Reviewerly/exHarmony huggingface dataset:
Instance-Specific Feature Propagation for Referring Segmentation

Referring segmentation aims to generate a segmentation mask for the target instance indicated by a natural language expression. There are typically two kinds of existing methods: one-stage methods that directly perform segmentation on the fused vision and language features; and two-stage methods that first utilize an instance segmentation model for instance proposal and then select one of these instances via matching them with language features. In this work, we propose a novel framework that simultaneously detects the target-of-interest via feature propagation and generates a fine-grained segmentation mask. In our framework, each instance is represented by an Instance-Specific Feature (ISF), and the target-of-referring is identified by exchanging information among all ISFs using our proposed Feature Propagation Module (FPM). Our instance-aware approach learns the relationship among all objects, which helps to better locate the target-of-interest than one-stage methods. Comparing to two-stage methods, our approach collaboratively and interactively utilizes both vision and language information for synchronous identification and segmentation. In the experimental tests, our method outperforms previous state-of-the-art methods on all three RefCOCO series datasets.
O&amp;O: A DIY toolkit for designing and rapid prototyping olfactory interfaces

Constructing olfactory interfaces on demand requires significant design proficiency and engineering effort. The absence of powerful and convenient tools that reduced innovation complexity posed obstacles for future research in the area. To address this problem, we proposed O&O, a modular olfactory interface DIY toolkit. The toolkit consists of: (1) a scent generation kit, a set of electronics and accessories that supported three common scent vaporization techniques; (2) a module construction kit, a set of primitive cardboard modules for assembling permutable functional structures; (3) a design manual, a step-by-step design thinking framework that directs the decision-making and prototyping process. We organized a formal workshop with 19 participants and four solo DIY trials to evaluate the capability of the toolkit, the overall user engagement, the creations in both sessions, and the iterative suggestions. Finally, design implications and future opportunities were discussed for further research.
On incorrectness logic for Quantum programs

Bug-catching is important for developing quantum programs. Motivated by the incorrectness logic for classical programs, we propose an incorrectness logic towards a logical foundation for static bug-catching in quantum programming. The validity of formulas in this logic is dual to that of quantum Hoare logics. We justify the formulation of validity by an intuitive explanation from a reachability point of view and a comparison against several alternative formulations. Compared with existing works focusing on dynamic analysis, our logic provides sound and complete arguments. We further demonstrate the usefulness of the logic by reasoning several examples, including Grover's search, quantum teleportation, and a repeat-until-success program. We also automate the reasoning procedure by a prototyped static analyzer built on top of the logic rules.
Adaptive Client Clustering for Efficient Federated Learning over Non-IID and Imbalanced Data

Federated learning (FL) is an emerging distributed and privacy-preserving machine learning framework. However, the performance of traditional FL methods is seriously impaired by the real-world data, which appear to be non-IID. The recent clustered federated learning (CFL) methods eliminate the impact of non-IID data by grouping clients with similar data distribution into the same cluster. Unfortunately, existing CFL methods heavily rely on the pre-setting of the cluster number, failing to achieve adaptive client clustering. We also experimentally observe that imbalanced data largely degrade their correctness of client clustering. In this paper, we present a novel CFL method without manual intervention, named AutoCFL, which can eliminate both effects of non-IID and imbalanced data simultaneously. To deal with imbalanced data, the local training adjustment strategy adaptively adjusts the number of local training epochs for each client. To further improve the clustering correctness and adaptability, the weighted voting-based client clustering strategy automatically groups each client into an appropriate cluster. Extensive experiments are conducted to evaluate the design of AutoCFL with three popular datasets under various data settings. Experimental results demonstrate that AutoCFL outperforms state-of-the-art methods, e.g., on average improving model accuracy by 9.24%, while reducing communication costs by 4.67 in an adaptive manner.
SOIT: Segmenting Objects with Instance-Aware Transformers

This paper presents an end-to-end instance segmentation framework, termed SOIT, that Segments Objects with Instance-aware Transformers. Inspired by DETR, our method views instance segmentation as a direct set prediction problem and effectively removes the need for many hand-crafted components like RoI cropping, one-to-many label assignment, and non-maximum suppression (NMS). In SOIT, multiple queries are learned to directly reason a set of object embeddings of semantic category, bounding-box location, and pixel-wise mask in parallel under the global image context. The class and bounding-box can be easily embedded by a fixed-length vector. The pixel-wise mask, especially, is embedded by a group of parameters to construct a lightweight instance-aware transformer. Afterward, a full-resolution mask is produced by the instance-aware transformer without involving any RoI-based operation. Overall, SOIT introduces a simple single-stage instance segmentation framework that is both RoI- and NMS-free. Experimental results on the MS COCO dataset demonstrate that SOIT outperforms state-of-the-art instance segmentation approaches significantly. Moreover, the joint learning of multiple tasks in a unified query embedding can also substantially improve the detection performance. Code is available at https://github.com/yuxiaodongHRI/SOIT.
Efficient Heuristic Algorithms for Single-Vehicle Task Planning With Precedence Constraints

