nils-herrmann/clean_scirepeval huggingface dataset:
Early Diagnosis of Dementia from Clinical Data by Machine Learning Techniques

Dementia is the most prevalent degenerative disease in seniors in which progression can be prevented or delayed by early diagnosis. In this study, we proposed a two-layer model inspired by the method used in dementia support centers for the early diagnosis of dementia and using machine learning techniques. Data were collected from patients who received dementia screening from 2008 to 2013 at the Gangbuk-Gu center for dementia in the Republic of Korea. The data consisted of the patient’s gender, age, education, the Mini-Mental State Examination in the Korean version of the CERAD Assessment Packet (MMSE-KC) for dementia screening test, and the Korean version of the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD-K) for the dementia precise test. In the proposed model, MMSE-KC data are initially classified into normal and abnormal. In the second stage, CERAD-K data are used to classify dementia and mild cognitive impairment. The performance of each algorithm is compared with that of Naive Bayes, Bayes Network, Begging, Logistic Regression, Random Forest, Support Vector Machine (SVM) and Multilayer Perceptron (MLP) using Precision, Recall and F-measure. Comparing the F-measure values of normal, mild cognitive impairment (MCI), and dementia, the MLP was the highest in the F-measure values of normal with 0.97, while the SVM appear to be the highest in MCI and dementia with 0.739. Using the proposed early diagnosis model for dementia reduces the time and economic burden and can help simplify the diagnosis method for dementia.
Probabilistic MDP-behavior planning for cars

This paper presents a method for high-level decision making in traffic environments. In contrast to the usual approach of modeling decision policies by hand, a Markov Decision Process (MDP) is employed to plan the optimal policy by assessing the outcomes of actions. Using probability theory, decisions are deduced automatically from the knowledge about how road users behave over time. This approach does neither depend on an explicit situation recognition nor is it limited to only a variety of situations or types of descriptions. Hence it is versatile and powerful. The contribution of this paper is a mathematical framework to derive abstract symbolic states from complex continuous temporal models encoded as Dynamic Bayesian Networks (DBN). For this purpose discrete MDP states are interpreted by random variables. To make computation feasible this space grows adaptively during planning and according to the problem to be solved.
Dense Dilated Network for Few Shot Action Recognition

Recently, video action recognition has been widely studied. Training deep neural networks requires a large amount of well-labeled videos. On the other hand, videos in the same class share high-level semantic similarity. In this paper, we introduce a novel neural network architecture to simultaneously capture local and long-term spatial temporal information. The dilated dense network is proposed with the blocks being composed of densely-connected dilated convolutions layers. The proposed framework is capable of fusing each layer's outputs to learn high-level representations, and the representations are robust even with only few training snippets. The aggregations of dilated dense blocks are also explored. We conduct extensive experiments on UCF101 and demonstrate the effectiveness of our proposed method, especially with few training examples.
Proofs of Work for Blockchain Protocols

One of the most impactful applications of proofs of work (POW) currently is in the design of blockchain protocols such as Bitcoin. Yet, despite the wide recognition of POWs as the fundamental cryptographic tool in this context, there is no known cryptographic formulation that implies the security of the Bitcoin blockchain protocol. Indeed, all previous works formally arguing the security of the Bitcoin protocol relied on direct proofs in the random oracle model, thus circumventing the di culty of isolating the required properties of the core POW primitive. In this work we ll this gap by providing a formulation of the POW primitive that implies the security of the Bitcoin blockchain protocol in the standard model. Our primitive entails a number of properties that parallel an e cient non-interactive proof system: completeness and fast veri cation, security against malicious provers (termed hardness against tampering and chosen message attacks ) and security for honest provers (termed uniquely successful under chosen key and message attacks ). Interestingly, our formulation is incomparable with previous formulations of POWs that applied the primitive to contexts other than the blockchain. Our result paves the way for proving the security of blockchain protocols in the standard model assuming our primitive can be realized from computational assumptions.
Security in Cloud Computing

Cloud computing refers to high scalable computing applications, storages and platforms as a service to companies, individuals and governments. Therefore, SMB (Small and Medium Business) organizations are adapting cloud computing services gradually to save cost and to increase efficiency in their business environment. While cloud service benefits and robustness are comprehensible, but now more concern about security in cloud computing &quot;How much secure is cloud computing environment?&quot;. Noted that security is one of the main barrier for continuing growth of cloud computing. For some major security risks and issues enterprises and individuals are unwilling to deploy their data and applications in cloud environment. In this paper, the main objective is to identified major security risks and issues those are need to think about during deployment and development of services in cloud and the way how to mitigate those security risks and issues. However, it is significant to know that, cloud computing is not insecure primarily, it just needs to be managed and accessed securely.
A 28-GHz 32-Element TRX Phased-Array IC With Concurrent Dual-Polarized Operation and Orthogonal Phase and Gain Control for 5G Communications

