Reviewerly/exHarmony huggingface dataset:
Instance-Wise Adaptive Tuning and Caching for Vision-Language Models

Large-scale vision-language models (LVLMs) pre-trained on massive image-text pairs have achieved remarkable success in visual representations. However, existing paradigms to transfer LVLMs to downstream tasks encounter two primary challenges. Firstly, the text features remain fixed after being calculated and cannot be adjusted according to image features, which decreases the model’s adaptability. Secondly, the model’s output solely depends on the similarity between the text and image features, leading to excessive reliance on LVLMs. To address these two challenges, we introduce a novel two-branch model named the Instance-Wise Adaptive Tuning and Caching (ATC). Specifically, one branch implements our proposed ConditionNet, which guides image features to form an adaptive textual cache that adjusts based on image features, achieving instance-wise inference and improving the model’s adaptability. The other branch introduces the similarities between images and incorporates a learnable visual cache, designed to decouple new and previous knowledge, allowing the model to acquire new knowledge while preserving prior knowledge. The model’s output is jointly determined by the two branches, thus overcoming the limitations of existing methods that rely solely on LVLMs. Additionally, our method requires limited computing resources to tune parameters, yet outperforms existing methods on 11 benchmark datasets.
Dynamic Causality

There have been a number of attempts to develop a formal definition of causality that accords with our intuitions about what constitutes a cause. Perhaps the best known is the “modified” definition of actual causality, HPm, due to Halpern. In this paper, we argue that HPm gives counterintuitive results for some simple causal models. We propose Dynamic Causality (DC), an alternative semantics for causal models that leads to an alternative definition of causes. DC ascribes the same causes as HPm on the examples of causal models widely discussed in the literature and ascribes intuitive causes for the kinds of causal models we consider. Moreover, we show that the complexity of determining a cause under the DC definition is lower than for the HPm definition.
One-Class Classification Approach to Variational Learning from Biased Positive Unlabeled Data

We discuss Empirical Risk Minimization approach in conjunction with one-class classification method to learn classifiers for biased Positive Unlabeled (PU) data. For such data, probability that an observation from a positive class is labeled may depend on its features. The proposed method extends Variational Autoencoder for PU data (VAE-PU) introduced in [16] by proposing another estimator of a theoretical risk of a classifier to be minimized, which has important advantages over the previous proposal. This is based on one-class classification approach using generated pseudo-observations, which turns out to be an effective method of detecting positive observations among unlabeled ones. The proposed method leads to more precise estimation of the theoretical risk than the previous proposal. Experiments performed on real data sets show that the proposed VAE-PU+OCC algorithm works very promisingly in comparison to its competitors such as the original VAE-PU, SAR-EM and LBE methods in terms of accuracy and F1 score. The advantage is especially strongly pronounced for small labeling frequencies.
Siamese Representation Learning for Unsupervised Relation Extraction

Unsupervised relation extraction (URE) aims at discovering underlying relations between named entity pairs from open-domain plain text without prior information on relational distribution. Existing URE models utilizing contrastive learning, which attract positive samples and repulse negative samples to promote better separation, have got decent effect. However, fine-grained relational semantic in relationship makes spurious negative samples, damaging the inherent hierarchical structure and hindering performances. To tackle this problem, we propose Siamese Representation Learning for Unsupervised Relation Extraction – a novel framework to simply leverage positive pairs to representation learning, possessing the capability to effectively optimize relation representation of instances and retain hierarchical information in relational feature space. Experimental results show that our model significantly advances the state-of-the-art results on two benchmark datasets and detailed analyses demonstrate the effectiveness and robustness of our proposed model on unsupervised relation extraction. We have released our code at https://github.com/gxxxzhang/siamese-ure.
Influences of Total Quality Management on Innovation Performance and Organizational Learning

Throughout history, businesses have struggled to maintain and improve organisational performance in an effort to maintain a competitive advantage. The important elements of the quality management system must be confirmed by the senior management of the organization to increase and enhance performance. The purpose of this article is to identify the most significant quality factors as predictors of innovation performance in Oman. It focuses on top management support, employee involvement, customer focus, and continuous improvement. The study’s empirical data, which came from a self-administered survey, served as its foundation. The Ministry of Public Authority for Special Economic Zones and Free Zones and the Ministry of Commerce, Industry, and Investment Promotion, which identify 260 enterprises, provided the information. Data gathering involved the use of convenient sampling. Moreover, SMART-PLS was used to analyse the data. The study found that top management, consumer support, and employee involvement have a favourable impact on organisational learning and innovation performance. The study did discover a negligible effect of continuous quality improvement on organisational learning and innovation performance. The study suggests that future research be conducted to investigate the factors that can have a positive impact on organisational performance and innovation through continuous quality improvement.
Exploring Information Bottleneck for Weakly Supervised Semantic Segmentation