This article investigates the task planning problem where one vehicle needs to visit a set of target locations while respecting the precedence constraints that specify the sequence orders to visit the targets. The objective is to minimize the vehicle's total travel distance to visit all the targets while satisfying all the precedence constraints. We show that the optimization problem is NP-hard, and consequently, to measure the proximity of a suboptimal solution from the optimal, a lower bound on the optimal solution is constructed based on the graph theory. Then, inspired by the existing topological sorting techniques, a new topological sorting strategy is proposed; in addition, facilitated by the sorting, we propose several heuristic algorithms to solve the task planning problem. The numerical experiments show that the designed algorithms can quickly lead to satisfying solutions and have better performance in comparison with popular genetic algorithms.
Integration model of English teaching resources based on artificial intelligence

Aiming at the problems of low accuracy and high redundancy in the integration of teaching resources, an artificial intelligence-based model of English teaching resources integration is proposed. Web crawler recognition technology is used to obtain web page information of English teaching resources and to establish index database. L-NCD is introduced to eliminate redundant data of English teaching resources. ISPO algorithm and K-means algorithm are combined to complete the integration of English teaching resources and output results. The experimental results show that the resource integration accuracy of the model is higher than 90%, which proves that the resource integration accuracy of the model is higher. The resource repetition rate of this model is less than 1%, which proves that this model has a good effect of de-redundancy.
Performance optimised architectures of Piccolo block cipher for low resource IoT applications

Radio frequency identification (RFID) and wireless sensor networks (WSNs) are the devices with constrained environments, have been expanded with the current trend of network capabilities and ubiquitous computing, which spread wings of internet of things (IoT). Piccolo is one of the ultra lightweight block cipher, uses 64-bit plaintext and two versions of keys 80 and 128-bit, makes them suitable for low computing devices. Different hardware architectures have been proposed to make them suitable for the low area, low power, and high speed applications. The strategies like loop rolled, parallel round based and pipelined architectures are employed to optimise the hardware design for low resource applications. The proposed architectures have been implemented on field programmable gate arrays (FPGA) achieving throughput of 691.54, 613.26, and 1195.54 Mbps as well as slice count of 47 results in low area and low power.
Extended opinion lexicon and ML-based sentiment analysis of tweets: a novel approach towards accurate classifier

Micro-blogging, today has become a very trendy communication tool among internet users. Millions of users share their opinions on diverse aspects of life which are rich sources for opinion mining. This paper addresses the sentiment analysis of twitter data on demonetisation. A new approach to sentiment analysis based on extended opinion lexicon-based-scores is presented in this paper. Naïve Bayes algorithm and the simple voter algorithm has been used along with supervised learning algorithm like SVM, maximum entropy and GLMNET which are further compared. An insights of demonetisation, that include positive, negative and neutral classification of tweets, emotions of the people behind the tweet using the sentiment package in R has also been discussed. Experimental analysis shows that the extended opinion lexicon method performs better amongst all the supervised and non-supervised machine learning algorithms.
SsciBERT: a pre-trained language model for social science texts

The academic literature of social sciences records human civilization and studies human social problems. With its large-scale growth, the ways to quickly find existing research on relevant issues have become an urgent demand for researchers. Previous studies, such as SciBERT, have shown that pre-training using domain-specific texts can improve the performance of natural language processing tasks. However, the pre-trained language model for social sciences is not available so far. In light of this, the present research proposes a pre-trained model based on the abstracts published in the Social Science Citation Index (SSCI) journals. The models, which are available on GitHub ( https://github.com/S-T-Full-Text-Knowledge-Mining/SSCI-BERT ), show excellent performance on discipline classification, abstract structure–function recognition, and named entity recognition tasks with the social sciences literature.
CGSNet: Contrastive Graph Self-Attention Network for Session-based Recommendation

