Reviewerly/exHarmony huggingface dataset:
On the Complexity of Arithmetic Secret Sharing

Since the mid 2000s, asymptotically-good strongly-multiplicative linear (ramp) secret sharing schemes over a fixed finite field have turned out as a central theoretical primitive in numerous constant-communication-rate results in multi-party cryptographic scenarios, and, surprisingly, in two-party cryptography as well. Known constructions of this most powerful class of arithmetic secret sharing schemes all rely heavily on algebraic geometry (AG), i.e., on dedicated AG codes based on asymptotically good towers of algebraic function fields defined over finite fields. It is a well-known open question since the first (explicit) constructions of such schemes appeared in CRYPTO 2006 whether the use of "heavy machinery" can be avoided here. i.e., the question is whether the mere existence of such schemes can also be proved by "elementary" techniques only (say, from classical algebraic coding theory), even disregarding effective construction. So far, there is no progress. In this paper we show the theoretical result that, (1) no matter whether this open question has an affirmative answer or not, these schemes can be constructed explicitly by elementary algorithms defined in terms of basic algebraic coding theory. This pertains to all relevant operations associated to such schemes, including, notably, the generation of an instance for a given number of players n, as well as error correction in the presence of corrupt shares. We further show that (2) the algorithms are quasi-linear time (in n); this is (asymptotically) significantly more efficient than the known constructions. That said, the analysis of the mere termination of these algorithms does still rely on algebraic geometry, in the sense that it requires "blackbox application" of suitable existence results for these schemes. Our method employs a nontrivial, novel adaptation of a classical (and ubiquitous) paradigm from coding theory that enables transformation of existence results on asymptotically good codes into explicit construction of such codes via concatenation, at some constant loss in parameters achieved. In a nutshell, our generating idea is to combine a cascade of explicit but "asymptotically-bad-yet-good-enough schemes" with an asymptotically good one in such a judicious way that the latter can be selected with exponentially small number of players in that of the compound scheme. This opens the door to efficient, elementary exhaustive search. In order to make this work, we overcome a number of nontrivial technical hurdles. Our main handles include a novel application of the recently introduced notion of Reverse Multiplication-Friendly Embeddings (RMFE) from CRYPTO 2018, as well as a novel application of a natural variant in arithmetic secret sharing from EUROCRYPT 2008.
An Efficient Cascaded Filter Design using Dragonfly Algorithm for Image Noise Reduction

This paper describes an optimal image denoising method with an efficient cascaded filter structure designed using dragonfly algorithm. Though there are several conventional filtering methods used for image denoising, the proposed method shows much improved result in terms of PSNR, IQI and SSIM values keeping the entire image attributes intact. This proposed image denoising technique exhibits its effectiveness not only in the matter of both quantitative and visual aspects of image but also the performance shows accuracy in presence of various types of noise like Gaussian, Salt and Pepper, and Speckle with different variance values. Furthermore, the experimental results with different real images establish the fact that this approach achieves better optimal solution than existing denoising techniques.
The Hough-Based Multibeamlet Transform

There are plenty of geometrical multiresolution transforms devoted to efficient edge representation. However, they have two drawbacks. The first one is that such transforms represent mono edge models. And the second one is that they are often based on approximations which are optimal according to the Mean Square Error what does not necessarily lead to optimal edge approximation. In this paper the multibeamlet transform based on the Hough transform is proposed. This transform is defined to properly detect multiedges present in images. Next, the method of image approximation with the use of the multibeamlet transform is described. Additionally, the modified bottom-up tree pruning algorithm is presented in order to properly approximate images with the use of multibeamlets. As follows from the performed experiments, this approach leads to image approximations with better quality than the state-of-the-art geometrical multiresolution transforms.
A Dialogue-System Using a Qur’anic Ontology

A dialogue-system is a medium of communication and interaction between humans and computers by exchanging questions and answers in natural languages. The performance of the latter depends on the ability to analyze the question, the good management of keywords to question the knowledge base, and finally the use of an efficient knowledge base. However, several problems related to developing dialogue systems are due to using traditional databases. In this project, we propose a dialogue-system based on a Quranic ontology. This system allows easy access to Qur'anic information by SPARQL queries from pre-existing domain ontology. The used ontology covers Quranic chapters and verses, each word of the Qur'an and its root and lemma.
Mitigating Data-only Attacks by Protecting Memory-resident Sensitive Data

