nils-herrmann/clean_scirepeval huggingface dataset:
Performance of H.264, H.265, VP8 and VP9 Compression Standards for High Resolutions

Recently multimedia services increase, especially in video domain, leads to requirements of quality assessment. The main factors effected that quality are compression technology and transmission link. This paper presents a coding efficiency comparison of the well-known video compression standards Advanced Video Coding (H.264/AVC), High-Efficiency Video Coding (H.265/HEVC), VP8 and VP9 using objective metrics. An extensive range of bitrates from low to high bitrates was selected and encoded. The evaluation was done for four types of sequences in high resolutions depending on content. All four video sequences were encoded by using same encoding configurations for all the examined video codecs. The results showed that the quality of all compression standards rises logarithmically with increasing bitrate - in low bitrates the quality grows faster than in high bitrates. The efficiency of the new compression standards outperforms the older ones. The efficiency of VP8 compression standard outperforms the H.264/AVC compression standard. The efficiency of H.265/HEVC and VP9 compression standards is almost the same. The results also showed that VP8 codec cannot allow encoding by low bitrates. According to the results can be also said that the efficiency of the new compression standards decreases with the resolution. The results showed that the effectiveness of compression depends on the type of test sequence.
METAMODELING OF COMBINED DISCRETE/CONTINUOUS RESPONSES

Metamodels are effective for providing fast-running surrogate approximations of product or system performance. Since these approximations are generally based on continuous functions, they can provide poor fits of discontinuous response functions. Many engineering models produce functions that are only piecewise continuous, due to changes in modes of behavior or other state variables. In this paper we investigate the use of a state-selecting metamodeling approach that provides an accurate approximation for piecewise continuous responses. The proposed approach is applied to a desk lamp performance model. Three types of metamodels—quadratic polynomials, spatial correlation (kriging) models, and radial basis functions—and five types of experimental designs—full factorial designs, D-best Latin hypercube designs, fractional Latin hypercubes, Hammersley sampling sequences, and uniform designs—are compared based on three error metrics computed over the design space. The state-selecting metamodeling approach outperforms a combined metamodeling approach in this example, and radial basis functions perform well for metamodel construction.
A Deep Learning Method for Pathological Voice Detection Using Convolutional Deep Belief Networks

Automatically detecting pathological voice disorders such as vocal cord paralysis or Reinke’s edema is a challenging and important medical classification problem. While deep learning techniques have achieved significant progress in the speech recognition field there has been less research work in the area of pathological voice disorders detection. A novel system for pathological voice detection using convolutional neural network (CNN) as the basic architecture is presented in this work. The novel system uses spectrograms of normal and pathological speech recordings as the input to the network. Initially Convolutional deep belief network (CDBN) are used to pre-train the weights of CNN system. This acts as a generative model to explore the structure of the input data using statistical methods. Then a CNN is trained using supervised back-propagation learning algorithm to fine tune the weights. It will be shown that a small amount of data can be used to achieve good results in classification with this deep learning approach. A performance analysis of the novel method is provided using real data from the Saarbrucken Voice database.
Wireless access monitoring and control system based on digital door lock

We propose a novel wireless access monitoring and control system based on the digital door lock, which is explosively used as a digital consumer device. Digital door lock is an electronic locking system operated by the combination of digital key, security password or number codes. This paper presents a prototype of the proposed system and shows a scheme for the implementation. To implement the system with ZigBee network protocol, four types of modules are developed, ZigBee module, digital door lock module, human detection module, and ZigBee relay module. ZigBee module is designed to support wireless sensor network and also used for the ZigBee tag to identify the access objects. Digital door lock module is implemented as a digital consumer device to control the access system as well as locking system. It is very convenient system for the consumers and has extensible and flexible characteristics. That is, it can be used as a home security system by the ZigBee network with additional sensor devices. Therefore, it can be a good practical product for the realization of an access monitoring and control system. It also can be applied to the real market for home networking system. Furthermore, the system can be extended to another service such as a connection between mobile phone and home networking system.
Wireless Power Transmission

