Reviewerly/exHarmony huggingface dataset:
Security Rights in Intellectual Property in Turkey

Although intellectual property rights are still not commonly used as security in Turkey, this usage does occur. Turkish law enables and, as of recently, even encourages the use of such rights as security, especially in the field of commerce. Two relatively new laws—namely, the Law on Industrial Property, No. 6769 of December 22, 2016 (Sınai Mülkiyet Kanunu) and the Law on Movable Pledge in Commercial Transactions, No. 6570 of October 20, 2016—are based, in general, on recent global developments and are in harmony with foreign and international legislation. Law on Movable Pledge in Commercial Transactions, No. 6750 has not introduced the system and solutions of the UNCITRAL Model Law on Secured Transactions. Nevertheless, Law No. 6750 has indeed expanded the possible practical uses of security rights over movables (including, inter alia, IP rights) that were formerly prescribed by the Turkish Civil Code and the relevant legislation. Accordingly, while the functions of the Registry (SMS) of the Turkish Patent and Trademark Office have been maintained, a second, quasi-parallel registry (TARES) has been established for certain commercial purposes. As mentioned before, the expanded possibilities provided by the new Turkish law do not include the Model Law concepts “registration of a notice”, “registration of an amendment or cancellation notice”, “search criteria in the Registry”, etc. The authors of this paper are of the opinion that Turkish lawmakers should eventually adopt the UNCITRAL Model Law on Secured Transactions and relevant UNCITRAL texts.
Crowd Density Estimation Method Using Reduced Features Set

A new crowd density estimation method with reduced feature set is proposed that enhances both prediction errors and computational time in terms of feature extraction, training, and prediction times. Based on the filter, wrapper, and embedded approaches to feature selection, 9 different sets of selected features were generating. Features categories were ranked based on the number of features selected across all selection methods. Using each set of features, 7 different regression models got trained. Each model performance was assessed based on MSE, MAE, and MRE. Feature selection methods were ranked based on its robustness in reducing the prediction errors. Based on the experimental results, the proposed method outperforms the previous works.
An Obstacle Avoidance Method for Autonomous Vehicle in Straight Road Based on Expanded Circle

In this paper, an algorithm is proposed to avoid online obstacle in a straight road or in an unknown environment. The algorithm is built upon the concept of expanded circle, which can be used to calculate the required steering angle for the vehicle. And then a lateral controller is designed to maintain the desired steering angle. Moreover, to keep the same posture of the vehicle after the obstacle avoidance manipulation, a constraint of the terminal point pose is added. The algorithm is computationally efficient, and guarantees online obstacle avoidance. Its performance is validated by actual experiments on autonomous vehicles in a laboratory setting and the results show that the proposed algorithm is more efficient than DWA, and even better than real human drivers.
Poster Abstract: Don't Wait For Weight: Towards Weight Inference of Passengers and Luggage using Smartphone Camera

Proposals on weighing passengers and their carry-on luggage prior to flights are gaining traction in the airline industry for fuel efficiency purposes. Adoption of such proposals are difficult in practice, however, as requiring passengers to step on weighing scales would incur significant overhead heavily affecting already busy airports. To solve this problem, we propose CamWeight, a novel vision-based weight inference system that takes video feed of off-the-shelf elastic mat (e.g., yoga mat) placed on the floor as the passenger walks over it while pulling his/her wheeled carry-on luggage. CamWeight makes use of inherent properties including amplitude and recovery time of strain, or mat deformation caused by footsteps and luggage wheels. Due to the inherent design of CamWeight, it incurs no additional time for weighing, while being cost effective. We present a preliminary proof-of-concept evaluation by varying weights in a luggage from 2.5 kg to 20 kg to achieve prediction mean absolute error of approximately 2 kg.
Research On Tag Recommendation Based on Multiple Keywords

The user inquiry system is a tool that we often use in our daily life. The effect of a inquiry system directly affects the user's experience. But the user will enter multiple keywords in the query to express what they really want to search, but this is usually a lot of noise caused by the impact of individual keywords, so this paper proposes a keyword query intention of mixed probability model, the model can be recommended result of query words together with the query intention fusion, and thus to minimize noise on the result of recommended keywords. This article also add tags to key words, as the query keywords supplement,. This method can not only make up for the noise interference of multiple keywords, but also calculate the similarity between the tag and the tag that has been marked on the web page, so as to judge the relevance between the user's query intention and the web page content more accurately. Finally, the results that are more in line with the user's query interest are ranked in the front position. Experimental results show that the proposed method is more effective.
Towards Adaptive Enterprise

