Reviewerly/exHarmony huggingface dataset:
A Weakly Supervised Segmentation Network Embedding Cross-scale Attention Guidance and Noise-sensitive Constraint for Detecting Tertiary Lymphoid Structures of Pancreatic Tumors

The presence of tertiary lymphoid structures (TLSs) on pancreatic pathological images is an important prognostic indicator of pancreatic tumors. Therefore, TLSs detection on pancreatic pathological images plays a crucial role in diagnosis and treatment for patients with pancreatic tumors. However, fully supervised detection algorithms based on deep learning usually require a large number of manual annotations, which is time-consuming and labor-intensive. In this paper, we aim to detect the TLSs in a manner of few-shot learning by proposing a weakly supervised segmentation network. We firstly obtain the lymphocyte density maps by combining a pretrained model for nuclei segmentation and a domain adversarial network for lymphocyte nuclei recognition. Then, we establish a cross-scale attention guidance mechanism by jointly learning the coarse-scale features from the original histopathology images and fine-scale features from our designed lymphocyte density attention. A noise-sensitive constraint is introduced by an embedding signed distance function loss in the training procedure to reduce tiny prediction errors. Experimental results on two collected datasets demonstrate that our proposed method significantly outperforms the state-of-the-art segmentation-based algorithms in terms of TLSs detection accuracy. Additionally, we apply our method to study the congruent relationship between the density of TLSs and peripancreatic vascular invasion and obtain some clinically statistical results.
CLEVA: Chinese Language Models EVAluation Platform

Yanyang Li, Jianqiao Zhao, Duo Zheng, Zi-Yuan Hu, Zhi Chen, Xiaohui Su, Yongfeng Huang, Shijia Huang, Dahua Lin, Michael Lyu, Liwei Wang. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. 2023.
INGENIOUS: Using Informative Data Subsets for Efficient Pre-Training of Language Models

H S V N S Kowndinya Renduchintala, Krishnateja Killamsetty, Sumit Bhatia, Milan Aggarwal, Ganesh Ramakrishnan, Rishabh Iyer, Balaji Krishnamurthy. Findings of the Association for Computational Linguistics: EMNLP 2023. 2023.
Steganography With Generated Images: Leveraging Volatility to Enhance Security

The development of generative AI applications has revolutionized the data environment for steganography, providing a new source of steganographic cover. However, existing generative data-based steganography methods typically require white-box access, rendering them unsuitable for black-box generative models. To overcome this limitation, we propose a novel steganography method for generated images, which leverages the volatility of generative models and is applicable in black-box scenarios. The volatility of generative models refers to the ability to generate a series of images with slight variations by fine-tuning the input parameters of the model. These generated images exhibit varying degrees of volatility in different areas. To resist steganalysis, we mask steganographic modifications by confusing them with the inherent volatility of the model. Specifically, by modeling distributions of generated pixels and estimating the parameters of the distributions, the occurrence probabilities of generated pixels can be obtained, which serve as an effective measure for steganographic modification probabilities to render stego images as indistinguishable as possible from the images producible by the model. Moreover, we further combine it with existing costs to develop a more comprehensive steganographic algorithm. Experimental results show that the proposed method significantly outperforms baseline and comparative methods in resisting both feature-based and CNN-based steganalyzers.
Text Classification Based on the Heterogeneous Graph Considering the Relationships between Documents

Text classification is the task of estimating the genre of a document based on information such as word co-occurrence and frequency of occurrence. Text classification has been studied by various approaches. In this study, we focused on text classification using graph structure data. Conventional graph-based methods express relationships between words and relationships between words and documents as weights between nodes. Then, a graph neural network is used for learning. However, there is a problem that conventional methods are not able to represent the relationship between documents on the graph. In this paper, we propose a graph structure that considers the relationships between documents. In the proposed method, the cosine similarity of document vectors is set as weights between document nodes. This completes a graph that considers the relationship between documents. The graph is then input into a graph convolutional neural network for training. Therefore, the aim of this study is to improve the text classification performance of conventional methods by using this graph that considers the relationships between document nodes. In this study, we conducted evaluation experiments using five different corpora of English documents. The results showed that the proposed method outperformed the performance of the conventional method by up to 1.19%, indicating that the use of relationships between documents is effective. In addition, the proposed method was shown to be particularly effective in classifying long documents.
The Bethe Ansatz as a Quantum Circuit

