Reviewerly/exHarmony huggingface dataset:
DEVELOPMENT OF E-BOOKLET THEME OF FOOD SECURITY BASED ON SCIENCE LITERACY FOR STUDENTS OF JUNIOR HIGH SCHOOL CLASS IX

Scientific literacy is the answer to prepare students to face the challenges of the 21st century. The resulting e-booklet product with the theme of food security based on science literacy is an alternative solution to improve students scientific literacy skills. The food security e-booklet contains food sources, substances contained in food and food processing methods which are compiled based on the integration of aspects of scientific literacy and problem based learning models. The research method used is the 4D development model developed by S. Thiagarajan, et al. (1974). However, the research on the development of this e-booklet was modified according to needs, consisting of the define, design, and develop stages. The assessment was carried out using a validation questionnaire that was assessed by a team of experts with two science education lecturers and one science teacher. The results of this study obtained the average value of the feasibility of the e-booklet on the theme of food security based on scientific literacy as a whole with a percentage of 84% in the very feasible category. Therefore, this e-booklet with the theme of food security reflects students' scientific literacy as a whole and is suitable for use. Keywords: e-booklet, scientific literacy, food security, problem based learning models
Attack Directories on ARM big.LITTLE Processors

Eviction-based cache side-channel attacks take advantage of inclusive cache hierarchies and shared cache hardware. Processors with the template ARM big.LITTLE architecture do not guarantee such preconditions and therefore will not usually allow cross-core attacks let alone cross-cluster attacks. This work reveals a new side-channel based on the snoop filter (SF), an unexplored directory structure embedded in template ARM big.LITTLE processors. Our systematic reverse engineering unveils the undocumented structure and property of the SF, and we successfully utilize it to bootstrap cross-core and cross-cluster cache eviction. We demonstrate a comprehensive methodology to exploit the SF side-channel, including the construction of eviction sets, the covert channel, and attacks against RSA and AES. When attacking TrustZone, we conduct an interrupt-based side-channel attack to extract the key of RSA by a single profiling trace, despite the strict cache clean defense. Supported by detailed experiments, the SF side-channel not only achieves competitive performance but also overcomes the main challenge of cache side-channel attacks on ARM big.LITTLE processors.
About one approach to increasing the security of computer systems against intrusion

The article considers an approach to increasing the security of computer systems from intrusion. The importance of the problem of information security and cybersecurity is substantiated. Strategies of attack and destructive actions used by intruders (hackers) are considered. 16 different attack and intrusion strategies are identified. The reasons for the vulnerability of computer systems are analyzed. Two groups of reasons are considered: objective reasons depending on the hardware and software components of the computer system and subjective reasons depending on the human factor. It is noted that one of the main reasons that objectively complicate the protection of a computer from intrusion is that the same computer is assigned the solution of two unrelated problems: the direct solution of tasks, which are essentially an internal problem, and the tasks of communication, which can be seen as an external problem. An approach is proposed to increase the security of computer systems from intrusion through hardware redundancy, in particular, instead of one computer, use a computer unit consisting of two computers – internal and external. The internal computer is used for the direct solution of tasks. The external computer is responsible for performing communication tasks. Additionally, the issues of physical protection of computer systems are considered.
Information currency converter based on Telegram messenger

The work is dedicated to the development of a mobile chatbot containing an information currency converter, designed for use by a wide range of people. A chatbot is a subject-oriented text-based dialog interface that allows a user to perform a limited set of tasks: getting information about the current rate of currencies (USD or EUR) relative to the national currency and finding out the current rate of cryptocurrencies (Bitcoin, Ethereum, Litecoin) in dollars or euros. To achieve this goal, the selected subject area was analyzed and appropriate conclusions were made. A corresponding study of analogs who perform tasks similar in complexity was carried out, only a few chatbots were identified, as a certain number of bots posted in Telegram no longer provide their services or work incorrectly. The algorithm of the service for currency conversion based on the Telegram messenger is described. The chatbot is implemented in the Python programming language and uses the Pycharm development environment, as it is best suited for programming the intended project and is easy to use. There are two options available to the user: the cryptocurrency rate from the CoinGecko site or the exchange rate from PrivatBank. The article examines the development and improvement of chatbots. Similar Telegram bots, which function similarly to the created one, are reviewed. The author’s bot has been developed, and the architecture and algorithm of the CurrencyBot currency conversion service are presented.
KL Regularized Normalization Framework for Low Resource Tasks

