Reviewerly/exHarmony huggingface dataset:
Lossless Compression Approach for Reversible Data Hiding in Encrypted Images

The popularity of multimedia and internet communication has increased abruptly. It is not only vital to save original media without losing information. Establishing secure communication between the sender and receiver is also necessary. A new EI-RDH (reversible data hiding in the encrypted image) approach is proposed, which is based on pixel pair-wise compression. The content owner encrypts the cover image by shuffling pixel pairs and sends the encrypted image to the data hider. Using the compression method, the data hider creates void space to embed the secret data and generate a compressed encrypted image. After then, the data hider hides the secret data bit with the data-hiding key at the MSB bit of the first pixel of pixel pairs and compressed embedded encrypted image sent to the receiver. With no error, the recipient retrieves the secret data and restores the cover image using data hiding and decryption keys. Compare with existing methods, the experimental results have shown better embedding capacity for the proposed approach.
An Efficient Human Face Detection Technique Based on CNN with SVM Classifier

Face recognition is a growing technology that has been broadly employed in forensics applications such as unlawful person identification, security, and authentication. Computer vision problems now find application in all spheres of the digital world ranging from a normal person’s routine mobile face ID login to institutional facial attendance system to national security-based software to identify criminals. The proposed work examines the performance of face reorganization model using CNN with SVM classifier. Two phases are involved in the creation of the facial recognition system. The first stage involves picking up or extracting facial features, while the second step involves pattern classification. The convolutional neural network (CNN) has made significant strides in FR technology in recent years but most of the existing models considered only one or two parameters but the proposed work computed all important performance parameters, i.e., accuracy, precision, recall, and F-score. Further, the time required to train the model and its prediction time are also computed in this study. The efficiency and dominance of the proposed method are compared with several face detection algorithms, i.e., Eigenface, Fisherface, and Lbph, and results clearly show the supremacy of the proposed approach over the traditional approaches.
Fake Image Detection Using Ensemble Learning

The volume of photos generated has increased dramatically in the last decade as a result of technological advancements and easy access to the Internet. The authenticity of these photos must be guaranteed because they have such a huge impact on people's lives and are sometimes used as evidence in the investigation of serious crimes. Image fraud must be detected in order to protect the image’s integrity and legitimacy. To create fabricated images, Deep Fakes, copy-move forgery, picture splicing, Generative Adversarial Networks (GANs), and other methods are used. Most forgery detection approaches and detectors are focused on only one sort of fraud, whether computer- or human-generated, and little progress has been made in constructing robust detectors that can successfully and efficiently deal with various types of forgery. To add to this, routine compression of images, manipulation of metadata, and changes in the resolution of the image tend to impair the performance of many image forgery detectors. Furthermore, any image that one comes across rarely comes along with information on how it was forged. Therefore, it is necessary to design a robust fake image detector. As a result, this research proposes an ensemble learning-based reliable fake picture detector that can recognise fake photos created in any manner. The experimental results show that the proposed ensemble learning model is accurately able to classify 87.28% of images, irrespective of the method of forgery.
A Framework for Syntactic Error Detection for Punjabi and Hindi Languages Using Statistical Pattern Matching Approach

Grammar checking is the fundamental requirement of each word processor system application like Ms-Word. The purpose of a grammar checker is to detect sentences that are grammatically incorrect based on the syntax of the language. As enormous data is available on the internet, it motivates the development of grammar checkers using a statistical approach. In this study, the authors proposed a pattern matching statistical approach for finding grammatical errors in Hindi and Punjabi texts. The author used Hindi and Punjabi parallel annotated corpus, taken from the Indian languages corpora Initiative (ILCI) for training the system by generating POS patterns of the sentences and then these part-of-speech tag patterns are used to check the accuracy of an input sentence. On testing, this system, on Hindi and Punjabi datasets author claimed an overall precision of 92.32%, recall of 94.82%, and f-measure of 93.56%.
Google Bard's Physical Capabilities in Vietnamese High Schools

This paper presents an evaluation of the physics performance of Google Bard on the physics test of the Vietnamese National High School Graduation Examination (VNHSGE). The objective was to assess the accuracy and effectiveness of Google Bard in solving physical problems commonly found in the Vietnamese curriculum. To facilitate this evaluation, the VNHSGE dataset in Vietnamese was translated into English since Bard does not currently support Vietnamese. The results indicate that Bard achieved a performance of 52%, lagging behind ChatGPT and BingChat, which achieved relatively higher scores of 61% and 66%, respectively. It is important to note that this performance may be influenced by the translation process from Vietnamese to English. As a result, it is not recommended for Vietnamese students to rely on Google Bard for physical problem-solving if Bard does not support Vietnamese. Instead, they should use a language model that is specifically designed for physics, such as ChatGPT or BingChat.
ROI Segmentation Using Two-Fold Image with Super-Resolution Technique

