Reviewerly/exHarmony huggingface dataset:
Towards an Effective and Interpretable Refinement Approach for DNN Verification

Recently, several abstraction refinement techniques have been proposed to improve the verification precision for deep neural networks (DNNs). However, these techniques usually take many refinement steps to verify a property and the refinement decision in each step is hard to interpret, thus hindering their analysis, reasoning and optimization.In this work, we propose SURGEON, a novel DNN verification refinement approach that is both effective and interpretable, allowing analyst to understand why and how each refinement decision is made. The main insight is to leverage the ‘interpretable’ nature of debugging processes and formulate the verification refinement problem as a debugging problem. Given a failed verification procedure, SURGEON refines it in an iterative manner and, in each iteration, it effectively identifies the root cause of the failure and heuristically generates fixes according to abstract transformers.We have implemented SURGEON in a prototype and evaluated it using a set of local robustness verification problems. Besides the interpretability, the experimental results show our approach can improve the precision of base verification methods and is more effective than existing refinement techniques.
Just-In-Time Method Name Updating With Heuristics and Neural Model

Ensuring the quality and conciseness of method names is pivotal for the readability and maintainability of source code. However, for developers, it often presents challenges, particularly during the course of code evolution. Throughout this process, developers sometimes may neglect to update the method name, resulting in inconsistency which could potentially mislead developers and introduce future bugs. In this paper, we propose the task of “Just-In-Time (JIT) Method Name Updating” which automatically performs method name updates to avoid inconsistent names and fix them before being introduced into code bases. Specifically, we propose an approach that combines heuristic rules and a neural model. The heuristic rule-based component mainly focuses on the single-token changes for our empirical findings that the proportion of single-token modifications is extensive, and often corresponds to code-indicative updates. The neural model-based component is a customized Seq2seq model considering code changes and the new method body’s token type. To evaluate our approach, we conduct extensive experiments on the collected dataset with over 108K method name-body co-change samples from popular Java projects. The results show that our method outperforms the three baselines on all metrics. In particular, our approach achieves a significant improvement in Accuracy and improves method generation baseline by 23.5%.
Design and Implementation of Image Processing Platform Based on GUI

Image processing technology covers a wide range of application fields, with in-depth research on various image theories, technologies, equipment, and a wide range of research fields. In order to facilitate researchers to visually and quickly analyze the effectiveness of relevant algorithms, an image processing simulation platform is designed using a GUI visual interaction interface. The design principles and processes are conceptualized, and the overall design of the platform is constructed. The functions of each sub interface are implemented based on the division of modules. The processing effects of different algorithms can be compared visually through running the results, and the application of image processing technology in license plate recognition can be demonstrated through comprehensive examples Comprehensive application in smart classroom attendance process. The test results of the simulation platform indicate that the image processing platform is easy to operate and expand, effectively assisting operators in deepening their understanding and application of image processing technology.
Absolute Positioning Method Based on Tunnel Wall Image Similarity Matching

This paper conducts an in-depth analysis of the positioning environment inside tunnels and proposes an absolute coordinate positioning method based on tunnel wall image similarity matching. In this chapter, we first analyze the existing problems in the current positioning process and propose solutions. Then, we introduce the construction method of the tunnel feature database and the tunnel wall image feature matching method based on motion smoothness constraints. Finally, experimental validation is performed.
Machine Learning Across Network-Connected FPGAs

FPGAs often cannot implement machine learning inference with high accuracy models due to significant storage and computing requirements. The corresponding hardware accelerators of such models are large designs which cannot be deployed on a single platform. In this research, we implement ResNet-50 with 4 bit precision for weights and 5 bit precision for activations, which has a good trade-off between precision and accuracy. We train ResNet-50 using the quantization-aware training library Brevitas and build a hardware accelerator with the FINN framework from AMD. We map the result to three FPGAs that communicate directly with one another over the network via the User Datagram Protocol (UDP). The multi-FPGA implementation is compared to a single FPGA ResNet-50 design with lower precision of 1 bit weights and 2 bit activations. While the latter can fit on a single FPGA, the former pays for higher accuracy with a three times increase in the required number of BRAM tiles and can only be deployed on multiple FPGAs. We show the difference in accuracy, resource utilization, and throughput for the designs deployed on AMD/Xilinx Alveo U280 data center accelerator cards available in the Open Cloud Testbed images/s.
Experimental demonstration of online learning in deep photonic neural networks

