Reviewerly/exHarmony huggingface dataset:
Character Detection and Segmentation of Historical Uchen Tibetan Documents in Complex Situations

Tibetan is a low-resource language, and Tibetan culture carried by historical Tibetan documents is an important part of Chinese civilization. The study of historical Tibetan documents is of great significance to the protection of Tibetan culture and the promotion of Chinese culture. Character segmentation is an important step in image analysis and recognition of historical Tibetan documents. However, the following three challenges prevent solving problems of character segmentation in historical Tibetan documents: 1) the text lines have different degrees of tilt and twist; 2) there are many complex situations such as overlapping, crossing, touching and breaking character strokes; and 3) these documents are written by different people with different stroke styles. To resolve these problems, we propose a character segmentation method based on key feature information for historical Tibetan documents. The proposed method consists of three parts: 1) projection and syllable point location information are used to shorten the text lines of historical Tibetan documents and establish a character block database; 2) the local baseline of the character block is detected by using the location information of syllable points or combined with horizontal projection and straight line detection, and the character block is divided into two areas above and below the baseline, and different segmentation methods are adopted; and 3) in view of the large difference in stroke styles, three stroke attribution distances are used to complete the attribution. The experimental results show that the method proposed in this paper can effectively solve the problem of character segmentation of historical Tibetan documents and achieve a better character segmentation effect, which also provides a reference for the relevant document character segmentation.
ORSCA-GPU: one round stream cipher algorithm for GPU implementation

Data confidentiality is one of the most critical security services. Many encryption algorithms are currently used to provide data confidentiality. That is why there are continuous research efforts on the design and implementation of efficient cipher schemes. For this purpose, different lightweight cipher algorithms have been presented and implemented on GPUs with different optimizations to reach high performance. Some examples of these ciphers are Speck, Simon which both require less latency compared to Advanced Encryption Standard (AES). However, these solutions require a higher number of rounds but with a more simple round function compared to AES. Therefore, in this paper, a new cipher scheme called “ORSCA” is defined which only requires one round with the dynamic key-dependent approach. The proposed cipher is designed according to the GPU characteristics. The proposed one-round stream cipher solution is suitable for the high data rate applications. According to the performance results, it can achieve high data throughput compared to existing ones, with throughput greater than 5 Terabits/s on a Tesla A100 GPU. Thus, this approach can be considered as a promising candidate for real-time applications. Finally, the security level is ensured by using the dynamic cryptographic primitives that can be changed for each new input message (or for a set of messages: sub-session key). Thus, the proposed solution is a promising candidate for high secure GPU cryptographic algorithms.
Efficient Two-stage Label Noise Reduction for Retrieval-based Tasks

The existence of noisy labels in datasets has always been an essential dilemma in deep learning studies. Previous works detected noisy labels by analyzing the predicted probability distribution generated by the model trained on the same data and calculating the probabilities of each label to be regarded as noise. However, the predicted probability distribution from the whole dataset may introduce overfitting, and the overfitting on noisy labels may induce the probability distribution of clean and noisy items to be not conditional independent, making identification more challenging. Additionally, label noise reduction on image datasets has received much attention, while label noise reduction on text datasets has not. This paper proposes a noisy label reduction method for text datasets, which could be applied at retrieval-based tasks by getting a conditional independent probability distribution to identify noisy labels accurately. The method first generates a candidate set containing noisy labels, predicts the category probabilities by the model trained on the rest cleaner data, and then identifies noisy items by analyzing a confidence matrix. Moreover, we introduce a warm-up module and a sharpened cross-entropy loss function for efficiently training in the first stage. Empirical results on different rates of uniform and random label noise in five text datasets demonstrate that our method can improve the label noise reduction accuracy and end-to-end classification accuracy. Further, we find that the iteration of the label noise reduction method is efficient to high-rate label noise datasets, and our method will not hurt clean datasets too much.
Lightweight and Secure Branch Predictors against Spectre Attacks

Spectre attacks endanger most of CPUs, operating systems and cloud services due to the sharing of branch predic- tors in modern processors, while existing defenses fail to balance the security and overhead. This paper designs a lightweight and secure branch predictor (LS-BP), which provides lightweight hardware isolation for different branch entries of same-address- space and cross-address-space. Therefore, it is difficult for the attacker to establish branch conflicts. Experimental results show the average performance overhead is less than 3% while providing strong protection.
Privacy-Preserving Aggregate Mobility Data Release: An Information-Theoretic Deep Reinforcement Learning Approach