Gaining reliable arbitrary code execution through the exploitation of memory corruption vulnerabilities is becoming increasingly more difficult in the face of modern exploit mitigations. Facing this challenge, adversaries have started shifting their attention to data leakage attacks, which can lead to equally damaging outcomes, such as the disclosure of private keys or other sensitive data. In this work, we present a compiler-level defense against data leakage attacks for user-space applications. Our approach strikes a balance between the manual effort required to protect sensitive application data, and the performance overhead of achieving strong data confidentiality. To that end, we require developers to simply annotate those variables holding sensitive data, after which our framework automatically transforms only the fraction of the entire program code that is related to sensitive data operations. We implemented this approach by extending the LLVM compiler, and used it to protect memory-resident private keys in the MbedTLS server, ssh-agent, and a Libsodium-based file signing program, as well as user passwords for Lighttpd and Memcached. Our results demonstrate the feasibility and practicality of our technique: a modest runtime overhead (e.g., 13% throughput reduction for MbedTLS) that is on par with, or better than, existing state-of-the-art memory safety approaches for selective data protection.
Method of Adaptive Multi-scale Transformation for Image Data Compression

The article is devoted to the development of a digital image compression algorithm. Image compression is a type of data compression applied to digital images, to reduce their cost for storage or transmission. Algorithms may take advantage of visual perception and the statistical properties of image data. We will consider a lossy compression algorithm that is based on multi-scale analysis using a spline as a basis function. An algorithm for compressing image data with an adaptive calculation of detail coefficients has been developed. Its advantages and prospects of use for the image compression in the online communication are considered. The application of this approach give an increase in the compression ratio with the same quality of compressed images.
Target recognition algorithm based on improved depth separable convolution

Abstract Realizing accurate target recognition in the fields of drones, vehicle-mounted systems, robots, etc. has increasingly become a hot research direction for researchers. Aiming at the problem of using deep learning algorithms to identify targets on mobile devices with limited size, the network model has a large number of parameters resulting in a large calculation cost, a complicated recognition process, and a large number of iterations resulting in a long recognition time. This paper proposes an improved deep separable convolution algorithm, which consists of deep convolution and point-by-point convolution, which is based on convolution kernels for step-by-step filtering and combination. After deep convolution, batchnorm is added, and the activation function uses a linear function. Add batchnorm and nonlinear activation function ReLU after 1×1 convolution to ensure the accuracy of target detection. This algorithm reduces the amount of parameters while ensuring recognition accuracy, reduces the amount of recognition calculations, and reduces the weight of the network model. It provides a network foundation for implementing image recognition on devices with limited size.
An Improved Comprehensive Evaluation Method for Software Development Ability

This article in view of the existing software development capability assessment method based on CMMI in the limitations of technical ability and attention on the quality of the product is not enough, an improved software development ability comprehensive evaluation method, the method on the basis of software development capability maturity evaluation, joined the organization developed the project value of the process of developing software and software problem evaluation after delivery, in a more objective, accurately reflect the software development organization's software development capabilities.
Towards the Use of a Framework to Make Technical Debt Visible

Technical debt concept has been in use since the 90s's decade. Several processes, techniques and tools, such as those related with software maintenance and risk control, are used to manage, prevent, measure and reduce technical debt. Technical debt management includes activities to identify, measure, prioritize, repay, and monitor it, but one of the main issues related with management resides in the complexity to make technical debt visible to organizations. In this paper is presented the application of the Normative Process Framework to make technical debt visible with a large system developed by students of software engineering. The Normative Process Framework is used in conjunction with a process to find technical debt and document it in a simple format. Results show how technical debt was made visible for that system in a simplified way, by using documentation generated during development, and considering not only code, but also other software assets. Once technical debt is made visible is easier to evaluate and prioritize it, to establish a convenient set of actions to control it.
Collection and Analyses of Exemplary Speech Data to Establish Easy-to-Understand Speech Synthesis for Japanese Elderly Adults

This paper describes a newly developed Japanese speech database in order to find the features of speech and speaking styles that elderly adults actually think is easy to understand for establishing speech synthesis for elderly adults. This speech database is characterized by two features: i) its sentences are largely taken from newsletters beyond just the contents that elderly adults tend to know; ii) the sentences are spoken by exemplary speakers selected through an audition process from the perspective that elderly adults actually think is easy to understand. This paper describes the design of our database and the basic characteristics measured by applying conventional theories. Finally it indicates the extension directions of the conventional theories to establish an easy-to-understand speech synthesis method for elderly adults.
An approach for semantic-based searching in learning resources

