Reviewerly/exHarmony huggingface dataset:
Perceived Integrity of Distributed Streaming Media Based on AWTC-TT Algorithm Optimization

With the development of economy, more and more attention has been paid to the monitoring system, which provides a reliable and powerful guarantee for people’s daily life, property security, and national security. The intelligent video surveillance introduces computer vision-related technologies into traditional video surveillance and realizes the analysis and understanding of video data without artificial dependence to obtain valuable target information in the perceived video data. On this basis, functions such as abnormal event monitoring and real-time alarm are realized. Distributed streaming media monitoring has changed the manual-based monitoring and content analysis modes of traditional monitoring, but the high-complexity calculations such as motion estimation and motion compensation in the encoding process increase the burden of monitoring and sensing equipment. Especially with the development of wireless multimedia technology, the traditional video coding has been unable to meet the requirements of monitoring and sensing equipment in the monitoring system based on wireless technology. This paper proposes an adaptive weighted tensor completion algorithm to complete the repair of streaming media data perceived by ordinary sensing devices. In the proposed algorithm, considering the unbalanced information distribution and data redundancy problems that may exist in the data, the tensor data is adjusted according to the approximate solution algorithm to obtain tensor data that only retains important information and the information distribution is more balanced and reasonable. In the iterative solution process, in order to better map the impact of each dimension of data in the repair process, an adaptive weighting mechanism is proposed according to the data characteristics to obtain the corresponding weight value of each dimension of data. Finally, the proposed approximate tensor solving algorithm and adaptive weighting mechanism are applied to a simple low-rank tensor completeness algorithm based on tensor columns to form the algorithm of this paper, and it is used to repair perceptual streaming media data with data missing problems. The experimental results show that the algorithm in this paper can improve the perceived streaming media data quality by 3% based on the known data information and maintain an advantage of 2% in average processing time. It avoids the replacement of sensing equipment and also provides data quality assurance for subsequent sensing streaming media content analysis. It has certain research significance for the development of monitoring system with artificial intelligence management for target perception and tracking.
I3A: An Intelligent Interactive Information Agent Model for Information Retrieval

Information Retrieval (IR) can be broadly interpreted as an interactive process for connecting users with the right information at the right time to finish a task, where interaction can be multimodal (e.g., using text, speech, and gestures) and connection can be made in multiple ways (e.g., querying, browsing, and recommendation). Although many formal IR models have been developed, the existing models are generally restricted to modeling the problem of ranking information items in response to a user's query without much consideration of user interaction. As a result, how to develop a general formal model that can cover all the variations of interactive IR (IIR) remains an open challenge. In this talk, I will discuss how we can address this challenge and present a general formal model for IR, called Intelligent Interactive Information Agent (I3A) model, which provides a unified theoretical foundation for both optimizing and evaluating sophisticated IIR algorithms and application systems. In I3A, an IIR system is modeled generally as an intelligent interactive information agent which plays an interactive cooperative "game" with its user(s), where both parties would take turns to "make moves" and interact with each other with a common objective of helping a user finish a task with minimum overall user effort. The optimization of IIR can be formally modelled as the agent optimizing a sequence of interaction decisions in response to each user action in a Bayesian decision framework. I will discuss how to refine the various components of the decision framework to make I3A operational and how multiple existing models, such as the Interface Card Model, the Probability Ranking Principle for IIR, formal models of users, and online learning to rank, can all be covered in the general I3A model. The I3A model also naturally suggests a new general methodology of evaluating IIR systems using search simulation.
An Online Extraction Algorithm for Image Feature Information Based on Convolutional Neural Network

As a result of fast technological improvement and the rise of online social media, image data have grown rapidly. Because of their rich content and intuitiveness as one of the key modes of people's daily communication, as a result, images are often used as communication vehicles. When it comes to image recognition, picture feature extraction is a critical stage, and the effect of image feature extraction directly impacts the effectiveness of image recognition. Furthermore, feature extraction is a key factor to consider that influences picture recognition accuracy. Unfortunately, due to the effects of individual variations and lighting, certain elements that are significantly connected to alterations in the image are hard to extract. As a result, features that appropriately show the changing interface are urgently needed. For this purpose, this research proposes an expression identification approach based on a deep convolutional neural network for the job of facial expression recognition under online picture feature information extraction. It uses the VGG19 and Resnet18 to recognize and classify facial expressions. After that, the DCNs have combined feature extraction and classification into a single network using deep convolutional neural networks (DCNs). The proposed model is compared to the most recent approach in the context of the FER2013 and CK + databases. The experimental results reveal that this method outperforms the competition, and the amount of useful image feature information that can be extracted is substantial.
Face Recognition and Automatic Attendance Management System using Machine Learning Techniques