Wireless Power Transmission (WPT) has been an ongoing research area since 1873 when James Maxwell first theorized transferring power could be achieved through electromagnetic radiation. While microwave systems were first to be developed, in recent years the viability and demand for optical systems has grown significantly. In the space elevator contest sponsored by NASA in 2009, LaserMotive successfully demonstrated the potential of a laser based WPT system. The defense and space communities have started to recognize the impact that this technology could have given the multitude of battery-reliant unmanned systems that exist today. The current research is a study analyzing the background and benefits of laser based WPT systems, the system components, demonstrated/proposed WPT systems, and proposed research area needs within laser based WPT systems.
Decision support for Cybersecurity risk planning

a r t i c l e i n f o Security countermeasures help ensure the confidentiality, availability, and integrity of information systems by preventing or mitigating asset losses from Cybersecurity attacks. Due to uncertainty, the financial impact of threats attacking assets is often difficult to measure quantitatively, and thus it is difficult to prescribe which countermeasures to employ. In this research, we describe a decision support system for calculating the uncertain risk faced by an organization under cyber attack as a function of uncertain threat rates, countermeasure costs, and impacts on its assets. The system uses a genetic algorithm to search for the best combination of countermeasures, allowing the user to determine the preferred tradeoff between the cost of the portfolio and resulting risk. Data collected from manufacturing firms provide an example of results under realistic input conditions. Assuring a secure information technology (IT) environment for the transaction of commerce is a major concern. The magnitude of the task is growing yearly, as attackers become more knowledgeable, more determined, and bolder in their efforts. According to a lead security expert at International Data Corporation, a global provider of market intelligence and advisory services for the IT community, " Emerging new attack vectors, more targeted campaigns, compliance, and the layering of new technologies on the corporate infrastructure are all factors having a tremendous impact on the overall risk to an organization " . Given that corporate expenditures are usually a good indicator of the level of concern about an issue, security is obviously near the top of many IT executives' lists. According to IDC, the global market for providers of information security services is projected to exceed $32.5 billion in 2010 [21]. The New York Times recently noted that " the intrusion into Google's computers and related attacks from within China on some thirty other companies point to the rising sophistication of such assaults and the vulnerability of even the best defenses " . The Times further states that, according to the Computer Security Institute, malware infections are up from one-half to almost two-thirds of companies surveyed last year, at an average cost of $235,000 for each organization. Finally, the newspaper observes, malware is being exploited in new terrains with malicious code that turns on cellphone microphones and cameras for the purpose of industrial spying . Because of the variety of methods attackers use to try to infiltrate and disrupt …
Cyber-physical System Risk Assessment

Cyber Physical System (CPS) is a combination of physical systems with cyber systems, where there is a tight coupling between the two systems. It is widely used in critical national infrastructure, such as electric power, petroleum and chemical industries. Once an attack against the CPS obtains success, the consequence will be unimaginable. A well-designed risk assessment of CPS will provide an overall view of CPS security status and support efficient allocations of safeguard resources. Though there is much relationship between CPS and IT system, they are still different in various aspects, especially the requirement for real-time. Therefore, traditional risk assessment method for IT system can't be directly applied in CPS. New ideas on CPS risk assessment are in urgent need and one idea about this is addressed in this paper. Firstly, it presents a depict description of a three-level CPS architecture and makes an analysis on the corresponding security features in each level. Secondly, it sums up traditional risk assessment methods analyzes the differences between cyber physical system security and traditional IT system security. Finally, the authors blaze a trail under the new perspective of CPS after breaking the restriction of traditional risk assessment methods and propose a risk assessment idea for CPS.
GRS: The green, reliability, and security of emerging machine to machine communications

Machine-to-machine communications is characterized by involving a large number of intelligent machines sharing information and making collaborative decisions without direct human intervention. Due to its potential to support a large number of ubiquitous characteristics and achieving better cost efficiency, M2M communications has quickly become a market-changing force for a wide variety of real-time monitoring applications, such as remote e-healthcare, smart homes, environmental monitoring, and industrial automation. However, the flourishing of M2M communications still hinges on fully understanding and managing the existing challenges: energy efficiency (green), reliability, and security (GRS). Without guaranteed GRS, M2M communications cannot be widely accepted as a promising communication paradigm. In this article, we explore the emerging M2M communications in terms of the potential GRS issues, and aim to promote an energy-efficient, reliable, and secure M2M communications environment. Specifically, we first formalize M2M communications architecture to incorporate three domains - the M2M, network, and application domains - and accordingly define GRS requirements in a systematic manner. We then introduce a number of GRS enabling techniques by exploring activity scheduling, redundancy utilization, and cooperative security mechanisms. These techniques hold promise in propelling the development and deployment of M2M communications applications.
A systematic review and meta-analysis of dressings used for wound healing: the efficiency of honey compared to silver on burns.

Honey has the antibacterial effect of silver without the toxic effect of silver on the skin. Even so, silver is the dominant antibacterial dressing used in wound healing.



To evaluate the healing effects of honey dressings compared to silver dressings for acute or chronic wounds.



