Reviewerly/exHarmony huggingface dataset:
Deep Learning Based Face Recognition Method using Siamese Network

Achieving state-of-the-art results in face verification systems typically hinges on the availability of labeled face training data, a resource that often proves challenging to acquire in substantial quantities. In this research endeavor, we proposed employing Siamese networks for face recognition, eliminating the need for labeled face images. We achieve this by strategically leveraging negative samples alongside nearest neighbor counterparts, thereby establishing positive and negative pairs through an unsupervised methodology. The architectural framework adopts a VGG encoder, trained as a double branch siamese network. Our primary aim is to circumvent the necessity for labeled face image data, thus proposing the generation of training pairs in an entirely unsupervised manner. Positive training data are selected within a dataset based on their highest cosine similarity scores with a designated anchor, while negative training data are culled in a parallel fashion, though drawn from an alternate dataset. During training, the proposed siamese network conducts binary classification via cross-entropy loss. Subsequently, during the testing phase, we directly extract face verification scores from the network's output layer. Experimental results reveal that the proposed unsupervised system delivers a performance on par with a similar but fully supervised baseline.
Asynchronous Authentication

A myriad of authentication mechanisms embody a continuous evolution from verbal passwords in ancient times to contemporary multi-factor authentication. Nevertheless, digital asset heists and numerous identity theft cases illustrate the urgent need to revisit the fundamentals of user authentication. We abstract away credential details and formalize the general, common case of asynchronous authentication, with unbounded message propagation time. Our model, which might be of independent interest, allows for eventual message delivery, while bounding execution time to maintain cryptographic guarantees. Given credentials' fault probabilities (e.g., loss or leak), we seek mechanisms with the highest success probability. We show that every mechanism is dominated by some Boolean mechanism -- defined by a monotonic Boolean function on presented credentials. We present an algorithm for finding approximately optimal mechanisms. Previous work analyzed Boolean mechanisms specifically, but used brute force, which quickly becomes prohibitively complex. We leverage the problem structure to reduce complexity by orders of magnitude. The algorithm is readily applicable to practical settings. For example, we revisit the common approach in cryptocurrency wallets that use a handful of high-quality credentials. We show that adding low-quality credentials improves security by orders of magnitude.
Mutual averaged non-commutativity of quantum operator algebras

We introduce an elementary measure of non-commutativity between two algebras of quantum operators acting on the same Hilbert space. This quantity, which we call Mutual Averaged Non-commutativity (MAN), is a simple generalization of a type of averaged Out-of-Time-Order-Correlators used in the study of quantum scrambling and chaos. MAN is defined by a Haar averaged squared norm of a commutator and for some types of algebras is manifestly of entropic nature. In particular, when the two algebras coincide the corresponding self-MAN can be fully computed in terms of the structural data of the associated Hilbert space decomposition. Properties and bounds of MAN are established in general and several concrete examples are discussed. Remarkably, for an important class of algebras, -- which includes factors and maximal abelian ones -- MAN can be expressed in the terms of the algebras projections CP-maps. Assuming that the latter can be enacted as physical processes, one can devise operational protocols to directly estimate the MAN of a pair of algebras.
Near-ideal Microwave Photon to Electron Conversion in a High Impedance Quantum Circuit

Photoelectric detectors cover a wide frequency spectrum spanning from the far ultraviolet to the infrared light with high sensitivity, large quantum efficiency and low dark current. The equivalent photoelectric detection of microwave frequency photons has remained elusive due to inherent differences between microwave photon energy and the interband transition energies exploited in standard photoelectric detectors. Here we present the realization of a near-ideal microwave photon to electron converter at a frequency typical of circuit quantum electrodynamics. These unique properties are enabled by the use of a high kinetic inductance disordered superconductor, granular aluminium, to enhance the light-matter interaction. This experiment constitutes an important proof of concept regarding low energy microwave photon to electron conversion unveiling new possibilities such as the detection of single microwave photons using charge detection. It finds significance in quantum research openning doors to a wide array of applications, from quantum-enhanced sensing to exploring the fundamental properties of quantum states.
High-fidelity, multi-qubit generalized measurements with dynamic circuits

Generalized measurements, also called positive operator-valued measures (POVMs), can offer advantages over projective measurements in various quantum information tasks. Here, we realize a generalized measurement of one and two superconducting qubits with high fidelity and in a single experimental setting. To do so, we propose a hybrid method, the "Naimark-terminated binary tree," based on a hybridization of Naimark's dilation and binary tree techniques that leverages emerging hardware capabilities for mid-circuit measurements and feed-forward control. Furthermore, we showcase a highly effective use of approximate compiling to enhance POVM fidelity in noisy conditions. We argue that our hybrid method scales better toward larger system sizes than its constituent methods and demonstrate its advantage by performing detector tomography of symmetric, informationally complete POVM (SIC-POVM). Detector fidelity is further improved through a composite error mitigation strategy that incorporates twirling and a newly devised conditional readout error mitigation. Looking forward, we expect improvements in approximate compilation and hardware noise for dynamic circuits to enable generalized measurements of larger multi-qubit POVMs on superconducting qubits.
DUSt3R: Geometric 3D Vision Made Easy

