Reviewerly/exHarmony huggingface dataset:
Addressing the programming challenges of practical interferometric mesh based optical processors

We demonstrate a novel mesh of Mach-Zehnder interferometers (MZIs) for programmable optical processors. The proposed mesh, referred to as Bokun mesh, is an architecture that merges the attributes of the prior topologies Diamond and Clements. Similar to Diamond, Bokun provides diagonal paths passing through every individual MZI enabling direct phase monitoring. However, unlike Diamond and similar to Clements, Bokun maintains a minimum optical depth leading to better scalability. Providing the monitoring option, Bokun's programming is faster improving the total energy efficiency of the processor. The performance of Bokun mesh enabled by an optimal optical depth is also more resilient to the loss and fabrication imperfections compared to architectures with longer depth such as Reck and Diamond. Employing an efficient programming scheme, the proposed architecture improves energy efficiency by 83% maintaining the same computation accuracy for weight matrix changes at 2 kHz.
Asteria-Pro: Enhancing Deep Learning-based Binary Code Similarity Detection by Incorporating Domain Knowledge

Widespread code reuse allows vulnerabilities to proliferate among a vast variety of firmware. There is an urgent need to detect these vulnerable codes effectively and efficiently. By measuring code similarities, AI-based binary code similarity detection is applied to detecting vulnerable code at scale. Existing studies have proposed various function features to capture the commonality for similarity detection. Nevertheless, the significant code syntactic variability induced by the diversity of IoT hardware architectures diminishes the accuracy of binary code similarity detection. In our earlier study and the tool Asteria , we adopted a Tree-LSTM network to summarize function semantics as function commonality, and the evaluation result indicates an advanced performance. However, it still has utility concerns due to excessive time costs and inadequate precision while searching for large-scale firmware bugs. To this end, we propose a novel deep learning-enhancement architecture by incorporating domain knowledge-based pre-filtration and re-ranking modules, and we develop a prototype named Asteria-Pro based on Asteria . The pre-filtration module eliminates dissimilar functions, thus reducing the subsequent deep learning-model calculations. The re-ranking module boosts the rankings of vulnerable functions among candidates generated by the deep learning model. Our evaluation indicates that the pre-filtration module cuts the calculation time by 96.9%, and the re-ranking module improves MRR and Recall by 23.71% and 36.4%, respectively. By incorporating these modules, Asteria-Pro outperforms existing state-of-the-art approaches in the bug search task by a significant margin. Furthermore, our evaluation shows that embedding baseline methods with pre-filtration and re-ranking modules significantly improves their precision. We conduct a large-scale real-world firmware bug search, and Asteria-Pro manages to detect 1,482 vulnerable functions with a high precision 91.65%.
AI and IP: Theory to Policy and Back Again – Policy and Research Recommendations at the Intersection of Artificial Intelligence and Intellectual Property

Abstract The interaction between artificial intelligence and intellectual property rights (IPRs) is one of the key areas of development in intellectual property law. After much, albeit selective, debate, it seems to be gaining increasing practical relevance through intense AI-related market activity, an initial set of case law on the matter, and policy initiatives by international organizations and lawmakers. Against this background, Zurich University’s Center for Intellectual Property and Competition Law is conducting, together with the Swiss Intellectual Property Institute, a research and policy project that explores the future of intellectual property law in an AI context. This paper briefly describes the AI/IP Research Project and presents an initial set of policy recommendations for the development of IP law with a view to AI. The recommendations address topics such as AI inventorship in patent law; AI authorship in copyright law; the need for sui generis rights to protect innovative AI output; rules for the allocation of AI-related IPRs; IP protection carve-outs in order to facilitate AI system development, training, and testing; the use of AI tools by IP offices; and suitable software protection and data usage regimes.
MRCN: A Novel Modality Restitution and Compensation Network for Visible-Infrared Person Re-identification

Visible-infrared person re-identification (VI-ReID), which aims to search identities across different spectra, is a challenging task due to large cross-modality discrepancy between visible and infrared images. The key to reduce the discrepancy is to filter out identity-irrelevant interference and effectively learn modality-invariant person representations. In this paper, we propose a novel Modality Restitution and Compensation Network (MRCN) to narrow the gap between the two modalities. Specifically, we first reduce the modality discrepancy by using two Instance Normalization (IN) layers. Next, to reduce the influence of IN layers on removing discriminative information and to reduce modality differences, we propose a Modality Restitution Module (MRM) and a Modality Compensation Module (MCM) to respectively distill modality-irrelevant and modality-relevant features from the removed information. Then, the modality-irrelevant features are used to restitute to the normalized visible and infrared features, while the modality-relevant features are used to compensate for the features of the other modality. Furthermore, to better disentangle the modality-relevant features and the modality-irrelevant features, we propose a novel Center-Quadruplet Causal (CQC) loss to encourage the network to effectively learn the modality-relevant features and the modality-irrelevant features. Extensive experiments are conducted to validate the superiority of our method on the challenging SYSU-MM01 and RegDB datasets. More remarkably, our method achieves 95.1% in terms of Rank-1 and 89.2% in terms of mAP on the RegDB dataset.
On the Effectiveness of Curriculum Learning in Educational Text Scoring