This paper presents the first reported 28-GHz phased-array IC for 5G communications. Implemented in 130-nm SiGe BiCMOS, the IC includes 32 TRX elements and features concurrent independent beams in two polarizations in either TX or RX operation. Circuit techniques to enable precise beam steering, orthogonal phase and amplitude control at each front end, and independent tapering and beam steering at the array level are presented. A TX/RX switch design is introduced which minimizes TX path loss resulting in 13.5 dBm/16 dBm Op1dB/Psat per front end with >20% peak power added efficiency of the power amplifier (including switch and off-mode LNA) while maintaining a 6 dB noise figure in the low noise amplifier (including switch and off-mode PA). Comprehensive on-wafer measurement results for the IC across multiple samples and temperature variation are presented. A package with four ICs and 64 dual-polarized antennas provides eight 16-element or two 64-element concurrent beams with 1.4°/step beam steering (<0.6° rms error) across a ±50° steering range without requiring calibration. A maximum saturated effective isotropic radiated power of 54 dBm is measured in the broadside direction for each polarization. Tapering control without requiring calibration achieves up to 20-dB sidelobe rejection without affecting the main lobe direction.
Detection and Visualization of Android Malware Behavior

Malware analysts still need to manually inspect malware samples that are considered suspicious by heuristic rules. They dissect software pieces and look for malware evidence in the code. The increasing number of malicious applications targeting Android devices raises the demand for analyzing them to find where the malcode is triggered when user interacts with them. In this paper a framework to monitor and visualize Android applications’ anomalous function calls is described. Our approach includes platformindependent application instrumentation, introducing hooks in order to trace restricted API functions used at runtime of the application. These function calls are collected at a central server where the application behavior filtering and a visualization take place. This can help Android malware analysts in visually inspecting what the application under study does, easily identifying such malicious functions.
A performance comparison of SQL and NoSQL databases

With the current emphasis on “Big Data”, NoSQL databases have surged in popularity. These databases are claimed to perform better than SQL databases. In this paper we aim to independently investigate the performance of some NoSQL and SQL databases in the light of key-value stores. We compare read, write, delete, and instantiate operations on key-value stores implemented by NoSQL and SQL databases. Besides, we also investigate an additional operation: iterating through all keys. An abstract key-value pair framework supporting these basic operations is designed and implemented using all the databases tested. Experimental results measure the timing of these operations and we summarize our findings of how the databases stack up against each other. Our results show that not all NoSQL databases perform better than SQL databases. Some are much worse. And for each database, the performance varies with each operation. Some are slow to instantiate, but fast to read, write, and delete. Others are fast to instantiate but slow on the other operations. And there is little correlation between performance and the data model each database uses.
UMH: A Hardware-Based Unified Memory Hierarchy for Systems with Multiple Discrete GPUs

In this article, we describe how to ease memory management between a Central Processing Unit (CPU) and one or multiple discrete Graphic Processing Units (GPUs) by architecting a novel hardware-based Unified Memory Hierarchy (UMH). Adopting UMH, a GPU accesses the CPU memory only if it does not find its required data in the directories associated with its high-bandwidth memory, or the NMOESI coherency protocol limits the access to that data. Using UMH with NMOESI improves performance of a CPU-multiGPU system by at least 1.92 × in comparison to alternative software-based approaches. It also allows the CPU to access GPUs modified data by at least 13 × faster.
End-to-end Learning of Latent Dirichlet Allocation by Mirror-Descent Back Propagation

We develop a fully discriminative learning approach for supervised Latent Dirichlet Allocation (LDA) model, which maximizes the posterior probability of the prediction variable given the input document. Different from traditional variational learning or Gibbs sampling approaches, the proposed learning method applies (i) the mirror descent algorithm for exact maximum a posterior inference and (ii) back propagation with stochastic gradient descent for model parameter estimation, leading to scalable learning of the model in an end-to-end discriminative manner. As a byproduct, we also apply this technique to develop a new learning method for the traditional unsupervised LDA model. Experimental results on two real-world regression and classification tasks show that the proposed methods significantly outperform the previous supervised/unsupervised LDA learning methods.
Eyeriss: a spatial architecture for energy-efficient dataflow for convolutional neural networks

