Reviewerly/exHarmony huggingface dataset:
An efficient image encryption scheme combining Rubik's cube principle with masking

In the last two decades, chaos mapping-centered image enciphering techniques acquired approval against various crypt analytical attacks, but these enciphering algorithms are confined by their insignificant key spaces and weak security particularly if the used chaotic map is one-dimensional. In this chapter, an efficient private key enciphering scheme for encrypting gray and color images has been suggested, which is centered on the concept of integrating Rubik’s cube encryption principle with the chaos-based masking of image. Image is first permutated through the Rubik’s cube encryption technique and further through masking, which enhances the security and efficacy of method. The key for creating the mask of image is obtained through Rubik’s cube keys and a chaotic technique. The projected technique can encipher both gray and color images irrespective of size. Security and time analysis are brought off on the scheme to corroborate its efficiency, robustness, and feasibility. Analysis shows the scheme has a faster rate of encryption and is more capable of resisting various crypt crypt analytical and statistical attacks in comparison to recent related techniques. Simulation and investigation of the results are done using MATLAB.
A Study of Nonverbal Communication in China-U.S. Business Negotiations

In the context of the rapid development of the global economy, business trades between countries are experiencing a sustained growth. So, international business trades between China and the United States increase continuously, for the reason that China is the biggest developing country in the world and the United States has the world’s most advanced economy. Business negotiations are necessary in global trade, in which the benefit between two sides can reach to the climax. In the process of business negotiation, nonverbal communication stays an important situation because of rich content and incomparable advantages, which is a significant part. This thesis tries to analyze the different cultural backgrounds between China and the United States. Then, focusing on the different aspects of the nonverbal communication such as expression, body languages and paralanguages. In that way, this thesis can help to understand the crucial position of nonverbal communication in international business negotiations and master the main methods of nonverbal communication between China and the United States.
Monocular Fisheye Depth Estimation for Automated Valet Parking: Dataset, Baseline and Deep Optimizers

Due to the significant non-linear distortions of fisheye camera models, supervised learning techniques for monocular depth estimation usually do not generalize well when applied to this kind of projection models. Additionally, there is no publicly available dataset for monocular fisheye camera depth estimation emphasizing automated valet parking. As a result, we initiate a unique depth estimate fisheye dataset optimized for automated valet parking. On this premise, we also construct a system for monocular fisheye depth estimation using an enhanced deep optimizer for improving the results of supervised monocular depth estimation on fisheye camera images. The proposed method produces impressive results on our ZMDepth dataset. We also evaluate our optimizer-based method on the KITTI dataset and achieved state-of-the-art results. We will make a partial ZMDepth dataset publicly available at: https://github.com/NamespaceMain/ZMDepth.
Security Analysis of Shamir’s Secret Sharing

The aim of the project is to study, analyze and research different security models. The main goal is to propose a solution that checks authenticity by addressing different cipher-suites, and adding more optimizations specifi-cally relevant to secretly parsing session data. The methods are based on Shamir’s Secret Sharing and AES with Galois/Counter Mode (AES-GCM) which provides both authenticated encryption and the ability to check the integrity and authentication of additional authenticated data that is sent in the clear.
Variational Bayes for Robust Radar Single Object Tracking

We address object tracking by radar and the robustness of the current state-of-the-art methods to process outliers. The standard tracking algorithms extract detections from radar image space to use it in the filtering stage. Filtering is performed by a Kalman filter, which assumes Gaussian distributed noise. However, this assumption does not account for large modeling errors and results in poor tracking performance during abrupt motions. We take the Gaussian Sum Filter (single-object variant of the Multi Hypothesis Tracker) as our baseline and propose a modification by modelling process noise with a distribution that has heavier tails than a Gaussian. Variational Bayes provides a fast, computationally cheap inference algorithm. Our simulations show that - in the presence of process outliers - the robust tracker outperforms the Gaussian Sum filter when tracking single objects.
Textual and Graph Representation Learning-based Service Recommendation for Application Construction

As a new computing paradigm, service-oriented computing has seen fast development in recent years. As a result, a proliferation of both traditional Web-based and cloud-based service models have appeared online. These open services will play a crucial role in developing applications. However, the difficulty of service selection in developing a service-oriented system grows in proportion to the growing number of available services. This research suggests expanding upon the foundation of the original collaborative filtering recommendation algorithm by developing a model for service recommendations based on the study of representation learning of knowledge graphs and texts. In this research, we focus on modeling the semantic features of applications and services through the use of Translating Embedding, a knowledge graph feature learning approach, and the textual features learned from topic models. We conducted experiments on a real-world dataset, and the results demonstrate that the suggested method is able to locate services that are pertinent to developer needs.
A Cross-platform Errand Service Application for Campus with a Dual-user Identity Model

