Reviewerly/exHarmony huggingface dataset:
Violence Detection Enhancement by Involving Convolutional Block Attention Modules Into Various Deep Learning Architectures: Comprehensive Case Study for UBI-Fights Dataset

The violence detection in surveillance videos is a complicated task, due to the requirements of extracting the spatio-temporal features in different videos environment, and various videos prospective cases. Hereby, in this paper, different architectures are proposed to perform this task in high performance, by using the UBI-Fights dataset as a comprehensive case study. The proposed architectures are based on involving the Convolutional Block Attention Modules (CBAM) with other simple layers (e.g., ConvLSTM2D or Conv2D&LSTM). In addition, using the Categorical Focal Loss (CFL) as loss function during architectures training to increase the focus on the most important features. To evaluate the proposed architectures, the performance metrics like are Area Under the Curve (AUC), and Equal Error Rate (EER); are mainly used, to declare the architecture ability of identifying the violence correctly, with low interaction value between classes. The performance results declare the ability of the proposed architectures, to achieve higher results that the state of art techniques. For example, the Conv2D&LSTM based architecture, get AUC value of 0.9493, and EER value of 0.0507; that outperform the most of the other proposed ones, and the state of art performance.
Transfer Learning for Automatic Modulation Recognition Using a Few Modulated Signal Samples

This letter proposes a transfer learning model for automatic modulation recognition (AMR) with only a few modulated signal samples. The transfer model is trained with the audio signal UrbanSound8K as the source domain, and then fine-tuned with a few modulated signal samples as the target domain. For improving the classification performance, the signal-to-noise ratio (SNR) is utilized as a feature to facilitate the classification of signals. Simulation results indicate that the transfer model has a significant superiority in terms of classification accuracy.
Quantum Computer-Aided Design Automation

Quantum computing is an essential issue for taking advantage of quantum properties in real-world applications. Realizing quantum computing with a corresponding quantum circuit is a critical step. This study introduces the AngelQ system, which provides quantum computing-as-a-service (QCaaS) for the cloud. The AngelQ system integrates quantum design automation (QDA) to achieve quantum and reversible circuit synthesis by a quantum-inspired optimization (QiO) technique and has visualization software interfaces to verify the QiO process and the correctness of the synthesized quantum and reversible circuits. First, the proposed novel QiO method has four significant features: Adaptive strategy, quantum-Not gate, update mechanism by Global solutions, and Entanglement Local search, named Angel QiO. which is a flexible technique to realize various circuits under different gate libraries with a few alterations. Angel QiO can construct diversified function-equivalent circuits to assess the circuit composition and boost the development of circuit synthesis. Second, researchers can observe the QiO algorithm process and inspect the intermediate result formula through AngelQ system's visualization interface to further adjust the parameters and improve the performance. Moreover, it benefits beginners’ learning and adds convenience to education. In summary, the AngelQ system can extend and facilitate the future design and automation of quantum computing.
A Deep Learning Model for Detection Cancer in Breast

It is exceedingly difficult to identify and classify breast cancer. In reality, a tumour or cancer is a complicated process that involves several changes to the mammography images. Additionally, distinct tissues are used to describe various sections of the image that exhibit variable and high appearance. The primary advantage of this method is image classification for cancer prediction and performance improvement. On an open-source dataset, we trained and tested the application of our research. Python 3 will be used to create this project. The Jupyter IDE will be used to deploy the project. The overall goal of this project is to provide the highest level of performance and efficiency.
Optimal Security for Keyed Hash Functions: Avoiding Time-Space Tradeoffs for Finding Collisions

Cryptographic hash functions map data of arbitrary size to a fixed size digest, and are one of the most commonly used cryptographic objects. As it is infeasible to design an individual hash function for every input size, variable-input length hash functions are built by designing and bootstrapping a single fixed-input length function that looks sufficiently random. To prevent trivial preprocessing attacks, applications often require not just a single hash function but rather a family of keyed hash functions. The most well-known methods for designing variable-input length hash function families from a fixed idealized function are the Merkle-Damgård and Sponge designs. The former underlies the SHA-1 and SHA-2 constructions and the latter underlies SHA-3. Unfortunately, recent works (Coretti et al. EUROCRYPT 2018, Coretti et al. CRYPTO 2018) show non-trivial time-space tradeoff attacks for finding collisions for both. Thus, this forces a parameter blowup (i.e., efficiency loss) for reaching a certain desired level of security. We ask whether it is possible to build families of keyed hash functions which are provably resistant to any non-trivial time-space tradeoff attacks for finding collisions, without incurring significant efficiency costs. We present several new constructions of keyed hash functions that are provably resistant to any non-trivial time-space tradeoff attacks for finding collisions. Our constructions provide various tradeoffs between their efficiency and the range of parameters where they achieve optimal security for collision resistance. Our main technical contribution is proving optimal security bounds for converting a hash function with a fixed-sized input to a keyed hash function with (potentially larger) fixed-size input. We then use this keyed function as the underlying primitive inside the standard Merkle-Damgård and Merkle tree constructions. We strongly believe that this paradigm of using a keyed inner hash function in these constructions is the right one, for which non-uniform security has not been analyzed prior to this work.
SNARGs and PPAD Hardness from the Decisional Diffie-Hellman Assumption

