nils-herrmann/clean_scirepeval huggingface dataset:
Markov Decision Processes With Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are used to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs.
In Defense of Single-column Networks for Crowd Counting

Crowd counting usually addressed by density estimation becomes an increasingly important topic in computer vision due to its widespread applications in video surveillance, urban planning, and intelligence gathering. However, it is essentially a challenging task because of the greatly varied sizes of objects, coupled with severe occlusions and vague appearance of extremely small individuals. Existing methods heavily rely on multi-column learning architectures to extract multi-scale features, which however suffer from heavy computational cost, especially undesired for crowd counting. In this paper, we propose the single-column counting network (SCNet) for efficient crowd counting without relying on multi-column networks. SCNet consists of residual fusion modules (RFMs) for multi-scale feature extraction, a pyramid pooling module (PPM) for information fusion, and a sub-pixel convolutional module (SPCM) followed by a bilinear upsampling layer for resolution recovery. Those proposed modules enable our SCNet to fully capture multi-scale features in a compact single-column architecture and estimate high-resolution density map in an efficient way. In addition, we provide a principled paradigm for density map generation and data augmentation for training, which shows further improved performance. Extensive experiments on three benchmark datasets show that our SCNet delivers new state-of-the-art performance and surpasses previous methods by large margins, which demonstrates the great effectiveness of SCNet as a singlecolumn network for crowd counting.  The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms. Equal contribution 2 WANG et al.: IN DEFENSE OF SINGLE-COLUMN NETWORKS FOR
Collaboration- and Fairness-Aware Big Data Management in Distributed Clouds

With the advancement of information and communication technology, data are being generated at an exponential rate via various instruments and collected at an unprecedented scale. Such large volume of data generated is referred to as big data, which now are revolutionizing all aspects of our life ranging from enterprises to individuals, from science communities to governments, as they exhibit great potentials to improve efficiency of enterprises and the quality of life. To obtain nontrivial patterns and derive valuable information from big data, a fundamental problem is how to properly place the collected data by different users to distributed clouds and to efficiently analyze the collected data to save user costs in data storage and processing, particularly the cost savings of users who share data. By doing so, it needs the close collaborations among the users, by sharing and utilizing the big data in distributed clouds due to the complexity and volume of big data. Since computing, storage and bandwidth resources in a distributed cloud usually are limited, and such resource provisioning typically is expensive, the collaborative users require to make use of the resources fairly. In this paper, we study a novel collaboration- and fairness-aware big data management problem in distributed cloud environments that aims to maximize the system throughout, while minimizing the operational cost of service providers to achieve the system throughput, subject to resource capacity and user fairness constraints. We first propose a novel optimization framework for the problem. We then devise a fast yet scalable approximation algorithm based on the built optimization framework. We also analyze the time complexity and approximation ratio of the proposed algorithm. We finally conduct experiments by simulations to evaluate the performance of the proposed algorithm. Experimental results demonstrate that the proposed algorithm is promising, and outperforms other heuristics.
Optic Disc and Optic Cup Segmentation Methodologies for Glaucoma Image Detection: A Survey

Glaucoma is the second leading cause of loss of vision in the world. Examining the head of optic nerve (cup-to-disc ratio) is very important for diagnosing glaucoma and for patient monitoring after diagnosis. Images of optic disc and optic cup are acquired by fundus camera as well as Optical Coherence Tomography. The optic disc and optic cup segmentation techniques are used to isolate the relevant parts of the retinal image and to calculate the cup-to-disc ratio. The main objective of this paper is to review segmentation methodologies and techniques for the disc and cup boundaries which are utilized to calculate the disc and cup geometrical parameters automatically and accurately to help the professionals in the glaucoma to have a wide view and more details about the optic nerve head structure using retinal fundus images. We provide a brief description of each technique, highlighting its classification and performance metrics. The current and future research directions are summarized and discussed.
Mathematical Formulas Extraction

As a universal technical language, mathematics has been widely applied in many fields, and it is more accurate than any other languages in describing information. Therefore, numerous mathematical formulas exist in all kinds of documents. There is no doubt that automatic mathematical formulas processing is very important and necessary, of which extract formulas from document images is the first step. In this paper, formulas extraction methods which are not based on recognition results are presented: isolated formulas are extracted based on Parzen window and embedded expressions are extracted based on 2-D structures detection. Experiments show that our methods are very effective in formulas extraction.
Numeric data frames and probabilistic judgments in complex real-world environments

