Reviewerly/exHarmony huggingface dataset:
Relation-Aware Compositional Zero-Shot Learning for Attribute-Object Pair Recognition

This paper proposes a novel model for recognizing images with composite attribute-object concepts, notably for composite concepts that are unseen during model training. We aim to explore the three key properties required by the task --- relation-aware, consistent, and decoupled --- to learn rich and robust features for primitive concepts that compose attribute-object pairs. To this end, we propose the Blocked Message Passing Network (BMP-Net). The model consists of two modules. The concept module generates semantically meaningful features for primitive concepts, whereas the visual module extracts visual features for attributes and objects from input images. A message passing mechanism is used in the concept module to capture the relations between primitive concepts. Furthermore, to prevent the model from being biased towards seen composite concepts and reduce the entanglement between attributes and objects, we propose a blocking mechanism that equalizes the information available to the model for both seen and unseen concepts. Extensive experiments and ablation studies on two benchmarks show the efficacy of the proposed model.
A Comprehensive Survey for Hadoop Distributed File System

In the last few days, data and the internet have become increasingly growing, occurring in big data. For these problems, there are many software frameworks used to increase the performance of the distributed system. This software is used for available ample data storage. One of the most beneficial software frameworks used to utilize data in distributed systems is Hadoop. This software creates machine clustering and formatting the work between them. Hadoop consists of two major components: Hadoop Distributed File System (HDFS) and Map Reduce (MR). By Hadoop, we can process, count, and distribute each word in a large file and know the number of affecting for each of them. The HDFS is designed to effectively store and transmit colossal data sets to high-bandwidth user applications. The differences between this and other file systems provided are relevant. HDFS is intended for low-cost hardware and is exceptionally tolerant to defects. Thousands of computers in a vast cluster both have directly associated storage functions and user programmers. The resource scales with demand while being cost-effective in all sizes by distributing storage and calculation through numerous servers. Depending on the above characteristics of the HDFS, many researchers worked in this field trying to enhance the performance and efficiency of the addressed file system to be one of the most active cloud systems. This paper offers an adequate study to review the essential investigations as a trend beneficial for researchers wishing to operate in such a system. The basic ideas and features of the investigated experiments were taken into account to have a robust comparison, which simplifies the selection for future researchers in this subject.&#x0D; According to many authors, this paper will explain what Hadoop is and its architectures, how it works, and its performance analysis in a distributed systems. In addition, assessing each Writing and compare with each other.
Fiber‐Integrated Phase Change Metasurfaces with Switchable Group Delay Dispersion

Abstract Demonstration of a fiber‐integrated non‐volatile reconfigurable metasurface providing high‐contrast group delay dispersion switching functionality is reported, which may be engineered to operate at wavelengths across the near‐infrared (telecoms) band. Light‐induced amorphous‐crystalline phase switching in a chalcogenide (germanium antimony telluride) metasurface, only a fraction of a wavelength thick, fabricated on the end‐facet of a single‐mode optical fiber, enables intensity and phase modulation of the guided wave at metasurface designated bands. Such devices present a range of opportunities in fiberized remotely programmable phase/intensity multiplexing and dynamic dispersion compensation for emerging telecommunications and data storage/processing applications, including in photonic neural network and neuromorphic computing architectures.
Discrete Robust Matrix Factorization Hashing for Large-scale Cross-media Retrieval

Cross-media hashing, which encodes data points from different modalities into a common Hamming space, has been successfully applied to solve large-scale multimedia retrieval issue due to storage efficiency and search effectiveness. Recently, matrix factorization based hashing methods have drawn considerable attention for their promising search accuracy. However, pioneer methods mainly focus on learning consensus hash codes for different modalities, but neglect the potential inconsistency among different modalities, \emph{e.g.,} the diversities of different modalities and noises, which may undermine the retrieval accuracy. To address this problem, we propose a novel unsupervised hashing model, namely, Discrete Robust Matrix Factorization Hashing (DRMFH), which simultaneously formulates the consistency and inconsistency across different modalities into a matrix factorization based model. Specifically, a homogenous space composed of a consistent Hamming space and an inconsistent diversity part, are generated by matrix factorization for each modality. Therefore, the consensus information across different modalities can be well captured in the learnt hash codes, leading to improved retrieval performance. Moreover, we design an effective optimization algorithm which is able to obtain an approximate discrete code matrix with linear time complexity. Comprehensive experimental results on three public multimedia retrieval datasets show that the proposed DRMFH outperforms several state-of-the-art methods.
Correlation with the fundamental PSO and PSO modifications to be hybrid swarm optimization

