Reviewerly/exHarmony huggingface dataset:
PLMmark: A Secure and Robust Black-Box Watermarking Framework for Pre-trained Language Models

The huge training overhead, considerable commercial value, and various potential security risks make it urgent to protect the intellectual property (IP) of Deep Neural Networks (DNNs). DNN watermarking has become a plausible method to meet this need. However, most of the existing watermarking schemes focus on image classification tasks. The schemes designed for the textual domain lack security and reliability. Moreover, how to protect the IP of widely-used pre-trained language models (PLMs) remains a blank. To fill these gaps, we propose PLMmark, the first secure and robust black-box watermarking framework for PLMs. It consists of three phases: (1) In order to generate watermarks that contain owners’ identity information, we propose a novel encoding method to establish a strong link between a digital signature and trigger words by leveraging the original vocabulary tables of PLMs. Combining this with public key cryptography ensures the security of our scheme. (2) To embed robust, task-agnostic, and highly transferable watermarks in PLMs, we introduce a supervised contrastive loss to deviate the output representations of trigger sets from that of clean samples. In this way, the watermarked models will respond to the trigger sets anomaly and thus can identify the ownership. (3) To make the model ownership verification results reliable, we perform double verification, which guarantees the unforgeability of ownership. Extensive experiments on text classification tasks demonstrate that the embedded watermark can transfer to all the downstream tasks and can be effectively extracted and verified. The watermarking scheme is robust to watermark removing attacks (fine-pruning and re-initializing) and is secure enough to resist forgery attacks.
FV-Train: Quantum Convolutional Neural Network Training with a Finite Number of Qubits by Extracting Diverse Features (Student Abstract)

Quantum convolutional neural network (QCNN) has just become as an emerging research topic as we experience the noisy intermediate-scale quantum (NISQ) era and beyond. As convolutional filters in QCNN extract intrinsic feature using quantum-based ansatz, it should use only finite number of qubits to prevent barren plateaus, and it introduces the lack of the feature information. In this paper, we propose a novel QCNN training algorithm to optimize feature extraction while using only a finite number of qubits, which is called fidelity-variation training (FV-Training).
Build-a-Bot: Teaching Conversational AI Using a Transformer-Based Intent Recognition and Question Answering Architecture

As artificial intelligence (AI) becomes a prominent part of modern life, AI literacy is becoming important for all citizens, not just those in technology careers. Previous research in AI education materials has largely focused on the introduction of terminology as well as AI use cases and ethics, but few allow students to learn by creating their own machine learning models. Therefore, there is a need for enriching AI educational tools with more adaptable and flexible platforms for interested educators with any level of technical experience to utilize within their teaching material. As such, we propose the development of an open-source tool (Build-A-Bot) for students and teachers to not only create their own transformer-based chatbots based on their own course material but also learn the fundamentals of AI through the model creation process. The primary concern of this paper is the creation of an interface for students to learn the principles of artificial intelligence by using a natural language pipeline to train a customized model to answer questions based on their own school curriculums. The model uses contexts given by their instructor, such as chapters of a textbook, to answer questions and is deployed on an interactive chatbot/voice agent. The pipeline teaches students data collection, data augmentation, intent recognition, and question answering by having them work through each of these processes while creating their AI agent, diverging from previous chatbot work where students and teachers use the bots as black-boxes with no abilities for customization or the bots lack AI capabilities, with the majority of dialogue scripts being rule-based. In addition, our tool is designed to make each step of this pipeline intuitive for students at a middle-school level. Further work primarily lies in providing our tool to schools and seeking student and teacher evaluations.
Formalising the Robustness of Counterfactual Explanations for Neural Networks

The use of counterfactual explanations (CFXs) is an increasingly popular explanation strategy for machine learning models. However, recent studies have shown that these explanations may not be robust to changes in the underlying model (e.g., following retraining), which raises questions about their reliability in real-world applications. Existing attempts towards solving this problem are heuristic, and the robustness to model changes of the resulting CFXs is evaluated with only a small number of retrained models, failing to provide exhaustive guarantees. To remedy this, we propose ∆-robustness, the first notion to formally and deterministically assess the robustness (to model changes) of CFXs for neural networks. We introduce an abstraction framework based on interval neural networks to verify the ∆-robustness of CFXs against a possibly infinite set of changes to the model parameters, i.e., weights and biases. We then demonstrate the utility of this approach in two distinct ways. First, we analyse the ∆-robustness of a number of CFX generation methods from the literature and show that they unanimously host significant deficiencies in this regard. Second, we demonstrate how embedding ∆-robustness within existing methods can provide CFXs which are provably robust.
Sparse Coding in a Dual Memory System for Lifelong Learning