This thesis investigates human probabilistic judgment in complex real-world settings to identify processes underpinning biases across groups which relate to numerical frames and formats. Experiments are conducted replicating real-world environments and data to test judgment performance based on framing and format. Regardless of background skills and experience, people in professional and consumer contexts show a strong tendency to perceive the world from a linear perspective, interpreting information in concrete, absolute terms and making judgments based on seeking and applying linear functions. Whether predicting sales, selecting between financial products, or forecasting refugee camp data, people use minimal cues and systematically apply additive methods amidst non-linear trends and percentage points to yield linear estimates in both rich and sparse informational contexts. Depending on data variability and temporality, human rationality and choice may be significantly helped or hindered by informational framing and format. The findings deliver both theoretical and practical contributions. Across groups and individual differences, the effects of informational format and the tendency to linearly extrapolate are connected by the bias to perceive values in concrete terms and make sense of data by seeking simple referent points. People compare and combine referents using additive methods when inappropriate and adhere strongly to defaults when applied in complex numeric environments. The practical contribution involves a framing manipulation which shows that format biases (i.e., additive processing) and optimism (i.e., associated with intertemporal effects) can be counteracted in judgments involving percentages and exponential growth rates by using absolute formats and positioning defaults in future event context information. This framing manipulation was highly effective in improving loan choice and repayment judgments compared to information in standard finance industry formats. There is a strong potential to increase rationality using this data format manipulation in other financial settings and domains such as health behaviour change in which peoples’ erroneous interpretation of percentages and non-linear relations negatively impact choice and behaviours in both the short and long-term.
Fast Priority Queues for Cached Memory

The cache hierarchy prevalent in todays high performance processors has to be taken into account in order to design algorithms that perform well in practice. This paper advocates the adaption of external memory algorithms to this purpose. This idea and the practical issues involved are exemplified by engineering a fast priority queue suited to external memory and cached memory that is based on <i>k</i>-way merging. It improves previous external memory algorithms by constant factors crucial for transferring it to cached memory. Running in the cache hierarchy of a workstation the algorithm is at least two times faster than an optimized implementation of binary heaps and 4-ary heaps for large inputs.
The Austrian eID Ecosystem in the Public Cloud: How to Obtain Privacy While Preserving Practicality

The Austrian eID system constitutes a main pillar within the Austrian eGovernment strategy. The eID system ensures unique identification and secure authentication for citizens protecting access to applications where sensitive and personal data is involved. In particular, the Austrian eID system supports three main use cases: Identification and authentication of Austrian citizens, electronic representation, and foreign citizen authentication at Austrian public sector applications. For supporting all these use cases, several components – either locally deployed in the applications’ domain or centrally deployed – need to communicate with each other. While local deployments have some advantages in terms of scalability, still a central deployment of all involved components would be advantageous, e.g. due to less maintenance efforts. However, a central deployment can easily lead to load bottlenecks because theoretically the whole Austrian population as well as – for foreign citizens – the whole EU population could use the provided services. To mitigate the issue on scalability, in this paper we propose the migration of main components of the ecosystem into a public cloud. However, a move of trusted services into a public cloud brings up new obstacles, particular with respect to privacy. To bypass the issue on privacy, in this paper we propose an approach on how the complete Austrian eID ecosystem can be moved into a public cloud in a privacy-preserving manner by applying selected cryptographic technologies (in particular using proxy ∗Corresponding author Email address: bernd.zwattendorfer@iaik.tugraz.at (Bernd Zwattendorfer)  Security and Applications October 1, 2015 c  1 4 Ja n 20 16 re-encryption and redactable signatures). Applying this approach, no sensitive data will be disclosed to a public cloud provider by still supporting all three main eID system use cases. We finally discuss our approach based on selected criteria.
Estimating county health statistics with twitter