Multi-view stereo reconstruction (MVS) in the wild requires to first estimate the camera parameters e.g. intrinsic and extrinsic parameters. These are usually tedious and cumbersome to obtain, yet they are mandatory to triangulate corresponding pixels in 3D space, which is the core of all best performing MVS algorithms. In this work, we take an opposite stance and introduce DUSt3R, a radically novel paradigm for Dense and Unconstrained Stereo 3D Reconstruction of arbitrary image collections, i.e. operating without prior information about camera calibration nor viewpoint poses. We cast the pairwise reconstruction problem as a regression of pointmaps, relaxing the hard constraints of usual projective camera models. We show that this formulation smoothly unifies the monocular and binocular reconstruction cases. In the case where more than two images are provided, we further propose a simple yet effective global alignment strategy that expresses all pairwise pointmaps in a common reference frame. We base our network architecture on standard Transformer encoders and decoders, allowing us to leverage powerful pretrained models. Our formulation directly provides a 3D model of the scene as well as depth information, but interestingly, we can seamlessly recover from it, pixel matches, relative and absolute camera. Exhaustive experiments on all these tasks showcase that the proposed DUSt3R can unify various 3D vision tasks and set new SoTAs on monocular/multi-view depth estimation as well as relative pose estimation. In summary, DUSt3R makes many geometric 3D vision tasks easy.
LingoQA: Video Question Answering for Autonomous Driving

Autonomous driving has long faced a challenge with public acceptance due to the lack of explainability in the decision-making process. Video question-answering (QA) in natural language provides the opportunity for bridging this gap. Nonetheless, evaluating the performance of Video QA models has proved particularly tough due to the absence of comprehensive benchmarks. To fill this gap, we introduce LingoQA, a benchmark specifically for autonomous driving Video QA. The LingoQA trainable metric demonstrates a 0.95 Spearman correlation coefficient with human evaluations. We introduce a Video QA dataset of central London consisting of 419k samples that we release with the paper. We establish a baseline vision-language model and run extensive ablation studies to understand its performance.
Quantum Algorithms for the Pathwise Lasso

We present a novel quantum high-dimensional linear regression algorithm with an $\ell_1$-penalty based on the classical LARS (Least Angle Regression) pathwise algorithm. Similarly to available classical numerical algorithms for Lasso, our quantum algorithm provides the full regularisation path as the penalty term varies, but quadratically faster per iteration under specific conditions. A quadratic speedup on the number of features/predictors $d$ is possible by using the simple quantum minimum-finding subroutine from D\"urr and Hoyer (arXiv'96) in order to obtain the joining time at each iteration. We then improve upon this simple quantum algorithm and obtain a quadratic speedup both in the number of features $d$ and the number of observations $n$ by using the recent approximate quantum minimum-finding subroutine from Chen and de Wolf (ICALP'23). As one of our main contributions, we construct a quantum unitary based on quantum amplitude estimation to approximately compute the joining times to be searched over by the approximate quantum minimum finding. Since the joining times are no longer exactly computed, it is no longer clear that the resulting approximate quantum algorithm obtains a good solution. As our second main contribution, we prove, via an approximate version of the KKT conditions and a duality gap, that the LARS algorithm (and therefore our quantum algorithm) is robust to errors. This means that it still outputs a path that minimises the Lasso cost function up to a small error if the joining times are only approximately computed. Finally, in the model where the observations are generated by an underlying linear model with an unknown coefficient vector, we prove bounds on the difference between the unknown coefficient vector and the approximate Lasso solution, which generalises known results about convergence rates in classical statistical learning theory analysis.
A New Model for Image Inpainting for Different Color Spaces

The newly developed method of inpainting the missing region in digital images that is based on the isotropic model was applied to a variety of color spaces, including XYZ, YCbCr, NTSC, and HSV color spaces. Tests were performed using masks that the researcher had created based on the original (clear) images; these masks range in both size and shape. In order to evaluate the quality of image inpainting, statistical quality metrics such as MSE (Mean Square Error), PSNR (Peak Signal to Noise Ratio), and SSIM (Structural Similarity Index) are utilized. The performance of this method was superior to the state-of-the-art PDEs (partial differential equations) inpainting methods.
URFormer: Unified Representation LiDAR-Camera 3D Object Detection with Transformer