We construct succinct non-interactive arguments (SNARGs) for bounded-depth computations assuming that the decisional Diffie-Hellman (DDH) problem is sub-exponentially hard. This is the first construction of such SNARGs from a Diffie-Hellman assumption. Our SNARG is also unambiguous: for every (true) statement x, it is computationally hard to find any accepting proof for x other than the proof produced by the prescribed prover strategy. We obtain our result by showing how to instantiate the Fiat-Shamir heuristic, under DDH, for a variant of the Goldwasser-Kalai-Rothblum (GKR) interactive proof system. Our new technical contributions are (1) giving a $$TC^0$$ circuit family for finding roots of cubic polynomials over a special family of characteristic-2 fields (Healy-Viola, STACS 2006) and (2) constructing a variant of the GKR protocol whose invocations of the sumcheck protocol (Lund-Fortnow-Karloff-Nisan, STOC 1990) only involve degree 3 polynomials over said fields. Along the way, since we can instantiate the Fiat-Shamir heuristic for certain variants of the sumcheck protocol, we also show the existence of (sub-exponentially) hard problems in the complexity class $$\textsf{PPAD}$$ , assuming the sub-exponential hardness of DDH. Previous $$\textsf{PPAD}$$ hardness results required either bilinear maps or the learning with errors assumption.
On the Optimal Succinctness and Efficiency of Functional Encryption and Attribute-Based Encryption

We investigate the best-possible (asymptotic) efficiency of functional encryption (FE) and attribute-based encryption (ABE) by proving inherent space-time trade-offs and constructing nearly optimal schemes. We consider the general notion of partially hiding functional encryption (PHFE), capturing both FE and ABE, and the most efficient computation model of random-access machine (RAM). In PHFE, a secret key $$\textsf{sk}_f$$ is associated with a function f, whereas a ciphertext $$\textsf{ct}_x(y)$$ is tied to a public input x and encrypts a private input y. Decryption reveals f(x, y) and nothing else about y. We present the first PHFE for RAM solely based on the necessary assumption of FE for circuits. Significantly improving upon the efficiency of prior schemes, our construction achieves nearly optimal succinctness and computation time: As a corollary, we obtain the first ABE with both keys and ciphertexts being constant-size, while enjoying the best-possible decryption time matching the lower bound by Luo [ePrint ’22]. We also separately achieve several other optimal ABE subject to the known lower bound. We study the barriers to further efficiency improvements. We prove the first unconditional space-time trade-offs for (PH-)FE: Our lower bounds apply even to the weakest secret-key 1-key 1-ciphertext selective schemes. Furthermore, we show a conditional barrier towards the optimal decryption time $${T_\textsf{Dec}=T{\text {poly}}(\lambda )}$$ while keeping linear size dependency — any such (PH-)FE scheme implies doubly efficient private information retrieval (DE-PIR) with linear-size preprocessed database, for which so far there is no candidate.
Development and Verification of a ROS-Based Multi-DOF Flight Test System for Unmanned Aerial Vehicles

Unmanned aerial vehicles (UAVs) are used in many research fields for diverse operations, requiring the system to be safe and efficient. Therefore, the UAV system’s robustness and integrity are paramount in its design and operation. For these design and operational requirements, we propose a robot operating system (ROS) based multi-degree of freedom (DOF) flight test framework for the safe development, verification, and validation of UAVs. The developed flight test system includes a test bench capable of moving freely in 4 DOF, an electronic control unit (ECU) for collecting data from sensors and running the operation software, a power supply unit (PSU) to power up each device, an operation software tools developed in ROS for seamless integration of the software and hardware systems, and a wind tunnel for simulating the flight environment. The fidelity of the flight test framework was verified through experimental tests conducted on a rocket-propelled foldable unmanned aerial vehicle under development.
LiteWSEC: A Lightweight Framework for Web-Scale Spectral Ensemble Clustering