Abstract The Bethe ansatz is an analytical method to solve exactly solvable models in quantum mechanics. It has been shown that the states of the Bethe ansatz can be prepared by a deterministic quantum circuit whose quantum gates were determined numerically. We report our progress in recasting the Bethe ansatz as a deterministic quantum circuit. We present the analytical expressions of the quantum gates. Formulae rely upon diagrammatic rules that define the wave functions of the Bethe ansatz by matrix product states. Based on the analytical expressions, we prove the unitarity of the quantum gates. We use our results to clarify on the equivalence between the coordinate and algebraic Bethe ansatze in light of matrix-product states.
Provably Fair Cooperative Scheduling

The context of this work is cooperative scheduling, a concurrency paradigm, where task execution is not arbitrarily preempted. Instead, language constructs exist that let a task voluntarily yield the right to execute to another task. The inquiry is the design of provably fair schedulers and suitable notions of fairness for cooperative scheduling languages. To the best of our knowledge, this problem has not been addressed so far. Our approach is to study fairness independently from syntactic constructs or environments, purely from the point of view of the semantics of programming languages, i.e., we consider fairness criteria using the formal definition of a program execution. We develop our concepts for classic structural operational semantics (SOS) as well as for the recent locally abstract, globally concrete (LAGC) semantics. The latter is a highly modular approach to semantics ensuring the separation of concerns between local statement evaluation and scheduling decisions. The new knowledge contributed by our work is threefold: first, we show that a new fairness notion, called quiescent fairness, is needed to characterize fairness adequately in the context of cooperative scheduling; second, we define a provably fair scheduler for cooperative scheduling languages; third, a qualitative comparison between the SOS and LAGC versions yields that the latter, while taking higher initial effort, is more amenable to proving fairness and scales better under language extensions than SOS. The grounding of our work is a detailed formal proof of quiescent fairness for the scheduler defined in LAGC semantics. The importance of our work is that it provides a formal foundation for the implementation of fair schedulers for cooperative scheduling languages, an increasingly popular paradigm (for example: akka/Scala, JavaScript, async Rust). Being based solely on semantics, our ideas are widely applicable. Further, our work makes clear that the standard notion of fairness in concurrent languages needs to be adapted for cooperative scheduling and, more generally, for any language that combines atomic execution sequences with some form of preemption.
Potential of virtual reality to meaningfully engage adults living with dementia in care settings: A scoping review

Older adults living with dementia in residential aged care homes receive physical care but may have limited access to meaningful engagement. Due to its multimodal and versatile nature, virtual reality (VR) is speculated to be an effective means of meaningfully engaging residents individually and/or in groups. However, there is little evidence regarding its efficacy as a means of meaningfully engaging older adults living with dementia. The objective of this scoping review is to identify and synthesise published research investigating the use of VR technology in promoting the meaningful engagement of older adults living with dementia in residential aged care facilities.A scoping review following Arksey and O'Malley's (2005) established process was conducted and included identifying and selecting relevant studies from seven databases (i.e., AgeLine, CINAHL, Medline, Cochrane, Embase, PsycINFO and Scopus), data charting, and collating, summarising and reporting findings. Two independent reviewers used Covidence and Excel to support data extraction and analysis.The 20 eligible studies revealed discrepancies in the classification of VR and strengths, weaknesses, opportunities and threats associated with its implementation. Findings indicate that the most influential factor in eliciting meaningful engagement is when VR use facilitates interpersonal interactions and opportunities for human connection.Although VR seems to be a promising medium in engaging older adults living with dementia, the efficacy of implementing VR technology within care facilities should be considered alongside potential environmental enablers and barriers. The apparent strength of implementing VR as a means of encouraging restoration, pleasure and productivity in older adults living with dementia lies in the fact that it creates opportunities for meaningful engagement on social and occupational levels.
Search Engine Results Optimization for Supply Chain SMEs Through Digital Content Management and Fuzzy Cognitive Models

