Reviewerly/exHarmony huggingface dataset:
Refinement Modal Logic Based on Finite Approximation of Covariant-Contravariant Refinement

The notion of covariant-contravariant refinement (CC-refinement) is a generalization of the notions of bisimulation and refinement, which is coinductively defined to describe behavior relation, i.e., two related processes are required to be always able to provide matched transitions each other. The notion of CC-n-refinement, where n is a natural number, presented in this paper, finitely approximates the notion of CC-refinement through weakening the above-mentioned requirement. A process is said to CC-n-refine another process whenever they can, within n-steps, match the transitions each other, and a CC-n-refinement relation may be considered to be a CC-refinement relation if n is big enough. Based on this kind of finite approximation, this paper presents CC-n-refinement modal logic (nCCRML) obtained from the modal system K by adding CC-n-refinement quantifiers, establishes an axiom system for nCCRML, and explores some important properties of this axiom system: soundness, completeness, and decidability. CC-n-refinement quantifiers can be used to formalize some interesting problems in the field of formal method.
3D deformation monitor with a distributed high-speed videogrammetry system based on shaking table experiment

In recent years, high-speed videogrammetry has been used to monitor the three-dimension behavior of large vibrating structures. Traditionally, this has been accomplished by transferring image sequences, captured by several cameras, to a central computer after each test has been conducted. Only then have the images been postprocessed and the required kinematic data extracted. This process is slow and inefficient because a large amount of data (image sequences) must be transferred to the host before the data analysis can begin. In order to address this problem, we have developed a novel system that adopts a distributed data processing strategy. The system which combining the processing power built into each of the individual cameras and the processing power of a central computer, uses a wired local area network. the communication between the nodes and the host is achieved using the TCP/IP protocol and a custom application layer. In this way, the processing power of the entire system is more fully utilized and the overall performance of the video processing system is improved. We describe how we have employed two cameras, operating simultaneously, to test the proposed concept. The experimental results from a series of tests showed that the average time required to perform the necessary image processing was reduced 58.7% by using the distributed processing system instead of a traditionally configured system.
Real-Time Fidelity Measurement of JPEG2000 Coded Images

The progressive nature of the JPEG2000 coded bitstream allows the reconstruction of images of different qualities from a single coded bitstream. This feature is utilized in this work to estimate the mean-squared-error (MSE) of reconstructed images without requiring the original image. It is based on the fact that if the MSE between the original image and a lower quality image is known, the MSE for higher quality images can be estimated from a quality scalable bitstream. The proposed method is highly accurate and is very simple as no complex statistical modeling is needed. Therefore, it is suitable to measure the fidelity of JPEG2000 decoded images at any desired quality in a real-time scenario.
What Arrow’s Information Paradox Says (to Philosophers)

Arrow’s information paradox features the most radical kind of information asymmetry by diagnosing an inherent conflict between two parties inclined to exchange information. In this paper, we argue that this paradox is more richly textured than generally supposed by current economic discussion on it and that its meaning encroaches on philosophy. In particular, we uncovers the ‘epistemic’ and more genuine version of the paradox, which looms on our cognitive lives like a sort of tax on curiosity. Finally, we sketch the relation between Arrow’s information paradox and the notion of zero-knowledge proofs in cryptography: roughly speaking, zero-knowledge proofs are protocols that enable a prover to convince a verifier that a statement is true, without conveying any additional information.
What we talk about when we talk about pcap expressions

Pcap expressions are a popular domain-specific language for packet filters, used extensively to capture network traffic during network monitoring or testing. The language is simple but made nontrivial by implicit subformulas and other features that can lead to unexpected and unwanted results.
A low-cost, accurate strain measurement using multi-view amplification mechanism and visual polydimethylsiloxane lens

Strain sensors are widely applied in the industry requiring specific signal amplifiers and signal readers. However, the devices are unable to read data quickly and intuitively. The present study introduces a highly accurate, repeatable, mold-free strain sensor comprising of an amplification mechanism and a smart-phone microscope. Generation of the amplification mechanism using rapid prototyping especially 3D printing is summarized and evaluated. The sensitivity of the proposed amplifier is (39.47 ± 1.34) for nylon and (37.74 ± 2.41) for ABS. A visual polydimethylsiloxane (PDMS) lens with a focal length of 7.23 mm is attached to the iphone6 camera, performing as a microscope for image acquisition, which provides an equivalent focal length of 6.7 mm and a resolution of 0.691 µm.
An Adaptive Frequency Based Steganography Technique