We demonstrate supervised and unsupervised online learning of a deep photonic neural network. While the present system employs a fiber-optics setup with variable optical attenuators as synapses and software neurons, it can be adapted to chip-scale implementations with optical neurons and synapses.
Edge Computing Applied on Real-Time Manatee Detection Using Microcontrollers

This paper explores the process of implementing Convolutional Neural Networks (CNNs) on resource-constrained microcontroller for real-time manatee detection in an edge computing application. The steps taken to optimize the CNN model and reduce its computational burden without compromising its performance are outlined. Techniques such as weight pruning and model quantization are employed to enhance the efficiency of the deployed model. The results of our implementation demonstrate the feasibility of using CNNs on resource-constrained microcontrollers for edge computing applications. The proposed approach opens up new possibilities for deploying intelligent applications directly at the network edge, enabling faster decision-making and reducing the need for constant data transmission to centralized servers.
Recommendation Algorithm Based on Social Information and Global Average Matrix Decomposition

Traditional recommendation systems typically rely solely on user content information to predict product ratings while ignoring the impact of other factors on recommendation effectiveness. Proposes a recommendation algorithm based on product attributes and social attributes that leverages product attribute information to obtain user preferences and product features, thereby reducing rating data sparsity. The algorithm also considers the effect of social attributes on the recommendation system and uses linear weighting with global rating average, item rating average, user rating average, and matrix decomposition to predict user ratings, thus improving prediction accuracy. The algorithm was tested on three public datasets, and the results indicate that it improves recommendation performance to a certain extent.
Spread-photon architecture for quantum-secure communications

We introduce a practical architecture for unitary evolution and recovery of quantum optical states that consists of widely available passive linear optical elements. We experimentally verify the proposed approach in free space. We evaluate its performance for quantum-secure communication over a free space optical link.
Spread Photon Transceiver for Quantum Secure Communications

Quantum communication protocols provide capabilities for private information exchange despite adversarial access to quantum resources. However, existing protocols can be limited by speed, and considerations around their practical implementation. We report on a novel quantum communications protocol, called the spread photon transceiver, which operates on the physical layer of a communications link with a pair of matched receivers, to achieve quantum security. The sensitivity of the matched receiver architecture to experimental error is evaluated. A security analysis of the protocol is conducted, evaluating the private information rate achievable between the intended sender and receiver, assuming a quantum-powerful adversary, with receiver losses reflective of scenarios which may be encountered in a military field environment.
Can Hierarchical Physics-Based Machine Learning De-Anonymize Solar Farm Locations?

In the last decade, machine learning techniques powered by big data have revolutionized data-driven methods to explore and exploit underlying patterns in data. For private entities sharing data, some guarantee of anonymity is essential. In this paper, we discuss a scheme to de-anonymize solar farms only based on their monthly yield profiles. By hierarchically training physics-guided convolution neural networks in a federated setting, the models attain above 90% accuracy when mapping yield profiles to geographical regions. However, the maximum accuracy attainable is limited. We argue that although the monthly yield profile can indicate a farm's latitude, it does not provide sufficient information to uniquely identify its location.
Searchlight: An accurate, sensitive, and fast radio frequency energy detection system

Detecting signals is often presented as a simple and solved problem. However, signal detection is typically not the only goal, as signal localization in time and frequency is often desired. This detection and localization process must also occur in the presence of non-ideal radio frequency (RF) receiver effects such as receiver non-flat noise floors, front-end inphase-quadrature (IQ) imbalance, local oscillator (LO) leakage, and in some cases power saturation. A robust signal detection system must overcome these hardware impairments and report only signals that correspond to true emitter devices. Additionally, some unspoken requirements take the form of detecting signals that are below the noise floor, very narrowband, very wideband, bursty, or frequency hopping. We present Searchlight, a signal detection system that solves these problems by using novel techniques that enable classical signal detection to work well using software defined radios (SDR). Performance results are presented for synthetically generated and over-the-air (OTA) data sets, which were obtained using commercially available hardware in a complicated signal scenario. Searchlight is shown to be an important enabler for true spectral monitoring platforms that desire to detect anomalous signals or apply machine learning (ML) algorithms to classify the various types of wireless activity in a given area.
AD-CLIP: Adapting Domains in Prompt Space Using CLIP

