Reviewerly/exHarmony huggingface dataset:
Cryptanalysis of Reduced Round ChaCha – New Attack &amp; Deeper Analysis

In this paper we present several analyses on ChaCha, a software stream cipher. First, we consider a divide-and-conquer approach on the secret key bits by partitioning them. The partitions are based on multiple input-output differentials to obtain a significantly improved attack on 6-round ChaCha256 with a complexity of 299.48. It is 240 times faster than the currently best known attack. This is the first time an attack on a round reduced ChaCha with a complexity smaller than 2k/2, where the secret key is of k bits, has been successful.Further, all the attack complexities related to ChaCha are theoretically estimated in general and there are several questions in this regard as pointed out by Dey, Garai, Sarkar and Sharma in Eurocrypt 2022. In this regard, we propose a toy version of ChaCha, with a 32-bit secret key, on which the attacks can be implemented completely to verify whether the theoretical estimates are justified. This idea is implemented for our proposed attack on 6 rounds. Finally, we show that it is possible to estimate the success probabilities of these kinds of PNB-based differential attacks more accurately. Our methodology explains how different cryptanalytic results can be evaluated with better accuracy rather than claiming that the success probability is significantly better than 50%.
Improving Multilingual Neural Machine Translation System for Indic Languages

The Machine Translation System (MTS) serves as effective tool for communication by translating text or speech from one language to another language. Recently, neural machine translation (NMT) has become popular for its performance and cost-effectiveness. However, NMT systems are restricted in translating low-resource languages as a huge quantity of data is required to learn useful mappings across languages. The need for an efficient translation system becomes obvious in a large multilingual environment like India. Indian languages (ILs) are still treated as low-resource languages due to unavailability of corpora. In order to address such an asymmetric nature, the multilingual neural machine translation (MNMT) system evolves as an ideal approach in this direction. The MNMT converts many languages using a single model, which is extremely useful in terms of training process and lowering online maintenance costs. It is also helpful for improving low-resource translation. In this article, we propose an MNMT system to address the issues related to low-resource language translation. Our model comprises two MNMT systems, i.e., for English-Indic (one-to-many) and for Indic-English (many-to-one) with a shared encoder-decoder containing 15 language pairs (30 translation directions). Since most of IL pairs have a scanty amount of parallel corpora, not sufficient for training any machine translation model, we explore various augmentation strategies to improve overall translation quality through the proposed model. A state-of-the-art transformer architecture is used to realize the proposed model. In addition, the article addresses the use of language relationships (in terms of dialect, script, etc.), particularly about the role of high-resource languages of the same family in boosting the performance of low-resource languages. Moreover, the experimental results also show the advantage of back-translation and domain adaptation for ILs to enhance the translation quality of both source and target languages. Using all these key approaches, our proposed model emerges to be more efficient than the baseline model in terms of evaluation metrics, i.e., BLEU (BiLingual Evaluation Understudy) score for a set of ILs.
All-fiber telecom band energy-time entangled biphoton source

We report an all-fiber telecom-band energy-time entangled biphoton source with all physical elements integrated into a compact cabinet. At a pump power of 800 µW, the photon pairs generation rate reaches 6.9 MHz with the coincidence-to-accidental ratio (CAR) better than 1150. The long-term stability of the biphoton source is characterized by measuring the Hong–Ou–Mandel interference visibility and CAR within a continuous operation period of more than 10 h. Benefiting from the advantages of compact size, light weight, and high stability, this device provides a convenient resource for various field turnkey quantum communication and metrology applications.
An evidence-based roadmap for IoT software systems engineering

The Internet of Things (IoT) has brought expectations for software inclusion in everyday objects. However, it has challenges and requires multidisciplinary technical knowledge involving different areas that should be combined to enable IoT software systems engineering. To present an evidence-based roadmap for IoT development to support developers in specifying, designing, and implementing IoT systems. An iterative approach based on experimental studies to acquire evidence to define the IoT Roadmap. Next, the Systems Engineering Body of Knowledge life cycle was used to organize the roadmap and set temporal dimensions for IoT software systems engineering. The studies revealed seven IoT Facets influencing IoT development. The IoT Roadmap comprises 117 items organized into 29 categories representing different concerns for each Facet. In addition, an experimental study was conducted observing a real case of a healthcare IoT project, indicating the roadmap applicability. The IoT Roadmap can be a feasible instrument to assist IoT software systems engineering because it can (a) support researchers and practitioners in understanding and characterizing the IoT and (b) provide a checklist to identify the applicable recommendations for engineering IoT software systems.
Blockchain enabled data security in vehicular networks