It is crucial to protect users’ location traces against inference attacks on aggregate mobility data collected from multiple users in various real-world applications. Most of the existing works on aggregate mobility data are focusing on inference attacks rather than designing privacy-preserving release mechanisms, and a few differential private release mechanisms suffer from poor utility-privacy tradeoffs. In this paper, we propose optimal centralized privacy-preserving aggregate mobility data release mechanisms (PAMDRMs) that minimize the leakage from an information-theoretic perspective by releasing perturbed versions of the raw aggregate location. Specifically, we use mutual information to measure user-level and aggregate-level privacy leakage separately, and formulate leakage minimization problems under utility constraints. As directly solving the optimization problems incur exponential complexity w.r.t. users’ trace length, we transform them into belief state Markov Decision Processes (MDPs), with a focus on the MDP formulation for the user-level privacy problem. We build reinforcement learning (RL) models and leverage the efficient Asynchronous Advantage Actor-Critic RL algorithm to derive the solutions to the MDPs as our optimal PAMDRMs. We compare them with two state-of-the-art privacy protection mechanisms PDPR (context-aware local design) and DMLM (context-free centralized design) in terms of mutual information leakage and adversary’s attack success (evaluated by her expected estimation error and Jensen-Shannon Divergence-based error). Extensive experimental results on both synthetic and real-world datasets demonstrate that the user-level PAMDRM performs the best on both measures thanks to its context-aware property and centralized design. Even though the aggregate-level PAMDRM achieves better privacy-utility tradeoff than the other two, it does not always perform better than them on adversarial success, highlighting the necessity of considering privacy measures from different perspectives to avoid overestimating the level of privacy offered to users. Lastly, we discuss an alternative, fully data-driven approach to derive the optimal PAMDRM by leveraging adversarial training on limited data samples.
Improve Image Captioning by Estimating the Gazing Patterns from the Caption

Recently, there has been much interest in developing image captioning models. State-of-the-art models reached a good performance in producing human-like descriptions from image features that are extracted from neural network models such as CNN and R-CNN. However, none of the previous methods have encapsulated explicit features that reflect a human perception of the images such as gazing patterns without the use of the eye-tracking systems. In this paper, we hypothesize that the nouns (i.e. entities) and their orders in the image description reflect human gazing patterns and perception. To this end, we estimate the sequence of the gazed objects from the words in the captions and then train a pointer network to learn to produce such sequence automatically given a set of objects in new images. We incorporate the suggested sequence by pointer network in existing image caption models and investigate its performance. Our experiments show a significant increase in the performance of the image captioning models when the sequence of the gazed objects are utilized as additional features (up to 13 points improvement in CIDEr score when combined with Neural Image Caption model).
The Usability of Mobile Application for Interior Design via Augmented Reality

Interior design plays an essential part in raising the morale of the person, where the moving of the feeling met color, changes of shapes and lighting influences the design details within the space. Furniture catalogue is paper based containing textual information and images, does not provide interaction for the user. The purpose of this study is to implement a new design approach interior design for decorative furniture. The augmented reality environment will allow the user to select from a range of furniture and then display the virtual furniture selected on the real environment. This will provide a better view of the furniture placement and simplify the process of interior designing for users to save their time and effort. In this project, ADDIE methodology has been implemented as a guideline to develop the product throughout this project. Augmented reality technology would potentially to be the future technology trend to the public.
SpectraNet: Learned Recognition of Artificial Satellites from High Contrast Spectroscopic Imagery

Effective space domain awareness requires positive identification of artificial satellites. Current methods for extracting object identification from observed data require spatially resolved imagery which limits identification to objects in low earth orbits. Many artificial Earth satellites, however, operate in geostationary orbits at distances which prohibit ground based observatories from resolving spatial information. This paper demonstrates an object identification solution leveraging modified residual convolutional neural networks to map distance-invariant spectroscopic data to object identity. We report classification accuracies exceeding 80% for a simulated 64-class satellite problem−even in the case of satellites undergoing constant, random re-orientation. An astronomical observing campaign driven by these results returned accuracies of ∼72% for a nine-class problem with an average of 100 examples per class, performing as expected from simulation. We demonstrate the application of variational Bayesian inference by dropout, stochastic weight averaging (SWA), and SWA-focused deep ensembling to measure classification uncertainties−critical components in space domain awareness where routine decisions risk expensive space assets and carry geopolitical consequences.
Characterization of Sunshine Duration in Western Equatorial Africa: In Situ Measurements versus SARAH-2 Satellite Estimates