Throughout crises and financial prosperity, the role and influence of small and medium supply chain enterprises (SMEs) have been solid and key factors for the economic growth of global markets. Hence, a great need arises for digital promotion to keep up with recent technological advancements. Such an aim could be achieved by SMEs in the supply chain by optimizing the results of their websites in search engines and utilizing the digital content (DC) of their web pages. This research is focused on the optimization of supply chain SMEs’ search engine results from the efficient management of their DC. The authors collected big data from the DC of five supply chain SMEs’ websites and performed statistical analysis (correlation and linear regression analysis), followed by six fuzzy cognitive mapping simulation scenarios. Supply chain SMEs’ customers enter their website and are found capable of producing DC metrics, originating from their interaction with the webpage, which is valuable for supply chain SMEs since they impact the performance of their search engine results. The outcomes of this study indicate that specific DC metrics, related to website customers’ activity, can accurately simulate and predict the course of supply chain SMEs’ digital marketing performance KPIs (global rank, organic, and paid traffic). It has been discerned that supply chain SMEs could enhance their search engine results, by increasing traffic originating from social sources, organic keywords, and the costs required for paid advertising campaigns. Accordingly, the increased number of pages opened by website visitors also indicates enhanced search engine results. Conflicts of Interest The authors declare that they have no conflicts of interest to this work.
Minimum-time generation of a uniform superposition in a qubit with only transverse field control

We consider a two-level system with a fixed energy spacing (detuning) between the two levels and a single transverse control field which can take values between zero and a maximum amplitude. Using Pontryagin's maximum principle, we completely solve the problem of generating in minimum time a uniform superposition of the two quantum states when starting from one of them, for all the values of the ratio between the maximum control amplitude and the detuning. For each value of this ratio we find the optimal pulse sequence to have the bang-bang form and calculate the durations of the pulses composing it. The suggested framework is not only restricted to the problem at hand, but it can be also exploited in the problem of fast charging a quantum battery based on a two-level system, as well as for the optimization of pulse sequences used for the controlled preparation of the excited state in a quantum emitter, which is a prerequisite for its usage as a single-photon source.
ICD-Face: Intra-class Compactness Distillation for Face Recognition

Knowledge distillation is an effective model compression method to improve the performance of a lightweight student model by transferring the knowledge of a well-performed teacher model, which has been widely adopted in many computer vision tasks, including face recognition (FR). The current FR distillation methods usually utilize the Feature Consistency Distillation (FCD) (e.g., L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> distance) on the learned embeddings extracted by the teacher and student models. However, after using FCD, we observe that the intra-class similarities of the student model are lower than the intra-class similarities of the teacher model a lot. Therefore, we propose an effective FR distillation method called ICD-Face by introducing intra-class compactness distillation into the existing distillation framework. Specifically, in ICD-Face, we first propose to calculate the similarity distributions of the teacher and student models, where the feature banks are introduced to construct sufficient and high-quality positive pairs. Then, we estimate the probability distributions of the teacher and student models and introduce the Similarity Distribution Consistency (SDC) loss to improve the intra-class compactness of the student model. Extensive experimental results on multiple benchmark datasets demonstrate the effectiveness of our proposed ICD-Face for face recognition.
CL-MVSNet: Unsupervised Multi-view Stereo with Dual-level Contrastive Learning

Unsupervised Multi-View Stereo (MVS) methods have achieved promising progress recently. However, previous methods primarily depend on the photometric consistency assumption, which may suffer from two limitations: indistinguishable regions and view-dependent effects, e.g., low-textured areas and reflections. To address these issues, in this paper, we propose a new dual-level contrastive learning approach, named CL-MVSNet. Specifically, our model integrates two contrastive branches into an unsupervised MVS framework to construct additional supervisory signals. On the one hand, we present an image-level contrastive branch to guide the model to acquire more context awareness, thus leading to more complete depth estimation in indistinguishable regions. On the other hand, we exploit a scene-level contrastive branch to boost the representation ability, improving robustness to view-dependent effects. Moreover, to recover more accurate 3D geometry, we introduce an ℒ0.5 photometric consistency loss, which encourages the model to focus more on accurate points while mitigating the gradient penalty of undesirable ones. Extensive experiments on DTU and Tanks&Temples benchmarks demonstrate that our approach achieves state-of-the-art performance among all end-to-end unsupervised MVS frameworks and outperforms its supervised counterpart by a considerable margin without fine-tuning.
Comparative approaches to the assessment of writing: Reliability and validity of benchmark rating and comparative judgement

In the past years, comparative assessment approaches have gained ground as a viable method to assess text quality. Instead of providing absolute scores to a text as in holistic or analytic scoring methods, raters in comparative assessments rate text quality by comparing texts either to pre-selected benchmarks representing different levels of writing quality (i.e., benchmark rating method) or by a series of pairwise comparisons to other texts in the sample (i.e., comparative judgement; CJ). In the present study, text quality scores from the benchmarking method and CJ are compared in terms of their reliability, convergent validity and scoring distribution. Results show that benchmark ratings and CJ-ratings were highly consistent and converged to the same construct of text quality. However, the distribution of benchmark ratings showed a central tendency. It is discussed how both methods can be integrated and used such that writing can be assessed reliably, validly, but also efficiently in both writing research and practice.
Domain Knowledge Distillation from Large Language Model: An Empirical Study in the Autonomous Driving Domain