With the development of the food delivery industry and the impact of the global Covid-19 pandemic in recent years, the demand for errand service platform has been progressively growing. However, due to crowd control policies during epidemics in China, off-campus personnel are unable to enter the campus, resulting in the current public errand service application invalid in campus. In this circumstance, students who need the errand service can only post the errand requests in chatting groups or use the mini-programs embedded in WeChat. Both approaches, however, have significant flaws. Therefore, a specific errand service application for campus is desired. In this paper, an errand services application named AoAoRun is developed for university students and faculties on campus using a dual-user identity model, making it possible for both students and faculties to become the service provider and the service recipients. It integrates and organizes the needs of errand services, providing a safe and user-friendly platform. In addition, unlike current errand service platforms, this application uses blockchain technology for data backup and credit scores system. This credit system gives higher priority to orders posted by users with higher credit scores. Finally, 53 volunteers participate in the user testing of the application. The test results show that AoAoRun greatly improves the utilization of resources and increases the efficiency of students’ study and life.
Stochastic Automata and Its Application

In this paper we consider the role of hidden Markov models (HMM) in speech recognition and analyze its benefits and disadvantages in this process. We compare it with multilayer perceptron networks (MPNs) and consider also the hybrid model, which includes both of them.
Temperature sensor research as a part of a microprocessor system by statistical analysis methods

The paper considers the solution of the scientific and practical problem of assessing the accuracy and stability of the sensors working as part of the microprocessor control system for the parameters of the microclimate of an artificial ecosystem by using statistical methods for analyzing measurement results using the example of a temperature sensor. The importance of this task lies in the fact that the organization of accurate control of the parameters of the microclimate will ensure optimal climatic conditions for the development of plants and fish, and create an environment as similar as possible to the natural one by timely control of parameters and a quick reaction to the output of parameters at critical values. For the analysis, three series of measurement results of the control indicator (water temperature) were used. The analysis confirmed the hypothesis that there are no violations of the stability of the temperature sensor, which makes it possible to recognize the metrological sensor as reliable. It is proved that the problems of increasing the likelihood of controlling objects with stochastic parameters, and controlling their metrological reliability are relevant and are important for the development of the theory and practice of non-dismantling control.
Balancing Multiple Object Tracking Objectives based on Learned Weighting Factors

Advanced multiple object tracking requires multi-task learning in order to solve object detection and data association tasks simultaneously. One fundamental characteristic of multi-task learning is that there is correlated information among tasks, and leveraging this property in training the model can result in better generalization performance. However, in multiple object tracking, most existing methods learn such property by treating multiple task losses equally and independently. In this paper, we take the weighting of multiple object tracking losses into consideration, relying on the related information among object detection and data association tasks. Firstly, this paper introduces a simple but effective Learned Weighting Factors (LWF) method where the weighting factors are learned through shallow neural networks. These learned factors are used to balance multi-task losses during training dynamically. Thus, our LWF method avoids manually tuning these weighting factors because this process is difficult and expensive caused by the high dimension of the search space. To the best of our knowledge, the proposed LWF method is a new and different perspective in the literature. Secondly, we conduct extensive experiments on two benchmark datasets, i.e., MOT16 and MOT20, surpassing state-of-the-art trackers without extra training samples. Video surveillance demos are available at https://bit.ly/3hkgBxo.
ViEmBot: An Empathetic Voicebot System for the Vietnamese Language

In this article, we introduce ViEmBot, a voicebot system that can interact and express empathy with humans in the Vietnamese language. Compared with current voicebot systems or virtual voice assistants that support Vietnamese, ViEmBot has several outstanding differences. First, the Bot module of ViEmBot was trained on the UIT-VED-27K dataset, the first Vietnamese empathetic-dialogues dataset. Second, ViEmBot can distinguish personal pronouns in Vietnamese and respond with appropriate pronouns, which is a common practice in Vietnamese culture. This makes ViEmBot sound more natural and friendlier than other voicebot systems that apply the same personalized pronoun responses in all situations. Third, ViEmBot can understand the content and emotion of the human and respond with appropriate emotional verbal responses to empathize with the human speaker.
Work-in-Progress: CAMiSE: Content Addressable Memory-integrated Searchable Encryption