Abstract Western Equatorial Africa is one of the least sunny areas in the world. Yet, this has attracted little research so far. As in many other parts of Africa, light availability is mainly estimated using in situ measurements of sunshine duration (SDU). Therefore, this study conducts the first characterization of SDU evolution during the annual cycle for the region. It also evaluates the skill of satellite-based estimates of SDU from the Surface Solar Radiation Data Set–Heliosat, edition 2.1 (SARAH-2.1). Mean annual SDU levels are low: less than 5 h day −1 at the regional scale, with the sunniest stations in the northeast (Cameroon and Central African Republic) and the least sunny in an ∼150-km-wide coastal strip in Gabon and Republic of the Congo (RoC). For most of the stations except the southeast ones in the Democratic Republic of Congo, the lowest SDU levels are recorded in July–September, during the main dry season, with persistent overcast conditions. They are as low as 2.5 h day −1 , especially on the windward slopes of the Massifs du Chaillu and du Mayombé, and of the Batéké Plateaus in Gabon and RoC. Although the mean annual and monthly spatial patterns are well reproduced in SARAH-2.1, SDU levels are systematically overestimated by 1–2 h day −1 . The largest positive biases are recorded during the December–February dry season, especially at the northernmost stations. Analyses at the daily time scale show that SARAH-2.1 biases arise from a twofold problem: the number of dark days (SDU &lt; 1 h day −1 ) is 50% lower than observed whereas that of sunny days (SDU &gt; 9 h day −1 ) is 50% higher than observed.
Lightweight Composite Re-Ranking for Efficient Keyword Search with BERT

Recently transformer-based ranking models have been shown to deliver high relevance for document search and the relevance-efficiency tradeoff becomes important for fast query response times. This paper presents BECR (BERT-based Composite Re-Ranking), a lightweight composite re-ranking scheme that combines deep contextual token interactions and traditional lexical term-matching features. BECR conducts query decomposition and composes a query representation using pre-computable token embeddings based on uni-grams and skip-n-grams, to seek a tradeoff of inference efficiency and relevance. Thus it does not perform expensive transformer computations during online inference, and does not require the use of GPU. This paper describes an evaluation of relevance and efficiency of BECR with several TREC datasets.
Generating Multi-Issued Session Key by Using Semi Quantum Key Distribution With Time-Constraint

Information security refers to protect the information from unauthorized access or modification. Quantum Key Distribution (QKD) is a way to generate a key preventing those malicious activities. One of QKD protocol, namely Semi-quantum key distribution (SQKD) protocol, is designed to allow two users to establish a secure secret key when either of them is limited to performing certain “classical” operations. It is proven to be secure from any type of attack. However, it will be a problem in the multi-session communication since the SQKD activities follow the number of the session. In this paper, we propose two modified SQKDs with time-constraint approach. Time-constraint is beneficial in QKD activity since it could generate session key between two parties within a certain time-constraint. By setting the number of session key and its time-constraint before QKD activities, many scheduled communications would be prepared well. Furthermore, BAN Logic analysis is applied to analyze the goal of the protocol, the considered assumptions, wasted phase, and the demand for data encryption. Finally, the performance analysis of the protocols is presented, and it shows a better performance compared with other certain QKDs.
A Voltage Template Attack on the Modular Polynomial Subtraction in Kyber

Kyber is one of the four final Key Encapsulation Mechanism (KEM) competitors of the National Institute of Standards and Technology PostQuantum Cryptography standardization competition. This paper reveals the vulnerability of Kyber under a voltage template side channel attack: the modular polynomial subtraction operation in Kyber.CCAKEM.Dec. In this paper, by splicing data under different selected ciphertexts, a small number of traces are required to recover the secret key. Experiments show that the recovering accuracy of secret key achieves 100% when using 330 traces, and it still achieves 98% when only using 44 traces.
Task-Motion Planning for Navigation in Belief Space

We present an integrated Task-Motion Planning (TMP) framework for navigation in large-scale environment. Autonomous robots operating in real world complex scenarios require planning in the discrete (task) space and the continuous (motion) space. In knowledge intensive domains, on the one hand, a robot has to reason at the highest-level, for example the regions to navigate to; on the other hand, the feasibility of the respective navigation tasks have to be checked at the execution level. This presents a need for motion-planning-aware task planners. We discuss a probabilistically complete approach that leverages this task-motion interaction for navigating in indoor domains, returning a plan that is optimal at the task-level. Furthermore, our framework is intended for motion planning under motion and sensing uncertainty, which is formally known as belief space planning. The underlying methodology is validated with a simulated office environment in Gazebo. In addition, we discuss the limitations and provide suggestions for improvements and future work.
HERMES: Hardware-Efficient Speculative Dataflow Architecture for Bonsai Merkle Tree-Based Memory Authentication