A swarm is a group of a single species in which the members interact with one another and with the immediate environment without a principle for control or the emergence of a global intriguing behavior. Swarm-based metaheuristics, including nature-inspired populace-based methods, have been developed to aid the creation of quick, robust, and low-cost solutions for complex problems. Swarm intelligence was proposed as a computational modeling of swarms and has been successfully applied to numerous optimization tasks since its introduction. A correlation with the fundamental Particle Swarm Optimization (PSO) and PSO modifications demonstrates that hybrid swarm optimization outperforms existing strategies. The downside of hybrid swarm optimization is that it frequently tends to arrive at suboptimal solutions. As such, efforts are being made into combining HSO and other algorithms to arrive at better quality solutions
Open Aspect Target Sentiment Classification with Natural Language Prompts

For many business applications, we often seek to analyze sentiments associated with any arbitrary aspects of commercial products, despite having a very limited amount of labels or even without any labels at all.However, existing aspect target sentiment classification (ATSC) models are not trainable if annotated datasets are not available.Even with labeled data, they fall short of reaching satisfactory performance.To address this, we propose simple approaches that better solve ATSC with natural language prompts, enabling the task under zero-shot cases and enhancing supervised settings, especially for few-shot cases.Under the few-shot setting for SemEval 2014 Task 4 laptop domain, our method of reformulating ATSC as an NLI task outperforms supervised SOTA approaches by up to 24.13 accuracy points and 33.14 macro F1 points.Moreover, we demonstrate that our prompts could handle implicitly stated aspects as well: our models reach about 77% accuracy on detecting sentiments for aspect categories (e.g., food), which do not necessarily appear within the text, even though we trained the models only with explicitly mentioned aspect terms (e.g., fajitas) from just 16 reviews -while the accuracy of the no-prompt baseline is only around 65%.
ConRPG: Paraphrase Generation using Contexts as Regularizer

A long-standing issue with paraphrase generation is how to obtain reliable supervision signals.In this paper, we propose an unsupervised paradigm for paraphrase generation based on the assumption that the probabilities of generating two sentences with the same meaning given the same context should be the same.Inspired by this fundamental idea, we propose a pipelined system which consists of paraphrase candidate generation based on contextual language models, candidate filtering using scoring functions, and paraphrase model training based on the selected candidates.The proposed paradigm offers merits over existing paraphrase generation methods: (1) using the context regularizer on meanings, the model is able to generate massive amounts of high-quality paraphrase pairs; and (2) using human-interpretable scoring functions to select paraphrase pairs from candidates, the proposed framework provides a channel for developers to intervene with the data generation process, leading to a more controllable model.Experimental results across different tasks and datasets demonstrate that the effectiveness of the proposed model in both supervised and unsupervised setups.
Electronic evidence and its authenticity in forensic evidence

Abstract Background The basis for criminal trials is the judge’s conviction of the evidence presented in a case. His belief is based on the context or evidence he is satisfactory with and understands. However, the law may establish certain evidence for the judge to adhere to. This study aims to identify the extent of the authenticity and strength of digital or electronic evidence in criminal trials, by identifying legislative trends in the various legal systems, and examining what legal jurists have done to determine the extent of the authenticity of the electronic evidence of cybercrimes. Results This study will research the legitimacy of electronic evidence and the conditions for its verification, the extent of the authenticity of electronic evidence found during an investigation, the difficulties of obtaining electronic evidence that can be presented before the courts, and the extent of a presiding judge’s freedom to determine if electronic materials presented in court should be used as evidence. Conclusions Two conditions must be met: first, the electronic evidence must be legally obtained based on written permission from the competent investigation authorities; second, it must be verified as valid by computer science and information technology experts. If those two conditions are not met, the evidence is invalid. The study gives reason to talk about the need for the adoption of international agreements on cooperation in the development and exchange of computer and information technologies, aimed at preserving electronic evidence from destruction and oblige countries to implement and comply with these agreements.
ExMrec2vec: Explainable Movie Recommender System based on Word2vec

According to the user profile, a recommender system intends to offer items to the user that may interest him.The recommendations have been applied successfully in various fields.Recommended items include movies, books, travel and tourism services, friends, research articles, research queries, and much more.Hence the presence of recommender systems in many areas, in particular, movies recommendations.Most current Machine Learning recommender systems serve as black boxes that do not provide the user with any insight into or justification for the system's logic.What puts users at risk of losing their confidence.Recommender systems suffer from an overload of information, which poses numerous problems, including high cost, slow data processing, and low time complexity.That is why researchers in have been using graph embeddings algorithms in the recommendation field to reduce the quantity of data, as these algorithms have been successful in the last few years.This work aims to improve the quality of recommendation and the simplicity of recommendation explanation based on the word2vec graph embeddings model.
Contrastive Domain Adaptation for Question Answering using Limited Text Corpora