Searchable symmetric encryption (SSE) aims to support efficient query-execution directly over encrypted databases. Practical implementations of SSE suffer from performance bottlenecks due to randomised memory accesses and computation-intensive cryptographic operations. We propose CAMISE – a fully associative memory-integrated framework for designing SSE systems with fast query processing over large databases. We show a novel usage of custom-designed Content Addressable Memory (CAM) to minimise storage-access latencies during query execution in SSE systems. We prototype a well-known SSE scheme, namely Oblivious Cross Tags (OXT), within this framework. Our implementation achieves 5x-7x speed-up over traditional software-based implementations while scaling smoothly to real-world databases with millions of records.
Patching up Network Data Leaks with Sweeper

Datacenters have witnessed a staggering evolution in networking technologies, driven by insatiable application demands for larger datasets and inter-server data transfers. Modern NICs can already handle 100s of Gbps of traffic, a bandwidth capability equivalent to several memory channels. Direct Cache Access mechanisms like DDIO that contain network traffic inside the CPU’s caches are therefore essential to effectively handle growing network traffic rates. However, a growing body of work reveals instances of a critical DDIO weakness known as “leaky DMA”, occurring when a significant fraction of network traffic leaks from the CPU’s caches to memory. We find that such network data leaks cap the network bandwidth a server can effectively utilize. We identify that a major culprit for such network data leaks are evictions of already consumed dirty network buffers. Our key insight is that buffers already consumed by the application typically need not be written back to memory, as their next reuse will be a full overwrite with new network data by the NIC. We introduce Sweeper, a hardware extension and API that allows applications to mark such consumed network buffers. Hardware then skips writing marked buffers back to memory, drastically reducing memory bandwidth consumption and mitigating the performance penalty of network data leaks. Sweeper boosts a 24-core server’s peak sustainable network bandwidth by up to $2. 6 \times $ as compared to DDIO-based configurations.
Focus! Provisioning Attention-aware Detection for Real-time On-device Video Analytics

The detection of objects via neural networks plays a key role in various video analytics, but consumes huge resources. Due to the limited on-device computing capability, such real-time detections should be precisely used for the objects that need the most attention. Unfortunately, as the target objects keep moving, existing systems fail to conduct adaptive detections over multiple regions in a video, and existing tracking mechanisms are easily affected by background contents. Therefore, we propose to design attention-aware on-device detection for analytics, to increase the quality of detections for those targets. However, the uncertainty of detections, the attention shift over regions, and the provisioning of on-device resources essentially hamper us from efficient analytics. We formulate such a scenario as a non-linear integer program in long-term scope, to maximize the detection accuracy. Afterwards, we design an online mechanism to orchestrate the detection model for each region in the video to cope with the moves of the targets, via a queue-based adaptation and the randomized rounding. Via rigorous proof, both dynamic regret regarding detection accuracy and the real-time requirement for the video analytics are ensured. The testbed experiments confirm the superiority of our approach over multiple state-of-the-art algorithms.
Quantum Netlist Compiler (QNC)

Over the last decade, Quantum Computing hardware has rapidly developed and become a very intriguing, promising, and active research field among scientists world-wide. To achieve the desired quantum functionalities, quantum algorithms require translation from a high-level description to a machine-specific physical operation sequence. In contrast to classical compilers, state-of-the-art quantum compilers are in their infancy. We believe there is a research need for a quantum compiler that can deal with generic unitary operators and generate basic unitary operations according to quantum machines' diverse underlying technologies and characteristics. In this work, we introduce the Quantum Netlist Compiler (QNC) that converts arbitrary unitary operators or desired initial states of quantum algorithms to OpenQASM-2.0 circuits enabling them to run on actual quantum hardware. Extensive simulations were run on the IBM quantum systems. The results show that QNC is well suited for quantum circuit optimization and produces circuits with competitive success rates in practice.
A roadmap for building quantum key distribution devices

Adopting the emerging quantum communication technologies seems to be a logical answer to the security challenges implied by the ever expanding of our communication networks and computing and storage facilities. Quantum communications are based on quantum key distribution, which uses quantum mechanical processes to generate cryptographic keys and to securely distribute them to the users in the networks. There already are quantum key distribution devices on the market, but it is ill advised to fully rely on such devices, in the national security use cases. Thus, each country in EU is actively trying or has already succeeded in developing their own prototypes. We present our roadmap for building such a quantum key distribution prototype and the current status of the R&D activities undertaken by our collaboration ITIM-UTCN.
Complete OCR Solution for Image Analysis of World War 2 Documents