An automated attendance management system maintains a record of the attendance of a particular student, whether he/she is present in the class. The most important element of the virtual platform for students is maintaining attendance in an educational institution. When done manually, it wastes a substantial amount of time and effort. Due to a lack of a reliable attendance mechanism, there were also numerous fraudulent attendances. The face is the most significant feature that distinguishes a person. As a result, the focus of this study is on an attendance system that uses a real time face identification method based on the frontal face recognition idea, as well as an efficient Haar cascade algorithm based on an open-source image processing framework as well as the Local Binary Pattern Histogram.
Research on Image Restoration Method of Underwater Robot Based on Dark Channel Prior Algorithm

Compared with the air imaging environment, the underwater imaging environment is more complicated. Aiming at the problems of color distortion, blurring and low contrast in the images taken in the water, a water based on an improved dark channel prior and color correction is proposed. The following image restoration method. First, a simplified underwater imaging model is constructed based on the dark channel prior algorithm, and then based on the gray world hypothesis theory, color correction is performed on the RGB three channels, and then the adaptive histogram equalization algorithm is used to enhance the contrast and output the restored image. Finally, the algorithm proposed in this paper is applied to the actual scene pictures collected by the underwater robot. The experimental results show that the proposed algorithm can effectively improve the image contrast and improve the color distortion, and can be applied to the actual application scene of the underwater robot.
An Improved Two-Stage Based Multi-frame Track-Before-Detect Algorithm in Radar systems

The detection and tracking of dim targets in complex environments show great importance in radar systems. The track-before-detect (TBD) technology has been widely researched in the scenario where the target signal-to-noise ratio (SNR) is low, however, there has been no suitable solution to the trade-off between tracking accuracy and computational complexity during the process of multi-frame joint detection in radar systems. In this paper, we propose an efficient two-stage based multi-frame detection and tracking algorithm in radar systems. The proposed algorithm consists of a low threshold pre-processing stage, and a TBD processor, which searches possible target tracks from multiple scans and declares the final estimated tracks. The proposed algorithm provides an accurate evolution of target states over time in polar coordinates to avoid the performance loss and model mismatch due to the nonlinear conversion in mixed coordinates. In addition, we further propose a greedy-based recursive algorithm to implement fast track formation from the over-threshold multi-frame measurement points. Simulation results show that the proposed method achieves a better detection and tracking performance with a low computational complexity.
Partnership of Educational Establishment with Sociocultural Institutions in Open Cultural and Educational Environment

The aim of the study is to substantiate the need for partnership between an educational establishment and various sociocultural institutions. The article provides a typology of models of their educational interaction, proposes a relevant model of interaction between the school and sociocultural institutions; the educational functions of the sociocultural institution as a subject of educational activity are actualized; the goals of interaction between educational and cultural establishments are substantiated. The scientific novelty lies in the identification of fundamental changes in the interaction of educational establishments and sociocultural institutions in open cultural and educational environment, which can be extrapolated to any establishments of cultural inheritance. The result of the study is the proposed step-by-step algorithm for the partnership of the mentioned institutions ensuring, on the one hand, the “pedagogical identity” of each educational institution, and on the other hand, building a fundamentally new, systematic work to modernize the educational process in accordance with the trends of the cultural and educational environment.
Analysis of Image Compression Techniques for IoT Applications

Image compression is an important application in medical fields, research, commercial, and defense. The large files of image can't be quickly processed and cannot be stored efficiently. Thus, compressing of images will maintain the maximum possible quality which is essential for the applications of real-world such as IoT (Internet of Things). In this work., two most popular image compression techniques namely Discrete Cosine Transform (DCT) and Lempel-Ziv-Welch (LZW) are used to compress sample images and their efficiency are compared. The compression ratio of later is found to be smaller than the former. This indicates DCT as better technique and applicable to various fields.
Large-Dimensional Convex Optimization

This chapter discusses the generalized linear classifier that results from convex optimization problem and takes in general nonexplicit form. Random matrix theory is combined with leave-one-out arguments to handle the technical difficulty due to implicity. Again, counterintuitive phenomena arise in popular machine learning methods such as logistic regression or SMV in the large-dimensional setting, a well-defined solution may not even exist, and if it does, it behaves dramatically from its small-dimensional counterpart.
Mixture distribution based real-coded crossover: A hybrid probabilistic approach for global optimization

