nils-herrmann/clean_scirepeval huggingface dataset:
Throughput Optimization, Spectrum Allocation, and Access Control in Two-Tier Femtocell Networks

The deployment of femtocells in a macrocell network is an economical and effective way to increase network capacity and coverage. Nevertheless, such deployment is challenging due to the presence of inter-tier and intra-tier interference, and the ad hoc operation of femtocells. Motivated by the flexible subchannel allocation capability of OFDMA, we investigate the effect of spectrum allocation in two-tier networks, where the macrocells employ closed access policy and the femtocells can operate in either open or closed access. By introducing a tractable model, we derive the success probability for each tier under different spectrum allocation and femtocell access policies. In particular, we consider joint subchannel allocation, in which the whole spectrum is shared by both tiers, as well as disjoint subchannel allocation, whereby disjoint sets of subchannels are assigned to both tiers. We formulate the throughput maximization problem subject to quality of service constraints in terms of success probabilities and per-tier minimum rates, and provide insights into the optimal spectrum allocation. Our results indicate that with closed access femtocells, the optimized joint and disjoint subchannel allocations provide the highest throughput among all schemes in sparse and dense femtocell networks, respectively. With open access femtocells, the optimized joint subchannel allocation provides the highest possible throughput for all femtocell densities.
Coupling Matrix Synthesis for a New Class of Microwave Filter Configuration

In this paper a new approach to the synthesis of coupling matrices for microwave filters is presente d. The new approach represents an advance on existing direct a nd optimization methods for coupling matrix synthesis in that it will exhaustively discover all possible coupling matrix solutions for a network if more than one exists. This enables a se lection to be made of the set of coupling values, resonator frequ ency offsets, parasitic coupling tolerance etc that will be best suited to the technology it is intended to realize the microwave filter with. To demonstrate the use of the method, the case of the r cently – introduced ‘extended box’ (EB) coupling matrix configuration is taken. The EB represents a new class of filter con figuration featuring a number of important advantages, one of which is the existence of multiple coupling matrix solutions for each prototype filtering function, eg 16 for 8 degree cases. This case is taken as an example to demonstrate the use of the synthesis method – yielding one solution suitable for dual-mode realiz ation and one where some couplings are small enough to neglect. Index Terms — Coupling matrix, filter synthesis, Groebner basis, inverted characteristic, multiple solutions.
Efficiency and harmonics generation in microwave to DC conversion circuits of half-wave and full-wave rectifier types

A Rectifying Antenna (Rectenna) is one of the most important components for a wireless power transmission. It has been developed for many applications such as Space Solar Power System (SSPS), and Radio Frequency Identification (RFID) etc. The Rectenna consisting of RF-DC conversion circuits and receiving antennas needs to be designed for high conversion efficiency to achieve efficient power transmission. we try to design the mw-Class RF-DC conversion circuit using half-and full-wave rectification. The measured conversion efficiencies is 59.3 % and 65.3 % athalf- and full-wave type, respectively. And the full-wave type has lower 2nd harmonic reflection coefficient.
Application of Machine Learning Algorithms to Predict Flight Arrival Delays

Growth in aviation industry has resulted in air-traffic congestion causing flight delays. Flight delays not only have economic impact but also harmful environmental effects. Air-traffic management is becoming increasingly challenging. In this project we apply machine learning algorithms like decision tree, logistic regression and neural networks classifiers to predict if a given flight’s arrival will be delayed or not. We show that with only three features we were able achieve a test accuracy of approximately 91% for all three classifiers.
Maintainability defects detection and correction: a multi-objective approach

Software defects often lead to bugs, runtime errors and software maintenance difficulties. They should be systematically prevented, found, removed or fixed all along the software lifecycle. However, detecting and fixing these defects is still, to some extent, a difficult, time-consuming and manual process. In this paper, we propose a two-step automated approach to detect and then to correct various types of maintainability defects in source code. Using Genetic Programming, our approach allows automatic generation of rules to detect defects, thus relieving the designer from a fastidious manual rule definition task. Then, we correct the detected defects while minimizing the correction effort. A correction solution is defined as the combination of refactoring operations that should maximize as much as possible the number of corrected defects with minimal code modification effort. We use the Non-dominated Sorting Genetic Algorithm (NSGA-II) to find the best compromise. For six open source projects, we succeeded in detecting the majority of known defects, and the proposed corrections fixed most of them with minimal effort.
Technochange management: using IT to drive organizational change