Efficient continual learning in humans is enabled by a rich set of neurophysiological mechanisms and interactions between multiple memory systems. The brain efficiently encodes information in non-overlapping sparse codes, which facilitates the learning of new associations faster with controlled interference with previous associations. To mimic sparse coding in DNNs, we enforce activation sparsity along with a dropout mechanism which encourages the model to activate similar units for semantically similar inputs and have less overlap with activation patterns of semantically dissimilar inputs. This provides us with an efficient mechanism for balancing the reusability and interference of features, depending on the similarity of classes across tasks. Furthermore, we employ sparse coding in a multiple-memory replay mechanism. Our method maintains an additional long-term semantic memory that aggregates and consolidates information encoded in the synaptic weights of the working model. Our extensive evaluation and characteristics analysis show that equipped with these biologically inspired mechanisms, the model can further mitigate forgetting. Code available at \url{https://github.com/NeurAI-Lab/SCoMMER}.
Probabilistic Programs as an Action Description Language

Actions description languages (ADLs), such as STRIPS, PDDL, and RDDL specify the input format for planning algorithms. Unfortunately, their syntax is familiar to planning experts only, and not to potential users of planning technology. Moreover, this syntax limits the ability to describe complex and large domains. We argue that programming languages (PLs), and more specifically, probabilistic programming languages (PPLs), provide a more suitable alternative. PLs are familiar to all programmers, support complex data types and rich libraries for their manipulation, and have powerful constructs, such as loops, sub-routines, and local variables with which complex, realistic models and complex objectives can be simply and naturally specified. PPLs, specifically, make it easy to specify distributions, which is essential for stochastic models. The natural objection to this proposal is that PLs are opaque and too expressive, making reasoning about them difficult. However, PPLs also come with efficient inference algorithms, which, coupled with a growing body of work on sampling-based and gradient-based planning, imply that planning and execution monitoring can be carried out efficiently in practice. In this paper, we expand on this proposal, illustrating its potential with examples.
AdaBoost.C2: Boosting Classifiers Chains for Multi-Label Classification

During the last decades, multi-label classification (MLC) has attracted the attention of more and more researchers due to its wide real-world applications. Many boosting methods for MLC have been proposed and achieved great successes. However, these methods only extend existing boosting frameworks to MLC and take loss functions in multi-label version to guide the iteration. These loss functions generally give a comprehensive evaluation on the label set entirety, and thus the characteristics of different labels are ignored. In this paper, we propose a multi-path AdaBoost framework specific to MLC, where each boosting path is established for distinct label and the combination of them is able to provide a maximum optimization to Hamming Loss. In each iteration, classifiers chain is taken as the base classifier to strengthen the connection between multiple AdaBoost paths and exploit the label correlation. Extensive experiments demonstrate the effectiveness of the proposed method.
Online Hyperparameter Optimization for Class-Incremental Learning

Class-incremental learning (CIL) aims to train a classification model while the number of classes increases phase-by-phase. An inherent challenge of CIL is the stability-plasticity tradeoff, i.e., CIL models should keep stable to retain old knowledge and keep plastic to absorb new knowledge. However, none of the existing CIL models can achieve the optimal tradeoff in different data-receiving settings—where typically the training-from-half (TFH) setting needs more stability, but the training-from-scratch (TFS) needs more plasticity. To this end, we design an online learning method that can adaptively optimize the tradeoff without knowing the setting as a priori. Specifically, we first introduce the key hyperparameters that influence the tradeoff, e.g., knowledge distillation (KD) loss weights, learning rates, and classifier types. Then, we formulate the hyperparameter optimization process as an online Markov Decision Process (MDP) problem and propose a specific algorithm to solve it. We apply local estimated rewards and a classic bandit algorithm Exp3 to address the issues when applying online MDP methods to the CIL protocol. Our method consistently improves top-performing CIL methods in both TFH and TFS settings, e.g., boosting the average accuracy of TFH and TFS by 2.2 percentage points on ImageNet-Full, compared to the state-of-the-art. Code is provided at https://class-il.mpi-inf.mpg.de/online/
CLIPVG: Text-Guided Image Manipulation Using Differentiable Vector Graphics