Automatic Text Scoring (ATS) is a widely-investigated task in education. Existing approaches often stressed the structure design of an ATS model and neglected the training process of the model. Considering the difficult nature of this task, we argued that the performance of an ATS model could be potentially boosted by carefully selecting data of varying complexities in the training process. Therefore, we aimed to investigate the effectiveness of curriculum learning (CL) in scoring educational text. Specifically, we designed two types of difficulty measurers: (i) pre-defined, calculated by measuring a sample's readability, length, the number of grammatical errors or unique words it contains; and (ii) automatic, calculated based on whether a model in a training epoch can accurately score the samples. These measurers were tested in both the easy-to-hard to hard-to-easy training paradigms. Through extensive evaluations on two widely-used datasets (one for short answer scoring and the other for long essay scoring), we demonstrated that (a) CL indeed could boost the performance of state-of-the-art ATS models, and the maximum improvement could be up to 4.5%, but most improvements were achieved when assessing short and easy answers; (b) the pre-defined measurer calculated based on the number of grammatical errors contained in a text sample tended to outperform the other difficulty measurers across different training paradigms.
MetaShard: A Novel Sharding Blockchain Platform for Metaverse Applications

Due to its security, transparency, and flexibility in verifying virtual assets, blockchain has been identified as one of the key technologies for Metaverse. Unfortunately, blockchain-based Metaverse faces serious challenges such as massive resource demands, scalability, and security/privacy concerns. To address these issues, this paper proposes a novel sharding-based blockchain framework, namely MetaShard, for Metaverse applications. Particularly, we first develop an effective consensus mechanism, namely Proof-of-Engagement, that can incentivize MUs' data and computing resource contribution. Moreover, to improve the scalability of MetaShard, we propose an innovative sharding management scheme to maximize the network's throughput while protecting the shards from 51% attacks. Since the optimization problem is NP-complete, we develop a hybrid approach that decomposes the problem (using the binary search method) into sub-problems that can be solved effectively by the Lagrangian method. As a result, the proposed approach can obtain solutions in polynomial time, thereby enabling flexible shard reconfiguration and reducing the risk of corruption from the adversary. Extensive numerical experiments show that, compared to the state-of-the-art commercial solvers, our proposed approach can achieve up to 66.6% higher throughput in less than 1/30 running time. Moreover, the proposed approach can achieve global optimal solutions in most experiments.
Anatomy-guided domain adaptation for 3D in-bed human pose estimation

3D human pose estimation is a key component of clinical monitoring systems. The clinical applicability of deep pose estimation models, however, is limited by their poor generalization under domain shifts along with their need for sufficient labeled training data. As a remedy, we present a novel domain adaptation method, adapting a model from a labeled source to a shifted unlabeled target domain. Our method comprises two complementary adaptation strategies based on prior knowledge about human anatomy. First, we guide the learning process in the target domain by constraining predictions to the space of anatomically plausible poses. To this end, we embed the prior knowledge into an anatomical loss function that penalizes asymmetric limb lengths, implausible bone lengths, and implausible joint angles. Second, we propose to filter pseudo labels for self-training according to their anatomical plausibility and incorporate the concept into the Mean Teacher paradigm. We unify both strategies in a point cloud-based framework applicable to unsupervised and source-free domain adaptation. Evaluation is performed for in-bed pose estimation under two adaptation scenarios, using the public SLP dataset and a newly created dataset. Our method consistently outperforms various state-of-the-art domain adaptation methods, surpasses the baseline model by 31%/66%, and reduces the domain gap by 65%/82%. Source code is available at https://github.com/multimodallearning/da-3dhpe-anatomy.
Comparative Analysis of Classical Test Theory and Item Response Theory using Chemistry Test Data