Abstract Recently, researchers have applied blockchain technology in vehicular networks to take benefit of its security features, such as confidentiality, authenticity, immutability, integrity, and non-repudiation. The resource-intensive nature of the blockchain consensus algorithm makes it a challenge to integrate it with vehicular networks due to the time-sensitive message dissemination requirements. Moreover, most of the researchers have used the Proof-of-Work consensus algorithm, or its variant to add a block to a blockchain, which is a highly resource-intensive process with greater latency. In this paper, we propose a consensus algorithm for vehicular networks named as Vehicular network Based Consensus Algorithm (VBCA) to ensure data security across the network using blockchain that maintains a secured pool of confirmed messages exchanged in the network. The proposed scheme, based on a consortium blockchain, reduces average transaction latency, and increases the number of confirmed transactions in a decentralized manner, without compromising the integrity and security of data. The simulation results show improved performance in terms of confirmed transactions, transaction latency, number of blocks, and block creation time.
Strategies for quantum-optimized construction of interpolating operators in classical simulations of lattice quantum field theories

It has recently been argued that noisy intermediate-scale quantum computers may be used to optimize interpolating operator constructions for lattice quantum field theory (LQFT) calculations on classical computers. Here, two concrete realizations of the method are developed and implemented. The first approach is to maximize the overlap, or fidelity, of the state created by an interpolating operator acting on the vacuum state to the target eigenstate. The second is to instead minimize the energy expectation value of the interpolated state. These approaches are implemented in a proof-of-concept calculation in ($1+1$)-dimensions for a single-flavor massive Schwinger model to obtain quantum-optimized interpolating operator constructions for a vector meson state in the theory. Although fidelity maximization is preferable in the absence of noise due to quantum gate errors, it is found that energy minimization is more robust to these effects in the proof-of-concept calculation. This work serves as a concrete demonstration of how quantum computers in the intermediate term might be used to accelerate classical LQFT calculations.
A survey on bipartite graphs embedding

Abstract Research on graph representation learning (a.k.a. embedding) has received great attention in recent years and shows effective results for various types of networks. Nevertheless, few initiatives have been focused on the particular case of embeddings for bipartite graphs. In this paper, we first define the graph embedding problem in the case of bipartite graphs. Next, we propose a taxonomy of approaches used to tackle this problem and draw a description of state-of-the-art methods. Then, we establish their pros and cons with respect to conventional network embeddings. Finally, we provide a description of available resources to lead experiments on the subject.
A Survey on Oversmoothing in Graph Neural Networks

Node features of graph neural networks (GNNs) tend to become more similar with the increase of the network depth. This effect is known as over-smoothing, which we axiomatically define as the exponential convergence of suitable similarity measures on the node features. Our definition unifies previous approaches and gives rise to new quantitative measures of over-smoothing. Moreover, we empirically demonstrate this behavior for several over-smoothing measures on different graphs (small-, medium-, and large-scale). We also review several approaches for mitigating over-smoothing and empirically test their effectiveness on real-world graph datasets. Through illustrative examples, we demonstrate that mitigating over-smoothing is a necessary but not sufficient condition for building deep GNNs that are expressive on a wide range of graph learning tasks. Finally, we extend our definition of over-smoothing to the rapidly emerging field of continuous-time GNNs.
A competitive learning-based Grey wolf Optimizer for engineering problems and its application to multi-layer perceptron training

This article presents a competitive learning-based Grey Wolf Optimizer (Clb-GWO) formulated through the introduction of competitive learning strategies to achieve a better trade-off between exploration and exploitation while promoting population diversity through the design of difference vectors. The proposed method integrates population sub-division into majority groups and minority groups with a dual search system arranged in a selective complementary manner. The proposed Clb-GWO is tested and validated through the recent CEC2020 and CEC2019 benchmarking suites followed by the optimal training of multi-layer perceptron’s (MLPs) with five classification datasets and three function approximation datasets. Clb-GWO is compared against the standard version of GWO, five of its latest variants and two modern meta-heuristics. The benchmarking results and the MLP training results demonstrate the robustness of Clb-GWO. The proposed method performed competitively compared to all its competitors with statistically significant performance for the benchmarking tests. The performance of Clb-GWO the classification datasets and the function approximation datasets was excellent with lower error rates and least standard deviation rates.
Improving Nighttime Driving-Scene Segmentation via Dual Image-Adaptive Learnable Filters