Using IT in ways that can trigger major organizational changes creates high-risk, potentially high-reward, situations that I call technochange (for technology-driven organizational change). Technochange differs from typical IT projects and from typical organizational change programs and therefore requires a different approach. One major risk in technochange—that people will not use information technology and related work practices—is not thoroughly addressed by the discipline of IT project management, which focuses on project cost, project schedule, and solution functionality. Organizational change management approaches are also generally not effective on their own, because they take as a given the IT ‘‘solutions’’ developed by a technical team. Consequently, the potential for the IT ‘‘solution’’ to be misaligned with important organizational characteristics, such as culture or incentives, is great. Merely combining IT project management and organizational change management approaches does not produce the best results, for two reasons. First, the additive approach does not effectively address the many failure-threatening problems that can arise over the lengthy sequential process of the typical technochange lifecycle. Second, the additive approach is not structured to produce the characteristics of a good technochange solution: a complete intervention consisting of IT and complementary organizational changes, an implementable solution with minimal misfits with the existing organization, and an organization primed to appropriate the potential bene¢ts of the technochange solution. With hard work and care, the combined IT project management plus organizational change approach can be made to work. However, an iterative, incremental approach to implementing technochange can be a better strategy in many situations. The essential characteristic of the technochange prototyping approach is that each phase involves both new IT functionality and related organizational changes, such as redesigned business processes, new performance metrics, and training.  (2004) 19, 4–20. doi:10.1057/palgrave.jit.2000002
Models and Statistical Inference : The Controversy between Fisher and Neyman – Pearson

The main thesis of the paper is that in the case of modern statistics, the differences between the various concepts of models were the key to its formative controversies. The mathematical theory of statistical inference was mainly developed by Ronald A. Fisher, Jerzy Neyman, and Egon S. Pearson. Fisher on the one side and Neyman–Pearson on the other were involved often in a polemic controversy. The common view is that Neyman and Pearson made Fisher’s account more stringent mathematically. It is argued, however, that there is a profound theoretical basis for the controversy: both sides held conflicting views about the role of mathematical modelling. At the end, the influential programme of Exploratory Data Analysis is considered to be advocating another, more instrumental conception of models.
/spl Sigma/High-frequency LC VCO design using capacitive degeneration

In this paper, we evaluate the high-frequency performance limitations of traditional LC voltage-controlled oscillators (VCOs) that use a cross-coupled negative resistance cell and propose a new topology that overcomes these limitations. The proposed cell is based on a capacitively emitter degenerated topology which uses a cross-coupled MOS pair as the degeneration cell. The cross-coupled MOS pair contributes additional conductance and results in a higher maximum attainable oscillation frequency and better negative resistance characteristics as compared to the other topologies at high frequencies. These properties combined with its small effective capacitance enable low-power low-noise high-frequency VCO implementations. The proposed topology is demonstrated through a 20-GHz fully integrated LC VCO implemented in the IBM SiGe 0.25-/spl mu/m BiCMOS process. A comparison of its figure of merit with previously reported 20-GHz VCOs shows the effectiveness of the proposed topology.
Noise and Bandwidth analysis for PMOS based wide swing cascode current mirror using 0.35 um CMOS-MEMS technology

In this work, we propose a PMOS based wide swing cascode current mirror (WSSCM) for 0.35 um CMOS-MEMS technology. The proposed circuit shows a high bandwidth current mirror with high output resistance capability operating at low voltage levels. Noise, output resistance and bandwidth analysis are presented. CADENCE SPECTRE circuit design tool using MIMOS 0.35 um and BSIM3 transistor models is used to carry out the simulations. The circuit achieves an amplification of 120.04 uA, the input referred noise of the circuit is 75.77 nV/√Hz with a minimum power consumption of 0.33 mW and a high output resistance of 15 MO .
T REE-STRUCTURED DECODING WITH DOUBLY-RECURRENT NEURAL NETWORKS

We propose a neural network architecture for generating tree-structured objects from encoded representations. The core of the method is a doubly recurrent neural network model comprised of separate width and depth recurrences that are combined inside each cell (node) to generate an output. The topology of the tree is modeled explicitly together with the content. That is, in response to an encoded vector representation, co-evolving recurrences are used to realize the associated tree and the labels for the nodes in the tree. We test this architecture in an encoderdecoder framework, where we train a network to encode a sentence as a vector, and then generate a tree structure from it. The experimental results show the effectiveness of this architecture at recovering latent tree structure in sequences and at mapping sentences to simple functional programs.
Prevention of falls and consequent injuries in elderly people