Although deep learning models have shown impressive performance on supervised learning tasks, they often struggle to generalize well when the training (source) and test (target) domains differ. Unsupervised domain adaptation (DA) has emerged as a popular solution to this problem. However, current DA techniques rely on visual backbones, which may lack semantic richness. Despite the potential of large-scale vision-language foundation models like CLIP, their effectiveness for DA has yet to be fully explored. To address this gap, we introduce AD-CLIP, a domain-agnostic prompt learning strategy for CLIP that aims to solve the DA problem in the prompt space. We leverage the frozen vision backbone of CLIP to extract both image style (domain) and content information, which we apply to learn prompt tokens. Our prompts are designed to be domain-invariant and class-generalizable, by conditioning prompt learning on image style and content features simultaneously. We use standard supervised contrastive learning in the source domain, while proposing an entropy minimization strategy to align domains in the embedding space given the target domain data. We also consider a scenario where only target domain samples are available during testing, without any source domain data, and propose a cross-domain style mapping network to hallucinate domain-agnostic tokens. Our extensive experiments on three benchmark DA datasets demonstrate the effectiveness of AD-CLIP compared to existing literature.
Enhancing CLIP with GPT-4: Harnessing Visual Descriptions as Prompts

Contrastive pretrained large Vision-Language Models (VLMs) like CLIP have revolutionized visual representation learning by providing good performance on downstream datasets. VLMs are 0-shot adapted to a downstream dataset by designing prompts that are relevant to the dataset. Such prompt engineering makes use of domain expertise and a validation dataset. Meanwhile, recent developments in generative pretrained models like GPT-4 mean they can be used as advanced internet search tools. They can also be manipulated to provide visual information in any structure. In this work, we show that GPT-4 can be used to generate text that is visually descriptive and how this can be used to adapt CLIP to downstream tasks. We show considerable improvements in 0-shot transfer accuracy on specialized fine-grained datasets like EuroSAT (~7%), DTD (~ 7%), SUN397 (~ 4.6%), and CUB ( ~3.3%) when compared to CLIP’s default prompt. We also design a simple few-shot adapter that learns to choose the best possible sentences to construct generalizable classifiers that outperform the recently proposed CoCoOP by ~2% on average and by over 4% on 4 specialized fine-grained datasets. The code, prompts, and auxiliary text dataset is available at github.com/mayug/VDT-Adapter.
Undercover Deepfakes: Detecting Fake Segments in Videos

The recent renaissance in generative models, driven primarily by the advent of diffusion models and iterative improvement in GAN methods, has enabled many creative applications. However, each advancement is also accompanied by a rise in the potential for misuse. In the arena of the deepfake generation, this is a key societal issue. In particular, the ability to modify segments of videos using such generative techniques creates a new paradigm of deepfakes which are mostly real videos altered slightly to distort the truth. This paradigm has been under-explored by the current deepfake detection methods in the academic literature. In this paper, we present a deepfake detection method that can address this issue by performing deepfake prediction at the frame and video levels. To facilitate testing our method, we prepared a new benchmark dataset where videos have both real and fake frame sequences with very subtle transitions. We provide a benchmark on the proposed dataset with our detection method which utilizes the Vision Transformer based on Scaling and Shifting [38] to learn spatial features, and a Timeseries Transformer to learn temporal features of the videos to help facilitate the interpretation of possible deepfakes. Extensive experiments on a variety of deepfake generation methods show excellent results by the proposed method on temporal segmentation and classical video-level predictions as well. In particular, the paradigm we address will form a powerful tool for the moderation of deepfakes, where human oversight can be better targeted to the parts of videos suspected of being deepfakes. All experiments can be reproduced at: github.com/rgb91/temporal-deepfake-segmentation.
Shapley Deep Learning: A Consensus for General-Purpose Vision Systems

Shapely Deep Learning (SDL) targets a new foundation for the design of general-purpose vision systems, by establishing a consensus method that facilitates self-adaptation and flexibility to deal with new computer vision tasks. Today, machine learning did not yet reach the flexible, general-purpose intelligence that biological vision has in mimicking visual descriptions and learning in general-purpose vision algorithms. Currently, each model is built using the domain knowledge of the application in question. Data scientists must consequently be well-versed in the relevant subject. This paper presents SDL as a consensus method for general-purpose intelligence without the help of a domain expert as the trained model has been developed utilizing a general deep learning approach that investigates the contribution of each model in the training process. First, several deep learning models have been trained for each image. The shapley value is then determined to compute the contribution of each subset of models in the training. The model selection is finally performed based on the shapley value and the joint model cost. Optimization of the shapley computation is also carried out by investigating the banzhaf function. We present the evaluation of the generality of SDL using the computer vision benchmarks: MNIST for Shapley value performance, ImageNet for image classification, and COCO for object detection. The results reveal the effectiveness of SDL in terms of accuracy and competitiveness of inference runtime. Concretely, SDL achieved 10%, and 8% over MViTv2 for classification, and object detection tasks, respectively.
Video Action Recognition with Adaptive Zooming Using Motion Residuals