Question generation has recently shown impressive results in customizing question answering (QA) systems to new domains.These approaches circumvent the need for manually annotated training data from the new domain and, instead, generate synthetic questionanswer pairs that are used for training.However, existing methods for question generation rely on large amounts of synthetically generated datasets and costly computational resources, which render these techniques widely inaccessible when the text corpora is of limited size.This is problematic as many niche domains rely on small text corpora, which naturally restricts the amount of synthetic data that can be generated.In this paper, we propose a novel framework for domain adaptation called contrastive domain adaptation for QA (CAQA).Specifically, CAQA combines techniques from question generation and domaininvariant learning to answer out-of-domain questions in settings with limited text corpora.Here, we train a QA system on both source data and generated data from the target domain with a contrastive adaptation loss that is incorporated in the training objective.By combining techniques from question generation and domain-invariant learning, our model achieved considerable improvements compared to stateof-the-art baselines.
Python Parallel Processing and Multiprocessing: A Rivew

Parallel and multiprocessing algorithms break down significant numerical problems into smaller subtasks, reducing the total computing time on multiprocessor and multicore computers. Parallel programming is well supported in proven programming languages such as C and Python, which are well suited to “heavy-duty” computational tasks. Historically, Python has been regarded as a strong supporter of parallel programming due to the global interpreter lock (GIL). However, times have changed. Parallel programming in Python is supported by the creation of a diverse set of libraries and packages. This review focused on Python libraries that support parallel processing and multiprocessing, intending to accelerate computation in various fields, including multimedia, attack detection, supercomputers, and genetic algorithms. Furthermore, we discussed some Python libraries that can be used for this purpose.
Quantum metrology with imperfect measurements

The impact of measurement imperfections on quantum metrology protocols has not been approached in a systematic manner so far. In this work, we tackle this issue by generalising firstly the notion of quantum Fisher information to account for noisy detection, and propose tractable methods allowing for its approximate evaluation. We then show that in canonical scenarios involving N probes with local measurements undergoing readout noise, the optimal sensitivity depends crucially on the control operations allowed to counterbalance the measurement imperfections-with global control operations, the ideal sensitivity (e.g., the Heisenberg scaling) can always be recovered in the asymptotic N limit, while with local control operations the quantum-enhancement of sensitivity is constrained to a constant factor. We illustrate our findings with an example of NV-centre magnetometry, as well as schemes involving spin-1/2 probes with bit-flip errors affecting their two-outcome measurements, for which we find the input states and control unitary operations sufficient to attain the ultimate asymptotic precision.
LM-Critic: Language Models for Unsupervised Grammatical Error Correction

Training a model for grammatical error correction (GEC) requires a set of labeled ungrammatical / grammatical sentence pairs, but manually annotating such pairs can be expensive.Recently, the Break-It-Fix-It (BIFI) framework has demonstrated strong results on learning to repair a broken program without any labeled examples, but this relies on a perfect critic (e.g., a compiler) that returns whether an example is valid or not, which does not exist for the GEC task.In this work, we show how to leverage a pretrained language model (LM) in defining an LM-Critic, which judges a sentence to be grammatical if the LM assigns it a higher probability than its local perturbations.We apply this LM-Critic and BIFI along with a large set of unlabeled sentences to bootstrap realistic ungrammatical/grammatical pairs for training a corrector.We evaluate our approach on GEC datasets across multiple domains (CoNLL-2014, BEA-2019, GMEG-wiki and GMEG-yahoo) and show that it outperforms existing methods in both the unsupervised setting (+7.7 F 0.5 ) and the supervised setting (+0.5 F 0.5 ).
Exploring the Design Space for Human-Food-Technology Interaction: An Approach from the Lens of Eating Experiences

Embedded in everyday practices, food can be a rich resource for interaction design. This article focuses on eating experiences to uncover how bodily, sensory, and socio-cultural aspects of eating can be better leveraged for the design of user experience. We report a systematic literature review of 109 papers, and interviews with 18 professional chefs, providing new understandings of prior HFI research, as well as how professional chefs creatively design eating experiences. The findings inform a conceptual framework of designing for user experience leveraging eating experiences. These findings also inform implications for HFI design suggesting the value of multisensory flavor experiences, external and internal sensory stimulation and deprivation, aspects of eating for communicating meaning, and designing with contrasting pleasurable and uncomfortable experiences. The article concludes with six charts as novel generative design tools for HFI experiences focused on sensory, emotional, communicative, performative, and temporal experiences.
Comprehensive Review of K-Means Clustering Algorithms