Image-level weakly supervised semantic segmentation (WSSS) has attracted much attention due to the easily acquired class labels. Most existing methods resort to utilizing Class Activation Maps (CAMs) obtained from the classification network to play as the initial pseudo labels. However, the classifiers only focus on the most discriminative regions of the target objects, which is referred to as the information bottleneck from the perspective of the information theory. To alleviate this information bottleneck limitation, we propose an Information Perturbation Module (IPM) to explicitly obtain the information difference maps, which provide the accurate direction and magnitude of the information compression in the classification network. After that, an information bottleneck breakthrough mechanism with three branches is proposed to overcome the information bottleneck in the classification network for segmentation. Additionally, a diversity regularization on the generated two information difference maps is proposed to improve the diversity of the output CAMs. Extensive experiments on PASCAL VOC2012 val and test sets demonstrate that the proposed method can effectively improve the weakly supervised semantic segmentation performance of the advanced approaches.
SharpenNet: Detecting Anti-Forensics USM Sharpening Adversarial Examples Based on ConvNeXt

Image sharpening detection, as a crucial branch of image forensics research, has attained a satisfactory level of performance with the assistance of deep learning. However, due to the nature of convolutional neural network (CNN) models, adversarial examples synthesized by generative adversarial networks (GANs) can easily attack existing forensics models. Therefore, deep learning-based forensics faces new challenges. In this paper, a novel architecture inspired by ConvNext is proposed to detect synthesized adversarial USM sharpening images. Through practical demonstration, our proposed technique achieves satisfying performance in recognizing adversarial samples that outperform previous sharpened image forensic systems. In addition, we have undertaken an ablation analysis of our suggested network topology and analyzed the efficacy of different enhancements.
OECS: a deep convolutional neural network accelerator based on a 3D hybrid optical-electrical NoC

Various emerging technologies have been proposed and applied to deep convolutional neural networks (DCNNs) to enhance their execution efficiency and accuracy while reducing the number of parameters. However, previous dataflow-based DCNN accelerators have primarily focused on accelerating the convolution (CONV) layer while reducing the overhead of data movement through specific data reuse strategies to improve performance. When deploying these emerging DCNNs in conventional accelerators, frequent data access from both the external memory and internal processing units of the accelerator can result in significant data movement overhead, leading to decreased acceleration performance. To address these challenges, this paper proposes a novel three-dimensional hybrid optical-electrical network-on-chip (NoC) accelerator called the optical-electronic channel-stationary system (OECS). OECS leverages a channel stationary (CS) calculation mode and stay-at-local data storage strategy to minimize the movement and processing of all data in the local processing element (PE). This strategy reduces data movement between each PE in the accelerator and between the accelerator and external memory, resulting in improved acceleration performance. Simulation results demonstrate that, compared to state-of-the-art accelerators, OECS achieves a 53% improvement in execution speed and saves approximately 44% of energy consumption associated with data movement.
Understanding Responses to Embarrassing Questions in Chatbot-Facilitated Medical Interview Conversations Using Deep Language Models

Healthcare is one the fastest growing fields for conversational agents (CAs), also known as chatbots. Compared with traditional human-machine interfaces which focus on utilitarian features, chatbots offer unique advantage in understanding the user’s intent and acquiring critical information from the user via natural communications. More importantly, the perceived impartial and non-judgmental nature of machines may facilitate users’ honest self-disclosure when answering embarrassing or stigmatizing questions in medical interviews. To realize the potential of CAs, it is crucial to understand the content in users’ conversation such as self-disclosure of sensitive, personal information so that the agent can react appropriately by offering empathy or encouraging elaboration. In this exploratory study, we designed a web-based chatbot that automatically conducted medical interviews on colorectal health. The interview consisted of embarrassing questions about stool description, diarrhea, constipation, and anal sex behavior that participants needed to answer. Interview conversations from 552 participants above the age of 35 were recorded. The participants’ responses were then analyzed in terms of the level of detail and whether the response contained health or personal information that participants voluntarily disclosed. We tested the fine-tuned BERT model and the GPT-3 classification API to compare the capability of pre-trained deep language models for classifying medical interview conversations.
Hiding Full-Color Images into Audio with Visual Enhancement via Residual Networks