Deep convolutional neural networks (CNNs) are widely used in modern AI systems for their superior accuracy but at the cost of high computational complexity. The complexity comes from the need to simultaneously process hundreds of filters and channels in the high-dimensional convolutions, which involve a significant amount of data movement. Although highly-parallel compute paradigms, such as SIMD/SIMT, effectively address the computation requirement to achieve high throughput, energy consumption still remains high as data movement can be more expensive than computation. Accordingly, finding a dataflow that supports parallel processing with minimal data movement cost is crucial to achieving energy-efficient CNN processing without compromising accuracy.
 In this paper, we present a novel dataflow, called row-stationary (RS), that minimizes data movement energy consumption on a spatial architecture. This is realized by exploiting local data reuse of filter weights and feature map pixels, i.e., activations, in the high-dimensional convolutions, and minimizing data movement of partial sum accumulations. Unlike dataflows used in existing designs, which only reduce certain types of data movement, the proposed RS dataflow can adapt to different CNN shape configurations and reduces all types of data movement through maximally utilizing the processing engine (PE) local storage, direct inter-PE communication and spatial parallelism. To evaluate the energy efficiency of the different dataflows, we propose an analysis framework that compares energy cost under the same hardware area and processing parallelism constraints. Experiments using the CNN configurations of AlexNet show that the proposed RS dataflow is more energy efficient than existing dataflows in both convolutional (1.4× to 2.5×) and fully-connected layers (at least 1.3× for batch size larger than 16). The RS dataflow has also been demonstrated on a fabricated chip, which verifies our energy analysis.
A Spreadsheet Algebra for a Direct Data Manipulation Query Interface

A spreadsheet-like "direct manipulation" interface is more intuitive for many non-technical database users compared to traditional alternatives, such as visual query builders. The construction of such a direct manipulation interfacemay appear straightforward, but there are some significant challenges. First, individual direct manipulation operations cannot be too complex, so expressive power has to be achieved through composing (long) sequences of small operations. Second, all intermediate results are visible to the user, so grouping and ordering are material after every small step. Third, users often find the need to modify previously specified queries. Since manipulations are specified one step at a time, there is no actual queryexpression to modify. Suitable means must be provided to address this need. Fourth, the order in which manipulations are performed by the user should not affect the results obtained, to avoid user confusion. We address the aforementioned challenges by designing a new spreadsheet algebra that: i) operates on recursively grouped multi-sets, ii) contains a selectively designed set of operators capable of expressing at least all single-block SQL queries and can be intuitively implemented in a spreadsheet, iii) enables query modification by the notion of modifiable query state, and iv) requires no ordering in unary data manipulation operators since they are all designed to commute. We built a prototype implementation of the spreadsheet algebra and show, through user studies with non-technical subjects, that the resultant query interface is easier to use than a standard commercial visual query builder.
Exploiting Synergy Between Ontologies and Recommender Systems

Recommender systems learn about user preferences over time, automatically finding things of similar interest. This reduces the burden of creating explicit queries. Recommender systems do, however, suffer from cold-start problems where no initial information is available early on upon which to base recommendations. Semantic knowledge structures, such as ontologies, can provide valuable domain knowledge and user information. However, acquiring such knowledge and keeping it up to date is not a trivial task and user interests are particularly difficult to acquire and maintain. This paper investigates the synergy between a web-based research paper recommender system and an ontology containing information automatically extracted from departmental databases available on the web. The ontology is used to address the recommender systems cold-start problem. The recommender system addresses the ontology’s interest-acquisition problem. An empirical evaluation of this approach is conducted and the performance of the integrated systems measured. General Terms Design, Experimentation.
Static hand gesture recognition based on HOG characters and support vector machines

Gesture recognition technology has important significance in the field of human-computer interaction (HCI), the gesture recognition technology which is based on visual is sensitive to the impact of the experimental environment lighting, and so, the recognition result will produce a greater change; it makes this technology one of the most challenging topics. HOG feature which is successfully applied to pedestrian detection is operating on the local grid unit of image, so it can maintain a good invariance on geometric and optical deformation. In this paper, we extracted the gradient direction histogram (HOG) features of gestures, then, a Support Vector Machines is used to train these feature vectors, at testing time, a decision is taken using the previously learned SVMs, and compared the same gesture recognition rate in different light conditions. Experimental results show that the HOG feature extraction and multivariate SVM classification methods has a high recognition rate, and the system has a better robustness for the illumination.
Intelligent scissors for image composition