A systematic review with meta-analysis.



The search, conducted in seven databases, resulted in six randomised controlled trial studies from South Asia focusing on antibacterial properties and healing times of honey and silver.



Honey was more efficacious for wound healing than silver, as measured in the number of days needed for wounds to heal (pooled risk difference -20, 95% CI -0.29 to -0.11, p < .001). Honey turned out to have more antibacterial qualities than silver.



All the included studies based on burns showed the unequivocal result that honey had an even more positive effect than silver on wound healing.
Security and Privacy Preservation Scheme of Face Identification and Resolution Framework Using Fog Computing in Internet of Things

Face identification and resolution technology is crucial to ensure the identity consistency of humans in physical space and cyber space. In the current Internet of Things (IoT) and big data situation, the increase of applications based on face identification and resolution raises the demands of computation, communication, and storage capabilities. Therefore, we have proposed the fog computing-based face identification and resolution framework to improve processing capacity and save the bandwidth. However, there are some security and privacy issues brought by the properties of fog computing-based framework. In this paper, we propose a security and privacy preservation scheme to solve the above issues. We give an outline of the fog computing-based face identification and resolution framework, and summarize the security and privacy issues. Then the authentication and session key agreement scheme, data encryption scheme, and data integrity checking scheme are proposed to solve the issues of confidentiality, integrity, and availability in the processes of face identification and face resolution. Finally, we implement a prototype system to evaluate the influence of security scheme on system performance. Meanwhile, we also evaluate and analyze the security properties of proposed scheme from the viewpoint of logical formal proof and the confidentiality, integrity, and availability (CIA) properties of information security. The results indicate that the proposed scheme can effectively meet the requirements for security and privacy preservation.
Towards detecting fake user accounts in facebook

People are highly dependent on online social networks (OSNs) which have attracted the interest of cyber criminals for carrying out a number of malicious activities. An entire industry of black-market services has emerged which offers fake accounts based services for sale. We, therefore, in our work, focus on detecting fake accounts on a very popular (and difficult for data collection) online social network, Facebook. Key contributions of our work are as follows. The first contribution has been collection of data related to real and fake accounts on Facebook. Due to strict privacy settings and ever evolving API of Facebook with each version adding more restrictions, collecting user accounts data became a major challenge. Our second contribution is the use of user-feed information on Facebook to understand user profile activity and identifying an extensive set of 17 features which play a key role in discriminating fake users on Facebook with real users. Third contribution is the use these features and identifying the key machine learning based classifiers who perform well in detection task out of a total of 12 classifiers employed. Fourth contribution is the identifying which type of activities (like, comment, tagging, sharing, etc) contribute the most in fake user detection. Results exhibit classification accuracy of 79% among the best performing classifiers. In terms of activities, likes and comments contribute well towards detection task. Although the accuracy is not very high, however, our work forms a baseline for further improvement. Our results indicate that many fake users are classified as real suggesting clearly that fake accounts are mimicking real user behavior to evade detection mechanisms. Our work concludes by enlisting a number of future course of actions that can be undertaken.
A summary-statistic representation in peripheral vision explains visual crowding.

Peripheral vision provides a less faithful representation of the visual input than foveal vision. Nonetheless, we can gain a lot of information about the world from our peripheral vision, for example in order to plan eye movements. The phenomenon of crowding shows that the reduction of information available in the periphery is not merely the result of reduced resolution. Crowding refers to visual phenomena in which identification of a target stimulus is significantly impaired by the presence of nearby stimuli, or flankers. What information is available in the periphery? We propose that the visual system locally represents peripheral stimuli by the joint statistics of responses of cells sensitive to different position, phase, orientation, and scale. This "textural" representation by summary statistics predicts the subjective "jumble" of features often associated with crowding. We show that the difficulty of performing an identification task within a single pooling region using this representation of the stimuli is correlated with peripheral identification performance under conditions of crowding. Furthermore, for a simple stimulus with no flankers, this representation can be adequate to specify the stimulus with some position invariance. This provides evidence that a unified neuronal mechanism may underlie peripheral vision, ordinary pattern recognition in central vision, and texture perception. A key component of our methodology involves creating visualizations of the information available in the summary statistics of a stimulus. We call these visualizations "mongrels" and show that they are highly useful in examining how the early visual system represents the visual input. Mongrels enable one to study the "equivalence classes" of our model, i.e., the sets of stimuli that map to the same representation according to the model.
Neural Attentional Rating Regression with Review-level Explanations