Watermarking is a viable approach for safeguarding the proprietary rights of digital media. This study introduces an innovative fast Fourier transform (FFT)-based phase modulation (PM) scheme that facilitates efficient and effective blind audio watermarking at a remarkable rate of 508.85 numeric values per second while still retaining the original quality. Such a payload capacity makes it possible to embed a full-color image of 64 × 64 pixels within an audio signal of just 24.15 s. To bolster the security of watermark images, we have also implemented the Arnold transform in conjunction with chaotic encryption. Our comprehensive analysis and evaluation confirm that the proposed FFT–PM scheme exhibits exceptional imperceptibility, rendering the hidden watermark virtually undetectable. Additionally, the FFT–PM scheme shows impressive robustness against common signal-processing attacks. To further enhance the visual rendition of the recovered color watermarks, we propose using residual neural networks to perform image denoising and super-resolution reconstruction after retrieving the watermarks. The utilization of the residual networks contributes to noticeable improvements in perceptual quality, resulting in higher levels of zero-normalized cross-correlation in cases where the watermarks are severely damaged.
Flexible Project Management Methods: Types, Analysis, and Comparison

This article presents a study of flexible project management methods such as Agile, Scrum, Kanban, and Lean. The research aims to explore and compare these methods in order to determine their key characteristics, advantages, disadvantages, and areas of application. The article provides a literature review that establishes the theoretical foundation and core principles of each method. The analysis of practical cases examines successful implementation examples of each method and identifies challenges encountered by teams when using them. The comparative analysis is based on the principles, structure, roles, tools, and areas of application of each method. This allows for identifying similarities and differences between them and understanding in which cases the use of each method is most appropriate. The research methods include literature review, analysis of practical cases, and comparative analysis. The study provides a comprehensive overview of flexible project management methods and offers recommendations for selecting the most suitable method depending on the project context and team needs.
Optimal Topology of Vision Transformer for Real-Time Video Action Recognition in an End-To-End Cloud Solution

This study introduces an optimal topology of vision transformers for real-time video action recognition in a cloud-based solution. Although model performance is a key criterion for real-time video analysis use cases, inference latency plays a more crucial role in adopting such technology in real-world scenarios. Our objective is to reduce the inference latency of the solution while admissibly maintaining the vision transformer’s performance. Thus, we employed the optimal cloud components as the foundation of our machine learning pipeline and optimized the topology of vision transformers. We utilized UCF101, including more than one million action recognition video clips. The modeling pipeline consists of a preprocessing module to extract frames from video clips, training two-dimensional (2D) vision transformer models, and deep learning baselines. The pipeline also includes a postprocessing step to aggregate the frame-level predictions to generate the video-level predictions at inference. The results demonstrate that our optimal vision transformer model with an input dimension of 56 × 56 × 3 with eight attention heads produces an F1 score of 91.497% for the testing set. The optimized vision transformer reduces the inference latency by 40.70%, measured through a batch-processing approach, with a 55.63% faster training time than the baseline. Lastly, we developed an enhanced skip-frame approach to improve the inference latency by finding an optimal ratio of frames for prediction at inference, where we could further reduce the inference latency by 57.15%. This study reveals that the vision transformer model is highly optimizable for inference latency while maintaining the model performance.
A Circular Box-Based Deep Learning Model for the Identification of Signet Ring Cells from Histopathological Images

Signet ring cell (SRC) carcinoma is a particularly serious type of cancer that is a leading cause of death all over the world. SRC carcinoma has a more deceptive onset than other carcinomas and is mostly encountered in its later stages. Thus, the recognition of SRCs at their initial stages is a challenge because of different variants and sizes and illumination changes. The recognition process of SRCs at their early stages is costly because of the requirement for medical experts. A timely diagnosis is important because the level of the disease determines the severity, cure, and survival rate of victims. To tackle the current challenges, a deep learning (DL)-based methodology is proposed in this paper, i.e., custom CircleNet with ResNet-34 for SRC recognition and classification. We chose this method because of the circular shapes of SRCs and achieved better performance due to the CircleNet method. We utilized a challenging dataset for experimentation and performed augmentation to increase the dataset samples. The experiments were conducted using 35,000 images and attained 96.40% accuracy. We performed a comparative analysis and confirmed that our method outperforms the other methods.
CNOT circuits need little help to implement arbitrary Hadamard-free Clifford transformations they generate