In the present simulation-based study, a novel parent-centric real-coded crossover operator is introduced with a unique probabilistic aspect of the mixture distribution. Moreover, the mixture distribution is a co-integration of double Pareto and Laplace probability distributions with various parameters. The key objective of the newly proposed methodology is to obtained optimal solutions for complex multimodal optimization problems. Hence, for its global comparison, the newly proposed mixture distribution crossover operator (MDX) is compared with double Pareto (DPX), Laplace (LX), and simulated binary (SBX) crossover operators within the conjunction of three mutation operators (MTPM, PM, and NUM). After a descriptive comparison, a Quade multiple comparison test is also administered to examine its statistical significance. Furthermore, the performance of the genetic algorithm (GA) is also examined on a set of twenty-one unconstraint benchmark functions with diverse features. The empirical results of the simulation-based study reveal that the mixture-based crossover operator obtained a substantial dominance over all considered crossover operators in terms of computational complexity, robustness, scalability, and capability of exploration and exploitation. Moreover, the Quade multiple comparison test also showed a significant superiority with graphical authentication of the performance index (PI).
Detection of Error Images Based on Matching Edges with Strong Components

Research is being attempted to automatically perform quality testing of applications running on mobile platforms using image processing. This study introduces an algorithm that robustly extracts pixels that are not similar to reference images from the images tested by effectively using edge and template matching techniques. The introduced method first uses a Canny edge to detect strong edges from reference and test images. The template matching technique is then used to extract normalized correlation coefficients for the edges extracted in the previous step. Finally, the correlation coefficients extracted from the reference and test images are analyzed to determine whether the tested images are error images or not. The experimental results show that the introduced algorithm correctly extracts error images from the color images being tested by applying strong edge and template matching. The approach introduced in this study is expected to be useful in applications related to image processing, such as program testing, image search, object classification, etc.
How much is the fork? Fast Probability and Profitability Calculation during Temporary Forks

Estimating the probability, as well as the profitability, of different attacks is of utmost importance when assessing the security and stability of prevalent cryptocurrencies. Previous modeling attempts of classic chain-racing attacks have different drawbacks: they either focus on theoretical scenarios such as infinite attack durations, do not account for already contributed blocks, assume honest victims which immediately stop extending their chain as soon as it falls behind, or rely on computationally heavy approaches which render them ill-suited when fast decisions are required. In this paper, we present a simple yet practical model to calculate the success probability of finite attacks, while considering already contributed blocks and victims that do not give up easily. Hereby, we introduce a more fine grained distinction between different actor types and the sides they take during an attack. The presented model simplifies assessing the profitability of forks in practical settings, while also enabling fast and more accurate estimations of the economic security grantees in certain scenarios. By applying and testing our model in the context of bribing attacks, we further emphasize that approaches where the attacker compensates already contributed attack-chain blocks are particularly cheap. Better and more realistic attack models also help to spot and explain certain events observed in the empirical analysis of cryptocurrencies, or provide valuable directions for future studies. For better reproducibility and to foster further research in this area, all source code, artifacts and calculations are made available on GitHub.
Enhancing Multilingual Accessibility of Question Answering over Knowledge Graphs

There are more than 7000 languages spoken in the world today. Yet, English dominates in many research communities, in particular in the field of Knowledge Graph Question Answering (KGQA). The goal of a KGQA system is to provide natural-language access to a knowledge graph. While many research works aim to achieve the best possible QA quality over English benchmarks, only a small portion of them focuses on providing these systems in a way that different user groups (e.g., speakers of different languages) may use them with the same efficiency (i.e., accessibility). To address this research gap, we investigate the multilingual aspect of the accessibility, which enables speakers of different languages (including low-resource and endangered languages) to interact with KGQA systems with the same efficiency.
Accelerated synthesis of neural network-based barrier certificates using collaborative learning

Most of existing Neural Network (NN)-based barrier certificate synthesis methods cannot deal with high-dimensional continuous systems, since a large quantity of sampled data may easily result in inaccurate initial models coupled with slow convergence rate. To accelerate the synthesis of NN-based barrier certificates, this paper presents an effective two-stage approach named CL-BC, which fully exploits the parallel processing capability of underlying hardware to enable quick search for a barrier certificate. Unlike existing NN-based methods that adopt a random initial model for barrier certificate synthesis, in the first stage CL-BC pre-trains an initial model based on a small subset of sampling data. In this way, an approximate barrier certificate in an NN form can be quickly achieved with little overhead. Based on our proposed collaborative learning scheme, in the second stage CL-BC conducts the parallel learning on partitioned domains, where the learned knowledge from different partitions can be aggregated to accelerate the convergence of a global NN model for barrier certificate synthesis. In this way, the overall synthesis time of an NN-based barrier certificate can be drastically reduced. Experimental results show that our approach can not only drastically reduce barrier synthesis time, but also can synthesize barrier certificates for complex systems that cannot be handled by state-of-the-art.
Bipolar vector classifier for fault-tolerant deep neural networks