Engineering knowledge-based (or expert) systems require extensive manual effort and domain knowledge. As Large Language Models (LLMs) are trained using an enormous amount of cross-domain knowledge, it becomes possible to automate such engineering processes. This paper presents an empirical automation and semi-automation framework for domain knowledge distillation using prompt engineering and the LLM ChatGPT. We assess the framework empirically in the autonomous driving domain and present our key observations. In our implementation, we construct the domain knowledge ontology by “chatting” with ChatGPT. The key finding is that while fully automated domain ontology construction is possible, human supervision and early intervention typically improve efficiency and output quality as they lessen the effects of response randomness and the butterfly effect. We, therefore, also develop a web-based distillation assistant enabling supervision and flexible intervention at runtime. We hope our findings and tools could inspire future research toward revolutionizing the engineering of knowledge-based systems across application domains.
Pauline Rowson at CrimeFest 2012 with her publisher

Crime authors descend on Crimefest 2012 where Pauline Rowson appeared on a panel and attended the announcement of the CWA dagger shortlists
Face Tracking and Recognition with the Use of Particle-Filtered Local Features

A consistent particle filtering-based framework for the purpose of parallel face tracking and recognition from video sequences is proposed. A novel approach to defining randomized, particle filtering-driven local face features for the purpose of recognition is proposed. The potential of cumulating classification decisions based on the proposed feature set definition is evaluated. By applying cumulation mechanisms to the classification results determined from single frames and with the use of particle-filtered features, good recognition rates are obtained at the minimal computational cost. The proposed framework can operate in real-time on a typical modern PC. Additionally, the application of cumulation mechanisms makes the framework resistant to brief visual distortions, such as occlusions, head rotations or face expressions. A high performance is also obtained on low resolution images (video frames). Since the framework is based on the particle filtering principle, it is easily tunable to various application requirements (security level, hardware constraints).
Chapter 5. The Learning Curve

Chapter 7 of Library Technology Reports (vol. 49, no. 8), “Streamlining Information Services Using Chatbots,” discusses how to use the data generated by chatbots. Reviewing conversation logs is essential to good chatbot performance. Methods to efficiently review logs and improve the accuracy of responses are examined .
The effect of 3D visualization on mainframe application maintenance: A controlled experiment

Code written in 1960’s and 80’s are still being maintained by large software organizations. Such legacy systems play a significant role in supporting various data immense applications in banking, manufacturing, retail marketing, health care domains etc. The software maintenance engineer for such large systems has to undergo not trivial tasks of identifying and understanding relevant parts of system related to the developer/maintainer goals. As these systems were written before the evolution of software engineering principles, there is no standard documentation available for such systems and even best coding practices were not followed during the development of these systems. Hence, maintenance of such systems is a challenging task for young software engineers who need to spend a lot of time in comprehending the system before fixing the errors. The lack of tools that support legacy code comprehension makes the maintenance process to be time consuming and tedious. Our research is focused on understanding the impact of 3D software visualization for change impact analysis tasks on the mainframe during the maintenance phase. To conduct this research we have constructed a 3D visualization tool which generates a three dimensional call graph for a system written in COBOL using non immersive virtual reality (VRML). We have conducted a controlled experiment to test the effectiveness of the tool on subjects performing change impact analysis tasks. Our results show that subjects who used the tool performed more the tasks more accurately and faster than the group that did not use the tool.
Geometric insights into support vector machine behavior using the KKT conditions

The support vector machine (SVM) is a powerful and widely used classification algorithm. This paper uses the Karush-Kuhn-Tucker conditions to provide rigorous mathematical proof for new insights into the behavior of SVM. These insights provide unexpected relationships between SVM and two other linear classifiers: the mean difference and the maximal data piling direction. For example, we show that in many cases SVM can be viewed as a cropped version of these classifiers. By carefully exploring these connections we show how SVM tuning behavior is affected by data characteristics including: balanced vs. unbalanced classes, low vs. high dimension, separable vs. non-separable data. These results provide further insights into tuning SVM via cross-validation by explaining observed pathological behavior and motivating improved cross-validation methodology.