Reviewerly/exHarmony huggingface dataset:
NMF-based approach to automatic term extraction

• 5 Different NMF algorithms with different parameters are compared. • Kullback-Leibler NMF requiring the stationarity of objective value is the best. • The performance of NMF algorithms depends on a corpus imbalance. • NMF outperforms 4 from 6 baseline methods and second only to deep learning methods. This work describes automatic term extraction approach based on the combination of the probabilistic topic modelling (PTM) and non-negative matrix factorization (NMF). Topic modeling algorithms including NMF-based ones do not require expensive and time-consuming manual annotations for domain terms, but only a corpus of domain documents. The topics emerge from the corpus documents without any supervision as sets of most probable words. This work is aimed to investigate how fully and precisely these most probable words from topics can reflect domain terminology. We run a series of experiments on the novel, qualitatively annotated dataset ACTER that was first used in the TermEval 2020 Shared Task. We compare five different NMF algorithms and four different NMF initializations when changing the number of topics extracted from documents and the number of most probable words extracted from topics in order to determine optimal combinations for best performance of term extraction. Finally, we compare the obtained optimal combinations of NMF with the competitive methods in TermEval 2020 and prove that our approach is second only to two much more sophisticated, domain-dependent supervised methods.
Memory effect of a dephasing channel on measurement uncertainty, dense coding, teleportation, and quantum Fisher information

We investigate measurement uncertainty of two incompatible observables, dense coding capacity, teleportation fidelity, and quantum Fisher information for two qubits traversing sequentially a dephasing channel modeled by the multimode bosonic field. Using a memory factor determined by the time separation between two channel uses and the transmit time of each qubit, it is shown that the memory effect of this channel on these four quantum characteristics is state-dependent. It is beneficial for some initial states and detrimental for the other states. There is also circumstance in which the memory effects with different origins have different influences on these quantum characteristics. These results may enrich our comprehension on the interplay between memory effect of a channel and decoherence process of a multi-qubit system.
An evaluation of agile Ontology Engineering Methodologies for the digital transformation of companies

Ontologies are increasingly recognised among the key enablers of the digital transformation of knowledge management processes, but still with a low level of adoption in manufacturing companies. Because ontologies and underlying technologies are complex, Ontology Engineering Methodologies (OEMs) provide a set of guidelines to move from an informal to a formal representation of the company’s knowledge base. This study evaluates three agile OEMs, i.e. UPONLite, SAMOD and RapidOWL, in terms of their process and outcome features, i.e. the OEM steps and the expected quality of the ontological models produced. The assessment is performed from the viewpoint of developers of ontology-based technologies in real industrial use cases. Results show that the three agile OEMs reflect different features to effectively support the digital transformation of companies' knowledge management; thus, they cannot be interchangeable. UPONLite is more effective in contexts where there is a lack of skills in OE, with the need for a structured approach in involving domain experts and generating documentation. SAMOD requires a more extended development period, but with several cycles that allow to map different types of knowledge and enable a “try-and-learn” approach. Conversely, RapidOWL lacks a structured sequence of modelling activities and encourages developers to be creative, but at the same time requires higher expertise in OE. Thus, companies and personnel dedicated to OE should choose the methodology according to the main aims guiding their digitalisation process, the current development status, and the level of expertise. • Digitalization of knowledge management processes can be enhanced by Ontology Engineering Methodologies (OEM). • Three Agile OEMs are evaluated in terms of outcome and process features. • UPONLite supports documentation and knowledge reuse with clear and structured steps. • SAMOD provides adaptability and flexibility with additional iterative cycles. • RapidOWL mainly encourages creativity and innovation in ontology engineering.
Subjective and Objective Analyses of Collaboration and Co-Presence in a Virtual Reality Remote Environment

Remote collaboration in virtual reality has gained attention and proved to be a viable solution for providing effective collaboration environments for physically distant collaborators. This study compares head-mounted display (HMD)- and computer-based remote collaboration solutions that allow users to interact with each other through immersive environments. Analyzing remote collaboration in immersive environments requires understanding group interactions and personal experiences. For this purpose, a 3D object assembly task was performed by 10 participants using self-reported surveys and physiological measures to investigate the effectiveness of collaboration from the users&#x0027; perspective. The results showed that the HMD-based remote collaboration in a virtual reality environment increased the sense of co-presence among the users.
Perspectives Organize Information in Mind and Nature: Empirical Findings of Point-View Perspective (P) in Cognitive and Material Complexity