We present a new, interactive tool called Intelligent Scissors which we use for image segmentation and composition. Fully au mated segmentation is an unsolved problem, while manual trac is inaccurate and laboriously unacceptable. However, Intellige Scissors allow objects within digital images to be extracted quick and accurately using simple gesture motions with a mouse. Wh the gestured mouse position comes in proximity to an object ed a live-wire boundary “snaps” to, and wraps around the object o interest.
Assessing Code Authorship: The Case of the Linux Kernel

Code authorship is a key information in large-scale opensource systems. Among others, it allows maintainers to assess division of work and identify key collaborators. Interestingly, open-source communities lack guidelines on how to manage authorship. This could be mitigated by setting to build an empirical body of knowledge on how authorship-related measures evolve in successful open-source communities. Towards that direction, we perform a case study on the Linux kernel. Our results show that: (a) only a small portion of developers (26%) makes significant contributions to the code base; (b) the distribution of the number of files per author is highly skewed—a small group of topauthors (3%) is responsible for hundreds of files, while most authors (75%) are responsible for at most 11 files; (c) most authors (62%) have a specialist profile; (d) authors with a high number of co-authorship connections tend to collaborate with others with less connections.
Implementing it Service Management: A Case Study Focussing on Critical Success Factors

Queensland Health, a large Australian government agency, implemented a centralised IT service management model based on the ITIL framework. This paper presents an in-depth case study of the implementation. It sheds light on the challenges and breakthroughs, confirms a set of factors that contributed to the project’s success and offers a learning opportunity for other organisations. The study indicates that the commitment of senior management is crucial to the project’s success as is a project champion and the recognition of the need for an appropriate change management strategy to transform the organisational culture to a service-oriented focus. Maintaining close and forthright relationships with multiple vendors facilitates technology transfer to in-house staff while a benefits realisation plan is a valuable tool for tracking and communicating tangible and intangible project benefits to the project stakeholders. An effective project governance and execution process further contributes to the implementation success.
Deep Convolutional Neural Networks on Multichannel Time Series for Human Activity Recognition

This paper focuses on human activity recognition (HAR) problem, in which inputs are multichannel time series signals acquired from a set of bodyworn inertial sensors and outputs are predefined human activities. In this problem, extracting effective features for identifying activities is a critical but challenging task. Most existing work relies on heuristic hand-crafted feature design and shallow feature learning architectures, which cannot find those distinguishing features to accurately classify different activities. In this paper, we propose a systematic feature learning method for HAR problem. This method adopts a deep convolutional neural networks (CNN) to automate feature learning from the raw inputs in a systematic way. Through the deep architecture, the learned features are deemed as the higher level abstract representation of low level raw time series signals. By leveraging the labelled information via supervised learning, the learned features are endowed with more discriminative power. Unified in one model, feature learning and classification are mutually enhanced. All these unique advantages of the CNN make it outperform other HAR algorithms, as verified in the experiments on the Opportunity Activity Recognition Challenge and other benchmark datasets.
"I don't believe in word senses"

Word sense disambiguation assumes word senses. Within the lexicography and linguistics literature, they are known to be very slippery entities. The paper looks at problems with existing accounts of ‘word sense’ and describes the various kinds of ways in which a word’s meaning can deviate from its core meaning. An analysis is presented in which word senses are abstractions from clusters of corpus citations, in accordance with current lexicographic practice. The corpus citations, not the word senses, are the basic objects in the ontology. The corpus citations will be clustered into senses according to the purposes of whoever or whatever does the clustering. In the absence of such purposes, word senses do not exist. Word sense disambiguation also needs a set of word senses to disambiguate between. In most recent work, the set has been taken from a general-purpose lexical resource, with the assumption that the lexical resource describes the word senses of English/French/: : : , between which NLP applications will need to disambiguate. The implication of the paper is, by contrast, that word senses exist only relative to a task.
Retinal blood vessel segmentation using matched filter and Laplacian of Gaussian

Automated blood vessel segmentation of retinal images offers huge potential benefits for medical diagnosis of different ocular diseases. In this paper, 2D Matched Filters (MF) are applied to fundus retinal images to detect vessels which are enhanced by Contrast Limited Adaptive Histogram Equalization (CLAHE) method. Due to the Gaussian nature of blood vessel profile, the MF with Gaussian kernel often misclassifies non-vascular structures (e.g., step, ramp or other transients) as vessels. To avoid such false detection, this paper introduces Laplacian of Gaussian (LoG) filters in the vessel segmentation process. The inherent zero-crossing property of LoG filter is used in the algorithm, along with the MF, in order to extract vessels reliably from the retinal images. The proposed method is validated against three publicly available databases, STARE, DRIVE and HRF. Simulation results show that the proposed method is able to segment vessels accurately from the three database images with an average accuracy that is competitive to the existing methodologies.