Assessment of learning involves deciding whether or not the content and objectives of education are down pat by administering quality tests. This study assesses the standard of Chemistry action take a look at and compares the item statistics generated mistreatment CTT and IRT strategies. A descriptive survey was adopted involving a sample of N=530 students. The specialised XCALIBRE 4 and ITEMAN 4 softwares were used to conduct the item analysis. Results indicate that, both the two methods commonly identified 13(32.5%) items as “problematic” and 27(67.5%) were “good”. Similarly, a significantly higher correlation exists between item statistics derived from the CTT and IRT models, [(r=-0.985,) and (r=0.801) p&lt;0.05] for item difficulty and discrimination respectively; the study concludes that the Chemistry Achievement test used do not pass through the processes of standardisation. Secondly, CTT and IRT frameworks appeared to be effective and reliable in assessing test items as the two frameworks provide similar and comparable results. The study recommends that the teacher made Chemistry tests used in measuring students’ achievement should be made to pass through all the processes of standardisation. Meanwhile, CTT and IRT approaches of item analysis ought to be integrated within the aspects of item development and analysis because of their superiority within the investigation of reliability and minimising measurement errors.
Lightweight Bilateral Network for Real-Time Semantic Segmentation

Herein, a dual-branch semantic segmentation model based on depth-separable convolution and attention mechanism is proposed for the real-time and accuracy requirement of semantic segmentation. The proposed approach overcomes the problems of poor segmentation effect and over-simplification of feature fusion arising from the constant downsample operations in semantic segmentation. The network is divided into spatial detail and semantic information paths. The spatial detail path utilizes a smaller downsample multiplier to maintain resolution and efficiently extract spatial information. The semantic information path is constructed by a non-bottleneck residual unit with dilated convolution; it extracts semantic features. For the feature aggregation problem, the feature-guided fusion module is designed to assign different weights to the parts of the two paths and fuse them to obtain the final output. The proposed algorithm achieves a segmentation accuracy of 69.6% and speed of 70 fps on the Cityscapes dataset, with a model parameter count of only 0.76 M, thus indicating some advantages over recent real-time semantic segmentation algorithms. The proposed method with depth separable convolution and attention mechanism can effectively extract features and compensate for the loss of accuracy caused by downsampling. The experiments demonstrate that the proposed fusion module outperforms other methods in fusing different features.
Automated System for Colon Cancer Detection and Segmentation Based on Deep Learning Techniques

Colon cancer is one of the world's three most deadly and severe cancers. As with any cancer, the key priority is early detection. Deep learning (DL) applications have recently gained popularity in medical image analysis due to the success they have achieved in the early detection and screening of cancerous tissues or organs. This paper aims to explore the potential of deep learning techniques for colon cancer classification. This research will aid in the early prediction of colon cancer in order to provide effective treatment in the most timely manner. In this exploratory study, many deep learning optimizers were investigated, including stochastic gradient descent (SGD), Adamax, AdaDelta, root mean square prop (RMSprop), adaptive moment estimation (Adam), and the Nesterov and Adam optimizer (Nadam). According to the empirical results, the CNN-Adam technique produced the highest accuracy with an average score of 82% when compared to other models for four colon cancer datasets. Similarly, Dataset_1 produced better results, with CNN-Adam, CNN-RMSprop, and CNN-Adadelta achieving accuracy scores of 0.95, 0.76, and 0.96, respectively.
The Top-Down Solver—An Exercise in $$\text {A}^{2}$$I

The top-down solver TD is a convenient local generic fixpoint engine which is at the heart of static analysis frameworks such as Ciao and Goblint. Here, we show how Patrick Cousot’s idea of applying analysis to the analyzer itself allows to derive advanced versions of TD from a recursive descent fixpoint algorithm. A run of that fixpoint algorithm provides us with a trace whose dynamic analysis allows not only to identify semantic dependencies between unknowns on-the-fly, but also to choose appropriate widening/narrowing points. It is thus not only the sequence of iterates for individual unknowns which is taken into account, but the global trace of the fixpoint algorithm itself.
Fairness in Recommendation: Foundations, Methods, and Applications

As one of the most pervasive applications of machine learning, recommender systems are playing an important role on assisting human decision-making. The satisfaction of users and the interests of platforms are closely related to the quality of the generated recommendation results. However, as a highly data-driven system, recommender system could be affected by data or algorithmic bias and thus generate unfair results, which could weaken the reliance of the systems. As a result, it is crucial to address the potential unfairness problems in recommendation settings. Recently, there has been growing attention on fairness considerations in recommender systems with more and more literature on approaches to promote fairness in recommendation. However, the studies are rather fragmented and lack a systematic organization, thus making it difficult to penetrate for new researchers to the domain. This motivates us to provide a systematic survey of existing works on fairness in recommendation. This survey focuses on the foundations for fairness in recommendation literature. It first presents a brief introduction about fairness in basic machine learning tasks such as classification and ranking to provide a general overview of fairness research, as well as introduce the more complex situations and challenges that need to be considered when studying fairness in recommender systems. After that, the survey will introduce fairness in recommendation with a focus on the taxonomies of current fairness definitions, the typical techniques for improving fairness, as well as the datasets for fairness studies in recommendation. The survey also talks about the challenges and opportunities in fairness research with the hope of promoting the fair recommendation research area and beyond.
To Aggregate or Not? Learning with Separate Noisy Labels