Understanding the relationships among environment, behavior, and health is a core concern of public health researchers. While a number of recent studies have investigated the use of social media to track infectious diseases such as influenza, little work has been done to determine if other health concerns can be inferred. In this paper, we present a large-scale study of 27 health-related statistics, including obesity, health insurance coverage, access to healthy foods, and teen birth rates. We perform a linguistic analysis of the Twitter activity in the top 100 most populous counties in the U.S., and find a significant correlation with 6 of the 27 health statistics. When compared to traditional models based on demographic variables alone, we find that augmenting models with Twitter-derived information improves predictive accuracy for 20 of 27 statistics, suggesting that this new methodology can complement existing approaches.
A Novel CMOS High-Power Terahertz VCO Based on Coupled Oscillators: Theory and Implementation

We introduce a novel frequency tuning method for high-power terahertz sources in CMOS. In this technique, multiple core oscillators are coupled to generate, combine, and deliver their harmonic power to the output node without using varactors. By exploiting the theory of nonlinear dynamics, we control the coupling between the cores to set their phase shift and frequency. Using this method, two high-power terahertz VCOs are fabricated in a 65 nm LP bulk CMOS process. The first one has a measured output power of 0.76 mW at 290 GHz with 4.5% tuning range and the output power of the second VCO is 0.46 mW at 320 GHz with 2.6% tuning range. The output power of these signal sources is 4 orders of magnitude higher than previous CMOS VCOs and is even higher than VCOs implemented in compound semiconductors with much higher cut-off frequencies.
Sentence level emotion recognition based on decisions from subsentence segments

Emotion recognition from speech plays an important role in developing affective and intelligent systems. This study investigates sentence-level emotion recognition. We propose to use a two-step approach to leverage information from subsentence segments for sentence level decision. First we use a segment level emotion classifier to generate predictions for segments within a sentence. A second component combines the predictions from these segments to obtain a sentence level decision. We evaluate different segment units (words, phrases, time-based segments) and different decision combination methods (majority vote, average of probabilities, and a Gaussian Mixture Model (GMM)). Our experimental results on two different data sets show that our proposed method significantly outperforms the standard sentence-based classification approach. In addition, we find that using time-based segments achieves the best performance, and thus no speech recognition or alignment is needed when using our method, which is important to develop language independent emotion recognition systems.
Optimal Dynamic Pricing of Perishable Items by a Monopolist Facing Strategic Consumers

We introduce a dynamic pricing model for a monopolistic company selling a perishable product to a finite population of strategic consumers (customers who are aware that pricing is dynamic and may time their purchases strategically). This problem is modeled as a stochastic dynamic game in which the company’s objective is to maximize total expected revenues, and each customer maximizes the expected present value of utility. We prove the existence of a unique subgame-perfect equilibrium pricing policy, provide equilibrium optimality conditions for both customer and seller, and prove monotonicity results for special cases. We demonstrate through numerical examples that a company that ignores strategic consumer behavior may receive much lower total revenues than one that uses the strategic equilibrium pricing policy. We also show that, when the initial capacity is a decision variable, it can be used together with the appropriate pricing policy to effectively reduce the impact of strategic consumer behavior. The proposed model is computationally tractable for problems of realistic size.
Toward Black-Box Detection of Logic Flaws in Web Applications

Web applications play a very important role in many critical areas, including online banking, health care, and personal communication. This, combined with the limited security training of many web developers, makes web applications one of the most common targets for attackers. In the past, researchers have proposed a large number of whiteand black-box techniques to test web applications for the presence of several classes of vulnerabilities. However, traditional approaches focus mostly on the detection of input validation flaws, such as SQL injection and cross-site scripting. Unfortunately, logic vulnerabilities specific to particular applications remain outside the scope of most of the existing tools and still need to be discovered by manual inspection. In this paper we propose a novel black-box technique to detect logic vulnerabilities in web applications. Our approach is based on the automatic identification of a number of behavioral patterns starting from few network traces in which users interact with a certain application. Based on the extracted model, we then generate targeted test cases following a number of common attack scenarios. We applied our prototype to seven real world E-commerce web applications, discovering ten very severe and previouslyunknown logic vulnerabilities.
PRISM 4.0: Verification of Probabilistic Real-Time Systems