The field of Optical Character Recognition (OCR) consists of techniques that are mainly focused on document image analysis. Aside from generating significant speedups of everyday procedures, OCR has a considerable role in the preservation of historical sources of information. Most of the World War 2 (WW2) documents are of great importance, especially with applications in virtual archives, museums, and research. The situation asks for an efficient, yet not aggressive, transcribing method using OCR tools. This paper describes an approach in the context of the given problem. The focus is oriented towards extracting the information from documents affected by their age, but with simpler structures, mainly split into paragraphs, such as letters and military reports. The approach is based on combining the results of multiple OCR engines, with the final objective of achieving better performance compared to the individual performance of each engine.
A Novel Approach to Speech Signal Segmentation Based on Time-Frequency Analysis

The accuracy of speech signal segmentation depends directly on the parameters used to determine the boundaries of the beginning and the end of informative fragments in a continuous speech stream. The purpose of the work is to increase the efficiency of speech/pause segmentation due to the frequency-time analysis of speech signals. A novel original approach to speech/pause segmentation based on the analysis of the values of the mean frequency (in the frequency domain) and short-term energy of the Teager operator function (in the time domain) is proposed. The proposed approach is unique due to an auxiliary algorithm to correct speech/pause segmentation errors, developed on the basis of physiological functioning of the respiratory apparatus organs during the formation of a continuous speech stream. A brief overview of speech signal informative parameters used for speech/pause segmentation has been presented, and the proposed approach performance has been detailed. The suggested approach has been compared with the known methods of speech/pause segmentation for pure and noisy speech signals. The research findings have evidenced the best results of speech/pause segmentation for pure and noisy speech signals being achieved by the methods based on the proposed approach; the ratio of the short-term energy of the Teager operator function to the mean frequency as an informative parameter ensuring maximum relevance to the segmentation problem; an auxiliary algorithm to correct false states enhancing the efficiency of segmentation.
Change detection method using cluster transition probability

AI models such as anomaly detection are being incorporated into various systems, and it is essential to update such models in response to changes in the external environment. When trying to automate the determination of the update timing of the AI model, one promising solution is considered to be the use of a change detection method. However, constraints such as stationarity and iid imposed on target data by existing methods often become barriers to application to time-series data in telecommunications, which often show periodic fluctuations. In this paper, we propose a new change detection method that solves the problems of existing methods, focusing on time-series data showing daily and weekly fluctuations. The proposed method is based on clustering that imposes no restrictions on target data and detects changes by tracking cluster transitions and calculating the distance between cluster transition distributions for the past and current period. The effectiveness of the proposed method is shown by using actual telecommunications data with tens of millions of user connections to network devices.
Realizing a Cyber-Physical Systems Oriented Student Training for Ukrainian Students

In the framework of the Erasmus+ project “Development of practically-oriented student-centred education in the field of modelling of Cyber-Physical Systems”, in the first half of February 2022 a group of 12 Ukrainian engineering students (and 1 professor) participated in a two-week exchange program at KU Leuven Bruges Campus (Belgium). The mainly research-based training focused on a broad range of topics including renewable energy, sustainable material science and several applications of Cyber-Physical Systems.
Enhanced Local Optimization Framework for Light Field Disparity Estimation

Light Field depth estimation has become a very relevant topic over the last decade. With advances in augmented and virtual reality technologies, estimating the depth of a scene with accuracy became more and more important. One of the most successful families of algorithms in the field relies on minimising an objective function for all pixels by first computing the function for an arbitrary set of depth values. However, the choice of these values tends to constrain the accuracy of the disparity map. To overcome this problem, a local optimisation framework that enables accurate depth map estimation without requiring pre-selected depth values is proposed. The framework introduces a novel simulated annealing-based approach that takes advantage of neighbouring pixels to achieve good depth estimates in only 12 iterations, in the presented results. The framework is tested with three different state-of-the-art cost metrics, achieving an average reduction of 4.96 in Mean Squared Error ×100 and 5.53% in badpix 0.07 relative to the traditional approach.
Convolutional Neural Networks - Deterministic Systems

Chaotic states can occur in the convolutional neural network, which are caused by both an increase in the learning speed and an increase in the values of the filters in the convolutional layers. The appearance of local minima due to the increase in the speed of learning causes the appearance of bifurcations on the dependence of learning error on the number of epochs.