Considerable progress has recently been made in leveraging CLIP (Contrastive Language-Image Pre-Training) models for text-guided image manipulation. However, all existing works rely on additional generative models to ensure the quality of results, because CLIP alone cannot provide enough guidance information for fine-scale pixel-level changes. In this paper, we introduce CLIPVG, a text-guided image manipulation framework using differentiable vector graphics, which is also the first CLIP-based general image manipulation framework that does not require any additional generative models. We demonstrate that CLIPVG can not only achieve state-of-art performance in both semantic correctness and synthesis quality, but also is flexible enough to support various applications far beyond the capability of all existing methods.
Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation

Over the years, learning-based multi-view stereo methods have achieved great success based on their coarse-to-fine depth estimation frameworks. However, 3D CNN-based cost volume regularization inevitably leads to over-smoothing problems at object boundaries due to its smooth properties. Moreover, discrete and sparse depth hypothesis sampling exacerbates the difficulty in recovering the depth of thin structures and object boundaries. To this end, we present an Efficient edge-Preserving multi-view stereo Network (EPNet) for practical depth estimation. To keep delicate estimation at details, a Hierarchical Edge-Preserving Residual learning (HEPR) module is proposed to progressively rectify the upsampling errors and help refine multi-scale depth estimation. After that, a Cross-view Photometric Consistency (CPC) is proposed to enhance the gradient flow for detailed structures, which further boosts the estimation accuracy. Last, we design a lightweight cascade framework and inject the above two strategies into it to achieve better efficiency and performance trade-offs. Extensive experiments show that our method achieves state-of-the-art performance with fast inference speed and low memory usage. Notably, our method tops the first place on challenging Tanks and Temples advanced dataset and ETH3D high-res benchmark among all published learning-based methods. Code will be available at https://github.com/susuwj/EPNet.
An Improved Flow Direction Algorithm for Engineering Optimization Problems

Flow Direction Algorithm (FDA) has better searching performance than some traditional optimization algorithms. To give the basic Flow Direction Algorithm more effective searching ability and avoid multiple local minima under the searching space, and enable it to obtain better search results, an improved FDA based on the Lévy flight strategy and the self-renewable method (LSRFDA) was proposed in this paper. The Lévy flight strategy and the self-renewable approach were added to the basic Flow Direction Algorithm. Random parameters generated by the Lévy flight strategy can increase the algorithm’s diversity of feasible solutions in a short calculation time and greatly enhance the operational efficiency of the algorithm. The self-renewable method lets the algorithm quickly obtain a better possible solution and jump to the local solution space. Then, this paper tested different mathematical testing functions, including low-dimensional and high-dimensional functions, and the test results were compared with those of different algorithms. This paper includes iterative figures, box plots, and search paths to show the different performances of the LSRFDA. Finally, this paper calculated different engineering optimization problems. The test results show that the proposed algorithm in this paper has better searching ability and quicker searching speed than the basic Flow Direction Algorithm.
Agent-Centric Relation Graph for Object Visual Navigation

Object visual navigation aims to steer an agent toward a target object based on visual observations. It is highly desirable to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">reasonably perceive</i> the environment and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">accurately control</i> the agent. In the navigation task, we introduce an Agent-Centric Relation Graph (ACRG) for learning the visual representation based on the relationships in the environment. ACRG is a highly effective structure that consists of two relationships, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e., the horizontal relationship among objects</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">the distance relationship between the agent and objects</i> . On the one hand, we design the Object Horizontal Relationship Graph (OHRG) that stores the relative horizontal location among objects. On the other hand, we propose the Agent-Target Distance Relationship Graph (ATDRG) that enables the agent to perceive the distance between the target and objects. For ATDRG, we utilize image depth to obtain the target distance and imply the vertical location to capture the distance relationship among objects in the vertical direction. With the above graphs, the agent can perceive the environment and output navigation actions. Experimental results in the artificial environment AI2-THOR demonstrate that ACRG significantly outperforms other state-of-the-art methods in unseen testing environments.
Rethinking rights in social media governance: human rights, ideology and inequality

