2, 3, 4] are now scalable and can be used to estimate prediction uncertainty and model uncertainty . While many efforts focus on better approximation of the posterior, we believe that the quality of the uncertainty highly depends on the choice of the prior. Hence, we consider learning a prior from previous tasks by learning a probability distribution p(w|α) over the weights w of a network, parameterized by α, and leveraging this learned prior to reduce sample complexity on new tasks. More formally we consider a hierarchical Bayes approach across N tasks, with hyper-prior p(α). Each task has its own parameters wj , withW = {wj}j=1. Using all datasets D = {Sj}j=1, we have the following posterior:1 p(W, α|D) = p(α|D) ∏ j p(wj |α, Sj) ∝ p(D|W)p(W|α)p(α) ∝ ∏
An Optimized Floyd Algorithm for the Shortest Path Problem

There is too much iteration when using the traditional Floyd algorithm to calculate the shortest path, that is to say, the computation of traditional Floyd algorithm is too large for an urban road network with a large number of nodes. As for this disadvantage, the Floyd algorithm was improved and optimized further in this study; moreover, the improved Floyd algorithm was used to solve the shortest path problem in car navigation system, and it achieved good results. Two improvements were done: firstly, construct an iterative matrix for solving the shortest path, all the nodes in it were compared at first, and those nodes which have nothing to do with the results were removed, then search for the next node directly to reduce the frequency of iteration; Secondly, construct a serial number matrix for solving the shortest path, it was used to record the case of inserting node in the process of iteration. Finally, the frequency of iteration and the computational complexity were compared for both the traditional Floyd algorithm and the improved Floyd algorithm. The experimental results and analysis showed that the computational complexity of the improved Floyd algorithm has reduced to half of that of the traditional algorithm. What’s more, by checking the iterative matrix and the serial number matrix, the shortest path can be found out simply, intuitively and efficiently.
Impact of FinFET Technology Introduction in the 3T1D-DRAM Memory Cell

In this paper, the 3T1D-DRAM cell based on FinFET devices is studied as an alternative to the bulk one. We observe an improvement in its behavior when IG and SG FinFETs are properly mixed, since together they provide a relevant increase in the memory circuit retention time. Moreover, our FinFET cell shows larger variability robustness, better performance at low supply voltage, and higher tolerance to elevated temperatures.
Making Financial Trading by Recurrent Reinforcement Learning

In this paper we propose a financial trading system whose strategy is developed by means of an artificial neural network approach based on a recurrent reinforcement learning algorithm. In general terms, this kind of approach consists in specifying a trading policy based on some predetermined investor’s measure of profitability, and in setting the financial trading system while using it. In particular, with respect to the prominent literature, in this contribution: first, we take into account as measure of profitability the reciprocal of the returns weighted direction symmetry index instead of the wide-spread Sharpe ratio; second, we obtain the differential version of this measure of profitability and obtain all the related learning relationships; third, we propose a procedure for the management of drawdown-like phenomena; finally, we apply our financial trading approach to some of the major world financial market indices.
Transferring, Translating, and Transforming: An Integrative Framework for Managing Knowledge Across Boundaries

The paper examines managing knowledge across boundaries in settings where innovation is desired. Innovation is a useful context because it allows us to explore the negative consequences of the path-dependent nature of knowledge. A framework is developed that describes three progressively complex boundaries—syntactic, semantic, and pragmatic— and three progressively complex processes—transfer, translation, and transformation. The framework is used to specify the practical and political mismatches that occur when innovation is desired and how this relates to the common knowledge that actors use to share and assess each other’s domain-specific knowledge. The development and use of a collaborative engineering tool in the early stages of a vehicle’s development is presented to illustrate the conceptual and prescriptive value of the framework. The implication of this framework on key topics in the organization theory and strategy literatures is then discussed.
Architectural Innovation : The Reconfiguration of Existing Product Technologies and the Failure of Established Firms

The Reconfiguration of Existing Product Technologies and the Failure of Established Firms Author(s): Rebecca M. Henderson and Kim B. Clark Source: Administrative Science Quarterly, Vol. 35, No. 1, Special Issue: Technology, Organizations, and Innovation (Mar., 1990), pp. 9-30 Published by: Johnson Graduate School of Management, Cornell University Stable URL: http://www.jstor.org/stable/2393549 Accessed: 24/08/2008 13:09
An information security knowledge sharing model in organizations