Steganography is an art of embedding data in an imperceptible way. Steganography technique has two category based on transformation: spatial and frquency domain transformation based techniques. The requirements of steganography are capacity, quality and imperceptibility. This paper works on capacity and quality simultaneously by embedding data in transformed domain at selected locations. Here, secret message bits are embedded in middle frequency, few low frequency and high frequency components at alternate position of discrete cosine transformed image. By this method, secret message bits will less affect the cover image’s information part. Analyses of this method and other DCT based methods over same image data set shows that proposed work has good PSNR value and high embedding capacity.
Object Tracking Using Sparsity Based Discriminative Minimum-Spanning-Tree

An object tracking method based on a minimum-spanning-tree one-class classifier is proposed where the background is regarded as a collection of all other classes except the target. First, two kinds of different target class examples and outlier examples are collected in the recent few frames. Meanwhile, the candidates are sampled by a particle filter in the current frame. Second, using the sparsity-based local discriminant analysis, all examples and candidates are projected into a local discriminative subspace to improve the discriminative ability of one-class classifier. Then, a graph is built on all the target examples by adopting one-to-all sparse reconstruction coefficients rather than pair-wise Euclidean distance and a minimum-spanning-tree one-class classifier is trained on the graph. Finally, the similarity scores of the candidates are evaluated by the trained classifier. The one with the highest score is determined as the tracking result and used to update the training set. The experimental results demonstrate the superior performance of our method in robustness and accuracy to the state-of-the-art methods.
Diagnosing Misattribution of Commitments: A Normative and Pragmatic Model of for Assessing Straw Man

This paper builds a nine-step method for determining whether a straw man fallacy has been committed in a given case or not, by starting with some relatively easy textbook cases and moving to more realistic and harder cases. The paper shows how the type of argument associated with the fallacy can be proved to be a fallacy in a normative argumentation model, and then moves on to the practical task of building a hands-on method for applying the model to real examples of argumentation. Insights from linguistic pragmatics are used to distinguish the different pragmatic processes involved in reconstructing what is said and what is meant by an utterance, and to differentiate strong and weak commitments. In particular, the process of interpretation is analyzed in terms of an abductive pattern of reasoning, based on co-textual and contextual information, and assessable through the instruments of argumentation theory.
Robust Face Sketch Recognition Using Locality Sensitive Histograms

This letter proposes a new face sketch recognition method. Given a query sketch and face photos in a database, the proposed method first synthesizes pseudo sketches by computing the locality sensitive histogram and dense illumination invariant features from the resized face photos, then extracts discriminative features by computing histogram of averaged oriented gradients on the query sketch and pseudo sketches, and finally find a match with the shortest cosine distance in the feature space. It achieves accuracy comparable to the state-of-the-art while showing much more robustness than the existing face sketch recognition methods.
Research on Computing Efficiency of MapReduce in Big Data Environment

The emergence of big data has brought a great impact on traditional computing mode, the distributed computing framework represented by MapReduce has become an important solution to this problem. Based on the big data, this paper deeply studies the principle and framework of MapReduce programming. On the basis of mastering the principle and framework of MapReduce programming, the time consumption of distributed computing framework MapReduce and traditional computing model is compared with concrete programming experiments. The experiment shows that MapReduce has great advantages in large data volume.
Research on division and conquer algorithm

In computer science, divide-and-conquer is an important algorithm. The literal interpretation is "divide-and-conquer," in which a complex problem is divided into two or more identical or similar sub-problems, and then sub-problems are divided into smaller sub-problems, until the final sub-problem can be solved simply and directly. The combination of solutions of the solution of the original problem. This technique is the foundation of many efficient algorithms, such as sort algorithm (fast sort, merge sort), Fourier transform (fast Fourier transform) and so on. When it comes to divide-and-conquer algorithms, it’s necessary to say recursion. They’re like twin brothers, often used in algorithm design at the same time, and As a result, many efficient algorithms are generated. The direct or indirect call of its own algorithm is called a recursive algorithm. The function defined by the function itself is called recursive function. The sub-problem generated by divide-and-conquer method is often the smaller pattern of the original problem, which makes it convenient to use recursive technique. In this case, the repeated application of divide-and-conquer means can make the sub-problem consistent with the original problem type but its scale is constantly reduced, and finally the sub-problem can be reduced to a very easy solution. This naturally leads to recursion.
On the Optimization of Advanced DCF-Trackers

Trackers based on discriminative correlation filters (DCF) have recently seen widespread success and in this work we dive into their numerical core. DCF-based trackers interleave learning of the target detector and target state inference based on this detector. Whereas the original formulation includes a closed-form solution for the filter learning, recently introduced improvements to the framework no longer have known closed-form solutions. Instead a large-scale linear least squares problem must be solved each time the detector is updated. We analyze the procedure used to optimize the detector and let the popular scheme introduced with ECO serve as a baseline. The ECO implementation is revisited in detail and several mechanisms are provided with alternatives. With comprehensive experiments we show which configurations are superior in terms of tracking capabilities and optimization performance.
State-Affine Normal Forms