Deep Neural Networks (DNNs) surpass the human-level performance on specific tasks. The outperforming capability accelerate an adoption of DNNs to safety-critical applications such as autonomous vehicles and medical diagnosis. Millions of parameters in DNN requires a high memory capacity. A process technology scaling allows increasing memory density, however, the memory reliability confronts significant reliability issues causing errors in the memory. This can make stored weights in memory erroneous. Studies show that the erroneous weights can cause a significant accuracy loss. This motivates research on fault-tolerant DNN architectures. Despite of these efforts, DNNs are still vulnerable to errors, especially error in DNN classifier. In the worst case, because a classifier in convolutional neural network (CNN) is the last stage determining an input class, a single error in the classifier can cause a significant accuracy drop. To enhance the fault tolerance in CNN, this paper proposes a novel bipolar vector classifier which can be easily integrated with any CNN structures and can be incorporated with other fault tolerance approaches. Experimental results show that the proposed method stably maintains an accuracy with a high bit error rate up to 10−3 in the classifier.
Exploring Cross-Lingual Transfer to Counteract Data Scarcity for Causality Detection

Finding causal relations in text is an important task for many types of textual analysis. It is a challenging task, especially for the many languages with no or only little annotated training data available. To overcome this issue, we explore cross-lingual methods. Our main focus is on Swedish, for which we have a limited amount of data, and where we explore transfer from English and German. We also present additional results for German with English as a source language. We explore both a zero-shot setting without any target training data, and a few-shot setting with a small amount of target data. An additional challenge is the fact that the annotation schemes for the different data sets differ, and we discuss how we can address this issue. Moreover, we explore the impact of different types of sentence representations. We find that we have the best results for Swedish with German as a source language, for which we have a rather small but compatible data set. We are able to take advantage of a limited amount of noisy Swedish training data, but only if we balance its classes. In addition we find that the newer transformer-based representations can make better use of target language data, but that a representation based on recurrent neural networks is surprisingly competitive in the zero-shot setting.
FaSe

Caches are widely used to improve performance in modern processors. By carefully evicting cache lines and identifying cache hit/miss time, contention-based cache timing channel attacks can be orchestrated to leak information from the victim process. Existing hardware countermeasures explored cache partitioning and randomization, are either costly, not applicable for the L1 data cache, or are vulnerable to sophisticated attacks. Countermeasures using cache flush exist but are slow since all cache lines have to be evacuated during a cache flush. In this paper, we propose for the first time a hardware/software flush-based countermeasure, called fast selective flushing (FaSe). By utilizing an ISA extension and cache modification, FaSe selectively flushes cache lines and provides a mitigation method with a similar effect to methods using naive flush. FaSe is implemented on RISC-V Rocket Chip and evaluated on Xilinx FPGA running user programs and the Linux OS. Our experiments show that FaSe reduces time overhead by 36% for user programs and 42% for the OS compared to the methods with naive flushing, with less than 1% hardware overhead. Our security test shows FaSe can mitigate target cache timing attacks.
ROSE: Robust Caches for Amazon Product Search

Product search engines like Amazon Search often use caches to improve the customer user experience; caches can improve both the system's latency as well as search quality. However, as search traffic increases over time, the cache's ever-growing size can diminish the overall system performance. Furthermore, typos, misspellings, and redundancy widely witnessed in real-world product search queries can cause unnecessary cache misses, reducing the cache's utility. In this paper, we introduce ROSE, a RObuSt cachE, a system that is tolerant to misspellings and typos while retaining the look-up cost of traditional caches. The core component of ROSE is a randomized hashing schema that makes ROSE able to index and retrieve an arbitrarily large set of queries with constant memory and constant time. ROSE is also robust to any query intent, typos, and grammatical errors with theoretical guarantees. Extensive experiments on real-world datasets demonstrate the effectiveness and efficiency of ROSE. ROSE is deployed in the Amazon Search Engine and produced a significant improvement over the existing solutions across several key business metrics.