Large pre-trained models, such as Bert, GPT, and Wav2Vec, have demonstrated great potential for learning representations that are transferable to a wide variety of downstream tasks . It is difficult to obtain a large quantity of supervised data due to the limited availability of resources and time. In light of this, a significant amount of research has been conducted in the area of adopting large pre-trained datasets for diverse downstream tasks via fine tuning, linear probing, or prompt tuning in low resource settings. Normalization techniques are essential for accelerating training and improving the generalization of deep neural networks and have been successfully used in a wide variety of applications. A lot of normalization techniques have been proposed but the success of normalization in low resource downstream NLP and speech tasks is limited. One of the reasons is the inability to capture expressiveness by rescaling parameters of normalization. We propose KullbackLeibler(KL) Regularized normalization (KL-Norm) which make the normalized data well behaved and helps in better generalization as it reduces over-fitting, generalises well on out of domain distributions and removes irrelevant biases and features with negligible increase in model parameters and memory overheads. Detailed experimental evaluation on multiple low resource NLP and speech tasks, demonstrates the superior performance of KL-Norm as compared to other popular normalization and regularization techniques.
Target Conditioned Representation Independence (TCRI); From Domain-Invariant to Domain-General Representations

We propose a Target Conditioned Representation Independence (TCRI) objective for domain generalization. TCRI addresses the limitations of existing domain generalization methods due to incomplete constraints. Specifically, TCRI implements regularizers motivated by conditional independence constraints that are sufficient to strictly learn complete sets of invariant mechanisms, which we show are necessary and sufficient for domain generalization. Empirically, we show that TCRI is effective on both synthetic and real-world data. TCRI is competitive with baselines in average accuracy while outperforming them in worst-domain accuracy, indicating desired cross-domain stability.
Bent functions and strongly regular graphs

The family of bent functions is a known class of Boolean functions, which have a great importance in cryptography. The Cayley graph defined on $\mathbb{Z}_{2}^{n}$ by the support of a bent function is a strongly regular graph $srg(v,k\lambda,\mu)$, with $\lambda=\mu$. In this note we list the parameters of such Cayley graphs. Moreover, it is given a condition on $(n,m)$-bent functions $F=(f_1,\ldots,f_m)$, involving the support of their components $f_i$, and their $n$-ary symmetric differences.
Modelling Controllers for Cyber Physical Systems Using Neural Networks

Model Predictive Controllers (MPC) are widely used for controlling cyber-physical systems. It is an iterative process of optimizing the prediction of the future states of a robot over a fixed time horizon. MPCs are effective in practice, but because they are computationally expensive and slow, they are not well suited for use in real-time applications. Overcoming the flaw can be accomplished by approximating an MPC's functionality. Neural networks are very good function approximators and are faster compared to an MPC. It can be challenging to apply neural networks to control-based applications since the data does not match the i.i.d assumption. This study investigates various imitation learning methods for using a neural network in a control-based environment and evaluates their benefits and shortcomings.
Design Considerations of an Unmanned Aerial Vehicle for Aerial Filming

Filming sport videos from an aerial view has always been a hard and an expensive task to achieve, especially in sports that require a wide open area for its normal development or the ones that put in danger human safety. Recently, a new solution arose for aerial filming based on the use of Unmanned Aerial Vehicles (UAVs), which is substantially cheaper than traditional aerial filming solutions that require conventional aircrafts like helicopters or complex structures for wide mobility. In this paper, we describe the design process followed for building a customized UAV suitable for sports aerial filming. The process includes the requirements definition, technical sizing and selection of mechanical, hardware and software technologies, as well as the whole integration and operation settings. One of the goals is to develop technologies allowing to build low cost UAVs and to manage them for a wide range of usage scenarios while achieving high levels of flexibility and automation. This work also shows some technical issues found during the development of the UAV as well as the solutions implemented.
Improving Automated Program Repair with Domain Adaptation

Automated Program Repair (APR) is defined as the process of fixing a bug/defect in the source code, by an automated tool. APR tools have recently experienced promising results by leveraging state-of-the-art Neural Language Processing (NLP) techniques. APR tools such as TFix and CodeXGLUE combine text-to-text transformers with software-specific techniques are outperforming alternatives, these days. However, in most APR studies the train and test sets are chosen from the same set of projects. In reality, however, APR models are meant to be generalizable to new and different projects. Therefore, there is a potential threat that reported APR models with high effectiveness perform poorly when the characteristics of the new project or its bugs are different than the training set's(Domain Shift). In this study, we first define and measure the domain shift problem in automated program repair. Then, we then propose a domain adaptation framework that can adapt an APR model for a given target project. We conduct an empirical study with three domain adaptation methods FullFineTuning, TuningWithLightWeightAdapterLayers, and CurriculumLearning using two state-of-the-art domain adaptation tools (TFix and CodeXGLUE) and two APR models on 611 bugs from 19 projects. The results show that our proposed framework can improve the effectiveness of TFix by 13.05% and CodeXGLUE by 23.4%. Another contribution of this study is the proposal of a data synthesis method to address the lack of labelled data in APR. We leverage transformers to create a bug generator model. We use the generated synthetic data to domain adapt TFix and CodeXGLUE on the projects with no data (Zero-shot learning), which results in an average improvement of 5.76% and 24.42% for TFix and CodeXGLUE, respectively.
Offline Clustering Approach to Self-supervised Learning for Class-imbalanced Image Data