Knowledge sharing plays an important role in the domain of information security, due to its positive effect on employees' information security awareness. It is acknowledged that security awareness is the most important factor that mitigates the risk of information security breaches in organizations. In this research, a model has been presented that shows how information security knowledge sharing (ISKS) forms and decreases the risk of information security incidents. The Motivation Theory and Theory of Planned Behavior besides Triandis model were applied as the theoretical backbone of the conceptual framework. The results of the data analysis showed that earning a reputation, and gaining promotion as an extrinsic motivation and curiosity satisfaction as an intrinsic motivation have positive effects on employees' attitude toward ISKS. However, self-worth satisfaction does not influence ISKS attitude. In addition, the findings revealed that attitude, perceived behavioral control, and subjective norms have positive effects on ISKS intention and ISKS intention affects ISKS behavior. The outcomes also showed that organizational support influences ISKS behavior more than trust. The results of this research should be of interest to academics and practitioners in the domain of information security.
Depth-Gated LSTM

In this short note, we present an extension of long short-term memory (LSTM) neural networks to using a depth gate to connect memory cells of adjacent layers. Doing so introduces a linear dependence between lower and upper layer recurrent units. Importantly, the linear dependence is gated through a gating function, which we call depth gate. This gate is a function of the lower layer memory cell, the input to and the past memory cell of this layer. We conducted experiments and verified that this new architecture of LSTMs was able to improve machine translation and language modeling performances.
Variations in recorded acoustic gunshot waveforms generated by small firearms.

Analysis of recorded acoustic gunshot signals to determine firearm waveform characteristics requires an understanding of the impulsive signal events, how the waveforms vary among different sources, and how the waveforms are affected by the environment and the recording system. This paper presents empirical results from waveforms produced by different small firearms and an analysis of their variations under different and controlled conditions. Acoustic signals were generated using multiple firearm makes and models firing different ammunition types. Simultaneous recordings from the microphones located at different distances from the source and at different azimuth angles (from the line-of-fire) were used to study source characteristics and sound propagation effects. The results indicate that recorded gunshot waveforms generally consist of multiple acoustic events, and these are observable depending on the received distance and azimuth angle. The source blast size, microphone distance, and microphone azimuth angle are the primary factors affecting the recorded muzzle blast characteristics. Ground or object reflections and ballistic shockwaves and their reflections can interfere with the muzzle blast waveform and its measurements. This experiment confirmed and quantified the wide range of correlation results between waveforms recorded from different source, microphone distance, and microphone angle configurations.
Kickback noise reduction techniques for CMOS latched comparators

The latched comparator is a building block of virtually all analog-to-digital converter architectures. It uses a positive feedback mechanism to regenerate the analog input signal into a full-scale digital level. The large voltage variations in the internal nodes are coupled to the input, disturbing the input voltage-this is usually called kickback noise. This brief reviews existing solutions to minimize the kickback noise and proposes two new ones. HSPICE simulations of comparators implemented in a 0.18-/spl mu/m technology demonstrate their effectiveness.
Cognitive signals for brain-machine interfaces in posterior parietal cortex include continuous 3D trajectory commands.

Cortical neural prosthetics extract command signals from the brain with the goal to restore function in paralyzed or amputated patients. Continuous control signals can be extracted from the motor cortical areas, whereas neural activity from posterior parietal cortex (PPC) can be used to decode cognitive variables related to the goals of movement. Because typical activities of daily living comprise both continuous control tasks such as reaching, and tasks benefiting from discrete control such as typing on a keyboard, availability of both signals simultaneously would promise significant increases in performance and versatility. Here, we show that PPC can provide 3D hand trajectory information under natural conditions that would be encountered for prosthetic applications, thus allowing simultaneous extraction of continuous and discrete signals without requiring multisite surgical implants. We found that limb movements can be decoded robustly and with high accuracy from a small population of neural units under free gaze in a complex 3D point-to-point reaching task. Both animals' brain-control performance improved rapidly with practice, resulting in faster target acquisition and increasing accuracy. These findings disprove the notion that the motor cortical areas are the only candidate areas for continuous prosthetic command signals and, rather, suggests that PPC can provide equally useful trajectory signals in addition to discrete, cognitive variables. Hybrid use of continuous and discrete signals from PPC may enable a new generation of neural prostheses providing superior performance and additional flexibility in addressing individual patient needs.
Expressive power of recurrent neural networks

Deep neural networks are surprisingly efficient at solving practical tasks, but the theory behind this phenomenon is only starting to catch up with the practice. Numerous works show that depth is the key to this efficiency. A certain class of deep convolutional networks – namely those that correspond to the Hierarchical Tucker (HT) tensor decomposition – has been proven to have exponentially higher expressive power than shallow networks. I.e. a shallow network of exponential width is required to realize the same score function as computed by the deep architecture. In this paper, we prove the expressive power theorem (an exponential lower bound on the width of the equivalent shallow network) for a class of recurrent neural networks – ones that correspond to the Tensor Train (TT) decomposition. This means that even processing an image patch by patch with an RNN can be exponentially more efficient than a (shallow) convolutional network with one hidden layer. Using theoretical results on the relation between the tensor decompositions we compare expressive powers of the HTand TT-Networks. We also implement the recurrent TT-Networks and provide numerical evidence of their expressivity.
Job shop scheduling