Currently, online learning has been widely applied in education and training. Especially, when it is difficult for lecturers and learners to get close to each other in the context of Covid-19 epidemic period, online learning shows its availability and necessary. Learning materials provided in the educational institutions are diverse; almost lectures are stored as files but have not been totally arranged in a standard database system. Therefore, searching information about curriculum and lectures still face difficulties. This paper proposes a solution for semantic-based searching in learning resources. Firstly, ontologies are built to represent information of lectures. When users enter a query, the system pre-processes it (e.g., word segmentation, removing stop words), and then provides it to classifier (e.g., SVM) to identify the corresponding domain (or topic), aiming to narrow the search space in the ontology. After classifying, the key phrases will be queried in the appropriate ontology to result in related lectures. Experiments on lectures in the domains of information technology show that the proposed model is feasible.
Graph Visualization of the Characteristics of Complex Objects on the Example of the Analysis of Politicians

The paper considers the task of analyzing complex interconnected objects using graph construction. There is no unified tool for constructing graphs. Some solutions can build graphs limited by the number of nodes, while others do not visually display data. The Gephi application was used to construct graphs for the research. Gephi has great functionality for building and analyzing graphs. The subject of research is a politician with a certain set of characteristics. In the paper an algorithm that enables to automate data collection on politicians was developed. One of the main methods of data collecting on the Internet is web scraping. Web scraping software may access the World Wide Web directly using the HTTP, or through a web browser. While web scraping can be done manually by a software user, the term typically refers to automated processes implemented using a software agent. The data was necessary for constructing graphs and their analysis. The use of graphs enables to see various types of relationships, including mediate. This methodology enables to change the attitude towards the analysis of multidi-mensional objects.
Artificial Neural Network for Solving the Inventory Control Problem in Fuzzy Environments

In this chapter, different inventory control problems are formulated in fuzzy environment and solved by artificial neural network. Due to present the non-linearity associated with the differential equation in fuzzy environment, the solution procedure may be very complicated. To avoid the situation, artificial neural networks play an important role. In this chapter, different inventory control problems are formulated in fuzzy environment and solved by artificial neural network. Due to present the non-linearity associated with the differential equation in fuzzy environment, the solution procedure may be very complicated. To avoid the situation, artificial neural networks play an important role.
Does Instagram as Learning Media Affect Students’ Writing Skill on Recount Text?: An Experimental Research

This research's objectives were to examine whether Instagram as a Learning Media is useful toward students' writing recount text or not and measuring how significant the effect of Instagram on students’ writing recount text before and after treatment. This quasi-experimental research involved 35 students as participants from two different classes. Researchers took 10th graders from a selected private school in Purbalingga as research samples. Two IPS classes were selected, i.e., X IPS 1 was set as an experimental class, and X IPS 2 was set as a control class. The data were collected from the score of pre-test and post-test after the treatment. Researchers then compared the students’ achievement from control and experimental classes and analyzed the data using descriptive analysis and N-gain. The research findings indicated a significant achievement of students' competencies in using Instagram than conventional learning in writing recount text. The average N-gain score from experimental class and control class obtained 0.308 &gt; 0.057. The Mean score of pre-test in experimental and control class obtained 69.875 &gt; 64.818 and the Mean score of post-test in experimental and control class was 79.461 &gt; 67.142. As a result, the experimental class's mean score was higher than the control class means Instagram as a Learning Media is useful in writing recount text.
SG <sup>XL</sup>

Intel’s SGX architecture offers clients of public cloud computing platforms the ability to create hardware-protected enclaves whose contents are protected from privileged system software. However, SGX relies on system software for enclave memory management. In a sequence of recent papers, researchers have demonstrated that this reliance allows a malicious OS/hypervisor to snoop on the page addresses being accessed from within an enclave via various channels. This page address stream can then be used to infer secrets if the enclave’s page access pattern depends upon the secret and this constitutes an important class of side-channels. We propose SG XL , a hardware-software co-designed system that significantly increases the difficulty of any page address-based side-channels through the use of large pages. A large page maps address ranges at a much larger granularity than the default page size (at least 512× larger). SG XL thus significantly lowers resolution of the leaked page address stream and could practically throttle all flavors of page-address based side-channels. We detail the modifications needed to SGX’s software stack and the (minor) hardware enhancements required for SG XL to guarantee the use of large pages in the presence of adversarial system software. We empirically show that SG XL could be one of those rare systems that enhances security with the potential of improving performance as well.
Learning Systems and Gamification