Current LiDAR-camera 3D detectors adopt a 3D-2D design pattern. However, this paradigm ignores the dimensional gap between heterogeneous modalities (e.g., coordinate system, data distribution), leading to difficulties in marrying the geometric and semantic information of two modalities. Moreover, conventional 3D convolution neural networks (3D CNNs) backbone leads to limited receptive fields, which discourages the interaction between multi-modal features, especially in capturing long-range object context information. To this end, we propose a Unified Representation Transformer-based multi-modal 3D detector (URFormer) with better representation scheme and cross-modality interaction, which consists of three crucial components. First, we propose Depth-Aware Lift Module (DALM), which exploits depth information in 2D modality and lifts 2D representation into 3D at the pixel level, and naturally unifies inconsistent multi-modal representation. Second, we design a Sparse Transformer (SPTR) to enlarge effective receptive fields and capture long-range object semantic features for better interaction in multi-modal features. Finally, we design Unified Representation Fusion (URFusion) to integrate cross-modality features in a fine-grain manner. Extensive experiments are conducted to demonstrate the effectiveness of our method on KITTI benchmark and show remarkable performance compared to the state-of-the-art methods.
Towards Balanced RGB-TSDF Fusion for Consistent Semantic Scene Completion by 3D RGB Feature Completion and a Classwise Entropy Loss Function

Semantic Scene Completion (SSC) aims to jointly infer semantics and occupancies of 3D scenes. Truncated Signed Distance Function (TSDF), a 3D encoding of depth, has been a common input for SSC. Furthermore, RGB-TSDF fusion, seems promising since these two modalities provide color and geometry information, respectively. Nevertheless, RGB-TSDF fusion has been considered nontrivial and commonly-used naive addition will result in inconsistent results. We argue that the inconsistency comes from the sparsity of RGB features upon projecting into 3D space, while TSDF features are dense, leading to imbalanced feature maps when summed up. To address this RGB-TSDF distribution difference, we propose a two-stage network with a 3D RGB feature completion module that completes RGB features with meaningful values for occluded areas. Moreover, we propose an effective classwise entropy loss function to punish inconsistency. Extensive experiments on public datasets verify that our method achieves state-of-the-art performance among methods that do not adopt extra data.
EDI-C: Reputation-Model-Based Collaborative Audit Scheme for Edge Data Integrity

The emergence of mobile edge computing (MEC) has facilitated the development of data caching technology, which enables application vendors to cache frequently used data on the edge servers close to the user, thereby providing low-latency data access services. However, in an unstable MEC environment, the multi-replica data cached by different edge servers is prone to corruption, making it crucial to verify the consistency of multi-replica data on different edge servers. Although the existing research realizes data integrity verification based on the cooperation of multiple edge servers, the integrity proof generated by multiple copies of data is the same, which has low verification efficiency and is vulnerable to attacks such as replay and replace. To address the above issues, based on homomorphic hash and sampling algorithms, this paper proposes an efficient and lightweight multi-replica integrity verification algorithm, which has significantly less storage cost and computational cost and can resist forgery and replay and replace attacks. Based on the verification algorithm, this paper further proposes a multi-replica edge data integrity collaborative audit scheme EDI-C based on the reputation model. EDI-C realizes the efficient collaborative audit of multiple edge servers in a distributed discrete environment through an incentive mechanism to avoid the trust problem of both sides caused by centralized audit. Also, it supports batch auditing of multiple copies of original data files at the same time through parallel processing and data block auditing technology, which not only significantly improves the verification efficiency but also realizes the accurate location and repair of corrupted data at the data block level. Finally, the security analyses and performance evaluation show the security and practicability of EDI-C. Compared with the representative schemes, the results show that EDI-C can ensure the integrity verification of cached data more efficiently in an MEC environment.
Risk assessment method of power communication network based on gray cloud theory and matter-element extension model coupling

The failure of the power communication network often leads to a cascading failure of the power system. Evaluating the potential risks in the power communication network is crucial to ensure the stable operation of the power system. This paper proposes a comprehensive risk assessment method for qualitative and quantitative measures. Firstly, an index system for risk assessment, considering both static structure and dynamic operation, is established. The optimal weight of each index is determined using the combination game weighting method. Additionally, a risk assessment model for the power communication network is established by coupling gray cloud theory and matter-element extension model. The fuzzy theory’s triangular modulus operator is utilized to fuse three types of fault probabilities, quantify the risk value of the power communication network, and assign it a grade. Finally, experimental results demonstrate that this method improves discrimination degree by 1.2%, 92%, and 85% respectively compared to other traditional algorithms.
A Dynamic Model-Based Doppler-Adaptive Correlation Filter for Maritime Radar Target Tracking