The standard state-affine normal forms are presented. We dissociate the ones having a stationary linear part for which a simple Luenberger observer can be used, and the ones having a time-varying linear part for which a Kalman design with a time-varying gain is necessary.
Effects of Different Queueing Models on Migration of Virtual Machines

Virtualization is an act of creating virtual resources that are made accessible by the application of cloud computing. In case of failure of a virtual machine, it is imperative to shift the processes running on this machine to another. This activity is known as live virtual machine migration and is classified under the major issues of research. Xia (2015) proposed a mathematical model to analyze this problem in which the work has been done in two phases. It is showing a state transition model system in which the M/M/1/K model has been utilized in phase1 to find the rejection probability of the jobs in the phase2. Each virtual machine is considered to have a buffer to store the incoming buffer. The major issue being discussed in this paper is the relation of the rejection probability of jobs with the changing size of the buffer. It actually provides an exhaustive analysis of three different queueing models, i.e., M/M/1/∞, M/M/∞, and M/M/1/K. The simulations are carried out in MATLAB, and the results are analyzed based on the rejection probability of the jobs. It is observed that with an increase in the request arrival rate, the rejection probability of jobs increases. However, with an increase in execution rate, the rejection probability of jobs decreases. If we change the model to M/M/∞, actually, the formulas of request rejection probability and job rejection probability got changed that resulted in a continuous decrease in values of rejection rate lines as compared to the values of the author. Hence, we can say that changing the queueing model is beneficial.
Structred MDS Matrices, Additive Codes Over $$\textit{GF}(2)^m$$ and Symmetric Cryptography

In this paper, we study the construction of diffusion matrices with suitable symmetric properties for cryptographic applications. The most famous example of diffusion matrices with symmetries are the circulant ones. In this paper, we focus on the less known dyadic family. Considering the link between optimal diffusion matrices and MDS codes, we use a coding theory approach based on additive codes over $$\textit{GF}(2)^m$$ . With this method, we could build diffusion matrices not only derived from a finite field but derived from the whole linear group $$\textit{GL}(m,2)$$ . We present some theoretical and experimental results, particularly efficient in the context of hardware implementation.
A distributed hypervisor for resource aggregation

Scale-out has become the standard answer to data analysis, machine learning and many other fields. Contrary to common belief, scale-up machines can outperform scale-out clusters for a considerable portion of tasks. However, those scale-up machines are not economical and may not be affordable for small businesses. This paper presents GiantVM, a distributed hypervisor that aggregates resources from multiple physical machines, providing the guest OS with a uniform hardware abstraction. We propose techniques to deal with the challenges of CPU, Memory, and I/O virtualization in distributed environments.
Information architects

Every organization relies on information to communicate with prospects and customers - blog posts, articles, whitepapers, user manuals, web portals, videos, tweets, social media posts, moderated forums, and more. This means that many people are creating content and are delivering it in multiple ways. To meet our users' needs, we need information architecture (IA) to provide the framework for developing and delivering this information. Although most content creators do not think of themselves as information architects, many of them perform tasks that are information architecture responsibilities. If you decide what information gets created and delivered, identify keywords to support findability, or organize the hierarchy for a table of contents, you are performing IA tasks. To learn who was performing these tasks and how they ended up with this role, I conducted a survey. This article presents my analysis of the results based upon my experience and relevant industry sources.
Comparison of DCT and Gabor Filters in Residual Extraction of CNN Based JPEG Steganalysis

An effective feature selection method to capture the weak stego noise is essential to image steganalysis. In the conventional JPEG steganalysis, Gabor filter and DCT filter are both used for residual extraction. However, there are few comparisons in existing convolutional neural networks (CNNs) based JPEG steganalysis using Gabor filter or DCT filter in the pre-processing stage to extract residuals. In this paper, we compare the performance of DCT filter with Gabor filter in the pre-processing phase of the steganalysis CNN. Firstly, we choose the parameters empirically and theoretically for Gabor filters which are used in CNN. Secondly, we improve the performance by removing the ABS layer in the original XuNet. Finally, the experimental results show that using Gabor filters or DCT filter can achieve comparable performance whenever the parameters of pre-processing filters are fixed or learnable. It’s different from the conventional steganalysis method where Gabor filters have advantages over DCT filters. When the parameters of the pre-processing filters are learnable, both Gabor filter and DCT filter can achieve better performance compared with the condition where the parameters are fixed.
Communication-efficient private distance calculation based on oblivious transfer extensions