The key to a successful adaptive enterprise lies in techniques and algorithms that enable the enterprise to learn about its environment and use the learning to make decisions that maximize its objectives. The volatile nature of the contemporary business environment means that learning needs to be continuous and reliable, and the decision-making rapid and accurate. In this chapter, the authors investigate two promising families of tools that can be used to design such algorithms: adaptive control and reinforcement learning. Both methodologies have evolved over the years into mathematically rigorous and practically reliable solutions. They review the foundations, the state-of-the-art, and the limitations of these methodologies. They discuss possible ways to bring together these techniques in a way that brings out the best of their capabilities.
The computer desktop image compression based on clustering algorithm

Summary With the rapid development of computer software and hardware technology and network technology, aiming at the limitations of the traditional image compression standards and the deficiencies of the existing computer desktop image compression methods. By analyzing the characteristics of computer desktop image, according to the characteristics of desktop compression, a compression scheme based on high efficiency video coding (HEVC) and color clustering is proposed, which divides blocks into text/graphics blocks, natural image blocks and mixed blocks based on the features of histogram information and texture information of blocks. In block division and classification, an adaptive dynamic block classification algorithm is proposed which is different from the traditional block partitioning. Compared with the traditional method, the new block classification algorithm can save the code stream and improve the classification accuracy.
A Comparative Analysis on Artificial Intelligence Techniques for Web Phishing Classification

Abstract Over the last few years, the web has been expanded to serve millions of users for various purposes all over the world. The web content filtering is essential to filter offensive, unwanted web content from web pages, reduced inappropriate content to prevent access to content which could compromise the network and spread malware. It also to tightened network security where web content filtering adds a much-needed layer of security to the network by blocking access to sites that raise an alarm. However, there are lack of comparison between classification techniques in previous studies in order to find the best classifier for the web page classification and the analysis related to it. Thus, the purpose of this study was to apply web page classification techniques and their performances is compared as it is the initial step in data mining before going to web filtering. In this project, three classifiers called Artificial Neural Network, J48 Decision Tree and Support Vector Machine were used to web phishing dataset in order to find the best possible classifier with small computational efforts that will give the best result in classifying the web page.
Single-Image Super-Resolution Based on Morphological Component Analysis and Coupled Dictionary Learning In Wavelet Domain

High resolution image can provide good performance guarantee for other image processing work, so image super-resolution has always been a hot topic of research. Image super-resolution based on sparse representation and dictionary learning is a very hot technology, these method usually using first-order and second-order derivatives to extract features of image, but this extraction method can not effectively extract high-frequency features such as textures. For this problem, we propose a new single-image super-resolution algorithm that combines concepts from image decomposition theory, morphological component analysis, coupled dictionary learning and wavelet-based dictionary construction. The LR image and texture image layers are used to construct coupled dictionaries. Coupled dictionary learning is used to realize the transformation of the sparse representation coefficients of the two feature spaces through the mapping matrix. This learning scheme relaxes the constraint conditions, enhances the mapping relationship between the LR image and HR texture layers, and improves the reconstruction quality. A set of dictionaries are created in the wavelet domain, where a pair of sub-band dictionaries is designed for the structure and texture image layers in each wavelet sub-band. A total of 3 pairs of dictionaries have been constructed for the structure and texture image components. These dictionaries demonstrate the compactness, directionality, and multi-scale nature of the wavelet transform. Hence, our scheme captures image high-frequency features more effectively. Experimental comparisons show superior super-resolution results of the proposed scheme based on the peak signal-to-noise ratio and the structural similarity index.
IT Systems for the Digital Enterprise

Modern digital enterprises operate in a dynamic environment where the business objectives, underlying technologies, and expectations from the end-users change over the time. Therefore, developing agile and adaptive IT systems is a critical need for most of the large business-critical enterprises. However, it is observed that the traditional IT system development approaches are not capable of ensuring all desired characteristics. This chapter discusses a set of established concepts and techniques that collectively help to achieve the desired agility and adaptiveness. The chapter reflects on the core concept of model-driven engineering for agility, technology independence, and retargetability; focuses on component abstraction to introduce divide-and-concur and separation of concerns; and proposes the use of variability and the concept of productline for developing configurable and extensible IT system.
Developments in exploring set invariance for Hopfield neural networks