Class-imbalanced datasets are known to cause the problem of model being biased towards the majority classes. In this project, we set up two research questions: 1) when is the class-imbalance problem more prevalent in self-supervised pre-training? and 2) can offline clustering of feature representations help pre-training on class-imbalanced data? Our experiments investigate the former question by adjusting the degree of {\it class-imbalance} when training the baseline models, namely SimCLR and SimSiam on CIFAR-10 database. To answer the latter question, we train each expert model on each subset of the feature clusters. We then distill the knowledge of expert models into a single model, so that we will be able to compare the performance of this model to our baselines.
CHEM: Efficient Secure Aggregation with Cached Homomorphic Encryption in Federated Machine Learning Systems

Although homomorphic encryption can be incorporated into neural network layers for securing machine learning tasks, such as confidential inference over encrypted data samples and encrypted local models in federated learning, the computational overhead has been an Achilles heel. This paper proposes a caching protocol, namely CHEM, such that tensor ciphertexts can be constructed from a pool of cached radixes rather than carrying out expensive encryption operations. From a theoretical perspective, we demonstrate that CHEM is semantically secure and can be parameterized with straightforward analysis under practical assumptions. Experimental results on three popular public data sets show that adopting CHEM only incurs sub-second overhead and yet reduces the encryption cost by 48%--89% for encoding input data samples in confidential inference and 67%--87% for encoding local models in federated learning, respectively.
SALVE: Self-supervised Adaptive Low-light Video Enhancement

A self-supervised adaptive low-light video enhancement method, called SALVE, is proposed in this work. SALVE first enhances a few key frames of an input low-light video using a retinex-based low-light image enhancement technique. For each keyframe, it learns a mapping from low-light image patches to enhanced ones via ridge regression. These mappings are then used to enhance the remaining frames in the low-light video. The combination of traditional retinex-based image enhancement and learning-based ridge regression leads to a robust, adaptive and computationally inexpensive solution to enhance low-light videos. Our extensive experiments along with a user study show that 87% of participants prefer SALVE over prior work.
Trajectory Generation and Tracking Control for Aggressive Tail-Sitter Flights

We address the theoretical and practical problems related to the trajectory generation and tracking control of tail-sitter UAVs. Theoretically, we focus on the differential flatness property with full exploitation of actual UAV aerodynamic models, which lays a foundation for generating dynamically feasible trajectory and achieving high-performance tracking control. We have found that a tail-sitter is differentially flat with accurate aerodynamic models within the entire flight envelope, by specifying coordinate flight condition and choosing the vehicle position as the flat output. This fundamental property allows us to fully exploit the high-fidelity aerodynamic models in the trajectory planning and tracking control to achieve accurate tail-sitter flights. Particularly, an optimization-based trajectory planner for tail-sitters is proposed to design high-quality, smooth trajectories with consideration of kinodynamic constraints, singularity-free constraints and actuator saturation. The planned trajectory of flat output is transformed to state trajectory in real-time with consideration of wind in environments. To track the state trajectory, a global, singularity-free, and minimally-parameterized on-manifold MPC is developed, which fully leverages the accurate aerodynamic model to achieve high-accuracy trajectory tracking within the whole flight envelope. The effectiveness of the proposed framework is demonstrated through extensive real-world experiments in both indoor and outdoor field tests, including agile SE(3) flight through consecutive narrow windows requiring specific attitude and with speed up to 10m/s, typical tail-sitter maneuvers (transition, level flight and loiter) with speed up to 20m/s, and extremely aggressive aerobatic maneuvers (Wingover, Loop, Vertical Eight and Cuban Eight) with acceleration up to 2.5g.
A Theory of Conversion Relations for Prefixed Units of Measure

Units of measure with prefixes and conversion rules are given a formal semantic model in terms of categorial group theory. Basic structures and both natural and contingent semantic operations are defined. Conversion rules are represented as a class of ternary relations with both group-like and category-like properties. A hierarchy of subclasses is explored, each with better algebraic behavior than the preceding, culminating in a direct efficient conversion-by-rewriting algorithm.
Towards a generalization of the van der Waerden's conjecture for Sn-polynomials with integral coefficients over a fixed number field extension