Motivated by the mechanisms of selective visual attention in humans, we put forward an efficient method for learning spatial attention with adaptive zooming for video action recognition. The learnt module can be used as a plug-in with any 3D CNN action recognition model with clip-level processing. We propose to use relevant motion clues from video frames to adaptively learn input-clip optimal transformations, as these clues are hypothesized to be directly related to the action recognition task. We employ differentiable transformations and samplers and ensure end-to-end system differentiability. We render the proposed module light-weight and computationally efficient, by exploiting the motion information inherently present in compressed videos and readily available at both training and inference time. Highly informative motion-related content of compressed video domain modalities helps further boost action recognition accuracy. Our experimental work demonstrates clear benefits of the proposed method for adaptive spatial zooming and of utilizing the compressed domain for that purpose.
ANOPHONE: An Annotation Tool for Phonemes and L2 Annotation Systems for Czech

Abstract The goal of this text is the presentation of the ANOPHONE annotation system, which allows for the management and annotation of speech data to develop a tool for the automatic transcription of speech of non-native speakers of Czech. This system is currently designed for annotations on the segmental level of recordings of non-native speakers of Czech, with the aim to train automatic speech recognition (ASR) models used in this tool. After an introductory section that discusses the use of technology in pronunciation teaching and mentions some of the e-learning applications for teaching the pronunciation of second languages (L2), we address both general and more specific aspects of speech data annotation to train ASR models and mention attributive and synthetic segmental systems of speech data annotation for Czech as L2. We also briefly introduce the annotation system of non-native speakers of Czech called BV1, which is used for testing the ANOPHONE tool. The main part of this text focuses on presenting the annotation tool itself, while the conclusion describes the experience of testing the speech data annotation tool using BV1 annotation system for Czech as L2.
Project on salary classification

In this project, The results use three different machine learning algorithms to approach salary classification. The analyzed data used many different variables such as education level, age, and work-class to label each person into two categories, one with a salary greater than 50k and the other with a salary less than or equal to 50k. First of all, this work uses a single decision tree model to visualize data because it is more concise and understandable, and then by using the support vector machine method, the result becomes more accurate. After building two different models, The accuracy was found to be about 86.32%, which is relatively high and reliable. However, higher accuracy may be more persuasive. So, this project uses another model which is the random forest model. This algorithm is considered a highly accurate method because of the number of decision trees that participated. This model explained 87.03% of the accuracy of my result. According to my models, if a person desires a wage increase, that person should do his best to improve his education level, and he needs to have a stable marriage situation and be able to start his own business as much as possible between the ages of 20 to 60.
NEW INTEGRATION PRACTICES OF MATHEMATICS EDUCATION INTO UNIVERSITY DIGITAL EDUCATIONAL ENVIRONMENT FOR PRE-SERVICE TEACHER TRAINING

The need for integration and digitization in the field of mathematical education for bache­lor students in pedagogical disciplines is being actualized during the transition from the SPOD (Stable, Predictable, Orderly, and Democratic) world to the VUCA (Volatile, Uncer­tain, Complex, and Ambiguous) world. The directions for modernization and integration in the teaching of modules and disciplines in the mathematical and information cycles under the conditions of digitization of mathematical education are presented. These directions open up new possibilities in the preparation of future mathematics teachers who are in demand in the labor market. The authors attempted to analyze the potential of disciplines in the mathemati­cal and information cycles in the context of interdisciplinary integration in the field of math­ematical education. Three directions for modernizing the teaching of the fundamentals of queueing theory are proposed (target aspect, organizational processes, substantive aspect of knowledge transfer), which include updating the goals of mathematical education for bachelor students in pedagogical disciplines, creating organizational infrastructure to ensure digitiza­tion and informatization of mathematical education, and updating its substantive aspect with algorithms for solving problems using tools of general and special-purpose software. The in­ternational experience of teaching queueing theory is presented, including the introduction of VR technologies, web quests into the educational process, as well as the use of software prod­ucts such as AnyLogic, Wolfram Alpha, and others.