Emerging byte-addressable Non-Volatile Memory (NVM) technology, although promising superior memory density and ultra-low energy consumption, poses unique challenges to guaranteeing memory confidentiality, integrity, and crash-consistency. As such, extensive research has been conducted to transparently protect memory security through an FPGA-implemented middleware that effectively deploys encryption, authentication/integrity verification, and replay attack protection. Bonsai Merkle tree (BMT) has been proven to be highly effective in guaranteeing memory integrity and protecting against replay attack. However, when used in a strictly persistent trusted execution environment (TEE), BMT-based memory integrity protection severely bottlenecks memory performance because properly maintaining a BMT results in a deep traversal over the hash tree for every counter update. In this paper, we propose HERMES, a hardware-efficient memory integrity engine specifically designed to deliver a crash-consistent BMT for NVM, capable of processing multiple outstanding counter requests in flight, which significantly improves both latency and throughput of all BMT operations through leveraging an asynchronous dataflow architecture and speculative execution. HERMES incorporates three architectural innovations: (1) a speculative control logic and a speculative temporary buffer dedicatedly designed and deployed at each level; (2) an optimized hardware component verifying all BMT levels in parallel and an adaptive algorithm adapting to caching status of BMT levels; (3) a formalized message format transferred between BMT levels to accommodate both counter operations within a unified architecture where each level is able to adaptively behave. Experimented with Shuhai memory bandwidth benchmark, HERMES achieved up to 7.9x higher throughput and up to 3.5x shorter latency over the state-of-the-art ARES while consuming 2x resource utilization as a tradeoff.
Spatiotemporal variations of global solar radiation: Case Study Egypt

The spatio-temporal characteristic of the available global solar radiation (GSR) variability is essential for effective grid integration of photovoltaic (PV) power systems, particularly in developing countries such as Egypt. Thus, the long-term spatio-temporal variability of GSR and its trend assessment, fluctuations, and abrupt change detection during the climate period 1985-2018 based on eight ground-based stations in Egypt were statistically analyzed. The missing records in the observed GSR data were completed using the SoDa (Solar radiation data) database after its validation at the selected eight stations, which produced high accuracy and slight bias compared to the available observations. The results showed that the northern coast, particularly the Nile Delta region, has lower GSR values than the southern region. At all stations, winter had the largest coefficient of variation (COV) values while summer had the lowest COV values. Furthermore, the trend analysis for GSR using Mann-Kendall (MK) rank statistical test revealed that autumn, followed by winter and spring has a positive trend across Egypt, while the annual and summer GSR have positive trends over middle and northern Egypt. The annual GSR long-term variability measured by Gaussian and Binomial low-path filters shows a positive trend at all stations except Arish, Asyut and Kharga. Moreover, the detected first abrupt change point for the annual GSR data occurred during the 1990s at all stations except for Matrouh in 2002. This first abrupt change point is positive (increasing GSR values) at all stations except a negative trend for Bahtim and Kharga. The second and third abrupt change points are positive at all stations except Arish during 2003-2009 in the second change point and returns to its positive trend in the third change point in 2010.
A novel polynomial membership functions based control method for T–S fuzzy systems

To solve the state feedback stabilization problem for a class of T-S fuzzy systems, a new stability synthesis method is raised to further relax the conservatism existing in the analysis process. First, a series of nonlinear polynomials are used to approximate the system membership functions. And satisfied approximation results can be obtained. Then a polynomials transformation method is raised to change the polynomial membership functions into linear form. This method can facilitate the synthesis process and achieve better approximation precision. Therefore, by a membership function involving stability analysis approach, linear matrix inequality form stability conditions are given. Finally, by presenting and analyzing a numerical and a practical examples, the conservatism reduction effects of the proposed methods are verified.
Innovations in the Healthcare Industry Using Blockchain Technology

With blockchain technology, the healthcare sector can have data efficiency, data access flexibility, interconnection, transparency, and security. This chapter reviews the development of blockchain technology usage, current implementation challenges of blockchain technology, and includes references for the applications of blockchain technology in healthcare. A systematic review of current status, desired status called ideal status, and the research gap of use of blockchain technology in application areas of the healthcare industry are included along with identification of possible research agendas for future research.