This paper describes a major new release of the PRISM probabilistic model checker, adding, in particular, quantitative verification of (priced) probabilistic timed automata. These model systems exhibiting probabilistic, nondeterministic and real-time characteristics. In many application domains, all three aspects are essential; this includes, for example, embedded controllers in automotive or avionic systems, wireless communication protocols such as Bluetooth or Zigbee, and randomised security protocols. PRISM, which is open-source, also contains several new components that are of independent use. These include: an extensible toolkit for building, verifying and refining abstractions of probabilistic models; an explicit-state probabilistic model checking library; a discrete-event simulation engine for statistical model checking; support for generation of optimal adversaries/strategies; and a benchmark suite.
Ieee Comsoc Mmtc E-letter Multimedia Communications Technical Committee Ieee Communications Society E-letter Energy Efficiency and Spectrum Efficiency Co-design: from Noma to Network Noma21 Message from Mmtc Chair Emerging Topics: Special Issue on 5g Mobile Communications Technologies and Services

It is really a great honor for me to serve as the Asia vice-chair for this vital ComSoc Committee during the period 2014-2016! As part of my duties, I have contributed to the initial setting of the Interest Groups (IGs) and I am starting to work on the promotion of Special Issues  to develop the full proposal on SI or symposium or workshop. It is our hope to achieve the largest efficiency via our collaborative efforts, ideally each IG can at least organize one SI or symposium or workshop per year. We also encourage multiple IGs to collaboratively propose topics that are relevant. In addition, we also hope IGs can take more constructive roles in our TC activities, for example, advertising the IG members to submit their papers to the multimedia symposium in ICC/Globecom. I would like to thank all the IG chairs and co-chairs for the work they have already done and will be doing for the success of MMTC and hope that any of you will find the proper IG of interest to get …
Energy-Efficient Resource Allocation in Cellular Networks With Shared Full-Duplex Relaying

Recent advances in self-interference cancelation techniques enable full-duplex relaying (FDR) systems, which transmit and receive simultaneously in the same frequency band with high spectrum efficiency. Unlike most existing works, we study the problem of energy-efficient resource allocation in FDR networks. We consider a shared FDR deployment scenario, where an FDR relay is deployed at the intersection of several adjacent cell sectors. First, a simple but practical transmission strategy is proposed to deal with the involved interference, i.e., multiaccess interference, multiuser interference, and self-interference. Then, the problem of joint power and subcarrier allocation is formulated to maximize the network-level energy efficiency while taking the residual self-interference into account. Since the formulated problem is a mixed combinatorial and nonconvex optimization problem with high computation complexity, we use Dinkelbach and discrete stochastic optimization methods to solve the energy-efficient resource-allocation problem efficiently. Simulation results are presented to show the effectiveness of the proposed scheme.
Efficient and Expressive Knowledge Base Completion Using Subgraph Feature Extraction

We explore some of the practicalities of using random walk inference methods, such as the Path Ranking Algorithm (PRA), for the task of knowledge base completion. We show that the random walk probabilities computed (at great expense) by PRA provide no discernible benefit to performance on this task, so they can safely be dropped. This allows us to define a simpler algorithm for generating feature matrices from graphs, which we call subgraph feature extraction (SFE). In addition to being conceptually simpler than PRA, SFE is much more efficient, reducing computation by an order of magnitude, and more expressive, allowing for much richer features than paths between two nodes in a graph. We show experimentally that this technique gives substantially better performance than PRA and its variants, improving mean average precision from .432 to .528 on a knowledge base completion task using the NELL KB.
Design procedure for two-stage CMOS operational amplifiers employing current buffer

The design procedure of the two-stage CMOS operational amplifiers employing Miller capacitor in conjunction with the common-gate current buffer is presented. Unlike the previously reported design strategy of the opamp of this type, which results in the opamp with a pair of nondominant complex conjugate poles and a finite zero, the proposed procedure is based upon the design strategy that results in the opamp with only one dominant pole. Design example of the proposed procedure is given.
Double-layered siw filter with enhanced electric coupling structure using three short-ended striplines

This paper presents an electric coupling structure with enhanced coupling coefficient based on double-layered SIW cavity resonators. Short-ended striplines are used to achieve electric coupling between two SIW cavities. The electric coupling depends on the width of the stripline. The proposed structure can achieve coupling coefficient more than 0.14. A fourth-order cross-coupled filter having centre frequency of 9.7 GHz and fractional bandwidth of 12% has been designed to verify the structure.