Semantic segmentation on driving-scene images is vital for autonomous driving. Although encouraging performance has been achieved on daytime images, the performance on nighttime images are less satisfactory due to the insufficient exposure and the lack of labeled data. To address these issues, we present an add-on module called dual image-adaptive learnable filters (DIAL-Filters) to improve the semantic segmentation in nighttime driving conditions, aiming at exploiting the intrinsic features of driving-scene images under different illuminations. DIAL-Filters consist of two parts, including an image-adaptive processing module (IAPM) and a learnable guided filter (LGF). With DIAL-Filters, we design both unsupervised and supervised frameworks for nighttime driving-scene segmentation, which can be trained in an end-to-end manner. Specifically, the IAPM module consists of a small convolutional neural network with a set of differentiable image filters, where each image can be adaptively enhanced for better segmentation with respect to the different illuminations. The LGF is employed to enhance the output of segmentation network to get the final segmentation result. The DIAL-Filters are light-weight and efficient and they can be readily applied for both daytime and nighttime images. Our experiments show that DAIL-Filters can significantly improve the supervised segmentation performance on ACDC_Night and NightCity datasets, while it demonstrates the state-of-the-art performance on unsupervised nighttime semantic segmentation on Dark Zurich and Nighttime Driving testbeds. Codes and models are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/wenyyu/IA-Seg</uri> .
Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense

The rise in malicious usage of large language models, such as fake content creation and academic plagiarism, has motivated the development of approaches that identify AI-generated text, including those based on watermarking or outlier detection. However, the robustness of these detection algorithms to paraphrases of AI-generated text remains unclear. To stress test these detectors, we build a 11B parameter paraphrase generation model (DIPPER) that can paraphrase paragraphs, condition on surrounding context, and control lexical diversity and content reordering. Using DIPPER to paraphrase text generated by three large language models (including GPT3.5-davinci-003) successfully evades several detectors, including watermarking, GPTZero, DetectGPT, and OpenAI's text classifier. For example, DIPPER drops detection accuracy of DetectGPT from 70.3% to 4.6% (at a constant false positive rate of 1%), without appreciably modifying the input semantics. To increase the robustness of AI-generated text detection to paraphrase attacks, we introduce a simple defense that relies on retrieving semantically-similar generations and must be maintained by a language model API provider. Given a candidate text, our algorithm searches a database of sequences previously generated by the API, looking for sequences that match the candidate text within a certain threshold. We empirically verify our defense using a database of 15M generations from a fine-tuned T5-XXL model and find that it can detect 80% to 97% of paraphrased generations across different settings while only classifying 1% of human-written sequences as AI-generated. We open-source our models, code and data.
Securing medical image privacy in cloud using deep learning network

Abstract The worldwide usage of Internet of Things (IoT) applications enhanced the utilization of consumer devices, such as smartphones, computers, screening equipment used in hospitals that merely rely on imaging techniques. Numerous images got generated over the cloud platform in a daily basis ad create storage complexity. On the other hand, securing the data stored in the cloud is important. Instead of storing large amount of data into the cloud, lightweight dynamic processing of data suppresses the complex issues in cloud security. Here secure cloud-based image processing architecture is discussed. Privacy preserving medical data communication is considered as the specific research scope. Cryptographic technique used to encode the original data and decode the data at the other end is currently in usage as conventional design. Providing privacy to the medical records through adding noise and denoising the same records is the proposed idea. The proposed work is keenly focused on creating a light weight cloud architecture that communicates the medical data effectively with privacy perseverance using deep learning technique. In the proposed system, the design of an efficient image denoising scheme with a hybrid classification model is created to ensure reliable and secure communication. Deep learning algorithms merged to form a Pseudo-Predictive Deep Denoising Network (PPDD). The proposed system's benefit is ensuring added security in Dark Cloud using a newly structured algorithm. The original data is packed in the Deep cloud using the Gaussian noise act as a key. The complete packing and unpacking of medical data is encapsulated by the transformed images. Over the cloud premise, the data is highly secured and invisible to the malicious users. To reduce the storage complexity, the dynamic data is unpacked and denoise process is applied at the edge devices. During the authorized access period alone, the data is decrypted and accessible at the edge nodes. The maximum process is dynamically happen in the cloud without depending on the storage boundary. The performance of proposed PPDD network model is evaluated through Signal to noise ratio (SNR), Similarity index (SI),Error Rate(ER) and Contrast to noise ratio(CNR). The proposed architecture is comparatively validated with existing state-of-art approach.
GPT-4 vs. GPT-3.5: A Concise Showdown

&lt;p&gt;As the echoes of ChatGPT's remarkable success continue to permeate the AI community, its formidable successor, GPT-4, has emerged, showing off a wealth of novel features. In this concise paper, we elucidate the capabilities of GPT-4 and conduct a comparative analysis with its predecessor, ChatGPT, offering insights into their relative strengths and advancements in the rapidly evolving field of generative AI. We also present a comprehensive summary of the major performance results reported by OpenAI on GPT-4 across various Natural Language Processing (NLP) tasks. We place great emphasis on the innovative advancements offered by GPT-4 in comparison to its predecessors. Our focus is on highlighting its remarkable performance while also mentioning its limitations. The purpose of this paper is to deliver a succinct understanding of the new features and performance benchmarks of GPT-4.&lt;/p&gt;
An End-to-End CNN Approach for Enhancing Underwater Images Using Spatial and Frequency Domain Techniques