We propose a general framework for computing privacy-preserving distance metrics (PPDM) in the two-party setting in order to improve communication complexity by benefiting from 1-out-of-n oblivious transfers. We implement privacy-preserving Euclidean distance, Cosine similarity and Edit distance protocols while the PPDM framework is easily extendable to address other distance measures. These protocols have direct applications in privacy-preserving one-to-many biometric identification in which two parties known as the client and the server want to find the best match between their inputs. The client's input is compared to all the records in the server's database. We use the semi-honest adversary threat model. We extensively evaluate our PPDM framework. And, we theoretically show the improvement of PPDM over related work.
Multi-Context Automated Lemma Generation for Term Rewriting Induction with Divergence Detection

We present an automated lemma generation method for equational, inductive theorem proving based on the term rewriting induction of Reddy and Aoto as well as the divergence critic framework of Walsh. The method effectively works by using the divergence-detection technique to locate differences in diverging sequences, and generates potential lemmas automatically by analyzing these differences. We have incorporated this method in the multi-context inductive theorem prover of Sato and Kurihara to overcome the strategic problems resulting from the unsoundness of the method. The experimental results show that our method is effective especially for some problems diverging with complex differences (i.e., parallel and nested differences).
Big Data and Privacy State of the Art

The phenomenon of big data (massive data mining) refers to the exponential growth of the volume of data available on the web. This new concept has become widely used in recent years, enabling scalable, efficient, and fast access to data anytime, anywhere, helping the scientific community and companies identify the most subtle behaviors of users. However, big data has its share of the limits of ethical issues and risks that cannot be ignored. Indeed, new risks in terms of privacy are just beginning to be perceived. Sometimes simply annoying, these risks can be really harmful. In the medium term, the issue of privacy could become one of the biggest obstacles to the growth of big data solutions. It is in this context that a great deal of research is under way to enhance security and develop mechanisms for the protection of privacy of users. Although this area is still in its infancy, the list of possibilities continues to grow.
Certified ACKBO

Term rewriting in the presence of associative and commutative function symbols constitutes a highly expressive model of computation, which is for example well suited to reason about parallel computations. However, it is well known that the standard notion of termination does not apply any more: any term rewrite system containing a commutativity rule is nonterminating. Thus, instead of adding AC-rules to a rewrite system, we switch to the notion of AC-termination. AC-termination can for example be shown using AC-compatible reduction orders. One specific example of such an order is ACKBO. We present our Isabelle/HOL formalization of the ACKBO order. On an abstract level this gives us a mechanized proof of the fact that ACKBO is indeed an AC-compatible reduction order. Moreover, we integrated corresponding check functions into the verified certifier CeTA. This has the more practical consequence of enabling the machine certification of AC-termination proofs generated by automated termination tools.
Formation of Information Literacy of Younger Schoolchildren

The article deals with one of the priorities of the modern primary school, reflected in the requirements of the Federal State Educational Standard of the Primary Education of the Second Generation, the formation of information literacy of students. It describes the characteristics of three groups of informational educational activities, including the readiness of the younger schoolchildren: to use the source of information; understand and present information in different forms; evaluate the reliability, reliability of this or received information. The difficulties faced by elementary school students in mastering information and educational activities are analyzed. The directions of work of the teacher to overcome them and improve the information culture of students are outlined.
Consumer Innovation Shout Out: Warby Parker ‘Virtual Try-on’ comes to Life Thanks to iPhone X + AR - Viewpoints on Innovation

To solve the dependency of exploring a frame assortment with physical product, Warby Parker launched it’s ‘Virtual Try-on’ app.
Content-Adaptive Steganalysis via Augmented Utilization of Selection-Channel Information

Modern adaptive image steganographic schemes embed secret message into textural regions to make it difficult for steganalytic detection. To overcome the presented challenges, existing steganalytic methods incorporate selection-channel information into steganalytic features so as to improve detection capability. In this paper, we extended the maxSRM steganalytic scheme by better exploiting the selection-channel information in two aspects. On one hand, we processed the embedding change probabilities by highlighting the large probabilities to obtain the so called augmented coefficients. On the other hand, we used the augmented coefficients weighted by the approximated probabilities of occurrence of image residuals for computing co-occurrence matrix in steganalytic features. In this way, we further utilized the selection-channel information and make pixels with high embedding change probability contribute more to final steganalysis features. Experiments on BOSSBase image dateset showed that our proposed steganalytic method achieved the state-of-the-art performance against various steganographic schemes under different payloads.