Region of Interest (ROI) segmentation is one of the challenging steps during breast cancer detection. By calculating the threshold value, a binary image is constructed and is named a concealed image, where value 1 represents the presence of texture. With the help of a concealed image, a two-fold image is constructed, and to convert this image from low resolution to high-resolution Super-resolution technique is applied. This constructed high-resolution image can be used during developing Computer Aided Diagnosis Systems for breast cancer detection. The efficiency of the proposed approach is tested on the suspicious patches of the IRMA reference dataset. The testing of the work is performed on 762 ROIs, where 352 are from the benign class, and 410 are from the malignant class. The experiments have shown that the proposed two-fold image has attained the values 0.926 and 0.956 for quality measures for benign and malignant classes, respectively. A comparative analysis of our proposed method with two existing and similar methodologies also validates the correctness and accuracy of our result.
Additive Randomized Encodings and Their Applications

Addition of n inputs is often the easiest nontrivial function to compute securely. Motivated by several open questions, we ask what can be computed securely given only an oracle that computes the sum. Namely, what functions can be computed in a model where parties can only encode their input locally, then sum up the encodings over some Abelian group $${\mathbb G}$$ , and decode the result to get the function output. An additive randomized encoding (ARE) of a function $$f(x_1,\ldots ,x_n)$$ maps every input $$x_i$$ independently into a randomized encoding $$\hat{x}_i$$ , such that $$\sum _{i=1}^n$$ $$\hat{x}_i$$ reveals $$f(x_1,\ldots ,x_n)$$ and nothing else about the inputs. In a robust ARE, the sum of any subset of the $$\hat{x}_i$$ only reveals the residual function obtained by restricting the corresponding inputs. We obtain positive and negative results on ARE. In particular: We then describe several applications of ARE and the above results.
Revisiting the Constant-Sum Winternitz One-Time Signature with Applications to $$\text {SPHINCS}^+$$ and XMSS

Hash-based signatures offer a conservative alternative to post-quantum signatures with arguably better-understood security than other post-quantum candidates. As a core building block of hash-based signatures, the efficiency of one-time signature (OTS) largely dominates that of hash-based signatures. The WOTS $$^{+}$$ signature scheme (Africacrypt 2013) is the current state-of-the-art OTS adopted by the signature schemes standardized by NIST—XMSS, LMS, and SPHINCS $$^+$$ . A natural question is whether there is (and how much) room left for improving one-time signatures (and thus standard hash-based signatures). In this paper, we show that WOTS $$^{+}$$ one-time signature, when adopting the constant-sum encoding scheme (Bos and Chaum, Crypto 1992), is size-optimal not only under Winternitz's OTS framework, but also among all tree-based OTS designs. Moreover, we point out a flaw in the DAG-based OTS design previously shown to be size-optimal at Asiacrypt 1996, which makes the constant-sum WOTS $$^{+}$$ the most size-efficient OTS to the best of our knowledge. Finally, we evaluate the performance of constant-sum WOTS $$^{+}$$ integrated into the SPHINCS $$^+$$ (CCS 2019) and XMSS (PQC 2011) signature schemes which exhibit certain degrees of improvement in both signing time and signature size.
TreePIR: Sublinear-Time and Polylog-Bandwidth Private Information Retrieval from DDH

In Private Information Retrieval (PIR), a client wishes to retrieve the value of an index i from a public database of N values without leaking any information about i. In their recent seminal work, Corrigan-Gibbs and Kogan (EUROCRYPT 2020) introduced the first two-server PIR protocol with sublinear amortized server time and sublinear $$O(\sqrt{N} \log N)$$ bandwidth. In a followup work, Shi et al. (CRYPTO 2021) reduced the bandwidth to polylogarithmic by proposing a construction based on privately puncturable pseudorandom functions, a primitive whose only construction known to date is based on heavy cryptographic primitives such as LWE. Partly because of this, their PIR protocol does not achieve concrete efficiency. In this paper we propose TreePIR, a two-server PIR protocol with sublinear amortized server time and polylogarithmic bandwidth whose security can be based on just the DDH assumption. TreePIR can be partitioned in two phases that are both sublinear: The first phase is remarkably simple and only requires pseudorandom generators. The second phase is a single-server PIR protocol on only $$\sqrt{N}$$ indices, for which we can use the protocol by Döttling et al. (CRYPTO 2019) based on DDH, or, for practical purposes, the most concretely efficient single-server PIR protocol. Not only does TreePIR achieve better asymptotics than previous approaches while resting on weaker cryptographic assumptions, it also outperforms existing two-server PIR protocols in practice. The crux of our protocol is a new cryptographic primitive that we call weak privately puncturable pseudorandom functions, which we believe can have further applications.
Temporal Repetition Counting Based on Multi-stride Collaboration