The importance of perspective-taking crosses disciplines and is foundational to diverse phenomena such as point-of-view, scale, mindset, theory of mind, opinion, belief, empathy, compassion, analysis, and problem solving, etc. This publication gives predictions for and a formal description of point-view Perspectives (P) or the “P-rule”. This makes the P-rule foundational to systems, systems thinking and the consilience of knowledge. It is one of four universals of the organization of information as a whole. This paper presents nine empirical studies in which subjects were asked to complete a task and/or answer a question. The samples vary for each study (ranging from N = 407 to N = 34,398) and are generalizable to a normal distribution of the US population. As was evident in Cabrera, “These studies support—with high statistical significance—the predictions made by DSRP Theory (Distinctions, Systems Relationships, Perspectives) point-view Perspectives including its: universality as an observable phenomenon in both mind (cognitive complexity) and nature (material complexity) (i.e., parallelism); internal structures and dynamics; mutual dependencies on other universals (i.e., Distinctions, Systems, and Relationships); role in structural predictions; and, efficacy as a metacognitive skill”. These data suggest that point-view Perspectives (P) observably and empirically exist, and that universality, efficacy, and parallelism (between cognitive and material complexity) exist as well. The impact of this paper is that it provides empirical evidence for the phenomena of point-view perspective taking (“P-rule”) as a universal pattern/structure of systems thinking, a field in which scholarly debate is often based on invalidated opinioned frameworks; this sets the stage for theory building in the field.
Learning Privacy-Preserving Graph Convolutional Network with Partially Observed Sensitive Attributes

Recent studies have shown Graph Neural Networks (GNNs) are extremely vulnerable to attribute inference attacks. To tackle this challenge, existing privacy-preserving GNNs research assumes that the sensitive attributes of all users are known beforehand. However, due to different privacy preferences, some users (i.e., private users) may prefer not to reveal sensitive information that others (i.e., non-private users) would not mind disclosing. For example, in social networks, male users are typically less sensitive to their age information than female users. The age disclosure of male users can lead to the age information of female users in the network exposed. This is partly because social media users are connected, the homophily property and message-passing mechanism of GNNs can exacerbate individual privacy leakage. In this work, we study a novel and practical problem of learning privacy-preserving GNNs with partially observed sensitive attributes.
FIRE: Fast Incremental Recommendation with Graph Signal Processing

Recommender systems are incremental in nature. Recent progresses in incremental recommendation rely on capturing the temporal dynamics of users/items from temporal interaction graphs, so that their user/item embeddings can evolve together with the graph structures. However, these methods are faced with two key challenges: 1) model training and/or updating are time-consuming and 2) new users/items cannot be effectively handled. To this end, we propose the fast incremental recommendation (FIRE) method from a graph signal processing perspective. FIRE is non-parametric which does not suffer from the time-consuming back-propagations as in previous learning-based methods, significantly improving the efficiency of model updating. In addition, we encode user/item temporal information and side information by designing new graph filters in FIRE, which can capture the temporal dynamics of users/items and address the cold-start issue for new users/items, respectively. Experimental studies on four popular datasets demonstrate that FIRE can improve the accuracy by a large margin and improve the model updating efficiency by at least 3X compared with the state-of-the-art incremental recommendation algorithms. The Code is available at https://github.com/Yaveng/FIRE.
Reproducibility and Replicability of Web Measurement Studies

Web measurement studies can shed light on not yet fully understood phenomena and thus are essential for analyzing how the modern Web works. This often requires building new and adjusting existing crawling setups, which has led to a wide variety of analysis tools for different (but related) aspects. If these efforts are not sufficiently documented, the reproducibility and replicability of the measurements may suffer—two properties that are crucial to sustainable research. In this paper, we survey 117 recent research papers to derive best practices for Web-based measurement studies and specify criteria that need to be met in practice. When applying these criteria to the surveyed papers, we find that the experimental setup and other aspects essential to reproducing and replicating results are often missing. We underline the criticality of this finding by performing a large-scale Web measurement study on 4.5 million pages with 24 different measurement setups to demonstrate the influence of the individual criteria. Our experiments show that slight differences in the experimental setup directly affect the overall results and must be documented accurately and carefully.
Swift and Sure: Hardness-aware Contrastive Learning for Low-dimensional Knowledge Graph Embeddings

Knowledge graph embedding (KGE) has shown great potential in automatic knowledge graph (KG) completion and knowledge-driven tasks. However, recent KGE models suffer from high training cost and large storage space, thus limiting their practicality in real-world applications. To address this challenge, based on the latest findings in the field of Contrastive Learning, we propose a novel KGE training framework called Hardness-aware Low-dimensional Embedding (HaLE). Instead of the traditional Negative Sampling, we design a new loss function based on query sampling that can balance two important training targets, Alignment and Uniformity. Furthermore, we analyze the hardness-aware ability of recent low-dimensional hyperbolic models and propose a lightweight hardness-aware activation mechanism, which can help the KGE models focus on hard instances and speed up convergence. The experimental results show that in the limited training time, HaLE can effectively improve the performance and training speed of KGE models on five commonly-used datasets. After training just a few minutes, the HaLE-trained models are competitive compared to the state-of-the-art models in both low- and high-dimensional conditions.
Generating Perturbation-based Explanations with Robustness to Out-of-Distribution Data