Abstract A Hadamard-free Clifford transformation is a circuit composed of quantum Phase (P), CZ, and CNOT gates. It is known that such a circuit can be written as a three-stage computation, -P-CZ-CNOT-, where each stage consists only of gates of the specified type. In this paper, we focus on the minimization of circuit depth by entangling gates, corresponding to the important time-to-solution metric and the reduction of noise due to decoherence. We consider two popular connectivity maps: Linear Nearest Neighbor (LNN) and all-to-all. First, we show that a Hadamard-free Clifford operation can be implemented over LNN in depth 5 n , i.e., in the same depth as the -CNOT- stage alone. This allows us to implement arbitrary Clifford transformation over LNN in depth no more than 7 n − 4, improving the best previous upper bound of 9 n . Second, we report heuristic evidence that on average a random uniformly distributed Hadamard-free Clifford transformation over n &gt; 6 qubits can be implemented with only a tiny additive overhead over all-to-all connected architecture compared to the best-known depth-optimized implementation of the -CNOT- stage alone. This suggests the reduction of the depth of Clifford circuits from $$2n\,+\,O({\log }^{2}(n))$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>2</mml:mn> <mml:mi>n</mml:mi> <mml:mspace /> <mml:mo>+</mml:mo> <mml:mspace /> <mml:mi>O</mml:mi> <mml:mrow> <mml:mo>(</mml:mo> <mml:mrow> <mml:msup> <mml:mrow> <mml:mi>log</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msup> <mml:mrow> <mml:mo>(</mml:mo> <mml:mrow> <mml:mi>n</mml:mi> </mml:mrow> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> to $$1.5n\,+\,O({\log }^{2}(n))$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>1.5</mml:mn> <mml:mi>n</mml:mi> <mml:mspace /> <mml:mo>+</mml:mo> <mml:mspace /> <mml:mi>O</mml:mi> <mml:mrow> <mml:mo>(</mml:mo> <mml:mrow> <mml:msup> <mml:mrow> <mml:mi>log</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msup> <mml:mrow> <mml:mo>(</mml:mo> <mml:mrow> <mml:mi>n</mml:mi> </mml:mrow> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> over unrestricted architectures.
Detection of Unknown Polymorphic Patterns Using Feature-Extracting Part of a Convolutional Autoencoder

Background: The present paper proposes a novel approach for detecting the presence of unknown polymorphic patterns in random symbol sequences that also comprise already known polymorphic patterns. Methods: We propose to represent rules that define the considered patterns as regular expressions and show how these expressions can be modeled using filter cascades of neural convolutional layers. We adopted a convolutional autoencoder (CAE) as a pattern detection framework. To detect unknown patterns, we first incorporated knowledge of known rules into the CAE’s convolutional feature extractor by fixing weights in some of its filter cascades. Then, we executed the learning procedure, where the weights of the remaining filters were driven by two different objectives. The first was to ensure correct sequence reconstruction, whereas the second was to prevent weights from learning the already known patterns. Results: The proposed methodology was tested on sample sequences derived from the human genome. The analysis of the experimental results provided statistically significant information on the presence or absence of polymorphic patterns that were not known in advance. Conclusions: The proposed method was able to detect the existence of unknown polymorphic patterns.
On Linear Perfect b-Symbol Codes over Finite Fields

Motivated by the application of high-density data storage technologies, Cassuto and Blaum introduced codes for symbol-pair read channels in 2011, and Yaakobi et al. generalized the coding framework to that for b-symbol read channels where b≥2 in 2016. In this paper, we establish a b-sphere-packing bound and present a recurrence relationship for the b-weight enumerator. We determine all parameters of linear perfect b-symbol e-error-correcting codes over Fq for e&lt;2b and show that for 2b≤e&lt;3b, there exist at most finite such codes for a given b, e, and q. We construct a family of linear perfect b-symbol b-error-correcting codes over Fq using constacyclic codes.
Research on the Application of Translation Parallel Corpus in Interpretation Teaching

Large and organized sets of translated texts between languages are called parallel translation corpora (PTLs). Even though data-driven learning can generate insights from massive datasets and create more tailored learning experiences, it has gained in popularity. There are however a few problems with this strategy, such as poor data quality, privacy concerns, inability to scale, a lack of clear explanations, and high costs. To provide high-quality output, machine translation algorithms are generally trained utilizing parallel corpora generated by human translators. The written word can be deciphered from one language to another through translation. The spoken word can be conveyed from one language to another through translation. Based on the study of real samples, machine translation from corpus linguistics uses its translations to create its translations. Statistical approaches are only one of the many ways a corpus may be used. Translated texts from two or more languages are called parallel corpora. With the emergence of data-driven learning (DDL) in translation training and language instruction, they are becoming increasingly popular in translation and contrastive research. While working as a professional translator, you're likely to encounter a wide range of challenges. These are lexical-semantic, grammar, syntactic, rhetorical, practical, and cultural difficulties. There are limitations to the number of possible translations that dictionaries can provide and difficulty in doing a thorough search. When it comes to translating, contemporary technologies bring up a whole new world of possibilities thanks to the sheer volume of data and the speed at which it is available. Students of translation can benefit from this study's innovative way of employing PTL-DDL, which can help them improve the quality and speed of their translations. In addition, the parallel corpora's sample sentences are readily available, making it easier to choose the best translations from a pool of translation candidates. Because of these characteristics, the approach is well-suited to creating active or encoding dictionaries.