Given the changes that have been recently felt and the transformation of the classroom from a physical space, with one teacher and a set of students, to a mediating platform through which learning and teaching are performed online, it is necessary to get acquainted with the contexts in which these platforms are implemented and how to use them, as well as with models and systems of e-learning, b-learning, or m-learning. While looking for solutions that allow to implement and use gamification for online learning, one also intends to get acquainted with (some of) these platforms, as well as to integrate and combine “new” technologies such as augmented reality and virtual reality with gamification strategies for e-learning. In order to do so, this chapter presents a systematized reading of the studies that was carried out on this particular subject and of what has been published on this theme.
activIn: A novel Non-Intrusive Activity Inference Tool

The first step towards energy awareness is to gain the knowledge on how much energy each household device consumes. This paper introduces a new methodology for activity inference (activIn) estimating the action of a resident that may be directly associated with the use of a specific electric device. The devices' energy load is distinguished in low power consumption and high power consumption using an unsupervised Hidden Markov Model establishing the devices power consumption base-load. The device's operation mode is inferred using a Random Forest Classifier that extracts the device's daily operation pattern while inferring the device's associated activity. Finally, the proposed activIn tool is evaluated against real-life and realtime devices' consumption data, while the experimental results show that it is a powerful tool for user's activity, device profiling, energy consumption awareness and device-driven applications.
Artefact‐free image stitching via a better normed seam‐cutting energy function

Image stitching, as the important field of computer graphics and vision, has received much attention in recent years. Image stitching techniques are generally decomposed into two phases: image alignment, which aligns target images with the reference images; and image composition, which fixes ghosting and visual artefacts. This work aims to propose a new strategy for the seam-cutting method which provides visually appealing result. Seam-cutting is one of the most influential methods in image composition, which can relieve artefacts and produce plausible results. However, it is observed that the state-of-the-art seam-cutting approaches usually lead to undesirable seams in some challenging scenes. Here, the authors put forward a novel seam-cutting method by defining a new energy function. This method uses 5 / 2 power of L 1 norm as a colour difference which can magnify the weight of colour distinction to avoid undesirable seams and artefacts. The proposed method can be easily implemented. The test images are collected from the public available challenging datasets and taken by ourselves. Experiments demonstrate that the proposed method can create comparable or even better stitching results compared to other state-of-the-art seam-cutting approaches.
Determining Causal Skeletons with Information Theory

Modeling a causal association as arising from a communication process between cause and effect, simplifies the discovery of causal skeletons. The communication channels enabling these communication processes, are fully characterized by stochastic tensors, and therefore allow us to use linear algebra. This tensor-based approach reduces the dimensionality of the data needed to test for conditional independence, e.g., for systems comprising three variables, pair-wise determined tensors suffice to infer the causal skeleton. The only thing needed is a minor extension to information theory, namely the concept of path information.
Traffic Flow Estimation Using Deep Learning

In Vietnam, traffic is always a complex and challenging problem due to a mixture of different types of vehicle as well as the large number of vehicles on road. To improve the traffic management, it is critical to develop a real time traffic flow estimation system which can detect, classify and count vehicles, detect traffic violation at any given time. In this study, a multi-vehicle detection and tracking approach was proposed to achieve these requirements. The proposed model involved two major steps: detection and multiple-object tracking. In the first step, the vehicles were detected and classified into classes (motorbike, car, truck, bus and rudimentary vehicles) using Faster R-CNN model. Next, the movement of the detected objects was tracked with CSRT tracker. Then, all vehicle data is sent to an analyzer to estimate the traffic flow by counting and classifying vehicles by their driving direction. Results showed that the model can robustly work in realtime with an accuracy >86$%.
Refinement Network for unsupervised on the scene Foreground Segmentation

In this paper we present a network for foreground segmentation based on background subtraction which does not require specific scene training. The network is built as a refinement step on top of classic state of the art background subtraction systems. In this way, the system combines the possibility to define application oriented specifications as background subtraction systems do, and the highly accurate object segmentation abilities of deep learning systems. The refinement system is based on a semantic segmentation network. The network is trained on a common database and is not fine-tuned for the specific scenes, unlike existing solutions for foreground segmentation based on CNNs. Experiments on available databases show top results among unsupervised methods.