Injuries resulting from falls in elderly people are a major public-health concern, representing one of the main causes of longstanding pain, functional impairment, disability, and death in this population. The problem is going to worsen, since the rates of such injuries seem to be rising in many areas, as is the number of elderly people in both the developed and developing world. Many methods and programmes to prevent such injuries already exist, including regular exercise, vitamin D and calcium supplementation, withdrawal of psychotropic medication, cataract surgery, professional environment hazard assessment and modification, hip protectors, and multifactorial preventive programmes for simultaneous assessment and reduction of many of the predisposing and situational risk factors. To receive broader-scale effectiveness, these programmes will need systematic implementation. Care must be taken, however, to rigorously select the right actions for those people most likely to benefit, such as vitamin D and calcium supplementation and hip protectors for elderly people living in institutions.
Frequent Pattern Compression: A Significance-Based Compression Scheme for L2 Caches

With the widening gap between processor and memory speeds, memory system designers ma find cache compression beneficial to increase cache capacity and reduce off-chip bandwidth Most hardware compression algorithms fall into the dictionary-based category, which depend on building a dictionary and using its entries to encode repeated data values. Such algorithms are effective in compressing large data blocks and files. Cache lines, however, are typically short (32-256 bytes), and a per-line dictionary places a significant overhead that limits the compressibility and increases decompression latency of such algorithms. For such short lines significance-based compression is an appealing alternative. We propose and evaluate a simple significance-based compression scheme that has a low com pression and decompression overhead. This scheme, Frequent Pattern Compression (FPC compresses individual cache lines on a word-by-word basis by storing common word patterns in a compressed format accompanied with an appropriate prefix. For a 64-byte cache line, compression can be completed in three cycles and decompression in five cycles, assuming 1 FO4 gate delays per cycle. We propose a compressed cache design in which data is stored in compressed form in the L2 caches, but are uncompressed in the L1 caches. L2 cache lines ar compressed to predetermined sizes that never exceed their original size to reduce decompre sion overhead. This simple scheme provides comparable compression ratios to more comple schemes that have higher cache hit latencies.
Classification of Web Pages Using Naive Bayesian Approach

In World Wide Web there is huge amount of data available and the number is increasing. Every day millions of web pages being created and uploaded in the electronic form. So, one way to manage this large amount of data is to classify this data. Classification is the process that divides data into groups, which are either dependent or independent of each other and each class is acts as a class. High dimensionality is one of the issues in the web page classification. Dimensionality is nothing but the number of terms in a web page. High dimensionality of web pages causes problem while classifying them. The main objective of reducing dimensionality of web pages is to improve the performance of the classifier. In this survey paper, we describes a feature selection and dimensionality reduction to reduce the dimensionality with the help of information gain and rough set based Quick Reduct algorithm respectively and then with the help of Naive Bayesian classifier web pages are classified.
Measurement and Defect Detection of the Weld Bead Based on Online Vision Inspection

Weld bead inspection is important for high-quality welding. This paper summarizes our work on weld bead profile measurement, monitoring, and defect detection using a structured light-based vision inspection system. The configuration of the sensor is described and analyzed. In this configuration, the system presented in this paper can easily be calibrated. The image processing and extraction algorithms for laser profiles and feature points are presented. The dimensional parameters of the weld bead are measured, and the weld defects are detected during multilayer welding processes. Experiments using the vision inspection system were conducted with satisfactory results for online inspection.
Puncturable Attribute-Based Encryption for Secure Data Delivery in Internet of Things

While the Internet of Things (IoT) is embraced as important tools for efficiency and productivity, it is becoming an increasingly attractive target for cybercriminals. This work represents the first endeavor to develop practical Puncturable Attribute Based Encryption schemes that are light-weight and applicable in IoTs. In the proposed scheme, the attribute-based encryption is adopted for fine grained access control. The secret keys are puncturable to revoke the decryption capability for selected messages, recipients, or time periods, thus protecting selected important messages even if the current key is compromised. In contrast to conventional forward encryption, a distinguishing merit of the proposed approach is that the recipients can update their keys by themselves without key re-issuing from the key distributor. It does not require frequent communications between IoT devices and the key distribution center, neither does it need deleting components to expunge existing keys to produce a new key. Moreover, we devise a novel approach which efficiently integrates attribute-based key and punctured keys such that the key size is roughly the same as that of the original attribute-based encryption. We prove the correctness of the proposed scheme and its security under the Decisional Bilinear Diffie-Hellman (DBDH) assumption. We also implement the proposed scheme on Raspberry Pi and observe that the computation efficiency of the proposed approach is comparable to the original attribute-based encryption. Both encryption and decryption can be completed within tens of milliseconds.
Improving Offline Handwritten Chinese Character Recognition by Iterative Refinement