This paper presents a comprehensive review of existing techniques of k-means clustering algorithms made at various times. The kmeans algorithm is aimed at partitioning objects or points to be analyzed into well-separated clusters. There are different algorithms for k-means clustering of objects such as traditional k-means algorithm, standard k-means algorithm, basic k-means algorithm, and the conventional k-means algorithm, this is perhaps the most widely used version of the k-means algorithms. These algorithms use the Euclidean distance as their metric and minimum distance rule approach by assigning each data point (objects)to its closest centroids.
Distilling Linguistic Context for Language Model Compression

A computationally expensive and memory intensive neural network lies behind the recent success of language representation learning.Knowledge distillation, a major technique for deploying such a vast language model in resource-scarce environments, transfers the knowledge on individual word representations learned without restrictions.In this paper, inspired by the recent observations that language representations are relatively positioned and have more semantic knowledge as a whole, we present a new knowledge distillation objective for language representation learning that transfers the contextual knowledge via two types of relationships across representations: Word Relation and Layer Transforming Relation.Unlike other recent distillation techniques for the language models, our contextual distillation does not have any restrictions on architectural changes between teacher and student.We validate the effectiveness of our method on challenging benchmarks of language understanding tasks, not only in architectures of various sizes, but also in combination with DynaBERT, the recently proposed adaptive size pruning method.
Fast unconditional initialization for superconducting qubit and resonator using quantum-circuit refrigerator

In a quantum computation scheme, such as recycling a qubit, the initialization of the qubit is always required. At present, the initialization of the qubit is one of the bottlenecks in such computation, and high fidelity and fast initialization still remain important research subjects. In this study, by coupling a SINIS (superconductor–insulator–normal metal–insulator–superconductor) junction to a coupled system consisting of a qubit and a resonator, a photon is removed from the resonator by single-photon-assisted tunneling, and the effective relaxation rate of the resonator is increased. In addition, by applying an |e,0⟩−|f,0⟩ drive pulse and an |f,0⟩−|g,1⟩ drive pulse to the qubit, the energy of the qubit is rapidly transferred to the resonator to realize high-speed initialization. The present simulation shows that the qubit can be initialized with 99% fidelity in 80 ns and 99.9% in 137 ns when the electron temperature of the normal metal of the SINIS junction is 10 mK. The simulation also shows that the relaxation time of the qubit is about 13% longer when the resonator is interposed between the SINIS junction and the qubit than when the SINIS junction is directly coupled to the qubit.
Efficient Contact Tracing for pandemics using blockchain

Blockchain technologies have been benefiting many industries by being decentralized, secure and confidential. They offer great potential in pandemic impacted scenarios as well. Contact tracing helps to mitigate the transmission of disease by alerting people who may have been exposed so they can act on time to protect themselves. Contact tracing systems face some challenges related to issues of medical privacy, data security and transparency. Multiple researches show concern that contact tracing discourages people to seek medication because of the fear of loss of data, subsequent stigma, discrimination, or abuse. In this paper, we discuss how contact tracing can be improved using blockchain technology and could be able to solve these issues. The aim of our proposed system would be to reduce the impact of pandemic, to implement this merger of the blockchains and the Contact tracing app and to ensure user privacy and avoid data misuse by incorporating a symmetric key cryptographic mechanism. To tackle the scalability related issues of Blockchain this framework uses IPFS, a distributed file storage system also known as Interplanetary File System. This blockchain based solution system will enhance contact tracing by making it more stable, secure, performant, highly useable and above all effective in the fight against any pandemic.
An Open Framework for Analyzing and Modeling XR Network Traffic

Thanks to recent advancements in the technology, eXtended Reality (XR) applications are gaining a lot of momentum, and they will surely become increasingly popular in the next decade. These new applications, however, require a step forward also in terms of models to simulate and analyze this type of traffic sources in modern communication networks, in order to guarantee to the users state of the art performance and Quality of Experience (QoE). Recognizing this need, in this work, we present a novel open-source traffic model, which researchers can use as a starting point both for improvements of the model itself and for the design of optimized algorithms for the transmission of these peculiar data flows. Along with the mathematical model and the code, we also share with the community the traces that we gathered for our study, collected from freely available applications such as Minecraft VR, Google Earth VR, and Virus Popper. Finally, we propose a roadmap for the construction of an end-to-end framework that fills this gap in the current state of the art.