Perturbation-based techniques are promising for explaining black-box machine learning models due to their effectiveness and ease of implementation. However, prior works have faced the problem of Out-of-Distribution (OoD) — an artifact of randomly perturbed data becoming inconsistent with the original dataset, degrading the reliability of generated explanations, which is still under-explored according to our best knowledge. This work addresses the OoD issue by designing a simple yet effective module that can quantify the affinity between the perturbed data and the original dataset distribution. Specifically, we penalize the influences of unreliable OoD data for the perturbed samples by integrating the inlier scores and prediction results of the target models, thereby making the final explanations more robust. Our solution is shown to be compatible with the most popular perturbation-based XAI algorithms: RISE, OCCLUSION, and LIME. Extensive experiments confirmed that our methods exhibit superior performance in most cases with computational and cognitive metrics. In particular, we point out the degradation problem of RISE algorithm for the first time. With our design, the performance of RISE can be boosted significantly. Besides, our solution also resolves a fundamental problem with a faithfulness indicator, a commonly used evaluation metric of XAI algorithms that appears sensitive to the OoD issue.
Towards a super-resolution based approach for improved face recognition in low resolution environment

The video surveillance activity generates a vast amount of data, which can be processed to detect miscreants. The task of identifying and recognizing an object in surveillance data is intriguing yet difficult due to the low resolution of captured images or video. The super-resolution approach aims to enhance the resolution of an image to generate a desirable high-resolution one. This paper develops a robust real-time face recognition approach that uses super-resolution to improve images and detect faces in the video. Many previously developed face detection systems are constrained by the severe distortion in the captured images. Further, many systems failed to handle the effect of motion, blur, and noise on the images registered on a camera. The presented approach improves descriptor count of the image based on the super-resolved faces and mitigates the effect of noise. Furthermore, it uses a parallel architecture to implement a super-resolution algorithm and overcomes the efficiency drawback increasing face recognition performance. Experimental analysis on the ORL, Caltech, and Chokepoint datasets has been carried out to evaluate the performance of the presented approach. The PSNR (Peak Signal-to-Noise-Ratio) and face recognition rate are used as the performance measures. The results showed significant improvement in the recognition rates for images where the face didn't contain pose expressions and scale variations. Further, for the complicated cases involving scale, pose, and lighting variations, the presented approach resulted in an improvement of 5%-6% in each case.
Performance Evaluation of RSA, ElGamal, and Paillier Partial Homomorphic Encryption Algorithms

Information security has progressed dramatically over the past century because of the significant advancements in computer and communications research. As a result, numerous scholars have made it their major goal to examine, improve, implement, or compare its methods. Cryptography is one of the most critical information security topics, with many different encryption algorithms, such as symmetric key and public key encryption methods, branching out from it. Homomorphic encryption is a form of encryption that permits mathematical operations on a ciphertext; it has recently become famous for protecting data in the cloud. This paper offers essential knowledge to researchers who may want it before implementing homomorphic/public key methods. It analyses the unpadded RSA, Paillier, and ElGamal public-key algorithms. The evaluation is done according to several criteria, including secrecy, homomorphism property, encryption/decryption time, memory usage, encryption throughput criteria, and how different file sizes and key sizes affect these criteria. The results demonstrate that the three algorithms compete in the analysed criteria. Depending on the application and the required features, any one of the algorithms might be favoured over the other.
An Accelerator for Sparse Convolutional Neural Networks Leveraging Systolic General Matrix-matrix Multiplication