The paper considers the nonlinear dynamics of a large class of continuous-time Hopfield neural networks (abbreviated as HNNs). Our research proposes sufficient conditions for testing the existence of contractive invariant sets with general form, defined by p-norms, $ 1\leq p\leq\infty$, which are weighted by rectangular, full column rank, non-negative matrices. These sufficient conditions have algebraic form and use a test matrix built from the HNN coefficients. From the point of view of the mathematical constructions, this test matrix defines the dynamics of a comparison system (with linear form), whose trajectories ensure componentwise upper bounds for the HNN trajectories. These bounds play an intermediary role in proving that any HNN trajectory remains inside a contractive set, once initialized inside that set. Two theorems are stated for covering both the local and the global cases of invariance. The theoretical results are illustrated by numerical examples run in Matlab, which also offer a visual support for the invariance property.
The Information Security Risks of the BYOD, From Theoretical Point of View

Nowadays a vast of the working people cannot live their life without smart devices. A part of them use these smart phones and tablets for work. Even a part of them are using their self-owned devices for business, beside their private usage. This practice is commonly referred as “Bring your own device” (BYOD). For example, people tend to use these devices for accessing their e-mail inboxes. Or even to analyze crucial business data via reaching the network of their firm, while commuting, just after they checked in their favorite social app. In this paper, we analyze the IT risks of the firm arisen by the BYOD. After the theoretical point of view, we will continue this.
Optimizing HAS for 360-Degree Videos

In recent years, an increasing number of Internet-based applications have been released that use virtual reality for education, training, gaming, and various forms of entertainment. When transmitting an omnidirectional 360° video over the Inter-net, it consumes considerably more data compared to traditional video streaming. To overcome the high network requirements and still reach a high Quality of Experience, HTTP adaptive streaming technology is considered for 360° video streaming. In contrast to traditional streaming, the adaptation logic considers not only the current network conditions, but also the viewport of the user, i.e., which part of the 360° sphere the user is currently focusing on. However, as the viewport of the user might change anytime, the adaptation logic is required to accurately predict the viewport in the next seconds of the playback to allow for an efficient and smooth streaming with a high visual quality.In this paper, we present novel linear programs that determine the optimal visual quality, which is reachable in a given network scenario using different approaches for viewport prediction. Our results are an important contribution for designing adaptation logics for 360° video streaming, which allow for efficient data transmission in the network while reaching a high QoE in VR applications.
Distortion Signal Sensing Technology of Wireless Optical Fiber Communication Network Based on Greedy Algorithm

In order to solve the problem of low perceptual accuracy in the existing communication distortion signal sensing methods, a wireless fiber communication network distortion signal sensing technology based on greedy algorithm is proposed. The distortion signal acquisition system of wireless optical fiber communication network is constructed, and the distortion signal cyclic spectrum is detected based on the indirect learning block diagram to realize the distortion signal feature acquisition. Combining with the matching degree algorithm, the characteristic matching processing of the distorted signal is carried out, the region detection architecture of the distorted signal is designed, and the sensing process of the distorted signal in the wireless fiber communication network is constructed based on the greedy algorithm. The simulation results show that the designed distortion signal sensing technology is more accurate than the traditional method and more practical.
Audio Signal Transmission Over Low-Speed Communication Channels for Emergency Broadcast

Real-time audio signal processing is a particularly difficult task. It is requiring significant computing resources of signal processing tools, as well as a special approach to writing software. This article demonstrates a block diagram of the device and describes algorithm, which realize a recording of a signal, speech recognition process, compressing, coding and transmitting. LabView15 is used as the development environment, and myRIO-1900 with FPGA is used to develop the device. The Python programming language is used to implement the algorithm for detecting speech in a noisy signal. A method based on the formant theory of intelligibility was used as a method of recognizing a speech signal. The device allows to perform real-time analysis of the operation of signal processing algorithms.
Prediction of Short-term PV output power Based on PCA-Stacking under different weather conditions