Underwater image processing area has been a central point of interest to many people in many fields such as control of underwater vehicles, archaeology, marine biology research, etc. Underwater exploration is becoming a big part of our life such as underwater marine and creatures research, pipeline and communication logistics, military use, touristic and entertainment use.Underwater images are subject to poor visibility, distortion, poor quality, etc., due to several reasons such as light propagation.The real problem occurs when these images have to be taken at a depth which is more than 500 feet where artificial light needs to be introduced.This work tackles the underwater environment challenges such as as colour casts, lack of image sharpness, low contrast, low visibility, and blurry appearance in deep ocean images by proposing an end-to-end deep underwater image enhancement network (WGH-net) based on convolutional neural network (CNN) algorithm.Quantitative and qualitative metrics results proved that our method achieved competitive results with the previous work methods as it was experimentally tested on different images from several datasets.
Feeling Virtually Present Makes Me Happier: The Influence of Immersion, Sense of Presence, and Video Contents on Positive Emotion Induction

Immersive technologies, such as Virtual Reality (VR), have great potential for enhancing users' emotions and wellbeing. However, how immersion, Virtual Environment contents, and sense of presence (SoP) influence emotional responses remains to be clarified to efficiently foster positive emotions. Consequently, a total of 26 participants (16 women, 10 men, 22.73 ± 2.69 years old) were exposed to 360-degree videos of natural and social contents on both a highly immersive Head-Mounted Display and a low immersive computer screen. Subjective emotional responses and SoP were assessed after each video using self-reports, while a wearable wristband collected continuously electrodermal activity and heart rate to record physiological emotional responses. Findings supported the added value of immersion, as more positive emotions and greater subjective arousal were reported after viewing the videos in the highly immersive setting, regardless of the video contents. In addition to usually employed natural contents, the findings also provide initial evidence for the effectiveness of social contents in eliciting positive emotions. Finally, structural equation models shed light on the indirect effect of immersion, through spatial and spatial SoP on subjective arousal. Overall, these are encouraging results about the effectiveness of VR for fostering positive emotions. Future studies should further investigate the influence of user characteristics on VR experiences to foster efficiently positive emotions among a broad range of potential users.
Rabi- and Blockade-Error-Resilient All-Geometric Rydberg Quantum Gates

We propose a nontrivial two-qubit gate scheme in which Rydberg atoms are subject to designed pulses resulting from geometric evolution processes. By utilizing hybrid robust nonadiabatic and adiabatic geometric operations on the control atom and target atom, respectively, we improve the robustness of the two-qubit Rydberg gate against Rabi control errors as well as blockade errors in comparison with the conventional two-qubit blockade gate. Numerical results with the current state-of-the-art experimental parameters corroborates the above mentioned robustness. We also evaluate the influence induced by the motion-induced dephasing and the dipole-dipole interaction and imperfection-excitation-induced leakage errors, which both could decrease the gate fidelity. Our scheme provides a promising route towards systematic control error (Rabi error) as well as blockade-error-resilient geometric quantum computation on neutral atom system.
Improving Entity Linking in Chinese Domain by Sense Embedding Based on Graph Clustering

Entity linking refers to linking a string in a text to corresponding entities in a knowledge base through candidate entity generation and candidate entity ranking. It is of great significance to some NLP (natural language processing) tasks, such as question answering. Unlike English entity linking, Chinese entity linking requires more consideration due to the lack of spacing and capitalization in text sequences and the ambiguity of characters and words, which is more evident in certain scenarios. In Chinese domains, such as industry, the generated candidate entities are usually composed of long strings and are heavily nested. In addition, the meanings of the words that make up industrial entities are sometimes ambiguous. Their semantic space is a subspace of the general word embedding space, and thus each entity word needs to get its exact meanings. Therefore, we propose two schemes to achieve better Chinese entity linking. First, we implement an n-gram based candidate entity generation method to increase the recall rate and reduce the nesting noise. Then, we enhance the corresponding candidate entity ranking mechanism by introducing sense embedding. Considering the contradiction between the ambiguity of word vectors and the single sense of the industrial domain, we design a sense embedding model based on graph clustering, which adopts an unsupervised approach for word sense induction and learns sense representation in conjunction with context. We test the embedding quality of our approach on classical datasets and demonstrate its disambiguation ability in general scenarios. We confirm that our method can better learn candidate entities’ fundamental laws in the industrial domain and achieve better performance on entity linking through experiments.