Visual repetition occurs in various forms in our world, such as human activities, animal behaviors, and even natural phenomena. Visual repetition counting remains a challenging task, especially in long videos, where repetitions exhibit certain characteristics such as discontinuous actions and inconsistent cycles. The existing methods that focus on counting repetitive actions in short videos face challenges in accurately counting repetitions in long videos due to these characteristics. To tackle this challenge, we propose a multi-stride collaborative counting framework based on adaptive temporal correlation to estimate repetitions in short and long videos. Our framework predicts the final counting result based on the counting results of the same video sampled with different strides. Additionally, since existing repetition counting datasets do not adequately cover all the challenging scenarios considered in our work, we have collected and labeled a new dataset called ActCount, which includes 172 videos with approximately 1,870 annotated repetitive actions. Our dataset includes repetitions that are non-human-centric, making it more realistic and challenging. Specifically, our model outperforms all previous models on the RepCount dataset, achieving an MAE of 0.3053 and an OBO of 0.3708, setting a new state-of-the-art benchmark.
Hyperplane Knowledge Graph Embedding with Path Neighborhoods and Mapping Properties

Knowledge representation learning(KRL) is significant for the knowledge graph completion(or link prediction) task, it aims to project entities and relations to a low-dimensional vector space. There are two topics have been widely studied in KRL: one is the ability of the model to handle complex relations(i.e., N-to-1, 1-to-N and N-to-N), and the other is whether the model integrates multi-source information. However, the existing methods rarely consider both topics. To mitigate this problem, this paper proposes TransPMH, a hyperplane knowledge graph embedding model with path neighborhoods and mapping properties, which models the relation as a hyperplane. Besides, this paper introduces path neighborhoods as the multi-source information to improve the model’s knowledge representation ability, and also introduces the relational mapping properties to enhance the model’s ability to handle complex relations. We conducted extensive experiments for link prediction and triplet classification on benchmark datasets like WordNet and Freebase. Experimental results show that the proposed method achieves significant improvements in multiple evaluation metrics.
Joint Feature Selection and Classifier Parameter Optimization: A Bio-Inspired Approach

Feature selection has been proven to be an effective method for handling large amounts of data, which together with the parameter settings of the classifier determines the performance of the classifier. However, many studies have considered the two separately, ignoring the intrinsic connection between them. Thus, in this work, we formulate a joint feature selection and parameters optimization problem, which is NP-hard and mixed-variable structured. Then we propose an improved binary honey badger algorithm (IBHBA) to solve the formulated problem. First, a novel initialization strategy based on the fast correlation-based filter (FCBF) method is proposed to generate promising initial solutions. Second, IBHBA introduces a local search factor based on simulated annealing (SA), a crossover operator based on tournament selection, and a mutation mechanism to improve the performance of conventional HBA. Finally, a binary mechanism is adopted to make it suitable for the feature selection problem. Experiments conducted in 27 public datasets have demonstrated that the proposed approach can outperform some well-known swarm-based algorithms.
Algebraic Reductions of Knowledge

We introduce reductions of knowledge, a generalization of arguments of knowledge, which reduce checking knowledge of a witness in one relation to checking knowledge of a witness in another (simpler) relation. Reductions of knowledge unify a growing class of modern techniques as well as provide a compositional framework to modularly reason about individual steps in complex arguments of knowledge. As a demonstration, we simplify and unify recursive arguments over linear algebraic statements by decomposing them as a sequence of reductions of knowledge. To do so, we develop the tensor reduction of knowledge, which generalizes the central reductive step common to many recursive arguments. Underlying the tensor reduction of knowledge is a new information-theoretic reduction, which, for any modules U, $$U_1$$ , and $$U_2$$ such that $$U \cong U_1 \otimes U_2$$ , reduces the task of evaluating a homomorphism in U to evaluating a homomorphism in $$U_1$$ and evaluating a homomorphism in $$U_2$$ .
A Cross-Document Coreference Resolution Approach to Low-Resource Languages