The rawly collected training data often comes with separate noisy labels collected from multiple imperfect annotators (e.g., via crowdsourcing). A typical way of using these separate labels is to first aggregate them into one and apply standard training methods. The literature has also studied extensively on effective aggregation approaches. This paper revisits this choice and aims to provide an answer to the question of whether one should aggregate separate noisy labels into single ones or use them separately as given. We theoretically analyze the performance of both approaches under the empirical risk minimization framework for a number of popular loss functions, including the ones designed specifically for the problem of learning with noisy labels. Our theorems conclude that label separation is preferred over label aggregation when the noise rates are high, or the number of labelers/annotations is insufficient. Extensive empirical results validate our conclusions.
CFSum Coarse-to-Fine Contribution Network for Multimodal Summarization

Multimodal summarization usually suffers from the problem that the contribution of the visual modality is unclear.Existing multimodal summarization approaches focus on designing the fusion methods of different modalities, while ignoring the adaptive conditions under which visual modalities are useful.Therefore, we propose a novel Coarse-to-Fine contribution network for multimodal Summarization (CFSum) to consider different contributions of images for summarization.First, to eliminate the interference of useless images, we propose a pre-filter module to abandon useless images.Second, to make accurate use of useful images, we propose two levels of visual complement modules, word level and phrase level.Specifically, image contributions are calculated and are adopted to guide the attention of both textual and visual modalities.Experimental results have shown that CFSum significantly outperforms multiple strong baselines on the standard benchmark.Furthermore, the analysis verifies that useful images can even help generate nonvisual words which are implicitly represented in the image 1 .
A Survey on Arabic Named Entity Recognition: Past, Recent Advances, and Future Trends

As more and more Arabic texts emerged on the Internet, extracting important information from these Arabic texts is especially useful. As a fundamental technology, Named entity recognition (NER) serves as the core component in information extraction technology, while also playing a critical role in many other Natural Language Processing (NLP) systems, such as question answering and knowledge graph building. In this paper, we provide a comprehensive review of the development of Arabic NER, especially the recent advances in deep learning and pre-trained language model. Specifically, we first introduce the background of Arabic NER, including the characteristics of Arabic and existing resources for Arabic NER. Then, we systematically review the development of Arabic NER methods. Traditional Arabic NER systems focus on feature engineering and designing domain-specific rules. In recent years, deep learning methods achieve significant progress by representing texts via continuous vector representations. With the growth of pre-trained language model, Arabic NER yields better performance. Finally, we conclude the method gap between Arabic NER and NER methods from other languages, which helps outline future directions for Arabic NER.
Schrödinger-Heisenberg Variational Quantum Algorithms

Recent breakthroughs have opened the possibility of intermediate-scale quantum computing with tens to hundreds of qubits, and shown the potential for solving classical challenging problems, such as in chemistry and condensed matter physics. However, the high accuracy needed to surpass classical computers poses a critical demand on the circuit depth, which is severely limited by the non-negligible gate infidelity, currently around 0.1%--1%. The limited circuit depth places restrictions on the performance of variational quantum algorithms (VQA) and prevents VQAs from exploring desired nontrivial quantum states. To resolve this problem, we propose a paradigm of Schr\"odinger-Heisenberg variational quantum algorithms (SHVQA). Using SHVQA, the expectation values of operators on states that require very deep circuits to prepare can now be efficiently measured by rather shallow circuits. The idea is to incorporate a virtual Heisenberg circuit, which acts effectively on the measurement observables, into a real shallow Schr\"odinger circuit, which is implemented realistically on the quantum hardware. We choose a Clifford virtual circuit, whose effect on the Hamiltonian can be seen as efficient classical processing. Yet, it greatly enlarges the state's expressivity, realizing much larger unitary $t$ designs. Our method enables accurate quantum simulation and computation that otherwise are only achievable with much deeper circuits or more accurate operations conventionally. This has been verified in our numerical experiments for a better approximation of random states, higher-fidelity solutions to the $XXZ$ model, and the electronic structure Hamiltonians of small molecules. Thus, together with effective quantum error mitigation, our work paves the way for realizing accurate quantum computing algorithms with near-term quantum devices.