This article proposes a novel hardware accelerator for the inference task with sparse convolutional neural networks (CNNs) by building a hardware unit to perform Image to Column ( Im2Col ) transformation of the input feature map coupled with a systolic-array-based general matrix-matrix multiplication (GEMM) unit. Our design carefully overlaps the Im2Col transformation with the GEMM computation to maximize parallelism. We propose a novel design for the Im2Col unit that uses a set of distributed local memories connected by a ring network, which improves energy efficiency and latency by streaming the input feature map only once. The systolic-array-based GEMM unit in the accelerator can be dynamically configured as multiple GEMM units with square-shaped systolic arrays or as a single GEMM unit with a tall systolic array. This dynamic reconfigurability enables effective pipelining of Im2Col and GEMM operations and attains high processing element utilization for a wide range of CNNs. Further, our accelerator is sparsity aware, improving performance and energy efficiency by effectively mapping the sparse feature maps and weights to the processing elements, skipping ineffectual operations and unnecessary data movements involving zeros. Our prototype, SPOTS, is on average 2.16 \( \times \) , 1.74 \( \times \) , and 1.63 \( \times \) faster than Gemmini, Eyeriss, and Sparse-PE, which are prior hardware accelerators for dense and sparse CNNs, respectively. SPOTS is also 78 \( \times \) and 12 \( \times \) more energy-efficient when compared to CPU and GPU implementations, respectively.
Efficient scheme for realizing a multiplex-controlled phase gate with photonic qubits in circuit quantum electrodynamics

We propose an efficient scheme to implement a multiplex-controlled phase gate with multiple photonic qubits simultaneously controlling one target photonic qubit based on circuit quantum electrodynamics (QED). For convenience, we denote this multiqubit gate as MCP gate. The gate is realized by using a two-level coupler to couple multiple cavities. The coupler here is a superconducting qubit. This scheme is simple because the gate implementation requires only one step of operation. In addition, this scheme is quite general because the two logic states of each photonic qubit can be encoded with a vacuum state and an arbitrary non-vacuum state ∣φ〉 (e.g., a Fock state, a superposition of Fock states, a cat state, or a coherent state, etc.) which is orthogonal or quasi-orthogonal to the vacuum state. The scheme has some additional advantages: because only two levels of the coupler are used, i.e., no auxiliary levels are utilized, decoherence from higher energy levels of the coupler is avoided; the gate operation time does not depend on the number of qubits; and the gate is implemented deterministically because no measurement is applied. As an example, we numerically analyze the circuit-QED based experimental feasibility of implementing a three-qubit MCP gate with photonic qubits each encoded via a vacuum state and a cat state. The scheme can be applied to accomplish the same task in a wide range of physical system, which consists of multiple microwave or optical cavities coupled to a two-level coupler such as a natural or artificial atom.
Dynamic effects on reservoir computing with a Hopf oscillator

Limit cycle oscillators have the potential to be resourced as reservoir computers due to their rich dynamics. Here, a Hopf oscillator is used as a physical reservoir computer by discarding the delay line and time-multiplexing procedure. A parametric study is used to uncover computational limits imposed by the dynamics of the oscillator using parity and chaotic time-series prediction benchmark tasks. Resonance, frequency ratios from the Farey sequence, and Arnold tongues were found to strongly affect the computation ability of the reservoir. These results provide insights into fabricating physical reservoir computers from limit cycle systems.
Multi-Level Interaction Reranking with User Behavior History

As the final stage of the multi-stage recommender system (MRS), reranking directly affects users' experience and satisfaction, thus playing a critical role in MRS. Despite the improvement achieved in the existing work, three issues are yet to be solved. First, users' historical behaviors contain rich preference information, such as users' long and short-term interests, but are not fully exploited in reranking. Previous work typically treats items in history equally important, neglecting the dynamic interaction between the history and candidate items. Second, existing reranking models focus on learning interactions at the item level while ignoring the fine-grained feature-level interactions. Lastly, estimating the reranking score on the ordered initial list before reranking may lead to the early scoring problem, thereby yielding suboptimal reranking performance. To address the above issues, we propose a framework named Multi-level Interaction Reranking (MIR). MIR combines low-level cross-item interaction and high-level set-to-list interaction, where we view the candidate items to be reranked as a set and the users' behavior history in chronological order as a list. We design a novel SLAttention structure for modeling the set-to-list interactions with personalized long-short term interests. Moreover, feature-level interactions are incorporated to capture the fine-grained influence among items. We design MIR in such a way that any permutation of the input items would not change the output ranking, and we theoretically prove it. Extensive experiments on three public and proprietary datasets show that MIR significantly outperforms the state-of-the-art models using various ranking and utility metrics.
Local Information Modeling with Self-Attention for Speaker Verification

Transformer based on self attention mechanism has demonstrated its state-of-the-art performance in most natural language processing (NLP) tasks, but it’s not very competitive when applied for speaker verification in previous works. Generally, speaker identity is mostly reflected by the relationship between adjacent tokens, whose extraction mainly depends on local modeling ability. However, the self-attention module, as the key component of transformer, can help the model make full use of global information but insufficient to capture the local information. To tackle this limitation, in this paper, we strengthen the local information modeling from two different aspects: restricting the attention context to be local and introducing convolution operation into transformer. Experiments conducted on Voxceleb illustrate that our proposed methods can notably improve system performance, verifying the significance of local information for speaker verification task.