Then×m minimum-makespan general job-shop scheduling problem, hereafter referred to as the JSSP, can be described by a set of n jobs{Ji}1≤j≤n which is to be processed on a set of m machines{Mr}1≤r≤m. Each job has a technological sequence of machines to be processed. The processing of job Jj on machineMr is called theoperationOjr. OperationOjr requires the exclusive use of Mr for an uninterrupted durationpjr, its processing time. Ascheduleis a set of completion times for each operation {cjr}1≤j≤n,1≤r≤m that satisfies those constraints. The time required to complete all the jobs is called the makespanL. The objective when solving or optimizing this general problem is to determine t h schedule which minimizesL. An example of a3 × 3 JSSP is given in Table 7.1. The data includes the
Recent Development in Big Data Analytics for Business Operations and Risk Management

“Big data” is an emerging topic and has attracted the attention of many researchers and practitioners in industrial systems engineering and cybernetics. Big data analytics would definitely lead to valuable knowledge for many organizations. Business operations and risk management can be a beneficiary as there are many data collection channels in the related industrial systems (e.g., wireless sensor networks, Internet-based systems, etc.). Big data research, however, is still in its infancy. Its focus is rather unclear and related studies are not well amalgamated. This paper aims to present the challenges and opportunities of big data analytics in this unique application domain. Technological development and advances for industrial-based business systems, reliability and security of industrial systems, and their operational risk management are examined. Important areas for future research are also discussed and revealed.
Real-time automatic tag recommendation

Tags are user-generated labels for entities. Existing research on tag recommendation either focuses on improving its accuracy or on automating the process, while ignoring the efficiency issue. We propose a highly-automated novel framework for real-time tag recommendation. The tagged training documents are treated as triplets of (words, docs, tags), and represented in two bipartite graphs, which are partitioned into clusters by Spectral Recursive Embedding (SRE). Tags in each topical cluster are ranked by our novel ranking algorithm. A two-way Poisson Mixture Model (PMM) is proposed to model the document distribution into mixture components within each cluster and aggregate words into word clusters simultaneously. A new document is classified by the mixture model based on its posterior probabilities so that tags are recommended according to their ranks. Experiments on large-scale tagging datasets of scientific documents (CiteULike) and web pages del.icio.us) indicate that our framework is capable of making tag recommendation efficiently and effectively. The average tagging time for testing a document is around 1 second, with over 88% test documents correctly labeled with the top nine tags we suggested.
A review of RFID localization: Applications and techniques

Indoor localization has been actively researched recently due to security and safety as well as service matters. Previous research and development for indoor localization includes infrared, wireless LAN and ultrasonic. However, these technologies suffer either from the limited accuracy or lacking of the infrastructure. Radio Frequency Identification (RFID) is very attractive because of reasonable system price, and reader reliability. The RFID localization can be categorized into tag and reader localizations. In this paper, major localization techniques for both tag and reader localizations are reviewed to provide the readers state of the art of the indoor localization algorithms. The advantage and disadvantage of each technique for particular applications were also discussed.
Blood Code: The History and Future of Video Game Censorship

573 II. THE ANALOGOUS HISTORIES OF FILMS AND VIDEO GAMES ....................................................................................... 576 A. Film Controversy and the Formation of the MPAA ................ 576 B. Early Video Game Controversy and the Formation of the ESRB ................................................................................... 580 C. Doom and Columbine ........................................................... 584 D. Jack Thompson and Grand Theft Auto ................................... 586 III. WHY VIDEO GAMES SHOULD NOT BE TREATED DIFFERENTLY THAN FILMS .................................................... 593 A. Violent and Sexual Content in Video Games is Distinguishable from Pornography and Obscenity. .................. 594 B. Violent Game Content is Similar to Violent Film Content. ..... 596 C. Positive Social Aspects of Violent Gaming............................... 597 D. Desensitization Will Lead to a Decrease in Political Outrage. ............................................................................... 604 IV. EXISTING VIDEO GAME JURISPRUDENCE .............................. 605 V. RATINGS AND LABELS AS UNCONSTITUTIONAL CENSORSHIP.............................................................................. 607 CONCLUSION ....................................................................................... 609