The goal of session-based recommendation (SBR) is to predict the next item at a certain point in time for anonymous users. Previous methods usually learn session representations based on the item prediction loss, and session-based data consists of limited users’ short-term interactions. Consequently, the model trained with this loss often suffers from data sparsity. Contrastive learning can derive self-supervision signals from raw data, effectively alleviating this problem. However, existing contrastive recommendation approaches mainly generate self-supervision signals via feature mask, which is unfit for SBR because session-based data is too sparse to mine powerful self-supervision signals by masking features. In this paper, we propose a Contrastive Graph Self-Attention Network (abbreviated as CGSNet) for SBR. Specifically, we design three distinct graph encoders to capture different levels of item transition patterns, and obtain a collaborative session representation by aggregating these item representations related to the current session through an attention-based fusion module. Meanwhile, we devise a self-attention subnetwork to learn the complex item transition information, and obtain a local session representation by averaging the item representations within the current session. Since the above two session representations model a specific session from a global- and local-level perspective, respectively, we further introduce a contrastive learning paradigm to maximize the mutual information between the representations of collaborative session and local session to enhance the performance. Extensive experimental results on three widely used benchmark datasets validate the efficacy of our method, which outperforms the competing methods.
SemiSegSAR: A Semi-Supervised Segmentation Algorithm for Ship SAR Images

Automatic ship segmentation from high-resolution Synthetic Aperture Radar (SAR) remote sensing images has been a topic of interest that has gradually gained attention over the years due to the abundance of earth observation sensors. Recently, deep learning methods have provided a breakthrough increasing the performance greatly by using large amount of labeled data. Yet, the high cost related to the samples labeling and their scarcity result in significant limitation of their wide use. Therefore, it is crucial to overcome the unlabeled inputs challenge and develop semi-supervised learning approaches to enhance the machine learning models capacity. Our letter proposes a semi-supervised segmentation algorithm for SAR images named SemiSegSAR based on the use of Graph Signal Processing. This method includes instance segmentation; texture and statistical SAR features to represent the nodes of the graph; K-nearest neighbors to construct the graph; and Sobolev minimization algorithm to tackle the problem of semi-supervised semantic segmentation. The proposed algorithm is trained and tested using the publicly available SSDD and HRSID ship detection datasets. Experiments show that SemiSegSAR outperforms the current state-of-the-art semi-supervised and supervised methods while requiring only few labeled data.
Think Before You Speak: Explicitly Generating Implicit Commonsense Knowledge for Response Generation

Implicit knowledge, such as common sense, is key to fluid human conversations.Current neural response generation (RG) models are trained to generate responses directly, omitting unstated implicit knowledge.In this paper, we present Think-Before-Speaking (TBS), a generative approach to first externalize implicit commonsense knowledge (think) and use this knowledge to generate responses (speak).We expect that externalizing implicit knowledge allows more efficient learning, produces more informative responses, and enables more explainable models.We analyze different choices to collect knowledge-aligned dialogues, represent implicit knowledge, and transition between knowledge and dialogues.Empirical results show TBS models outperform end-to-end and knowledge-augmented RG baselines on most automatic metrics and generate more informative, specific, and commonsense-following responses, as evaluated by human annotators.TBS also generates knowledge that makes sense and is relevant to the dialogue around 85% of the time 1 .
Cloud Computing

The move to cloud computing has accelerated rapidly in recent years, and there’s no slowdown on the horizon. Organizations are moving their infrastructure, including mission-critical systems, out of their direct control and onto platforms that are delivered and supported by cloud service providers (CSP). When we consider the value of the data that’s being processed and stored in the cloud, this is a move that might have seemed impossible, or at least very improbable, not so long ago.
The SIGMORPHON 2022 Shared Task on Morpheme Segmentation

The SIGMORPHON 2022 shared task on morpheme segmentation challenged systems to decompose a word into a sequence of morphemes and covered most types of morphology: compounds, derivations, and inflections.Subtask 1, word-level morpheme segmentation, covered 5 million words in 9 languages (Czech, English, Spanish, Hungarian, French, Italian, Russian, Latin, Mongolian) and received 13 system submissions from 7 teams and the best system averaged 97.29% F1 score across all languages, ranging English (93.84%) to Latin (99.38%).Subtask 2, sentence-level morpheme segmentation, covered 18,735 sentences in 3 languages (Czech, English, Mongolian), received 10 system submissions from 3 teams, and the best systems outperformed all three state-of-the-art subword tokenization methods (BPE, ULM, Mor-fessor2) by 30.71% absolute.To facilitate error analysis and support any type of future studies, we released all system predictions, the evaluation script, and all gold standard datasets. 1'Afternoon I took my medicine.'where "эмээ" is a homonym of two different words; in the first sentence, it is "grandmother", and in the second sentence -an inflected form of "medicine".Thus, the form in the second case can be segmented.However, the modern subword segmentation tools consider no contextual differences in word forms.Key points of this subtask are:• Morpheme segmentation is contextdependent;• We organize it for three languages: English, Czech, and Mongolian;
A dominant set-informed interpretable fuzzy system for automated diagnosis of dementia