Spectral Clustering (SC) is an effective clustering method for its excellent performance in partitioning non-linearly distributed data. On the other hand, Ensemble Clustering (EC), a different clustering technology, can promote cluster quality by ensembling the results of base clusterings. In this work, we concentrate on an EC framework that utilizes SC as the base method. Nevertheless, SC suffers from scalability due to its high computational complexity in constructing the Laplacian graph and computing the corresponding eigendecomposition. In the past decades, many efforts have been made to it. However, SC suffers from the scalability issue in processing extensive data, especially in web-scale scenarios. Additionally, EC requires multiple clustering results as the ensemble bases, which further aggravates resource consumption. To address this issue, LiteWSEC, a simple yet efficient Lightweight Framework for Web-scale Spectral Ensemble Clustering, is proposed to cluster web-scale data with limited resource requirements. It adopts the Web-scale Spectral Clustering (WSC) as the base method, which has minimal space overhead without computing overall embedding explicitly. LiteWSEC is highly flexible in the memory requirement, which is adaptive to the available resource. It can partition web-scale data (e.g., <inline-formula><tex-math notation="LaTeX">$n $</tex-math></inline-formula> = 8,000 k) in an resource-limited host (e.g., memory is restricted to 1 GB). Experiments on real-world, large-scale, and web-scale datasets demonstrate both the efficiency and effectiveness of LiteWSEC over state-of-the-art SC and EC methods.
Semi-automatic Vehicle Detection System for Road Traffic Management

Indian roads incorporate a combination of crossroads and intersections. The traffic situation in India is very complex due to the ever-increasing vehicle density. There is a need for an autonomous system to detect and analyze the vehicles on the road. However, designing an accurate object/entity detection mechanism is not easy because of the need for high dependency factors. This paper aims to construct a system that can detect, count, and classify vehicles accurately in real time with limited resources. The paper proposes a Semi-Automatic Vehicle Detection System (SAVDS) framework to recognize objects using outdoor CCTV footage by combining strategies such as optical flow, background subtraction, and convolutional neural networks. The optimization achieved as a result of a reduced detection area and a detection grid system is the work's major feature. Various background removal methods and HSV color model processing were utilized to determine the optimal approach to perform in various ambient situations. The results show that the proposed work classifies vehicles with accuracy up to 85% for benchmark datasets.
Efficient Detection of High Probability Statistical Properties of Cryptosystems via Surrogate Differentiation

A central problem in cryptanalysis is to find all the significant deviations from randomness in a given n-bit cryptographic primitive. When n is small (e.g., an 8-bit S-box), this is easy to do, but for large n, the only practical way to find such statistical properties was to exploit the internal structure of the primitive and to speed up the search with a variety of heuristic rules of thumb. However, such bottom-up techniques can miss many properties, especially in cryptosystems which are designed to have hidden trapdoors. In this paper we consider the top-down version of the problem in which the cryptographic primitive is given as a structureless black box, and reduce the complexity of the best known techniques for finding all its significant differential and linear properties by a large factor of $$2^{n/2}$$ . Our main new tool is the idea of using surrogate differentiation. In the context of finding differential properties, it enables us to simultaneously find information about all the differentials of the form $$f(x) \oplus f(x \oplus \alpha )$$ in all possible directions $$\alpha $$ by differentiating f in a single randomly chosen direction $$\gamma $$ (which is unrelated to the $$\alpha $$ 's). In the context of finding linear properties, surrogate differentiation can be combined in a highly effective way with the Fast Fourier Transform. For 64-bit cryptographic primitives, this technique makes it possible to automatically find in about $$2^{64}$$ time all their differentials with probability $$p \ge 2^{-32}$$ and all their linear approximations with bias $$|p| \ge 2^{-16}$$ (using $$2^{64}$$ memory); previous algorithms for these problems required at least $$2^{96}$$ time. Similar techniques can be used to significantly improve the best known time complexities of finding related key differentials, second-order differentials, and boomerangs. In addition, we show how to run variants of these algorithms which require no memory, and how to detect such statistical properties even in trapdoored cryptosystems whose designers specifically try to evade our techniques.
Coefficient Grouping: Breaking Chaghri and More

We propose an efficient technique called coefficient grouping to evaluate the algebraic degree of the FHE-friendly cipher Chaghri, which has been accepted for ACM CCS 2022. It is found that the algebraic degree increases linearly rather than exponentially. As a consequence, we can construct a 13-round distinguisher with time and data complexity of $$2^{63}$$ and mount a 13.5-round key-recovery attack. In particular, a higher-order differential attack on 8 rounds of Chaghri can be achieved with time and data complexity of $$2^{38}$$ . Hence, it indicates that the full 8 rounds are far from being secure. Furthermore, we also demonstrate the application of our coefficient grouping technique to the design of secure cryptographic components. As a result, a countermeasure is found for Chaghri and it has little overhead compared with the original design. Since more and more symmetric primitives defined over a large finite field are emerging, we believe our new technique can have more applications in the future research.
My smart home: an auto-ethnography of learning to live with smart technologies