Abstract With the continuous expansion of photovoltaic scale, the accurate prediction of photovoltaic power generation is increasingly important for grid dispatching and grid optimization operations. In this paper, the photovoltaic power generation mainly uses meteorological factors and historical data as the input and output of the neural network. The input quantity is large, the data is redundant, and the network is difficult to converge, which always has a great adverse effect on the accuracy of photovoltaic output prediction. Firstly, different weather types are classified according to the trend graphs of different weather types. Principal components analysis (PCA) is used to analyze less comprehensive features from multiple meteorological factors and reduce the input of predictive models. At the same time, aiming at the problem that the prediction accuracy of a single prediction model such as the existing neural network and wavelet analysis method is limited, the idea and method of integrated learning are introduced, and a short-term prediction method based on Stacking method combined with SVM and Xgboost is proposed. Compared with the single model of SVM and Xgboost, the results show that the proposed method has a significant improvement compared with the accuracy of a single prediction model.
Smooth Kronecker

Graphs and graph-processing have become increasingly important. This has led to an explosion in the development of graph-processing systems, each of which is evaluated relative to its predecessors. In the absence of a large corpus of real-world graphs, synthetic generators provide an easy way to construct graphs of varying sizes. The most widely used generator is the Kronecker generator. Unfortunately, the Kronecker generator was not designed to produce graphs for benchmarking and when used in this fashion, it is problematic in two ways. First, the fraction of zero-degree vertices scales with the graph size, dramatically reducing the effective size of the connected graph. Second, the generator produces a vertex degree distribution not found in real world settings. We demonstrate these phenomena and present the Smooth Kronecker Generator, which remedies the vertex degree distribution problem without changing the statistical properties of the graph.
Reconstruction number of graphs with unique pendant vertex

A vertex-deleted subgraph of a graph G is called a card of G. The reconstruction number of G is the minimum number of cards of G that suffices to determine G uniquely. A P-graph (Yongzhi, 1988) is a connected graph of order p with exactly two blocks, only one of them is K2 and the other block has a vertex of degree p−2 other than the cut vertex. It is shown that the reconstruction number of P-graphs is three in most of the cases, which strengthens the result of Bollobas (1990). Finally, we conclude that the reconstruction number of most of the separable graphs (connected graphs with a cut vertex) with pendant vertices is three if the reconstruction number of all other connected graphs without pendant vertices is three.
Think Beyond the Word: Understanding the Implied Textual Meaning by Digesting Context, Local, and Noise

Implied semantics is a complex language act that can appear everywhere on the Cyberspace. The prevalence of implied spam texts, such as implied pornography, sarcasm, and abuse hidden within the novel, tweet, microblog, or review, can be extremely harmful to the physical and mental health of teenagers. The non-literal interpretation of the implied text is hard to be understood by machine models due to its high context-sensitivity and heavy usage of figurative language. In this study, inspired by human reading comprehension, we propose a novel, simple, and effective deep neural framework, called Skim and Intensive Reading Model (SIRM), for figuring out implied textual meaning. The proposed SIRM consists of three main components, namely the skim reading component, intensive reading component, and adversarial training component. N-gram features are quickly extracted from the skim reading component, which is a combination of several convolutional neural networks, as skim (entire) information. An intensive reading component enables a hierarchical investigation for both sentence-level and paragraph-level representation, which encapsulates the current (local) embedding and the contextual information (context) with a dense connection. More specifically, the contextual information includes the near-neighbor information and the skim information mentioned above. Finally, besides the common training loss function, we employ an adversarial loss function as a penalty over the skim reading component to eliminate noisy information (noise) arisen from special figurative words in the training data. To verify the effectiveness, robustness, and efficiency of the proposed architecture, we conduct extensive comparative experiments on an industrial novel dataset involving implied pornography and three sarcasm benchmarks. Experimental results indicate that (1) the proposed model, which benefits from context and local modeling and consideration of figurative language (noise), outperforms existing state-of-the-art solutions, with comparable parameter scale and running speed; (2) the SIRM yields superior robustness in terms of parameter size sensitivity; (3) compared with ablation and addition variants of the SIRM, the final framework is efficient enough.
Synthesis of Pseudorandom Number Generator by Combining Mentor Graphics HDL Designer and Xilinx Vivado FPGA Flow

Pseudorandom number generators are used in cryptographic as well as VLSI testing applications. Linear Feedback Shift Registers (LFSR) are circuits that can be used as pseudorandom number generators. This paper proposes a modified reseeding method for LFSR and also presents a design flow for the implementation. The work is done by combining Mentor Graphics HDL Designer FPGA flow and Xilinx Vivado. Different bit lengths of LFSR are generated using Mentor Graphics HDL Designer and synthesized using both Mentor Graphics Precision RTL synthesizer and Xilinx Vivado. The implementation targets Virtex-7 FPGA.