We present an iterative refinement module that can be applied to the output feature maps of any existing convolutional neural networks in order to further improve classification accuracy. The proposed module, implemented by an attention-based recurrent neural network, can iteratively use its previous predictions to update attention and thereafter refine current predictions. In this way, the model is able to focus on a sub-region of input images to distinguish visually similar characters . We evaluate its effectiveness on handwritten Chinese character recognition (HCCR) task and observe significant performance gain. HCCR task is challenging due to large number of classes and small differences between certain characters. To overcome these difficulties, we further propose a novel convolutional architecture that utilizes both low-level visual cues and high-level structural information. Together with the proposed iterative refinement module, our approach achieves an accuracy of 97.37%, outperforming previous methods that use raw images as input on ICDAR-2013 dataset .
A Survey of Link Prediction in Social Networks

Link prediction is an important task for analying social networks which also has applications in other domains like, information retrieval, bioinformatics and e-commerce. There exist a variety of techniques for link prediction, ranging from feature-based classi cation and kernel-based method to matrix factorization and probabilistic graphical models. These methods differ from each other with respect to model complexity, prediction performance, scalability, and generalization ability. In this article, we survey some representative link prediction methods by categorizing them by the type of the models. We largely consider three types of models: rst, the traditional (non-Bayesian) models which extract a set of features to train a binary classi cation model. Second, the probabilistic approaches which model the joint-probability among the entities in a network by Bayesian graphical models. And, nally the linear algebraic approach which computes the similarity between the nodes in a network by rank-reduced similarity matrices. We discuss various existing link prediction models that fall in these broad categories and analyze their strength and weakness. We conclude the survey with a discussion on recent developments and future research direction.
Advantages of thesaurus representation using the Simple Knowledge Organization System (SKOS) compared with proposed alternatives

The concept of thesaurus has evolved from a list of conceptually interrelated words to today's controlled vocabularies, where terms form complex structures through semantic relationships. This term comes from the Latin and has turn been derived from the Greek "θησαυρός", which means treasury according to the Spanish Royal Academy, in whose dictionary it is also defined as: 'name given by its authors to certain dictionaries, catalogues and anthologies'. The increase in scientific communication and productivity made it essential to develop keyword indexing systems. At that time, Howerton spoke of controlled lists to refer to concepts that were heuristically or intuitively related. According to Roberts (1984), Mooers was the first to relate thesauri to information retrieval systems; Taube established the foundations of post-coordination, while Luhn dealt, at a basic level, with the creation of thesauri using automatic techniques. Brownson (1957) was the first to use the term to refer to the issue of translating concepts and their relationships expressed in documents into a more precise language free of ambiguities in order to facilitate information retrieval. The ASTIA Thesaurus was published in the early 1960s (Currás, 2005), already bearing the characteristics of today's thesauri and taking on the need for a tool to administer a controlled vocabulary in terms of indexing, thereby giving rise to the concept of documentary language.
A computational cognition model of perception, memory, and judgment

The mechanism of human cognition and its computability provide an important theoretical foundation to intelligent computation of visual media. This paper focuses on the intelligent processing of massive data of visual media and its corresponding processes of perception, memory, and judgment in cognition. In particular, both the human cognitive mechanism and cognitive computability of visual media are investigated in this paper at the following three levels: neurophysiology, cognitive psychology, and computational modeling. A computational cognition model of Perception, Memory, and Judgment (PMJ model for short) is proposed, which consists of three stages and three pathways by integrating the cognitive mechanism and computability aspects in a unified framework. Finally, this paper illustrates the applications of the proposed PMJ model in five visual media research areas. As demonstrated by these applications, the PMJ model sheds some light on the intelligent processing of visual media, and it would be innovative for researchers to apply human cognitive mechanism to computer science.
Monolithic DC-DC boost converter with current-mode hysteretic control

A monolithic DC-DC boost converter with current-mode hysteretic control is designed and simulated in 0.18-μm CMOS technology. The system is simple, robust, and has a fast response to external changes. It does not require an external clock, and the output is regulated by voltage feedback in addition to limiting the inductor current by sensing it. A non-overlapping clock is internally generated to drive the power switches using buffers designed to minimize power dissipation. For conversion specifications of 1.8 V to 3.3 V at 150 mA, overall efficiency of 94.5% is achieved. Line regulation is 17.5 mV/V, load regulation is 0.33% for a 100 mA current step, while the output voltage ripple is below 30 mV for nominal conditions.