Coreference resolution is an important area of research in natural language processing that deals with the task of identifying and grouping all the expressions in a text that refer to the same entity. This work presents a system to improve and develop a coreference resolution model for Thai language, based on the existing English clustering-based model. Specifically, we introduce a method to convert Thai text into ECB + -equivalent datasets, which can be used as benchmark for the Thai language. This paper follows an existing model trained for English coreference resolution which uses agglomerative clustering to segment clusters of coreference entities across document. The model trained and evaluated using our data achieves the best CoNLL F1 score of 72.87. Finally, we present a comparative study of the effect of manual and automatic span extractors on Thai language model performance. The results of our study indicate that our proposed pipeline, which utilizes the fine-tuned longformer model as the encoder, offers a viable alternative to more complex and resource-intensive methods. Our work also suggests that the use of existing NER and entity recognizer models can help automate span annotation prior to the subsequent conference clustering module. This study offers a potential framework for the construction of coreference resolution models in other low-resource languages.
Template Shift and Background Suppression for Visual Object Tracking

In recent years, transformer-based object tracking methods have demonstrated strong potential for development and outperformed traditional convolutional neural networks (CNN)-based models. However, most transformer-based trackers ignored the contradiction between the position of the target appearance and the window partitioning of the template, which breaks the integrity of the object. In this paper, we propose a more effective way of performing cross-correlation between the template and search region. Furthermore, we also design a method to suppress the impact of background factors in the template. We conducted a series of ablation experiments to evaluate the effectiveness of our design choices. Our method greatly improves the performance of the model. Because our method can be easily ported to other models, which indicates the potential for further improvement and development.
Simple Tests of Quantumness Also Certify Qubits

A test of quantumness is a protocol that allows a classical verifier to certify (only) that a prover is not classical. We show that tests of quantumness that follow a certain template, which captures recent proposals such as [KCVY21, KLVY22], can in fact do much more. Namely, the same protocols can be used for certifying a qubit, a building-block that stands at the heart of applications such as certifiable randomness and classical delegation of quantum computation. Certifying qubits was previously only known to be possible based on families of post-quantum trapdoor claw-free functions (TCF) with an advanced "adaptive hardcore bit" property, which have only been constructed based on the hardness of the Learning with Errors problem [BCM+21] and recently isogeny-based group actions [AMR23]. Our framework allows certification of qubits based only on the existence of post-quantum TCF, without the adaptive hardcore bit property, or on quantum fully homomorphic encryption. These can be instantiated, for example, from Ring Learning with Errors. This has the potential to improve the efficiency of qubit certification and derived functionalities. On the technical side, we show that the quantum soundness of any such protocol can be reduced to proving a bound on a simple algorithmic task: informally, answering "two challenges simultaneously" in the protocol. Our reduction formalizes the intuition that these protocols demonstrate quantumness by leveraging the impossibility of rewinding a general quantum prover. This allows us to prove tight bounds on the quantum soundness of [KCVY21] and [KLVY22], showing that no quantum polynomial-time prover can succeed with probability larger than $$\cos ^2 \frac{\pi }{8}\approx 0.853$$ . Previously, only an upper bound on the success probability of classical provers, and a lower bound on the success probability of quantum provers, were known. We then extend this proof of quantum soundness to show that provers that approach the quantum soundness bound must perform almost anti-commuting measurements. This certifies that the prover holds a qubit.
A Comparative Study of Chatbot Response Generation: Traditional Approaches Versus Large Language Models

Chatbot responses can be generated using traditional rule-based conversation design or through the use of large language models (LLMs). In this paper we compare the quality of responses provided by LLM-based chatbots with those provided by traditional conversation design. The results suggest that in some cases the use of LLMs could improve the quality of chatbot responses. The paper concludes by suggesting that a combination of approaches is the best way forward and suggests some directions for future work.
RFLSem: A Lightweight Model for Textual Sentiment Analysis

In the current Internet age, lots of emotions are being expressed and recorded in various recommender systems, including the public reviews of movies and restaurants as well as those on social media such as Twitter and Weibo. Accordingly, sentiment analysis has become a main research topic in the WWW community. Currently, deep learning (DL)-based sentiment analysis is the most promising research trend in this context. However, DL-based sentiment analysis methods are still confronted with great challenges, such as controversial robustness and the difficulty in reproducing in the face of the analysis of sentiment classification and sentiment score. In this paper, we propose RFLSem, a text-based lightweight sentiment analysis model that uniformly considers the difference between sentiment classification and sentiment score, the robustness of the sentiment analysis model, and the reproducibility problem in the sentiment analysis process. Large-scale experiments have been conducted on script datasets in both Chinese and English, including Chinese and English screenplay datasets as well as IMDB and SST datasets. The experimental results demonstrate that RFLSem can achieve better results than state-of-the-art sentiment analysis techniques.