Dementia is an incurable neurodegenerative disease primarily affecting the older population, for which the World Health Organisation has set to promoting early diagnosis and timely management as one of the primary goals for dementia care. While a range of popular machine learning algorithms and their variants have been applied for dementia diagnosis, fuzzy systems, which have been known effective in dealing with uncertainty and offer to explicitly reason how a diagnosis can be inferred, sporadically appear in recent literature. Given the advantages of a fuzzy rule-based model, which could potentially result in a clinical decision support system that offers understandable rules and a transparent inference process to support dementia diagnosis, this paper proposes a novel fuzzy inference system by adapting the concept of dominant sets that arise from the study of graph theory. A peeling-off strategy is used to iteratively extract from the constructed edge-weighted graph a collection of dominant sets. Each dominant set is further converted into a parameterized fuzzy rule, which is finally optimized in a supervised adaptive network-based fuzzy inference framework. An illustrative example is provided that demonstrates the interpretable rules and the transparent reasoning process of reaching a decision. Further systematic experiments conducted on data from the Open Access Series of Imaging Studies (OASIS) repository, also validate its superior performance over alternative methods.
Quantum Neural Network Classifiers: A Tutorial

Machine learning has achieved dramatic success over the past decade, with applications ranging from face recognition to natural language processing. Meanwhile, rapid progress has been made in the field of quantum computation including developing both powerful quantum algorithms and advanced quantum devices. The interplay between machine learning and quantum physics holds the intriguing potential for bringing practical applications to the modern society. Here, we focus on quantum neural networks in the form of parameterized quantum circuits. We will mainly discuss different structures and encoding strategies of quantum neural networks for supervised learning tasks, and benchmark their performance utilizing Yao.jl, a quantum simulation package written in Julia Language. The codes are efficient, aiming to provide convenience for beginners in scientific works such as developing powerful variational quantum learning models and assisting the corresponding experimental demonstrations.
Boosting Climate Analysis With Semantically Uplifted Knowledge Graphs

Nowadays, the fast expansion of heterogeneous climate data resources accessible on the Internet has led to substantial data fragmentation on the web. For example, station-based sensor data from different sources are likely to be interrelated but may be stored in disparate formats, such as <monospace>CSV</monospace>, <monospace>JSON</monospace>, and <monospace>XML</monospace>. To address the data isolation problem, several semantically uplifted knowledge graphs are proposed for climate data exchange. While these knowledge graphs improve data interoperability, the advancement in multisource data interchange is limited to data included inside knowledge graphs. As a result, the exclusive interoperability of current climatic knowledge graphs hampers the flow of data into typical climate analysis workflows in contexts, where analytical models often need data in nonknowledge graph formats. This article addresses this issue by focusing on enhancing climate analysis workflows within the context of the Python machine learning environment, with a preference for tabular data. We propose an analysis workflow able to automatically integrate remote climate knowledge graph data with local tabular data so as to enhance the data usability with respect to certain climate analysis tasks. To underscore the importance of our study, we illustrate how the workflow streamlines the access to multisource climatic variables in the Python environment from a semantic perspective. The additional knowledge graph data have the potential to augment local datasets in the climate domain, as evidenced by an improvement in accuracy of up to 10&#x0025; for machine learning geared on rainfall detection.
Digital literacy in the university setting: A literature review of empirical studies between 2010 and 2021

The impact of digital devices and the Internet has generated various changes at social, political, and economic levels, the repercussion of which is a great challenge characterized by the changing and globalized nature of today's society. This demands the development of new skills and new learning models in relation to information and communication technologies. Universities must respond to these social demands in the training of their future professionals. This paper aims to analyze the empirical evidence provided by international studies in the last eleven years, related to the digital literacy of university students, including those pursuing degrees related to the field of education. Our findings highlight the fact that the digital literacy that is offered in universities to graduate/postgraduate students, in addition to treating digital literacy as a central theme, also focuses on perceived and developed self-efficacy. This is done by strengthening competencies related to digital writing and reading, the use of databases, the digital design of content and materials, and the skills to edit, publish or share them on the web, or applications aimed at treating digital literacy as emerging pedagogies and educational innovation. Secondly, we found studies related to digital competencies and use of the Internet, social networks, web 2.0, or the treatment of digital risks and their relationship with digital literacy. Thirdly, we found works that, in addition to focusing on digital literacy, also focused on different psychological constructs such as motivation, commitment, attitudes, or satisfaction. Systematic review registration: https://www.scopus.com/home.uri; https://www.recursoscientificos.fecyt.es/.