Smart home technology is expected to be widespread in the future and to accommodate a green transition to reduce and time-shift energy consumption. However, smart technologies also have social consequences, which are important to understand. At a basic level, we need to know more about learning to live with these technologies and how they influence our everyday practices and routines. Providing in-depth longitudinal insights into these processes, this paper presents an auto-ethnography of living with smart home technology: a 20-month diary kept by one of the authors. The paper uses theories of practice to investigate details of learning processes when interacting with three selected technologies: smart alarm and lighting management, smart control of heating, and a smart electric vehicle (EV). Theories of learning have a well-established tradition within theories of practice, and the concept of "knowing how to go on" and the concept of practical intelligibility are central in this work. This paper investigates the adoption of new smart technologies and how they interact with learning processes in different material and social contexts. Such an approach can lay the groundwork for further empirical research with a broader set of materials. It can also provide knowledge to assist in the design of better technologies and in developing policies and regulations to promote this.
Neural Architecture Search for Dense Prediction Tasks in Computer Vision

Abstract The success of deep learning in recent years has lead to a rising demand for neural network architecture engineering. As a consequence, neural architecture search (NAS), which aims at automatically designing neural network architectures in a data-driven manner rather than manually, has evolved as a popular field of research. With the advent of weight sharing strategies across architectures, NAS has become applicable to a much wider range of problems. In particular, there are now many publications for dense prediction tasks in computer vision that require pixel-level predictions, such as semantic segmentation or object detection. These tasks come with novel challenges, such as higher memory footprints due to high-resolution data, learning multi-scale representations, longer training times, and more complex and larger neural architectures. In this manuscript, we provide an overview of NAS for dense prediction tasks by elaborating on these novel challenges and surveying ways to address them to ease future research and application of existing methods to novel problems.
A framework for inherently interpretable optimization models

With dramatic improvements in optimization software, the solution of large-scale problems that seemed intractable decades ago are now a routine task. This puts even more real-world applications into the reach of optimizers. At the same time, solving optimization problems often turns out to be one of the smaller difficulties when putting solutions into practice. One major barrier is that the optimization software can be perceived as a black box, which may produce solutions of high quality, but can create completely different solutions when circumstances change leading to low acceptance of optimized solutions. Such issues of interpretability and explainability have seen significant attention in other areas, such as machine learning, but less so in optimization. In this paper we propose an optimization framework that inherently comes with an easily interpretable optimization rule, that explains under which circumstances certain solutions are chosen. Focusing on univariate decision trees to represent interpretable optimization rules, we propose integer programming formulations as well as a heuristic method that ensure applicability of our approach even for large-scale problems. By presenting several extensions to the univariate decision tree approach, we showcase the generality of the proposed framework. Computational experiments using random and real-world data of a road network indicate that the costs of inherent interpretability can be very small.
eSticky: An Advanced Remote Reminder System for People with Early Dementia

While the European population is ageing, the number of people with dementia is dramatically rising. With this comes an increased need for products that help affected people to be more independent and able to live in their own home for as long as possible. eSticky addresses this need by providing a sophisticated reminder system that replaces the old-fashioned sticky notes by electronic versions thereof, which can be programmed from near and far in a device-independent manner via the internet and using a standard web browser. For this purpose, a set of low-cost ePaper displays are used, accompanied by a small and unobtrusive base station situated at the user's home, and supported by a web platform for user management and the authoring and scheduling of the message reminders. The displays can be placed at several strategically useful places in a user's home, to enable users and/or care persons to place reminders that will, based on the user's daily routines, most probably be read. A sub-set of displays, named active displays, even enable the user to press a confirmation button to show that he or she has actually read the reminder. The confirmation is then made available to the authors of the particular reminder through the web platform. The system is developed using a user-centred design approach, to take all stakeholders' wishes and needs into account, in order to come up with a system that is easy to use and provides good service to many people. In this extended version of the paper, besides a detailed description of the technological advancements of the project, both in terms of software and hardware, we provide an insight of the eSticky device design methodology and results from the design user evaluation.
Fast Quantifier of High‐Dimensional Frequency Entanglement through Hong–Ou–Mandel Interference

Abstract High‐dimensional frequency entanglement is an enabling resource in quantum technology due to its high information capacity and error resilience. A concise yet efficient method for precisely quantifying its dimensionality remains an open challenge, owing to the difficulties for performing required superposition measurements in energy‐time domains, and the complexity associated with full quantum state tomography that scales unfavorably with dimensions. With the assistance of Hong–Ou–Mandel experiment that performs a Fourier transform between the entangled photons in terms of joint spectral intensities and the quantum interference in terms of biphoton temporal coincidences, the concept of Shannon dimensionality as a fast quantifier of bipartite continuous frequency entanglement is unlocked. This quantitative technique reveals the complete distribution of frequency entanglement but without suffering from any limitation of modal capacity of the detection geometry. These results may significantly facilitate the use of quantum interference for characterizing the high‐dimensional entanglement nature by avoiding some stringent conditions.