The van der Waerden's Conjecture states that the set $\mathscr{P}_{n,N}^0(\mathbb{Q})$ of monic integer polynomials $f(X)$ of degree $n$, with height $\le N$ such that the Galois group $G_{K_f/\mathbb{Q}}$ of the splitting field $K_f/\mathbb{Q}$ is the full symmetric group, has order $|\mathscr{P}_{n,N}^0(\mathbb{Q})|=(2N)^n+O_n(N^{n-1})$ as $N\rightarrow+\infty$. The conjecture has been shown previously for cubic and quartics polynomials by van der Waerden, Chow and Dietmann. Subsequently, Bhargava proved it for $n\ge6$. In this paper, we generalize the result for polynomials with coefficients in the ring of algebraic integers $\mathcal{O}_K$ of a fixed finite extension $K/\mathbb{Q}$ of degree $d$, for some values of $n$ and $d$.
Approximate Autonomous Quantum Error Correction with Reinforcement Learning

Autonomous quantum error correction (AQEC) protects logical qubits by engineered dissipation and thus circumvents the necessity of frequent, error-prone measurement-feedback loops. Bosonic code spaces, where single-photon loss represents the dominant source of error, are promising candidates for AQEC due to their flexibility and controllability. While existing proposals have demonstrated the in-principle feasibility of AQEC with bosonic code spaces, these schemes are typically based on the exact implementation of the Knill-Laflamme conditions and thus require the realization of Hamiltonian distances $d\geq 2$. Implementing such Hamiltonian distances requires multiple nonlinear interactions and control fields, rendering these schemes experimentally challenging. Here, we propose a bosonic code for approximate AQEC by relaxing the Knill-Laflamme conditions. Using reinforcement learning (RL), we identify the optimal bosonic set of codewords (denoted here by RL code), which, surprisingly, is composed of the Fock states $\vert 2\rangle$ and $\vert 4\rangle$. As we show, the RL code, despite its approximate nature, successfully suppresses single-photon loss, reducing it to an effective dephasing process that well surpasses the break-even threshold. It may thus provide a valuable building block toward full error protection. The error-correcting Hamiltonian, which includes ancilla systems that emulate the engineered dissipation, is entirely based on the Hamiltonian distance $d=1$, significantly reducing model complexity. Single-qubit gates are implemented in the RL code with a maximum distance $d_g=2$.
Predictive Coding Based Multiscale Network with Encoder-Decoder LSTM for Video Prediction

We present a multi-scale predictive coding model for future video frames prediction. Drawing inspiration on the ``Predictive Coding" theories in cognitive science, it is updated by a combination of bottom-up and top-down information flows, which can enhance the interaction between different network levels. However, traditional predictive coding models only predict what is happening hierarchically rather than predicting the future. To address the problem, our model employs a multi-scale approach (Coarse to Fine), where the higher level neurons generate coarser predictions (lower resolution), while the lower level generate finer predictions (higher resolution). In terms of network architecture, we directly incorporate the encoder-decoder network within the LSTM module and share the final encoded high-level semantic information across different network levels. This enables comprehensive interaction between the current input and the historical states of LSTM compared with the traditional Encoder-LSTM-Decoder architecture, thus learning more believable temporal and spatial dependencies. Furthermore, to tackle the instability in adversarial training and mitigate the accumulation of prediction errors in long-term prediction, we propose several improvements to the training strategy. Our approach achieves good performance on datasets such as KTH, Moving MNIST and Caltech Pedestrian. Code is available at https://github.com/Ling-CF/MSPN.
Depth Estimation maps of lidar and stereo images

This paper as technology report is focusing on evaluation and performance about depth estimations based on lidar data and stereo images(front left and front right). The lidar 3d cloud data and stereo images are provided by ford. In addition, this paper also will explain some details about optimization for depth estimation performance. And some reasons why not use machine learning to do depth estimation, replaced by pure mathmatics to do stereo depth estimation. The structure of this paper is made of by following:(1) Performance: to discuss and evaluate about depth maps created from stereo images and 3D cloud points, and relationships analysis for alignment and errors;(2) Depth estimation by stereo images: to explain the methods about how to use stereo images to estimate depth;(3)Depth estimation by lidar: to explain the methods about how to use 3d cloud datas to estimate depth;In summary, this report is mainly to show the performance of depth maps and their approaches, analysis for them.
Perfect State Transfer in Arbitrary Distance

Quantum Perfect State Transfer (PST) is a fundamental tool of quantum communication in a network. It is considered a rare phenomenon. The original idea of PST depends on the fundamentals of the continuous-time quantum walk. A path graph with at most three vertices allows PST. Based on the Markovian quantum walk, we introduce a significantly powerful method for PST in this article. We establish PST between the extreme vertices of a path graph of arbitrary length. Moreover, any pair of vertices $j$ and $n - j - 1$ in a path graph with $n$ vertices allow PST for $0 \leq j < \frac{n - 1}{2}$. Also, no cycle graph with more than $4$ vertices does not allow PST based on the continuous-time quantum walk. In contrast, we establish PSTs based on Markovian quantum walk between the pair of vertices $j$ and $j + m$ for $j = 0, 1, \dots (m - 1)$ in a cycle graph with $2m$ vertices.