Reviews information is dominant for users to make online purchasing decisions in e-commerces. However, the usefulness of reviews is varied. We argue that less-useful reviews hurt model’s performance, and are also less meaningful for user’s reference. While some existing models utilize reviews for improving the performance of recommender systems, few of them consider the usefulness of reviews for recommendation quality. In this paper, we introduce a novel attentionmechanism to explore the usefulness of reviews, and propose a Neural Attentional Regression model with Review-level Explanations (NARRE) for recommendation. Speci cally, NARRE can not only predict precise ratings, but also learn the usefulness of each review simultaneously. Therefore, the highly-useful reviews are obtained which provide review-level explanations to help users make better and faster decisions. Extensive experiments on benchmark datasets of Amazon and Yelp on di erent domains show that the proposed NARRE model consistently outperforms the state-ofthe-art recommendation approaches, including PMF, NMF, SVD++, HFT, and DeepCoNN in terms of rating prediction, by the proposed attention model that takes review usefulness into consideration. Furthermore, the selected reviews are shown to be e ective when taking existing review-usefulness ratings in the system as ground truth. Besides, crowd-sourcing based evaluations reveal that in most cases, NARRE achieves equal or even better performances than system’s usefulness rating method in selecting reviews. And it is exible to o er great help on the dominant cases in real ecommerce scenarios when the ratings on review-usefulness are not available in the system.
Who are my neighbors?: A perception model for selecting neighbors of pedestrians in crowds

Pedestrian trajectory prediction is a challenging problem. One of the aspects that makes it so challenging is the fact that the future positions of an agent are not only determined by its previous positions, but also by the interaction of the agent with its neighbors. Previous methods, like Social Attention have considered the interactions with all agents as neighbors. However, this ends up assigning high attention weights to agents who are far away from the queried agent and/or moving in the opposite direction, even though, such agents might have little to no impact on the queried agent's trajectory. Furthermore, trajectory prediction of a queried agent involving all agents in a large crowded scenario is not efficient. In this paper, we propose a novel approach for selecting neighbors of an agent by modeling its perception as a combination of a location and a locomotion model. We demonstrate the performance of our method by comparing it with the existing state-of-the-art method on publicly available datasets. The results show that our neighbor selection model overall improves the accuracy of trajectory prediction and enables prediction in scenarios with large numbers of agents in which other methods do not scale well.
On Compiling CNF into Decision-DNNF

Decision-DNNF is a strict subset of decomposable negation normal form (DNNF) that plays a key role in analyzing the complexity of model counters (the searches performed by these counters have their traces in Decision-DNNF). This paper presents a number of results on Decision-DNNF. First, we introduce a new notion of CNF width and provide an algorithm that compiles CNFs into Decision-DNNFs in time and space that are exponential only in this width. The new width strictly dominates the treewidth of the CNF primal graph: it is no greater and can be bounded when the treewidth of the primal graph is unbounded. This new result leads to a tighter bound on the complexity of model counting. Second, we show that the output of the algorithm can be converted in linear time to a sentential decision diagram (SDD), which leads to a tighter bound on the complexity of compiling CNFs into SDDs.
Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion

We explore an original strategy for building deep networks, based on stacking layers of denoising autoencoderswhich are trained locally to denoise corrupted versions of t heir inputs. The resulting algorithm is a straightforward variation on the stacking of ordinary autoencoders. It is however shown on a benchmark of classification problems to yield sign ificantly lower classification error, thus bridging the performance gap with deep belief networks (DBN), and in several cases surpassing it. Higher level representations learnt in this purely u ns pervised fashion also help boost the performance of subsequent SVM classifiers. Qualitative exp eriments show that, contrary to ordinary autoencoders, denoising autoencoders are able to lear n Gabor-like edge detectors from natural image patches and larger stroke detectors from digit images . Thi work clearly establishes the value of using a denoising criterion as a tractable unsupervised o bjective to guide the learning of useful higher level representations.
Evaluating The Performance of Symmetric Encryption Algorithms

Internet and networks applications are growing very fast, so the needs to protect such applications are increased. Encryption algorithms play a main role in information security systems. On the other side, those algorithms consume a significant amount of computing resources such as CPU time, memory, and battery power. This paper provides evaluation of six of the most common encryption algorithms namely: AES (Rijndael), DES, 3DES, RC2, Blowfish, and RC6. A comparison has been conducted for those encryption algorithms at different settings for each algorithm such as different sizes of data blocks, different data types, battery power consumption, different key size and finally encryption/decryption speed. Experimental results are given to demonstrate the effectiveness of each algorithm.