This article deals with the problem of tracking targets with X-band marine radars in the complicated sea clutter background. By jointly exploiting the target’s kinematic and appearance information, we propose a novel dynamic model-based Doppler-adaptive correlation filter. The proposed tracker mainly consists of two modules. First, a Doppler-adaptive correlation filter is developed based on the kernel correlation filter. This filter can effectively represent the appearance patterns of the target and is adaptive to the motion Doppler by using a multi-frequency centered filter bank. Second, the Bernoulli filter is employed to represent the kinematic patterns of the target. Within this filter, the converted measurement Kalman filter with range rate is applied to overcome the inconsistency between coordinate systems of the motion and measurement models. By exploiting the hybrid measurement likelihood, the two modules are then fused within the Bayesian framework to achieve improved tracking performance in the complicated sea clutter background. Experimental results based on both the simulated and real radar data demonstrate that the proposed tracker outperforms its representative counterparts.
A study on the impact of pre-trained model on Just-In-Time defect prediction

Previous researchers conducting Just-In-Time (JIT) defect prediction tasks have primarily focused on the performance of individual pre-trained models, without exploring the relationship between different pre-trained models as backbones. In this study, we build six models: RoBERTaJIT, CodeBERTJIT, BARTJIT, PLBARTJIT, GPT2JIT, and CodeGPTJIT, each with a distinct pre-trained model as its backbone. We systematically explore the differences and connections between these models. Specifically, we investigate the performance of the models when using Commit code and Commit message as inputs, as well as the relationship between training efficiency and model distribution among these six models. Additionally, we conduct an ablation experiment to explore the sensitivity of each model to inputs. Furthermore, we investigate how the models perform in zero-shot and few-shot scenarios. Our findings indicate that each model based on different backbones shows improvements, and when the backbone’s pre-training model is similar, the training resources that need to be consumed are closer. We also observe that Commit code plays a significant role in defect detection, and different pre-trained models demonstrate better defect detection ability with a balanced dataset under few-shot scenarios. These results provide new insights for optimizing JIT defect prediction tasks using pre-trained models and highlight the factors that require more attention when constructing such models. Additionally, CodeGPTJIT and GPT2JIT achieved better performance than DeepJIT and CC2Vec on the two datasets respectively under 2000 training samples. These findings emphasize the effectiveness of transformer-based pre-trained models in JIT defect prediction tasks, especially in scenarios with limited training data.
Design and Implementation of a Web-Based Library Management System

Education plays a crucial role in the development of a nation, and for its successful implementation, quality reading resources, such as books, magazines, journals, dictionaries, and other reading materials, are required to facilitate the teaching and learning process. A library serves as a hub for educational institutions that house diverse collections of books, periodicals, and documents. However, the conventional manual library management system utilized by some private schools in Jakarta City has limitations, such as time-consuming processes for searching, borrowing, returning books, and generating reports. This study proposes the implementation of a web-based library management system employing cutting-edge technology to address these issues. This paper’s primary objective is to design a web-based library management system that enables efficient book borrowing and return. To achieve this objective, the study employs a use-case diagram, an entity-relationship diagram, and library web design. A questionnaire survey is used to collect data, which is appropriate for the research context and offers a cost-effective, efficient, and quick method for collecting data from a large sample size. Designing the library web, creating a questionnaire, distributing it to testers, and analyzing their responses comprise the research methodology.
Efficient Configurable Modular Multiplier For RNS

Modular multipliers are usually the most critical units in Residue Number System implementations. With the widespread application of cryptographic algorithms based on the Residue Number System, the scale and variety of modulus channels have significantly increased, Consequently, the design of high-speed general-purpose modular multipliers has become increasingly important. In this paper, we propose a universal architecture for designing an efficient modular multiplier based on the Barrett reduction algorithm. The proposed modular multiplier can be configured to support different modular multiplications and is well-suited for accelerating applications in Residue Number System-based encryption schemes. Synthesized results demonstrate that the proposed modular multiplier exhibits up to a 35.7% improvement in performance compared to the improved Barrett modular multiplier.
History of Blockchain Technology and its Impact in Social Good

Blockchain technology can be considered a new engaging technology. Its diffusion starts from 2008 thanks to Satoshi Nakamoto, then its expansion happens rapidly. The goal of this paper is to analyze Blockchain technology and in particular its impact in the social good topic. This social impact of Blockchain can help to improve the way we live in our society and how we can feel part of a community. Other than the implicit characteristics of decentralization, immutability and anonimity, Blockchain is particularly useful in untrusted environments, because it helps keeping transactions happening between two or more parties as transparent as possible.