Abstract This paper aims to question the dominance of human rights as the primary normative framework for European social media regulation, and academic research in this field. Analysing EU legislation and recent ECJ cases, it shows that issues like discriminatory content moderation, profiling, and promotion of stereotypes cannot adequately be addressed within a human rights framework. The centrality of individual rights in the EU legal regime not only fails to address collective issues, like platforms’ influence on culture and social norms, but cannot even offer effective, equal protection to individuals. In policy debates, the depoliticised and individualistic language of human rights can legitimise corporate activities and downplay important questions about the political economy of this privatised, highly-concentrated, advertiser-funded industry. The paper also considers interpretations of human rights as structural conditions or collective values, and argues that they cannot fully overcome the limitations discussed here. Given the entrenched role of fundamental rights in EU law, critics of social media cannot avoid relying on them. However, academics should also seek to develop more explicitly political critiques, based on alternative normative visions.
Learning Evaluation for Intelligence

The field of machine learning is only getting started. Only in the last quarter of a century have researchers focused on more advanced forms of machine learning, and this has led to a rise in the popularity of data science. And so, the data science community continues to marvel at the boundless possibilities of AI and machine learning. The industry as a whole is learning, growing, and facing new challenges as a result of this, which is both exciting and confusing.
Heavy metal contamination of Batanghari River, Jambi, Indonesia: determination based on sediment enrichment factor value

Batanghari River is the primary source of water for the people of Jambi. Batanghari River’s condition nowadays is worrying because its color is no longer clear. This research focused on determining the content of heavy metals in the Batanghari River's water and sediment. The conducted analysis is an ICP-MS analysis to determine the level of heavy metal (Cu, Cr, Co, Cd, Hg), and an enrichment factor calculation is conducted to determine the source of pollution. The enrichment factor value from the Batanghari River sediment sample declined with the following sequence Cd&amp;gt;Hg&amp;gt;Cu&amp;gt;Cr&amp;gt;Co, in 10 pinpoint locations. These indicate a high degree of anthropogenic activities along the Batanghari River which become the source of heavy metals entering the Batanghari River. The average concentration of heavy metals in the Batanghari River showed that the metal concentrations of Cd, Cu, and Hg are higher than the quality standards that have been set, whereas the metal concentrations of Cr and Co are still by the quality standards that have been set in PP 22 the year 2021.
The Impact of Lean Manufacturing Practices on Green Sustainability: The Case of Abdulghani Company

&#x0D; This study examines the impact of lean manufacturing practices on green sustainability in the context of Abdulghani Company. The study used a sample size of 180 respondents to collect data on the implementation of lean practices and the company's green sustainability performance. The findings indicate that the implementation of lean practices has a significant positive impact on green sustainability performance. Specifically, lean practices such as waste reduction, energy conservation, and process improvement contribute to a reduction in the company's environmental impact and improved environmental sustainability. The study recommends that companies adopt lean practices as a means of achieving both operational excellence and sustainable development.&#x0D;
Frequency of hyperthermia in acute ischemic stroke patients presenting to tertiary care hospital.

Objective: To determine frequency of hyperthermia in acute ischemic stroke patients. Study Design: Descriptive Cross-sectional study. Setting: Department of Medicine, M T I-H M C, Peshawar. Period: January, 2020 to Dec, 2020. Material &amp; Methods: All patients admitted through emergency Deptt was included in the study keeping in view inclusion criteria. All patients' axillary temperatures were checked using a mercury thermometer at the time of admission and every six hours for three days. To determine whether hyperthermia met the practical meaning of the term, daily follow-up was conducted. All of the data was entered into a pre-designed proforma. Results: Mean age was recorded as 65.5+7.67 years and mean BMI was 26+1.65kg/m2. 107 (51.94%) patients were recorded as male patients, 99 (48.05%) patients were recorded female patients.32 (15.53%) patients were recorded with hyperthermia. Risk factors were traced in 91.26% had history of hypertension, 84.46% had diabetes mellitus and 78.15% had smoking history. P-value with advancing age (p=0.020) and severity of stroke (p=0.278). Conclusion: It has been concluded in our study that hyperthermia in ischemic stroke should be looked as it has significant impact on the outcome.